From e4ba9b87bf6706570be22479d3309f8710019c46 Mon Sep 17 00:00:00 2001
From: epifanio <epiesasha@gmail.com>
Date: Sat, 5 Mar 2016 19:31:57 -0500
Subject: [PATCH] readding the projects folder, cleaned from GRASS and moved
 readme on root dir

---
 README.md                                     |   10 +-
 projects/FIONA/fiona-ipynb-interop.ipynb      |  162 +
 projects/FIONA/fiona-shapely-mapnik.ipynb     |  203 +
 projects/FIONA/fiona-shapely-pyplot.ipynb     |  460 ++
 projects/FOLIUM/US_Unemployment_Oct2012.csv   |   51 +
 projects/FOLIUM/data.json                     |    1 +
 projects/FOLIUM/data1.json                    |    1 +
 projects/FOLIUM/data2.json                    |    1 +
 projects/FOLIUM/data3.json                    |    1 +
 .../FOLIUM/folium-clustered-markers.ipynb     |   98 +
 projects/FOLIUM/folium-examples.ipynb         |  288 +
 projects/FOLIUM/folium-markers-popup.ipynb    |  100 +
 projects/FOLIUM/folium-rotate.ipynb           |   83 +
 projects/FOLIUM/folium-wms.ipynb              |  121 +
 projects/FOLIUM/map_1.html                    |  165 +
 projects/FOLIUM/map_2.html                    |  165 +
 projects/FOLIUM/map_3.html                    |  165 +
 projects/FOLIUM/us-states.json                |   52 +
 projects/FOLIUM/us_counties_20m_topo.json     |    1 +
 projects/FOLIUM/us_county_data.csv            |    1 +
 projects/FOLIUM/us_state_map.html             |  163 +
 projects/FOLIUM/us_state_map_2.html           |  163 +
 projects/IRIS/cartopy-earth-at-night.ipynb    |   91 +
 projects/IRIS/cartopy-ne2-cache-test.ipynb    |   74 +
 projects/IRIS/cartopy-quiver-regrid.ipynb     |  127 +
 projects/IRIS/cartopy-quiver.ipynb            |  307 +
 projects/IRIS/cartopy-rasterio-plot.ipynb     |  197 +
 projects/IRIS/iris-basics.ipynb               |  183 +
 projects/IRIS/iris-contourf-exner.ipynb       |  166 +
 projects/IRIS/iris-contourf-gtmps.ipynb       |  216 +
 projects/IRIS/iris-gtmps-ensemble.ipynb       |  210 +
 projects/IRIS/iris-hovmoller.ipynb            |  116 +
 projects/IRIS/iris-iplt-tripolar.ipynb        |  138 +
 projects/IRIS/iris-np-agg.ipynb               |  165 +
 projects/IRIS/iris-pcolormesh.ipynb           |  175 +
 projects/IRIS/iris-polyfit.ipynb              |  120 +
 projects/IRIS/iris-projs.ipynb                |  228 +
 projects/IRIS/iris-qplt-contour.ipynb         |  146 +
 projects/IRIS/iris-qplt-netcdf-dem.ipynb      |  119 +
 projects/IRIS/iris-qplt-netcdf.ipynb          |  111 +
 projects/IRIS/iris-qplt-pp.ipynb              |  144 +
 projects/IRIS/iris-rpole.ipynb                |  134 +
 projects/PYSAL/README.md                      |  132 +
 projects/PYSAL/esda/00_introduction.ipynb     |  240 +
 projects/PYSAL/esda/00_notebook_intro.ipynb   |  400 ++
 .../esda/01_spatial_data_processing.ipynb     |  588 ++
 projects/PYSAL/esda/02_plotting.ipynb         |  473 ++
 .../PYSAL/esda/03_choropleth_mapping.ipynb    |  495 ++
 projects/PYSAL/esda/04_pysal_mapping.ipynb    |  296 +
 projects/PYSAL/esda/05_spatial_weights.ipynb  |  951 +++
 projects/PYSAL/esda/06_taz_example.ipynb      |  381 +
 .../07_global_spatial_autocorrelation.ipynb   |  769 ++
 projects/PYSAL/esda/08_sp_corr.ipynb          |  182 +
 projects/PYSAL/esda/09_global_south.ipynb     |  897 +++
 projects/PYSAL/esda/10_local_south.ipynb      |  977 +++
 projects/PYSAL/esda/11_gol.ipynb              |  642 ++
 projects/PYSAL/esda/12_spatial_dynamics.ipynb | 1065 +++
 projects/PYSAL/esda/data/NAT.dbf              |  Bin 0 -> 2501092 bytes
 projects/PYSAL/esda/data/NAT.shp              |  Bin 0 -> 1462216 bytes
 projects/PYSAL/esda/data/NAT.shx              |  Bin 0 -> 24780 bytes
 projects/PYSAL/esda/data/mexico.csv           |   33 +
 projects/PYSAL/esda/data/nat.json             | 3090 +++++++++
 projects/PYSAL/esda/data/nat_copy.gal         | 6171 +++++++++++++++++
 projects/PYSAL/esda/data/south.dbf            |  Bin 0 -> 1145962 bytes
 projects/PYSAL/esda/data/south.json           | 1417 ++++
 projects/PYSAL/esda/data/south.shp            |  Bin 0 -> 737448 bytes
 projects/PYSAL/esda/data/south.shx            |  Bin 0 -> 11396 bytes
 projects/PYSAL/esda/data/south.zip            |  Bin 0 -> 1887633 bytes
 projects/PYSAL/esda/data/usjoin.csv           |   49 +
 projects/PYSAL/esda/figures/10_arctool.png    |  Bin 0 -> 127581 bytes
 projects/PYSAL/esda/figures/ack.png           |  Bin 0 -> 172486 bytes
 projects/PYSAL/esda/figures/anaconda.png      |  Bin 0 -> 198093 bytes
 .../PYSAL/esda/figures/anaconda_pysal.png     |  Bin 0 -> 436058 bytes
 projects/PYSAL/esda/figures/cast.png          |  Bin 0 -> 353415 bytes
 projects/PYSAL/esda/figures/cgpysal.png       |  Bin 0 -> 246836 bytes
 projects/PYSAL/esda/figures/enthought.png     |  Bin 0 -> 210843 bytes
 .../PYSAL/esda/figures/enthought_pysal.png    |  Bin 0 -> 141980 bytes
 projects/PYSAL/esda/figures/gsgui.png         |  Bin 0 -> 80628 bytes
 projects/PYSAL/esda/figures/gsoutput.png      |  Bin 0 -> 102050 bytes
 projects/PYSAL/esda/figures/pysal_org.png     |  Bin 0 -> 269792 bytes
 projects/PYSAL/esda/figures/pysalgraph.png    |  Bin 0 -> 120991 bytes
 projects/PYSAL/esda/figures/pysalnb.png       |  Bin 0 -> 195024 bytes
 projects/PYSAL/esda/figures/qgis.png          |  Bin 0 -> 97304 bytes
 projects/PYSAL/esda/figures/testing.png       |  Bin 0 -> 167725 bytes
 projects/PYSAL/esda/figures/weightsviewer.png |  Bin 0 -> 220896 bytes
 projects/PYSAL/esda/images/dashboard.png      |  Bin 0 -> 148816 bytes
 projects/PYSAL/esda/images/help.png           |  Bin 0 -> 188244 bytes
 projects/PYSAL/esda/images/hist.png           |  Bin 0 -> 11362 bytes
 .../PYSAL/esda/images/ipython_powershell.png  |  Bin 0 -> 110164 bytes
 projects/PYSAL/esda/images/menu.png           |  Bin 0 -> 105604 bytes
 projects/PYSAL/esda/images/save.png           |  Bin 0 -> 231585 bytes
 projects/PYSAL/esda/images/shortcuts.png      |  Bin 0 -> 220318 bytes
 projects/PYSAL/figs/enthought_pysal.png       |  Bin 0 -> 141980 bytes
 projects/PYSAL/figs/materials.png             |  Bin 0 -> 462439 bytes
 projects/PYSAL/figs/pysalGraphic.png          |  Bin 0 -> 117005 bytes
 95 files changed, 24865 insertions(+), 5 deletions(-)
 create mode 100644 projects/FIONA/fiona-ipynb-interop.ipynb
 create mode 100644 projects/FIONA/fiona-shapely-mapnik.ipynb
 create mode 100644 projects/FIONA/fiona-shapely-pyplot.ipynb
 create mode 100644 projects/FOLIUM/US_Unemployment_Oct2012.csv
 create mode 100644 projects/FOLIUM/data.json
 create mode 100644 projects/FOLIUM/data1.json
 create mode 100644 projects/FOLIUM/data2.json
 create mode 100644 projects/FOLIUM/data3.json
 create mode 100644 projects/FOLIUM/folium-clustered-markers.ipynb
 create mode 100644 projects/FOLIUM/folium-examples.ipynb
 create mode 100644 projects/FOLIUM/folium-markers-popup.ipynb
 create mode 100644 projects/FOLIUM/folium-rotate.ipynb
 create mode 100644 projects/FOLIUM/folium-wms.ipynb
 create mode 100644 projects/FOLIUM/map_1.html
 create mode 100644 projects/FOLIUM/map_2.html
 create mode 100644 projects/FOLIUM/map_3.html
 create mode 100644 projects/FOLIUM/us-states.json
 create mode 100644 projects/FOLIUM/us_counties_20m_topo.json
 create mode 100644 projects/FOLIUM/us_county_data.csv
 create mode 100644 projects/FOLIUM/us_state_map.html
 create mode 100644 projects/FOLIUM/us_state_map_2.html
 create mode 100644 projects/IRIS/cartopy-earth-at-night.ipynb
 create mode 100644 projects/IRIS/cartopy-ne2-cache-test.ipynb
 create mode 100644 projects/IRIS/cartopy-quiver-regrid.ipynb
 create mode 100644 projects/IRIS/cartopy-quiver.ipynb
 create mode 100644 projects/IRIS/cartopy-rasterio-plot.ipynb
 create mode 100644 projects/IRIS/iris-basics.ipynb
 create mode 100644 projects/IRIS/iris-contourf-exner.ipynb
 create mode 100644 projects/IRIS/iris-contourf-gtmps.ipynb
 create mode 100644 projects/IRIS/iris-gtmps-ensemble.ipynb
 create mode 100644 projects/IRIS/iris-hovmoller.ipynb
 create mode 100644 projects/IRIS/iris-iplt-tripolar.ipynb
 create mode 100644 projects/IRIS/iris-np-agg.ipynb
 create mode 100644 projects/IRIS/iris-pcolormesh.ipynb
 create mode 100644 projects/IRIS/iris-polyfit.ipynb
 create mode 100644 projects/IRIS/iris-projs.ipynb
 create mode 100644 projects/IRIS/iris-qplt-contour.ipynb
 create mode 100644 projects/IRIS/iris-qplt-netcdf-dem.ipynb
 create mode 100644 projects/IRIS/iris-qplt-netcdf.ipynb
 create mode 100644 projects/IRIS/iris-qplt-pp.ipynb
 create mode 100644 projects/IRIS/iris-rpole.ipynb
 create mode 100644 projects/PYSAL/README.md
 create mode 100644 projects/PYSAL/esda/00_introduction.ipynb
 create mode 100644 projects/PYSAL/esda/00_notebook_intro.ipynb
 create mode 100644 projects/PYSAL/esda/01_spatial_data_processing.ipynb
 create mode 100644 projects/PYSAL/esda/02_plotting.ipynb
 create mode 100644 projects/PYSAL/esda/03_choropleth_mapping.ipynb
 create mode 100644 projects/PYSAL/esda/04_pysal_mapping.ipynb
 create mode 100644 projects/PYSAL/esda/05_spatial_weights.ipynb
 create mode 100644 projects/PYSAL/esda/06_taz_example.ipynb
 create mode 100644 projects/PYSAL/esda/07_global_spatial_autocorrelation.ipynb
 create mode 100644 projects/PYSAL/esda/08_sp_corr.ipynb
 create mode 100644 projects/PYSAL/esda/09_global_south.ipynb
 create mode 100644 projects/PYSAL/esda/10_local_south.ipynb
 create mode 100644 projects/PYSAL/esda/11_gol.ipynb
 create mode 100644 projects/PYSAL/esda/12_spatial_dynamics.ipynb
 create mode 100755 projects/PYSAL/esda/data/NAT.dbf
 create mode 100755 projects/PYSAL/esda/data/NAT.shp
 create mode 100755 projects/PYSAL/esda/data/NAT.shx
 create mode 100755 projects/PYSAL/esda/data/mexico.csv
 create mode 100644 projects/PYSAL/esda/data/nat.json
 create mode 100644 projects/PYSAL/esda/data/nat_copy.gal
 create mode 100644 projects/PYSAL/esda/data/south.dbf
 create mode 100644 projects/PYSAL/esda/data/south.json
 create mode 100644 projects/PYSAL/esda/data/south.shp
 create mode 100644 projects/PYSAL/esda/data/south.shx
 create mode 100644 projects/PYSAL/esda/data/south.zip
 create mode 100644 projects/PYSAL/esda/data/usjoin.csv
 create mode 100644 projects/PYSAL/esda/figures/10_arctool.png
 create mode 100644 projects/PYSAL/esda/figures/ack.png
 create mode 100644 projects/PYSAL/esda/figures/anaconda.png
 create mode 100644 projects/PYSAL/esda/figures/anaconda_pysal.png
 create mode 100644 projects/PYSAL/esda/figures/cast.png
 create mode 100644 projects/PYSAL/esda/figures/cgpysal.png
 create mode 100644 projects/PYSAL/esda/figures/enthought.png
 create mode 100644 projects/PYSAL/esda/figures/enthought_pysal.png
 create mode 100644 projects/PYSAL/esda/figures/gsgui.png
 create mode 100644 projects/PYSAL/esda/figures/gsoutput.png
 create mode 100644 projects/PYSAL/esda/figures/pysal_org.png
 create mode 100644 projects/PYSAL/esda/figures/pysalgraph.png
 create mode 100644 projects/PYSAL/esda/figures/pysalnb.png
 create mode 100644 projects/PYSAL/esda/figures/qgis.png
 create mode 100644 projects/PYSAL/esda/figures/testing.png
 create mode 100644 projects/PYSAL/esda/figures/weightsviewer.png
 create mode 100644 projects/PYSAL/esda/images/dashboard.png
 create mode 100644 projects/PYSAL/esda/images/help.png
 create mode 100644 projects/PYSAL/esda/images/hist.png
 create mode 100644 projects/PYSAL/esda/images/ipython_powershell.png
 create mode 100644 projects/PYSAL/esda/images/menu.png
 create mode 100644 projects/PYSAL/esda/images/save.png
 create mode 100644 projects/PYSAL/esda/images/shortcuts.png
 create mode 100644 projects/PYSAL/figs/enthought_pysal.png
 create mode 100644 projects/PYSAL/figs/materials.png
 create mode 100644 projects/PYSAL/figs/pysalGraphic.png

diff --git a/README.md b/README.md
index 4c5512d..f64a9b5 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
-###Jupyter Notebooks###
+###OSGeo-live###
+This directory host a set of **[IPython Notebook](http://ipython.org/notebook.html)** that are used in the **[OSGeo-live](http://live.osgeo.org/en/index.html)** project to demostrate the capabilities of *Open Source Geospatial Tools* installed on the *OSGeo-Live*.
+To start the *Jupyter Notebook* server on the *OSGeo-Live*, use the *GEOSPATIAL* menu *start Jupyter Notebook*. 
+To access to the server select in the same menu *Jupyter Notebook* it will open the browser pointing to the *Jupyter Notebook* dashboard running on ```htpp://localhost:8883``` .
 
-In this repository are stored a set of [Jupyter notebooks](http://jupyter.org/) to demostrate the use of open source geospatial tools.
-The ```OSGeo-live``` directory host notebooks that can be executed on the OSGeo live project. 
-Other Jupyter Notebooks, running open source tools not installed on the live will be stored in the current directory.
-This repository can be rendered as static html using the  [NBViewer](http://nbviewer.jupyter.org/github/OSGeo/OSGeoLive-Notebooks/tree/master/) service.
+From the OSGeo-Live you can upgrade to the latest version of this repository running the command ```git pull``` from the notebook directory : ```/home/user/jupyter/notebooks```.
diff --git a/projects/FIONA/fiona-ipynb-interop.ipynb b/projects/FIONA/fiona-ipynb-interop.ipynb
new file mode 100644
index 0000000..e3c8843
--- /dev/null
+++ b/projects/FIONA/fiona-ipynb-interop.ipynb
@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import fiona\n",
+    "\n",
+    "from matplotlib.patches import Polygon\n",
+    "from shapely.geometry import shape, box\n",
+    "from shapely.ops import cascaded_union\n",
+    "\n",
+    "## Fiona, IPython Notebook interaction\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##\n",
+    "## "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Terminal Commands\n",
+    "----------------------\n",
+    "``Shell script can be executed with results stored into python variables``\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shps = !ls /home/user/data/north_carolina/shape/*shp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Task: quickly examine the bounding areas of a directory of shapefiles\n",
+    "------------------------------------------------------------------\n",
+    "* use ``fiona.open()`` to read data files on disk\n",
+    "* save the filename and bounding box into a python ``dict``\n",
+    "\n",
+    ".\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "td = {}\n",
+    "\n",
+    "for shp in shps:\n",
+    "    with fiona.open( shp, 'r') as inp:\n",
+    "        td[ inp.name ] = inp.bounds\n",
+    "\n",
+    "## Fiona inp.bounds => ( lower_lng, lower_lat, upper_lng, upper_lat)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Create shapely geometry from the coords\n",
+    "## shapely/geometry/geo.py\n",
+    "##   box(minx, miny, maxx, maxy, ccw=True)\n",
+    "\n",
+    "nboxes = []\n",
+    "for k,v in iter(td.iteritems()):\n",
+    "    nboxes.append( box( v[0], v[1], v[2], v[3]) )\n",
+    "\n",
+    "print 'Found BBOXs: ',len(nboxes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## create a single cascaded UNION too\n",
+    "dst_poly = cascaded_union(nboxes)\n",
+    "dst_poly.bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Draw every BBOX for all files, transparently\n",
+    "## use matplotlib.Polygon to draw; let autoscale calculate the area\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(12,12))\n",
+    "for polygon in nboxes:\n",
+    "    mpl_poly = Polygon(np.array(polygon.exterior), facecolor=\"y\", alpha=0.02)\n",
+    "    ax.add_patch(mpl_poly)\n",
+    "\n",
+    "## Indicate the exterior of the study area with a heavy line\n",
+    "ax.add_patch( Polygon(np.array(dst_poly.exterior), fill=False, lw=4, ec=\"b\", alpha=0.9) )\n",
+    "\n",
+    "ax.relim()\n",
+    "ax.autoscale()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FIONA/fiona-shapely-mapnik.ipynb b/projects/FIONA/fiona-shapely-mapnik.ipynb
new file mode 100644
index 0000000..24d4be6
--- /dev/null
+++ b/projects/FIONA/fiona-shapely-mapnik.ipynb
@@ -0,0 +1,203 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.core.display import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import fiona\n",
+    "import mapnik\n",
+    "import shapely.geometry\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "## Fiona, mapnik demo\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##\n",
+    "##   based on UoLPythonGroup/data-hack-0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "BASE_FOLDER = '/home/user/data/north_carolina/shape'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c_shapefile = os.path.join(BASE_FOLDER, 'nc_state.shp')\n",
+    "f = fiona.open(c_shapefile)\n",
+    "shps = list(f)\n",
+    "print 'f: ',type(f)\n",
+    "print f.schema"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "type(f[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Use the Shapely geometry classes\n",
+    "##  instantiate a Polygon from the Fiona collection "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import shapely.geometry\n",
+    "\n",
+    "geo = shapely.geometry.shape(f[0]['geometry'])\n",
+    "geo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## plt.plot(*geo.xy)  ## hmm not implemented"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Mapnik\n",
+    "##  load the Mapnik python interfaces\n",
+    "##  read the shapefile directly with Mapnik libs\n",
+    "##  use the IPython Image interface as the drawing target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def show_mapnik(m):\n",
+    "    \"\"\"Returns an IPython Image of the rendered map.\"\"\"\n",
+    "    im = mapnik.Image(m.width, m.height)\n",
+    "    mapnik.render(m, im)\n",
+    "    return Image(data=im.tostring('png32'))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import mapnik\n",
+    "\n",
+    "m = mapnik.Map(600, 600)\n",
+    "\n",
+    "layer = mapnik.Layer('contour')\n",
+    "layer.datasource = mapnik.Shapefile(file=c_shapefile)\n",
+    "\n",
+    "style = mapnik.Style()\n",
+    "rule = mapnik.Rule()\n",
+    "\n",
+    "line_symbolizer = mapnik.LineSymbolizer(mapnik.Color('green'),0.4)\n",
+    "rule.symbols.append(line_symbolizer)\n",
+    "\n",
+    "m.layers.append(layer)\n",
+    "style.rules.append(rule)\n",
+    "m.append_style('My Style', style)\n",
+    "layer.styles.append('My Style')\n",
+    "\n",
+    "m.layers.append(layer)\n",
+    "m.zoom_all()\n",
+    "show_mapnik(m)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FIONA/fiona-shapely-pyplot.ipynb b/projects/FIONA/fiona-shapely-pyplot.ipynb
new file mode 100644
index 0000000..43911de
--- /dev/null
+++ b/projects/FIONA/fiona-shapely-pyplot.ipynb
@@ -0,0 +1,460 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Polygon\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import fiona\n",
+    "from shapely.geometry import shape\n",
+    "\n",
+    "import numpy as np\n",
+    "import os\n",
+    "from collections import defaultdict, OrderedDict \n",
+    "\n",
+    "## Fiona, ogr2ogr, pyplot demo\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`Demo Area Analysis in IPython Notebook`\n",
+    "\n",
+    "  * Use North Carolina sample data supplied on the OSGeo Live \n",
+    "  * Extract an area of interest\n",
+    "  * Extract a subset of a state soils record based on a bounding box (BBOX)\n",
+    "  * Plot geometry graphically\n",
+    "  * Buffer the geometry by a fixed amount; calculate the areas of intersection for soil types\n",
+    "  * Produce a chart of the totals with pyplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "infile = '/usr/local/share/data/north_carolina/shape/urbanarea.shp'\n",
+    "infile_soils = '/usr/local/share/data/north_carolina/shape/soils_general.shp'\n",
+    "\n",
+    "out_farmville = '/tmp/farmville_shp'\n",
+    "out_farmville_soils = '/tmp/farmville_soil_shp'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## -- Call the gdal/ogr tool suite to extract and subset the original statewide dataset\n",
+    "##     use a feature of IPython Notebook to pass python variables to a command line\n",
+    "\n",
+    "!rm -rf $out_farmville\n",
+    "!ogr2ogr -f 'ESRI Shapefile' $out_farmville $infile -where \"NAME='Farmville'\" -dim 2 -a_srs EPSG:3358\n",
+    "\n",
+    "##-- advanced example -- raleigh is multiple polygons\n",
+    "#!rm -rf /tmp/raleigh1_shp\n",
+    "#!ogr2ogr -f 'ESRI Shapefile' /tmp/raleigh1_shp $infile -where \"NAME='Raleigh'\" -dim 2 -a_srs EPSG:3358\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## -- use the IPython notebook to get help on py module Fiona\n",
+    "# fiona?\n",
+    "# fiona.open?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## open and inspect a Shapefile using Fiona\n",
+    "##  a POLYGON record could be very long, so dont print the record w/ geometry\n",
+    "with fiona.open( out_farmville ) as f:\n",
+    "    crs = f.crs\n",
+    "    print 'CRS:',crs\n",
+    "    rec = f.next()\n",
+    "    #print rec\n",
+    "    print 'SHAPELY REC:',rec.keys()\n",
+    "    print 'SHAPEFILE FLDS: ',rec['properties'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with fiona.open( out_farmville,'r') as inp:\n",
+    "    #print dir(inp)  ## whats inside this object?\n",
+    "    #print inp.bounds\n",
+    "    \n",
+    "    ## take the bounding box of the area of interest\n",
+    "    ##  add 300 meters on each side for aesthetics\n",
+    "    ## (left, bottom, right, top)\n",
+    "    left_bnds     = inp.bounds[0] - 300\n",
+    "    bottom_bnds   = inp.bounds[1] - 300\n",
+    "    right_bnds    = inp.bounds[2] + 300\n",
+    "    top_bnds      = inp.bounds[3] + 300\n",
+    "\n",
+    "## echo one variable to sanity check\n",
+    "#left_bnds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## ogr2ogr ... -clipsrc [xmin ymin xmax ymax] ...\n",
+    "\n",
+    "!rm -rf $out_farmville_soils\n",
+    "!ogr2ogr -f 'ESRI Shapefile' $out_farmville_soils $infile_soils -clipsrc $left_bnds $bottom_bnds $right_bnds $top_bnds -dim 2 -a_srs EPSG:3358\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## ----------------------------------------\n",
+    "## plot_multipolygon function\n",
+    "##   Author: Kelsey Jordahl, Enthought\n",
+    "##   Scipy 2013 geospatial tutorial\n",
+    "\n",
+    "def plot_polygon(ax, poly, color='black'):\n",
+    "    a = np.asarray(poly.exterior)\n",
+    "    ax.add_patch(Polygon(a, facecolor=color, alpha=0.5))\n",
+    "    ax.plot(a[:, 0], a[:, 1], color=color)\n",
+    "\n",
+    "def plot_multipolygon(ax, geom, color='red'):\n",
+    "    \"\"\" Can safely call with either Polygon or Multipolygon geometry\n",
+    "    \"\"\"\n",
+    "    if geom.type == 'Polygon':\n",
+    "        plot_polygon(ax, geom, color)\n",
+    "    elif geom.type == 'MultiPolygon':\n",
+    "        for poly in geom.geoms:\n",
+    "            plot_polygon(ax, poly, color)\n",
+    "## ----------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "soil_colors = ['green', 'brown', '#ceFFde']\n",
+    "\n",
+    "dst_geoms = OrderedDict()\n",
+    "src_geoms = OrderedDict()\n",
+    "\n",
+    "cnt = 0\n",
+    "with fiona.open( out_farmville ) as f:\n",
+    "    for rec in f:\n",
+    "        src_geoms[cnt] = shape(rec['geometry'])\n",
+    "        cnt += 1\n",
+    "\n",
+    "cnt = 0\n",
+    "with fiona.open( out_farmville_soils ) as f:\n",
+    "    for rec in f:\n",
+    "        ## check the geometry type if desired\n",
+    "        ##  intersections may result in POINT or LINESTRING\n",
+    "        if rec['geometry']['type'] != 'Polygon':\n",
+    "            continue\n",
+    "        gsl = rec['properties']['GSL_NAME']\n",
+    "        dst_geoms[gsl] = shape(rec['geometry'])\n",
+    "        cnt += 1\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "fig, ax = plt.subplots(figsize=(11,11))\n",
+    "plt.title(\"Soil Class and Urban Area via N. Carolina Sample Dataset (CC-by-SA) OSGeo\")\n",
+    "\n",
+    "cnt = 0\n",
+    "for key in dst_geoms :\n",
+    "    ## cnt mod (number of colors) is always a safe index into colors[]\n",
+    "    color = soil_colors[ cnt%len(soil_colors) ]\n",
+    "    plot_multipolygon(ax, dst_geoms[key], color=color)\n",
+    "    cnt += 1\n",
+    "    \n",
+    "cnt = 0\n",
+    "color = 'gray'\n",
+    "with fiona.open( out_farmville ) as f:\n",
+    "    for rec in f:\n",
+    "        plot_multipolygon(ax, src_geoms[cnt], color=color)\n",
+    "        cnt += 1\n",
+    "\n",
+    "#ax.add_patch(Polygon( src_geoms[0].centroid, facecolor='black', alpha=0.5))\n",
+    "\n",
+    "labels = ax.get_xticklabels() \n",
+    "for label in labels: \n",
+    "    label.set_rotation(90) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## show all the variables defined so far\n",
+    "%whos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## what geomtries have been created ?\n",
+    "print 'src_geoms len: ',len(src_geoms)\n",
+    "print 'dst_geoms len: ',len(dst_geoms )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## buffer the urbanarea by N meters\n",
+    "##  save the result in the source-geometry list\n",
+    "\n",
+    "src_geom_buffered = src_geoms[0].buffer(166)\n",
+    "src_geoms[1] = src_geom_buffered\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(11,11))\n",
+    "\n",
+    "cnt = 0\n",
+    "for key in dst_geoms :\n",
+    "    color = soil_colors[ cnt%len(soil_colors) ]\n",
+    "    plot_multipolygon(ax, dst_geoms[key], color=color)\n",
+    "    cnt += 1\n",
+    "\n",
+    "plot_multipolygon(ax, src_geoms[1], color='gray')\n",
+    "plot_multipolygon(ax, src_geoms[0], color='gray')\n",
+    "    \n",
+    "labels = ax.get_xticklabels() \n",
+    "for label in labels: \n",
+    "    label.set_rotation(90) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## take the intersection of each soil area against the buffered poly of interest\n",
+    "##  store into a third convenient list\n",
+    "res_geoms = OrderedDict()\n",
+    "for key in dst_geoms:\n",
+    "    res_geoms[key] = src_geom_buffered.intersection( dst_geoms[key] )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(11,11))\n",
+    "\n",
+    "cnt = 0\n",
+    "for key in res_geoms :\n",
+    "    color = soil_colors[ cnt%len(soil_colors) ]\n",
+    "    plot_multipolygon(ax, res_geoms[key], color=color)\n",
+    "    cnt += 1\n",
+    "    \n",
+    "labels = ax.get_xticklabels() \n",
+    "for label in labels: \n",
+    "    label.set_rotation(90) \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## define a lookup table of mock attributes of each soil type\n",
+    "##  (substitute a real data source here)\n",
+    "\n",
+    "soils_lkup = {\n",
+    "  'NC038' : { 'TEXTURE_CLAY':0.53, 'TEXTURE_SILT':0.33, 'TEXTURE_SAND':0.44 },\n",
+    "  'NC035' : { 'TEXTURE_CLAY':0.70, 'TEXTURE_SILT':0.33, 'TEXTURE_SAND':0.44 },\n",
+    "  'NC034' : { 'TEXTURE_CLAY':0.23, 'TEXTURE_SILT':0.74, 'TEXTURE_SAND':0.44 }\n",
+    "}\n",
+    "\n",
+    "## get a rough total area for display purposes\n",
+    "sum_areas = 0.0\n",
+    "for key in res_geoms:\n",
+    "    sum_areas += int(res_geoms[key].area)\n",
+    "\n",
+    "## record the area and percentage area in a convenient dictionary \n",
+    "tdd = {}\n",
+    "for key in res_geoms:\n",
+    "    tdd[key] = soils_lkup[key]\n",
+    "    tdd[key]['area'] = int(res_geoms[key].area)\n",
+    "    tdd[key]['perc'] = int((res_geoms[key].area/sum_areas)*100)/100.0\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Now all attributes for visualization are available in a single dict\n",
+    "##  in a larger system this could be delivered for serving graphical reports\n",
+    "tdd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## What matplotlib pylot graphs are available ?\n",
+    "## http://matplotlib.org/api/pyplot_api.html\n",
+    "##\n",
+    "##   Use IPython built-in help to discover pie chart attributes\n",
+    "\n",
+    "plt.pie?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# The slices will be ordered and plotted counter-clockwise.\n",
+    "labels = [ 'NC034', 'NC035', 'NC038' ]\n",
+    "sizes = [  tdd['NC034']['perc']*100, tdd['NC035']['perc']*100, tdd['NC038']['perc']*100 ]\n",
+    "pie_colors = ['lightcoral', '#eeefee', 'yellowgreen']\n",
+    "# only \"explode\" the 2nd slice (i.e. 'NC035')\n",
+    "explode = (0, 0.1, 0)\n",
+    "\n",
+    "plt.pie(sizes, explode=explode, labels=labels, colors=pie_colors,\n",
+    "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
+    "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
+    "plt.axis('equal')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FOLIUM/US_Unemployment_Oct2012.csv b/projects/FOLIUM/US_Unemployment_Oct2012.csv
new file mode 100644
index 0000000..d163410
--- /dev/null
+++ b/projects/FOLIUM/US_Unemployment_Oct2012.csv
@@ -0,0 +1,51 @@
+State,Unemployment
+AL,7.1
+AK,6.8
+AZ,8.1
+AR,7.2
+CA,10.1
+CO,7.7
+CT,8.4
+DE,7.1
+FL,8.2
+GA,8.8
+HI,5.4
+ID,6.6
+IL,8.8
+IN,8.4
+IA,5.1
+KS,5.6
+KY,8.1
+LA,5.9
+ME,7.2
+MD,6.8
+MA,6.7
+MI,9.1
+MN,5.6
+MS,9.1
+MO,6.7
+MT,5.8
+NE,3.9
+NV,10.3
+NH,5.7
+NJ,9.6
+NM,6.8
+NY,8.4
+NC,9.4
+ND,3.2
+OH,6.9
+OK,5.2
+OR,8.5
+PA,8
+RI,10.1
+SC,8.8
+SD,4.4
+TN,7.8
+TX,6.4
+UT,5.5
+VT,5
+VA,5.8
+WA,7.8
+WV,7.5
+WI,6.8
+WY,5.1
\ No newline at end of file
diff --git a/projects/FOLIUM/data.json b/projects/FOLIUM/data.json
new file mode 100644
index 0000000..25c9697
--- /dev/null
+++ b/projects/FOLIUM/data.json
@@ -0,0 +1 @@
+[{"WA": 7.8, "DE": 7.1, "WI": 6.8, "WV": 7.5, "HI": 5.4, "FL": 8.2, "WY": 5.1, "NH": 5.7, "NJ": 9.6, "NM": 6.8, "TX": 6.4, "LA": 5.9, "NC": 9.4, "ND": 3.2, "NE": 3.9, "TN": 7.8, "NY": 8.4, "PA": 8.0, "CA": 10.1, "NV": 10.3, "VA": 5.8, "CO": 7.7, "AK": 6.8, "AL": 7.1, "AR": 7.2, "VT": 5.0, "IL": 8.8, "GA": 8.8, "IN": 8.4, "IA": 5.1, "OK": 5.2, "AZ": 8.1, "ID": 6.6, "CT": 8.4, "ME": 7.2, "MD": 6.8, "MA": 6.7, "OH": 6.9, "UT": 5.5, "MO": 6.7, "MN": 5.6, "MI": 9.1, "RI": 10.1, "KS": 5.6, "MT": 5.8, "MS": 9.1, "SC": 8.8, "KY": 8.1, "OR": 8.5, "SD": 4.4}]
\ No newline at end of file
diff --git a/projects/FOLIUM/data1.json b/projects/FOLIUM/data1.json
new file mode 100644
index 0000000..5a30e94
--- /dev/null
+++ b/projects/FOLIUM/data1.json
@@ -0,0 +1 @@
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diff --git a/projects/FOLIUM/data2.json b/projects/FOLIUM/data2.json
new file mode 100644
index 0000000..3b96f28
--- /dev/null
+++ b/projects/FOLIUM/data2.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/projects/FOLIUM/data3.json b/projects/FOLIUM/data3.json
new file mode 100644
index 0000000..7744921
--- /dev/null
+++ b/projects/FOLIUM/data3.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/projects/FOLIUM/folium-clustered-markers.ipynb b/projects/FOLIUM/folium-clustered-markers.ipynb
new file mode 100644
index 0000000..f6d1782
--- /dev/null
+++ b/projects/FOLIUM/folium-clustered-markers.ipynb
@@ -0,0 +1,98 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import folium\n",
+    "\n",
+    "def inline_map(map):\n",
+    "    \"\"\"\n",
+    "    Embeds the HTML source of the map directly into the IPython notebook.\n",
+    "    \n",
+    "    This method will not work if the map depends on any files (json data). Also this uses\n",
+    "    the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
+    "    \"\"\"\n",
+    "    map._build_map()\n",
+    "    return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
+    "\n",
+    "def embed_map(map, path=\"m213map.html\"):\n",
+    "    \"\"\"\n",
+    "    Embeds a linked iframe to the map into the IPython notebook.\n",
+    "    \n",
+    "    Note: this method will not capture the source of the map into the notebook.\n",
+    "    This method should work for all maps (as long as they use relative urls).\n",
+    "    \"\"\"\n",
+    "    map.create_map(path=path)\n",
+    "    return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map = folium.Map(width=500,height=500,location=[44, -73], zoom_start=4)\n",
+    "\n",
+    "map.simple_marker([40.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='green',marker_icon='ok-sign',clustered_marker=True)\n",
+    "map.simple_marker([44.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='red',marker_icon='remove-sign',clustered_marker=True)\n",
+    "map.simple_marker([44.67, -71.94], popup='Add <b>popup</b> text here.',clustered_marker=True)\n",
+    "\n",
+    "map.circle_marker([44, -71], popup='', fill_color='#ff0000', radius=5000, line_color='#ff0000')\n",
+    "\n",
+    "points1 = [40,-71]\n",
+    "points2 = [42,-73]\n",
+    "map.line([points1, points2])\n",
+    "\n",
+    "inline_map(map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FOLIUM/folium-examples.ipynb b/projects/FOLIUM/folium-examples.ipynb
new file mode 100644
index 0000000..37cc498
--- /dev/null
+++ b/projects/FOLIUM/folium-examples.ipynb
@@ -0,0 +1,288 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import folium\n",
+    "import pandas as pd\n",
+    "\n",
+    "#folium.initialize_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Standard OSM\n",
+    "map_osm = folium.Map(location=[45.5236, -122.6750])\n",
+    "map_osm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "stamen = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner',\n",
+    "                    zoom_start=13)\n",
+    "stamen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mt_hood = folium.Map(location=[45.372, -121.6972], zoom_start=12,\n",
+    "                     tiles='Stamen Terrain')\n",
+    "mt_hood.simple_marker([45.3288, -121.6625], popup='Mt. Hood Meadows')\n",
+    "mt_hood.simple_marker([45.3311, -121.7113], popup='Timberline Lodge')\n",
+    "mt_hood"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "portland = folium.Map(location=[45.5236, -122.6750], tiles='Stamen Toner',\n",
+    "                      zoom_start=13)\n",
+    "portland.simple_marker(location=[45.5244, -122.6699], popup='The Waterfront')\n",
+    "portland.circle_marker(location=[45.5215, -122.6261], radius=500,\n",
+    "                       popup='Laurelhurst Park', line_color='#3186cc',\n",
+    "                       fill_color='#3186cc')\n",
+    "portland"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "latlng = folium.Map(location=[46.1991, -122.1889], tiles='Stamen Terrain',\n",
+    "                    zoom_start=13)\n",
+    "latlng.lat_lng_popover()\n",
+    "latlng"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "waypoints = folium.Map(location=[46.8527, -121.7649], tiles='Stamen Terrain',\n",
+    "                       zoom_start=13)\n",
+    "waypoints.simple_marker(location=[46.8354, -121.7325], popup='Camp Muir')\n",
+    "waypoints.click_for_marker(popup='Waypoint')\n",
+    "waypoints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "polygons = folium.Map(location=[45.5236, -122.6750], zoom_start=13)\n",
+    "polygons.polygon_marker(location=[45.5012, -122.6655], popup='Ross Island Bridge',\n",
+    "                        fill_color='#132b5e', num_sides=3, radius=10)\n",
+    "polygons.polygon_marker(location=[45.5132, -122.6708], popup='Hawthorne Bridge',\n",
+    "                        fill_color='#45647d', num_sides=4, radius=10)\n",
+    "polygons.polygon_marker(location=[45.5275, -122.6692], popup='Steel Bridge',\n",
+    "                        fill_color='#769d96', num_sides=6, radius=10)\n",
+    "polygons.polygon_marker(location=[45.5318, -122.6745], popup='Broadway Bridge',\n",
+    "                        fill_color='#769d96', num_sides=8, radius=10)\n",
+    "polygons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "state_geo = r'us-states.json'\n",
+    "state_unemployment = r'US_Unemployment_Oct2012.csv'\n",
+    "\n",
+    "state_data = pd.read_csv(state_unemployment)\n",
+    "\n",
+    "#Let Folium determine the scale\n",
+    "states = folium.Map(location=[48, -102], zoom_start=3)\n",
+    "states.geo_json(geo_path=state_geo, data=state_data,\n",
+    "                columns=['State', 'Unemployment'],\n",
+    "                key_on='feature.id',\n",
+    "                fill_color='YlGn', fill_opacity=0.7, line_opacity=0.2,\n",
+    "                legend_name='Unemployment Rate (%)')\n",
+    "states.create_map(path='us_state_map.html')\n",
+    "states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "states2 = folium.Map(location=[48, -102], zoom_start=3)\n",
+    "states2.geo_json(geo_path=state_geo, data=state_data,\n",
+    "                 columns=['State', 'Unemployment'],\n",
+    "                 threshold_scale=[5, 6, 7, 8, 9, 10],\n",
+    "                 key_on='feature.id',\n",
+    "                 fill_color='BuPu', fill_opacity=0.7, line_opacity=0.5,\n",
+    "                 legend_name='Unemployment Rate (%)',\n",
+    "                 reset=True)\n",
+    "states2.create_map(path='us_state_map_2.html')\n",
+    "states2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "county_data = r'us_county_data.csv'\n",
+    "county_geo = r'us_counties_20m_topo.json'\n",
+    "\n",
+    "#Read into Dataframe, cast to string for consistency\n",
+    "df = pd.read_csv(county_data, na_values=[' '])\n",
+    "df['FIPS_Code'] = df['FIPS_Code'].astype(str)\n",
+    "\n",
+    "\n",
+    "def set_id(fips):\n",
+    "    '''Modify FIPS code to match GeoJSON property'''\n",
+    "    if fips == '0':\n",
+    "        return None\n",
+    "    elif len(fips) <= 4:\n",
+    "        return ''.join(['0500000US0', fips])\n",
+    "    else:\n",
+    "        return ''.join(['0500000US', fips])\n",
+    "\n",
+    "#Apply set_id, drop NaN\n",
+    "df['GEO_ID'] = df['FIPS_Code'].apply(set_id)\n",
+    "df = df.dropna()\n",
+    "\n",
+    "#Number of employed with auto scale\n",
+    "map_1 = folium.Map(location=[48, -102], zoom_start=3)\n",
+    "map_1.geo_json(geo_path=county_geo, data_out='data1.json', data=df,\n",
+    "               columns=['GEO_ID', 'Employed_2011'],\n",
+    "               key_on='feature.id',\n",
+    "               fill_color='YlOrRd', fill_opacity=0.7, line_opacity=0.3,\n",
+    "               topojson='objects.us_counties_20m')\n",
+    "map_1.create_map(path='map_1.html')\n",
+    "map_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map_2 = folium.Map(location=[40, -99], zoom_start=4)\n",
+    "map_2.geo_json(geo_path=county_geo, data_out='data2.json', data=df,\n",
+    "               columns=['GEO_ID', 'Unemployment_rate_2011'],\n",
+    "               key_on='feature.id',\n",
+    "               threshold_scale=[0, 5, 7, 9, 11, 13],\n",
+    "               fill_color='YlGnBu', line_opacity=0.3,\n",
+    "               legend_name='Unemployment Rate 2011 (%)',\n",
+    "               topojson='objects.us_counties_20m')\n",
+    "map_2.create_map(path='map_2.html')\n",
+    "map_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map_3 = folium.Map(location=[40, -99], zoom_start=4)\n",
+    "map_3.geo_json(geo_path=county_geo, data_out='data3.json', data=df,\n",
+    "               columns=['GEO_ID', 'Median_Household_Income_2011'],\n",
+    "               key_on='feature.id',\n",
+    "               fill_color='PuRd', line_opacity=0.3,\n",
+    "               legend_name='Median Household Income 2011 ($)',\n",
+    "               topojson='objects.us_counties_20m')\n",
+    "map_3.create_map(path='map_3.html')\n",
+    "map_3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FOLIUM/folium-markers-popup.ipynb b/projects/FOLIUM/folium-markers-popup.ipynb
new file mode 100644
index 0000000..adea228
--- /dev/null
+++ b/projects/FOLIUM/folium-markers-popup.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def inline_map(map):\n",
+    "    \"\"\"\n",
+    "    Embeds the HTML source of the map directly into the IPython notebook.\n",
+    "    \n",
+    "    This method will not work if the map depends on any files (json data). Also this uses\n",
+    "    the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
+    "    \"\"\"\n",
+    "    map._build_map()\n",
+    "    return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
+    "\n",
+    "def embed_map(map, path=\"m213map.html\"):\n",
+    "    \"\"\"\n",
+    "    Embeds a linked iframe to the map into the IPython notebook.\n",
+    "    \n",
+    "    Note: this method will not capture the source of the map into the notebook.\n",
+    "    This method should work for all maps (as long as they use relative urls).\n",
+    "    \"\"\"\n",
+    "    map.create_map(path=path)\n",
+    "    return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map = folium.Map(width=500,height=500,location=[44, -73], zoom_start=4)\n",
+    "\n",
+    "map.simple_marker([40.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='green',marker_icon='ok-sign')\n",
+    "map.simple_marker([44.67, -73.94], popup='Add <b>popup</b> text here.',marker_color='red',marker_icon='remove-sign')\n",
+    "map.simple_marker([44.67, -71.94], popup='Add <b>popup</b> text here.')\n",
+    "inline_map(map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FOLIUM/folium-rotate.ipynb b/projects/FOLIUM/folium-rotate.ipynb
new file mode 100644
index 0000000..fcf9f34
--- /dev/null
+++ b/projects/FOLIUM/folium-rotate.ipynb
@@ -0,0 +1,83 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import folium\n",
+    "from IPython.display import HTML, Javascript, display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def inline_map(m):\n",
+    "    m._build_map()\n",
+    "    srcdoc = m.HTML.replace('\"', '&quot;')\n",
+    "    embed = HTML('<iframe srcdoc=\"{srcdoc}\" '\n",
+    "                 'style=\"width: 100%; height: 500px; '\n",
+    "                 'border: none\"></iframe>'.format(srcdoc=srcdoc))\n",
+    "    return embed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#station =  st_list[st_list.keys()[0]]\n",
+    "#map = folium.Map(location=[station[\"lat\"], station[\"lon\"]], zoom_start=4)\n",
+    "\n",
+    "map = folium.Map(location=[41, -71], zoom_start=4)\n",
+    "#map.line(get_coordinates(bounding_box, bounding_box_type), line_color='#FF0000', line_weight=5)\n",
+    "\n",
+    "#plot the obs station, \n",
+    "#for st in st_list:        \n",
+    "map.simple_marker([41,-72], popup='something something...<br>',marker_color=\"green\",marker_icon=\"arrow-up\",icon_angle=0)\n",
+    "inline_map(map)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FOLIUM/folium-wms.ipynb b/projects/FOLIUM/folium-wms.ipynb
new file mode 100644
index 0000000..d9f5813
--- /dev/null
+++ b/projects/FOLIUM/folium-wms.ipynb
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def inline_map(map):\n",
+    "    \"\"\"\n",
+    "    Embeds the HTML source of the map directly into the IPython notebook.\n",
+    "    \n",
+    "    This method will not work if the map depends on any files (json data). Also this uses\n",
+    "    the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
+    "    \"\"\"\n",
+    "    map._build_map()\n",
+    "    return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
+    "\n",
+    "def embed_map(map, path=\"map.html\"):\n",
+    "    \"\"\"\n",
+    "    Embeds a linked iframe to the map into the IPython notebook.\n",
+    "    \n",
+    "    Note: this method will not capture the source of the map into the notebook.\n",
+    "    This method should work for all maps (as long as they use relative urls).\n",
+    "    \"\"\"\n",
+    "    map.create_map(path=path)\n",
+    "    return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map = folium.Map(width=800,height=600,location=[44, -73], zoom_start=3)\n",
+    "\n",
+    "map.add_wms_layer(wms_name=\"Temperature\",\n",
+    "                  wms_url=\"http://gis.srh.noaa.gov/arcgis/services/NDFDTemps/MapServer/WMSServer\",\n",
+    "                  wms_format=\"image/png\",\n",
+    "                  wms_layers= 16\n",
+    "                  )\n",
+    "\n",
+    "map.add_tile_layer(tile_name='hfradar 1km',\n",
+    "                   tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-18 14:00:00&rez=1')\n",
+    "map.add_tile_layer(tile_name='hfradar 2km',\n",
+    "                   tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-18 14:00:00&rez=2')\n",
+    "map.add_tile_layer(tile_name='hfradar 6km',\n",
+    "                   tile_url='http://hfradar.ndbc.noaa.gov/tilesavg.php?s=10&e=100&x={x}&y={y}&z={z}&t=2014-8-18 14:00:00&rez=6')\n",
+    "\n",
+    "map.add_layers_to_map()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "inline_map(map)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/FOLIUM/map_1.html b/projects/FOLIUM/map_1.html
new file mode 100644
index 0000000..8eaa535
--- /dev/null
+++ b/projects/FOLIUM/map_1.html
@@ -0,0 +1,165 @@
+<!DOCTYPE html>
+<head>
+   <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
+
+   
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/topojson/1.6.9/topojson.min.js"></script>
+   
+   
+
+   <style>
+
+      html, body {
+        width: 100%;
+        height: 100%;
+        margin: 0;
+        padding: 0;
+      }
+
+      .legend {
+          padding: 0px 0px;
+          font: 10px sans-serif;
+          background: white;
+          background: rgba(255,255,255,0.8);
+          box-shadow: 0 0 15px rgba(0,0,0,0.2);
+          border-radius: 5px;
+      }
+
+      .key path {
+        display: none;
+      }
+
+   </style>
+</head>
+
+<body>
+
+   <div id="map" style="width: 100%; height: 100%"></div>
+
+   <script>
+
+      queue()
+          .defer(d3.json, 'data1.json')
+          .defer(d3.json, 'us_counties_20m_topo.json')
+          .await(makeMap)
+
+      function onEachFeature(feature, layer) {
+          // does this feature have a property named popupContent?
+          if (feature.properties && feature.properties.popupContent) {
+              layer.bindPopup(feature.properties.popupContent);
+              }
+          };
+
+      function makeMap(error, data_1,tjson_1) {
+
+          
+          topo_1 = topojson.feature(tjson_1, tjson_1.objects.us_counties_20m);
+          
+
+          
+
+          function matchKey(datapoint, key_variable){
+              if (typeof key_variable[0][datapoint] === 'undefined') {
+                  return null;
+              }
+              else {
+                  return parseFloat(key_variable[0][datapoint]);
+              };
+          };
+
+          
+          var color = d3.scale.threshold()
+              .domain([40.0, 10000.0, 30000.0, 70000.0, 100000.0])
+              .range(['#FFFFB2', '#FED976', '#FEB24C', '#FD8D3C', '#FC4E2A', '#E31A1C']);
+          
+
+          var map = L.map('map').setView([48, -102], 3);
+
+          L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
+              maxZoom: 18,
+              minZoom: 1,
+              attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
+          }).addTo(map);
+
+          
+          function style_1(feature) {
+    return {
+        fillColor: color(matchKey(feature.id, data_1)),
+        weight: 1,
+        opacity: 0.3,
+        color: 'black',
+        fillOpacity: 0.7
+    };
+}
+          
+
+          
+          gJson_layer_1 = L.geoJson(topo_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
+          
+
+          
+              var legend = L.control({position: 'topright'});
+
+    legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
+
+    legend.addTo(map);
+
+    var x = d3.scale.linear()
+    .domain([0, 110000])
+    .range([0, 400]);
+
+    var xAxis = d3.svg.axis()
+        .scale(x)
+        .orient("top")
+        .tickSize(1)
+        .tickValues([40.0, 10000.0, 30000.0, 70000.0, 100000.0]);
+
+    var svg = d3.select(".legend.leaflet-control").append("svg")
+        .attr("id", 'legend')
+        .attr("width", 450)
+        .attr("height", 40);
+
+    var g = svg.append("g")
+        .attr("class", "key")
+        .attr("transform", "translate(25,16)");
+
+    g.selectAll("rect")
+        .data(color.range().map(function(d, i) {
+          return {
+            x0: i ? x(color.domain()[i - 1]) : x.range()[0],
+            x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
+            z: d
+          };
+        }))
+      .enter().append("rect")
+        .attr("height", 10)
+        .attr("x", function(d) { return d.x0; })
+        .attr("width", function(d) { return d.x1 - d.x0; })
+        .style("fill", function(d) { return d.z; });
+
+    g.call(xAxis).append("text")
+        .attr("class", "caption")
+        .attr("y", 21)
+        .text('Employed_2011');
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+      };
+
+   </script>
+</body>
\ No newline at end of file
diff --git a/projects/FOLIUM/map_2.html b/projects/FOLIUM/map_2.html
new file mode 100644
index 0000000..374ee58
--- /dev/null
+++ b/projects/FOLIUM/map_2.html
@@ -0,0 +1,165 @@
+<!DOCTYPE html>
+<head>
+   <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
+
+   
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/topojson/1.6.9/topojson.min.js"></script>
+   
+   
+
+   <style>
+
+      html, body {
+        width: 100%;
+        height: 100%;
+        margin: 0;
+        padding: 0;
+      }
+
+      .legend {
+          padding: 0px 0px;
+          font: 10px sans-serif;
+          background: white;
+          background: rgba(255,255,255,0.8);
+          box-shadow: 0 0 15px rgba(0,0,0,0.2);
+          border-radius: 5px;
+      }
+
+      .key path {
+        display: none;
+      }
+
+   </style>
+</head>
+
+<body>
+
+   <div id="map" style="width: 100%; height: 100%"></div>
+
+   <script>
+
+      queue()
+          .defer(d3.json, 'data2.json')
+          .defer(d3.json, 'us_counties_20m_topo.json')
+          .await(makeMap)
+
+      function onEachFeature(feature, layer) {
+          // does this feature have a property named popupContent?
+          if (feature.properties && feature.properties.popupContent) {
+              layer.bindPopup(feature.properties.popupContent);
+              }
+          };
+
+      function makeMap(error, data_1,tjson_1) {
+
+          
+          topo_1 = topojson.feature(tjson_1, tjson_1.objects.us_counties_20m);
+          
+
+          
+
+          function matchKey(datapoint, key_variable){
+              if (typeof key_variable[0][datapoint] === 'undefined') {
+                  return null;
+              }
+              else {
+                  return parseFloat(key_variable[0][datapoint]);
+              };
+          };
+
+          
+          var color = d3.scale.threshold()
+              .domain([0, 5, 7, 9, 11, 13])
+              .range(['#FFFFCC', '#C7E9B4', '#7FCDBB', '#41B6C4', '#1D91C0', '#225EA8', '#0C2C84']);
+          
+
+          var map = L.map('map').setView([40, -99], 4);
+
+          L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
+              maxZoom: 18,
+              minZoom: 1,
+              attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
+          }).addTo(map);
+
+          
+          function style_1(feature) {
+    return {
+        fillColor: color(matchKey(feature.id, data_1)),
+        weight: 1,
+        opacity: 0.3,
+        color: 'black',
+        fillOpacity: 0.6
+    };
+}
+          
+
+          
+          gJson_layer_1 = L.geoJson(topo_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
+          
+
+          
+              var legend = L.control({position: 'topright'});
+
+    legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
+
+    legend.addTo(map);
+
+    var x = d3.scale.linear()
+    .domain([0, 14])
+    .range([0, 400]);
+
+    var xAxis = d3.svg.axis()
+        .scale(x)
+        .orient("top")
+        .tickSize(1)
+        .tickValues([0, 5, 7, 9, 11, 13]);
+
+    var svg = d3.select(".legend.leaflet-control").append("svg")
+        .attr("id", 'legend')
+        .attr("width", 450)
+        .attr("height", 40);
+
+    var g = svg.append("g")
+        .attr("class", "key")
+        .attr("transform", "translate(25,16)");
+
+    g.selectAll("rect")
+        .data(color.range().map(function(d, i) {
+          return {
+            x0: i ? x(color.domain()[i - 1]) : x.range()[0],
+            x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
+            z: d
+          };
+        }))
+      .enter().append("rect")
+        .attr("height", 10)
+        .attr("x", function(d) { return d.x0; })
+        .attr("width", function(d) { return d.x1 - d.x0; })
+        .style("fill", function(d) { return d.z; });
+
+    g.call(xAxis).append("text")
+        .attr("class", "caption")
+        .attr("y", 21)
+        .text('Unemployment Rate 2011 (%)');
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+      };
+
+   </script>
+</body>
\ No newline at end of file
diff --git a/projects/FOLIUM/map_3.html b/projects/FOLIUM/map_3.html
new file mode 100644
index 0000000..789c155
--- /dev/null
+++ b/projects/FOLIUM/map_3.html
@@ -0,0 +1,165 @@
+<!DOCTYPE html>
+<head>
+   <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
+
+   
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/topojson/1.6.9/topojson.min.js"></script>
+   
+   
+
+   <style>
+
+      html, body {
+        width: 100%;
+        height: 100%;
+        margin: 0;
+        padding: 0;
+      }
+
+      .legend {
+          padding: 0px 0px;
+          font: 10px sans-serif;
+          background: white;
+          background: rgba(255,255,255,0.8);
+          box-shadow: 0 0 15px rgba(0,0,0,0.2);
+          border-radius: 5px;
+      }
+
+      .key path {
+        display: none;
+      }
+
+   </style>
+</head>
+
+<body>
+
+   <div id="map" style="width: 100%; height: 100%"></div>
+
+   <script>
+
+      queue()
+          .defer(d3.json, 'data3.json')
+          .defer(d3.json, 'us_counties_20m_topo.json')
+          .await(makeMap)
+
+      function onEachFeature(feature, layer) {
+          // does this feature have a property named popupContent?
+          if (feature.properties && feature.properties.popupContent) {
+              layer.bindPopup(feature.properties.popupContent);
+              }
+          };
+
+      function makeMap(error, data_1,tjson_1) {
+
+          
+          topo_1 = topojson.feature(tjson_1, tjson_1.objects.us_counties_20m);
+          
+
+          
+
+          function matchKey(datapoint, key_variable){
+              if (typeof key_variable[0][datapoint] === 'undefined') {
+                  return null;
+              }
+              else {
+                  return parseFloat(key_variable[0][datapoint]);
+              };
+          };
+
+          
+          var color = d3.scale.threshold()
+              .domain([20000.0, 40000.0, 50000.0, 50000.0, 60000.0])
+              .range(['#F1EEF6', '#D4B9DA', '#C994C7', '#DF65B0', '#E7298A', '#CE1256']);
+          
+
+          var map = L.map('map').setView([40, -99], 4);
+
+          L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
+              maxZoom: 18,
+              minZoom: 1,
+              attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
+          }).addTo(map);
+
+          
+          function style_1(feature) {
+    return {
+        fillColor: color(matchKey(feature.id, data_1)),
+        weight: 1,
+        opacity: 0.3,
+        color: 'black',
+        fillOpacity: 0.6
+    };
+}
+          
+
+          
+          gJson_layer_1 = L.geoJson(topo_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
+          
+
+          
+              var legend = L.control({position: 'topright'});
+
+    legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
+
+    legend.addTo(map);
+
+    var x = d3.scale.linear()
+    .domain([0, 66000])
+    .range([0, 400]);
+
+    var xAxis = d3.svg.axis()
+        .scale(x)
+        .orient("top")
+        .tickSize(1)
+        .tickValues([20000.0, 40000.0, 50000.0, 50000.0, 60000.0]);
+
+    var svg = d3.select(".legend.leaflet-control").append("svg")
+        .attr("id", 'legend')
+        .attr("width", 450)
+        .attr("height", 40);
+
+    var g = svg.append("g")
+        .attr("class", "key")
+        .attr("transform", "translate(25,16)");
+
+    g.selectAll("rect")
+        .data(color.range().map(function(d, i) {
+          return {
+            x0: i ? x(color.domain()[i - 1]) : x.range()[0],
+            x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
+            z: d
+          };
+        }))
+      .enter().append("rect")
+        .attr("height", 10)
+        .attr("x", function(d) { return d.x0; })
+        .attr("width", function(d) { return d.x1 - d.x0; })
+        .style("fill", function(d) { return d.z; });
+
+    g.call(xAxis).append("text")
+        .attr("class", "caption")
+        .attr("y", 21)
+        .text('Median Household Income 2011 ($)');
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+      };
+
+   </script>
+</body>
\ No newline at end of file
diff --git a/projects/FOLIUM/us-states.json b/projects/FOLIUM/us-states.json
new file mode 100644
index 0000000..3036216
--- /dev/null
+++ b/projects/FOLIUM/us-states.json
@@ -0,0 +1,52 @@
+{"type":"FeatureCollection","features":[
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diff --git a/projects/FOLIUM/us_counties_20m_topo.json b/projects/FOLIUM/us_counties_20m_topo.json
new file mode 100644
index 0000000..11cf8a2
--- /dev/null
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diff --git a/projects/FOLIUM/us_county_data.csv b/projects/FOLIUM/us_county_data.csv
new file mode 100644
index 0000000..ba47d92
--- /dev/null
+++ b/projects/FOLIUM/us_county_data.csv
@@ -0,0 +1 @@
+FIPS_Code,State,Area_name,Civilian_labor_force_2011,Employed_2011,Unemployed_2011,Unemployment_rate_2011,Median_Household_Income_2011,Med_HH_Income_Percent_of_StateTotal_2011
00000,US,United States,154505871,140674478,13831393,9,50502,100
01000,AL,Alabama,2190519,1993977,196542,9,41427,100
01001,AL,Autauga County,25930,23854,2076,8,48863,117.9
01003,AL,Baldwin County,85407,78491,6916,8.1,50144,121
01005,AL,Barbour County,9761,8651,1110,11.4,30117,72.7
01007,AL,Bibb County,9216,8303,913,9.9,37347,90.2
01009,AL,Blount County,26347,24156,2191,8.3,41940,101.2
01011,AL,Bullock County,3762,3186,576,15.3,26038,62.9
01013,AL,Butler County,9311,8231,1080,11.6,30489,73.6
01015,AL,Calhoun County,53790,48846,4944,9.2,39060,94.3
01017,AL,Chambers County,14837,13148,1689,11.4,33500,80.9
01019,AL,Cherokee County,11696,10676,1020,8.7,34738,83.9
01021,AL,Chilton County,19913,18206,1707,8.6,38833,93.7
01023,AL,Choctaw County,5144,4557,587,11.4,32940,79.5
01025,AL,Clarke County,10130,8592,1538,15.2,32642,78.8
01027,AL,Clay County,5296,4673,623,11.8,32454,78.3
01029,AL,Cleburne County,6452,5917,535,8.3,39153,94.5
01031,AL,Coffee County,21871,20324,1547,7.1,42173,101.8
01033,AL,Colbert County,25208,22940,2268,9,37269,90
01035,AL,Conecuh County,4633,3949,684,14.8,26914,65
01037,AL,Coosa County,4588,4100,488,10.6,34391,83
01039,AL,Covington County,16808,15317,1491,8.9,32138,77.6
01041,AL,Crenshaw County,6818,6227,591,8.7,34534,83.4
01043,AL,Cullman County,38557,35435,3122,8.1,39395,95.1
01045,AL,Dale County,20790,19063,1727,8.3,41568,100.3
01047,AL,Dallas County,15335,12848,2487,16.2,25859,62.4
01049,AL,DeKalb County,29232,25954,3278,11.2,35487,85.7
01051,AL,Elmore County,35777,32803,2974,8.3,54866,132.4
01053,AL,Escambia County,14962,13390,1572,10.5,32838,79.3
01055,AL,Etowah County,45824,41662,4162,9.1,34927,84.3
01057,AL,Fayette County,6629,5939,690,10.4,32648,78.8
01059,AL,Franklin County,13538,12262,1276,9.4,33705,81.4
01061,AL,Geneva County,11347,10406,941,8.3,33835,81.7
01063,AL,Greene County,3167,2717,450,14.2,24738,59.7
01065,AL,Hale County,5866,5164,702,12,31044,74.9
01067,AL,Henry County,7273,6659,614,8.4,39788,96
01069,AL,Houston County,46521,42733,3788,8.1,40428,97.6
01071,AL,Jackson County,26716,24403,2313,8.7,36746,88.7
01073,AL,Jefferson County,306677,279911,26766,8.7,42053,101.5
01075,AL,Lamar County,5396,4862,534,9.9,34731,83.8
01077,AL,Lauderdale County,44897,41167,3730,8.3,40195,97
01079,AL,Lawrence County,15740,14150,1590,10.1,38132,92
01081,AL,Lee County,67892,62859,5033,7.4,42539,102.7
01083,AL,Limestone County,39912,36886,3026,7.6,46760,112.9
01085,AL,Lowndes County,4352,3630,722,16.6,28043,67.7
01087,AL,Macon County,8814,7708,1106,12.5,27591,66.6
01089,AL,Madison County,173962,160806,13156,7.6,55298,133.5
01091,AL,Marengo County,7894,6966,928,11.8,31995,77.2
01093,AL,Marion County,11664,10347,1317,11.3,30926,74.7
01095,AL,Marshall County,41602,38044,3558,8.6,38876,93.8
01097,AL,Mobile County,192780,173367,19413,10.1,41867,101.1
01099,AL,Monroe County,7995,6770,1225,15.3,31072,75
01101,AL,Montgomery County,105355,95784,9571,9.1,42962,103.7
01103,AL,Morgan County,57829,52813,5016,8.7,43615,105.3
01105,AL,Perry County,3620,3027,593,16.4,22175,53.5
01107,AL,Pickens County,7947,7093,854,10.7,31079,75
01109,AL,Pike County,16390,15109,1281,7.8,32345,78.1
01111,AL,Randolph County,9004,8012,992,11,34517,83.3
01113,AL,Russell County,23088,20745,2343,10.1,32632,78.8
01115,AL,St. Clair County,37634,34504,3130,8.3,48040,116
01117,AL,Shelby County,102675,96205,6470,6.3,66362,160.2
01119,AL,Sumter County,4696,4016,680,14.5,23531,56.8
01121,AL,Talladega County,37433,33580,3853,10.3,33856,81.7
01123,AL,Tallapoosa County,17488,15485,2003,11.5,37686,91
01125,AL,Tuscaloosa County,92173,84646,7527,8.2,42086,101.6
01127,AL,Walker County,27677,25058,2619,9.5,35423,85.5
01129,AL,Washington County,6893,5931,962,14,39203,94.6
01131,AL,Wilcox County,3328,2686,642,19.3,20990,50.7
01133,AL,Winston County,9266,8028,1238,13.4,31018,74.9
02000,AK,Alaska,367042,339330,27712,7.6,65699,100
02013,AK,Aleutians East Borough,1186,1054,132,11.1,51289,78.1
02016,AK,Aleutians West Census Area,3175,2928,247,7.8,59362,90.4
02020,AK,Anchorage Municipality,157210,147604,9606,6.1,71237,108.4
02050,AK,Bethel Census Area,7261,6191,1070,14.7,43314,65.9
02060,AK,Bristol Bay Borough,1093,1050,43,3.9,61905,94.2
02068,AK,Denali Borough,1391,1253,138,9.9,68899,104.9
02070,AK,Dillingham Census Area,2140,1921,219,10.2,46427,70.7
02090,AK,Fairbanks North Star Borough,46859,43737,3122,6.7,64396,98
02100,AK,Haines Borough,1389,1275,114,8.2,51350,78.2
02105,AK,"Hoonah-Angoon Census Area, AK",1089,909,180,16.5,40896,62.2
02110,AK,Juneau City and Borough,18610,17631,979,5.3,74219,113
02122,AK,Kenai Peninsula Borough,27645,25071,2574,9.3,60360,91.9
02130,AK,Ketchikan Gateway Borough,8329,7725,604,7.3,59092,89.9
02150,AK,Kodiak Island Borough,7025,6537,488,6.9,66935,101.9
02164,AK,Lake and Peninsula Borough,1147,1056,91,7.9,47612,72.5
02170,AK,Matanuska-Susitna Borough,43079,39239,3840,8.9,68999,105
02180,AK,Nome Census Area,4150,3639,511,12.3,44766,68.1
02185,AK,North Slope Borough,5444,5149,295,5.4,73744,112.2
02188,AK,Northwest Arctic Borough,3114,2657,457,14.7,54680,83.2
02195,AK,Petersburg Census area,1701,1521,180,10.6,54434,82.9
02198,AK,Price of Wales Hyder Census Area,2493,2112,381,15.3,42649,64.9
02201,AK,Prince of Wales-Outer Ketchikan Census A, , , , , , 
02220,AK,Sitka City and Borough,4617,4334,283,6.1,61956,94.3
02230,AK,Skagway Municipality,679,582,97,14.3,61025,92.9
02232,AK,Skagway-Hoonah-Angoon Census Area, , , , , , 
02240,AK,Southeast Fairbanks Census Area,3608,3215,393,10.9,55617,84.7
02261,AK,Valdez-Cordova Census Area,5281,4801,480,9.1,63887,97.2
02270,AK,Wade Hampton Census Area,2815,2241,574,20.4,33206,50.5
02275,AK,Wrangell City and Borough,1076,984,92,8.6,43120,65.6
02280,AK,Wrangell-Petersburg Census Area, , , , , , 
02282,AK,Yakutat City and Borough,298,266,32,10.7,47575,72.4
02290,AK,Yukon-Koyukuk Census Area,3142,2650,492,15.7,33578,51.1
04000,AZ,Arizona,3034262,2746634,287628,9.5,46710,100
04001,AZ,Apache County,22500,18252,4248,18.9,30837,66
04003,AZ,Cochise County,61397,55992,5405,8.8,41237,88.3
04005,AZ,Coconino County,72898,66160,6738,9.2,45051,96.4
04007,AZ,Gila County,22527,20088,2439,10.8,35898,76.9
04009,AZ,Graham County,14558,13024,1534,10.5,40548,86.8
04011,AZ,Greenlee County,4083,3743,340,8.3,51145,109.5
04012,AZ,La Paz County,7394,6630,764,10.3,30679,65.7
04013,AZ,Maricopa County,1895128,1735369,159759,8.4,50785,108.7
04015,AZ,Mohave County,88006,78357,9649,11,35998,77.1
04017,AZ,Navajo County,40447,33963,6484,16,32425,69.4
04019,AZ,Pima County,465613,426406,39207,8.4,44102,94.4
04021,AZ,Pinal County,139864,125398,14466,10.3,45170,96.7
04023,AZ,Santa Cruz County,18390,15238,3152,17.1,35500,76
04025,AZ,Yavapai County,91959,82784,9175,10,41437,88.7
04027,AZ,Yuma County,89500,65230,24270,27.1,37894,81.1
05000,AR,Arkansas,1369853,1259866,109987,8,38889,100
05001,AR,Arkansas County,11598,10340,1258,10.8,37922,97.5
05003,AR,Ashley County,9612,8531,1081,11.2,37170,95.6
05005,AR,Baxter County,17203,15716,1487,8.6,33312,85.7
05007,AR,Benton County,108995,102122,6873,6.3,52644,135.4
05009,AR,Boone County,17308,16033,1275,7.4,38411,98.8
05011,AR,Bradley County,5014,4457,557,11.1,31118,80
05013,AR,Calhoun County,2578,2336,242,9.4,35464,91.2
05015,AR,Carroll County,14223,13323,900,6.3,33579,86.3
05017,AR,Chicot County,4640,4086,554,11.9,27966,71.9
05019,AR,Clark County,10969,9930,1039,9.5,34478,88.7
05021,AR,Clay County,6352,5479,873,13.7,32564,83.7
05023,AR,Cleburne County,12040,11089,951,7.9,42265,108.7
05025,AR,Cleveland County,4125,3798,327,7.9,41864,107.6
05027,AR,Columbia County,10579,9583,996,9.4,33533,86.2
05029,AR,Conway County,10348,9491,857,8.3,37830,97.3
05031,AR,Craighead County,48428,44877,3551,7.3,39410,101.3
05033,AR,Crawford County,27814,25451,2363,8.5,37552,96.6
05035,AR,Crittenden County,21564,18852,2712,12.6,34905,89.8
05037,AR,Cross County,8476,7757,719,8.5,35268,90.7
05039,AR,Dallas County,3718,3307,411,11.1,30728,79
05041,AR,Desha County,5740,5065,675,11.8,30186,77.6
05043,AR,Drew County,8306,7336,970,11.7,35627,91.6
05045,AR,Faulkner County,57970,53899,4071,7,49314,126.8
05047,AR,Franklin County,7910,7320,590,7.5,38245,98.3
05049,AR,Fulton County,5239,4850,389,7.4,33696,86.6
05051,AR,Garland County,42968,39411,3557,8.3,35651,91.7
05053,AR,Grant County,8499,7891,608,7.2,46538,119.7
05055,AR,Greene County,18955,17067,1888,10,37893,97.4
05057,AR,Hempstead County,10968,10034,934,8.5,30922,79.5
05059,AR,Hot Spring County,15812,14610,1202,7.6,38082,97.9
05061,AR,Howard County,6228,5731,497,8,33079,85.1
05063,AR,Independence County,17068,15559,1509,8.8,34690,89.2
05065,AR,Izard County,5454,4943,511,9.4,31245,80.3
05067,AR,Jackson County,7424,6636,788,10.6,32750,84.2
05069,AR,Jefferson County,35085,31377,3708,10.6,37704,97
05071,AR,Johnson County,11737,10890,847,7.2,34182,87.9
05073,AR,Lafayette County,2899,2612,287,9.9,30057,77.3
05075,AR,Lawrence County,7370,6651,719,9.8,32678,84
05077,AR,Lee County,3358,2980,378,11.3,27894,71.7
05079,AR,Lincoln County,5134,4653,481,9.4,35854,92.2
05081,AR,Little River County,6083,5614,469,7.7,38937,100.1
05083,AR,Logan County,9888,9061,827,8.4,34000,87.4
05085,AR,Lonoke County,33083,30837,2246,6.8,49579,127.5
05087,AR,Madison County,7517,7049,468,6.2,34292,88.2
05089,AR,Marion County,6622,6001,621,9.4,32685,84
05091,AR,Miller County,20864,19398,1466,7,37728,97
05093,AR,Mississippi County,22177,19715,2462,11.1,33426,86
05095,AR,Monroe County,3538,3221,317,9,27758,71.4
05097,AR,Montgomery County,4159,3813,346,8.3,31603,81.3
05099,AR,Nevada County,4336,3939,397,9.2,31590,81.2
05101,AR,Newton County,3577,3298,279,7.8,31224,80.3
05103,AR,Ouachita County,12092,10961,1131,9.4,35095,90.2
05105,AR,Perry County,4788,4374,414,8.6,38858,99.9
05107,AR,Phillips County,8639,7651,988,11.4,26892,69.2
05109,AR,Pike County,4790,4339,451,9.4,30054,77.3
05111,AR,Poinsett County,10741,9773,968,9,30761,79.1
05113,AR,Polk County,8463,7764,699,8.3,31030,79.8
05115,AR,Pope County,30473,28145,2328,7.6,38095,98
05117,AR,Prairie County,4011,3682,329,8.2,36088,92.8
05119,AR,Pulaski County,189904,176257,13647,7.2,43898,112.9
05121,AR,Randolph County,7637,6850,787,10.3,33210,85.4
05123,AR,St. Francis County,10273,9145,1128,11,28467,73.2
05125,AR,Saline County,52960,49495,3465,6.5,54372,139.8
05127,AR,Scott County,4829,4497,332,6.9,31163,80.1
05129,AR,Searcy County,3483,3188,295,8.5,26990,69.4
05131,AR,Sebastian County,59513,54568,4945,8.3,38404,98.8
05133,AR,Sevier County,6943,6370,573,8.3,30899,79.5
05135,AR,Sharp County,6365,5711,654,10.3,28760,74
05137,AR,Stone County,4575,4107,468,10.2,29182,75
05139,AR,Union County,17910,16171,1739,9.7,37193,95.6
05141,AR,Van Buren County,7396,6672,724,9.8,34513,88.7
05143,AR,Washington County,103897,97643,6254,6,39230,100.9
05145,AR,White County,34997,31911,3086,8.8,42958,110.5
05147,AR,Woodruff County,3287,2906,381,11.6,27357,70.3
05149,AR,Yell County,10342,9669,673,6.5,33747,86.8
06000,CA,California,18384886,16226558,2158328,11.7,57275,100
06001,CA,Alameda County,760931,682028,78903,10.4,67295,117.5
06003,CA,Alpine County,504,428,76,15.1,46706,81.5
06005,CA,Amador County,17020,14841,2179,12.8,51553,90
06007,CA,Butte County,101686,87892,13794,13.6,39208,68.5
06009,CA,Calaveras County,19852,16956,2896,14.6,50599,88.3
06011,CA,Colusa County,11799,9396,2403,20.4,47469,82.9
06013,CA,Contra Costa County,524056,469605,54451,10.4,74241,129.6
06015,CA,Del Norte County,11450,9919,1531,13.4,35598,62.2
06017,CA,El Dorado County,90962,80252,10710,11.8,61970,108.2
06019,CA,Fresno County,442054,368936,73118,16.5,42572,74.3
06021,CA,Glenn County,12865,10834,2031,15.8,40221,70.2
06023,CA,Humboldt County,60628,53796,6832,11.3,39526,69
06025,CA,Imperial County,77561,54550,23011,29.7,36898,64.4
06027,CA,Inyo County,9488,8548,940,9.9,44928,78.4
06029,CA,Kern County,382029,325095,56934,14.9,44903,78.4
06031,CA,Kings County,61099,51245,9854,16.1,48319,84.4
06033,CA,Lake County,25810,21508,4302,16.7,35882,62.6
06035,CA,Lassen County,13480,11666,1814,13.5,47938,83.7
06037,CA,Los Angeles County,4924364,4318870,605494,12.3,52239,91.2
06039,CA,Madera County,66369,56235,10134,15.3,44795,78.2
06041,CA,Marin County,135339,125354,9985,7.4,78470,137
06043,CA,Mariposa County,9680,8540,1140,11.8,42175,73.6
06045,CA,Mendocino County,42837,38162,4675,10.9,41236,72
06047,CA,Merced County,110167,90046,20121,18.3,40016,69.9
06049,CA,Modoc County,3905,3325,580,14.9,34654,60.5
06051,CA,Mono County,8790,7909,881,10,48758,85.1
06053,CA,Monterey County,222857,195209,27648,12.4,52746,92.1
06055,CA,Napa County,76460,69572,6888,9,61179,106.8
06057,CA,Nevada County,50960,45550,5410,10.6,53833,94
06059,CA,Orange County,1603670,1464372,139298,8.7,72046,125.8
06061,CA,Placer County,175078,156183,18895,10.8,69581,121.5
06063,CA,Plumas County,9868,8297,1571,15.9,44923,78.4
06065,CA,Riverside County,938434,810616,127818,13.6,52491,91.6
06067,CA,Sacramento County,675639,594098,81541,12.1,52236,91.2
06069,CA,San Benito County,26391,22251,4140,15.7,62618,109.3
06071,CA,San Bernardino County,860571,747141,113430,13.2,51017,89.1
06073,CA,San Diego County,1583807,1426103,157704,10,59290,103.5
06075,CA,San Francisco County,462530,422719,39811,8.6,69354,121.1
06077,CA,San Joaquin County,297572,247446,50126,16.8,50376,88
06079,CA,San Luis Obispo County,138650,125796,12854,9.3,53877,94.1
06081,CA,San Mateo County,380315,350181,30134,7.9,81378,142.1
06083,CA,Santa Barbara County,225635,205784,19851,8.8,59494,103.9
06085,CA,Santa Clara County,896226,809326,86900,9.7,84741,148
06087,CA,Santa Cruz County,150740,132514,18226,12.1,61228,106.9
06089,CA,Shasta County,83452,71180,12272,14.7,41796,73
06091,CA,Sierra County,1677,1438,239,14.3,45060,78.7
06093,CA,Siskiyou County,19829,16535,3294,16.6,35175,61.4
06095,CA,Solano County,215472,190826,24646,11.4,63090,110.2
06097,CA,Sonoma County,257279,232143,25136,9.8,60792,106.1
06099,CA,Stanislaus County,236573,196782,39791,16.8,44287,77.3
06101,CA,Sutter County,43305,35152,8153,18.8,48749,85.1
06103,CA,Tehama County,25374,21561,3813,15,37297,65.1
06105,CA,Trinity County,5058,4160,898,17.8,33163,57.9
06107,CA,Tulare County,208098,173520,34578,16.6,40599,70.9
06109,CA,Tuolumne County,25890,22514,3376,13,43530,76
06111,CA,Ventura County,437011,393089,43922,10.1,74019,129.2
06113,CA,Yolo County,97750,85658,12092,12.4,50174,87.6
06115,CA,Yuba County,27995,22907,5088,18.2,43299,75.6
08000,CO,Colorado,2723027,2497297,225730,8.3,55530,100
08001,CO,Adams County,231640,209105,22535,9.7,52429,94.4
08003,CO,Alamosa County,8802,8092,710,8.1,36870,66.4
08005,CO,Arapahoe County,319084,292978,26106,8.2,57042,102.7
08007,CO,Archuleta County,6207,5633,574,9.2,48043,86.5
08009,CO,Baca County,2533,2433,100,3.9,34168,61.5
08011,CO,Bent County,2616,2416,200,7.6,35522,64
08013,CO,Boulder County,175874,164991,10883,6.2,68101,122.6
08014,CO,Broomfield County,30912,28645,2267,7.3,79051,142.4
08015,CO,Chaffee County,8555,7940,615,7.2,44156,79.5
08017,CO,Cheyenne County,1380,1337,43,3.1,46965,84.6
08019,CO,Clear Creek County,5666,5252,414,7.3,65420,117.8
08021,CO,Conejos County,3885,3516,369,9.5,32588,58.7
08023,CO,Costilla County,1428,1248,180,12.6,26125,47
08025,CO,Crowley County,1777,1610,167,9.4,30279,54.5
08027,CO,Custer County,1989,1860,129,6.5,45791,82.5
08029,CO,Delta County,16284,14804,1480,9.1,41442,74.6
08031,CO,Denver County,322078,292806,29272,9.1,47584,85.7
08033,CO,Dolores County,1047,928,119,11.4,39650,71.4
08035,CO,Douglas County,160069,149815,10254,6.4,95973,172.8
08037,CO,Eagle County,29425,26884,2541,8.6,67190,121
08039,CO,Elbert County,12851,11915,936,7.3,78029,140.5
08041,CO,El Paso County,300553,272831,27722,9.2,54857,98.8
08043,CO,Fremont County,20350,18312,2038,10,41493,74.7
08045,CO,Garfield County,33173,30261,2912,8.8,57617,103.8
08047,CO,Gilpin County,3582,3349,233,6.5,60537,109
08049,CO,Grand County,8644,7938,706,8.2,56980,102.6
08051,CO,Gunnison County,9211,8620,591,6.4,43069,77.6
08053,CO,Hinsdale County,643,619,24,3.7,49024,88.3
08055,CO,Huerfano County,3244,2878,366,11.3,30655,55.2
08057,CO,Jackson County,1275,1225,50,3.9,43206,77.8
08059,CO,Jefferson County,304791,280768,24023,7.9,64412,116
08061,CO,Kiowa County,1016,976,40,3.9,41427,74.6
08063,CO,Kit Carson County,4631,4408,223,4.8,43078,77.6
08065,CO,Lake County,3574,3158,416,11.6,42768,77
08067,CO,La Plata County,30380,28327,2053,6.8,52276,94.1
08069,CO,Larimer County,178043,166001,12042,6.8,55501,99.9
08071,CO,Las Animas County,7891,7121,770,9.8,40191,72.4
08073,CO,Lincoln County,3232,3075,157,4.9,42734,77
08075,CO,Logan County,11468,10709,759,6.6,43823,78.9
08077,CO,Mesa County,79048,71441,7607,9.6,47778,86
08079,CO,Mineral County,459,436,23,5,46956,84.6
08081,CO,Moffat County,8537,7809,728,8.5,52257,94.1
08083,CO,Montezuma County,13278,12155,1123,8.5,41817,75.3
08085,CO,Montrose County,20095,17913,2182,10.9,43035,77.5
08087,CO,Morgan County,15837,14837,1000,6.3,41246,74.3
08089,CO,Otero County,9367,8519,848,9.1,30420,54.8
08091,CO,Ouray County,2599,2398,201,7.7,57922,104.3
08093,CO,Park County,9099,8392,707,7.8,58565,105.5
08095,CO,Phillips County,2563,2449,114,4.4,43826,78.9
08097,CO,Pitkin County,10793,9946,847,7.8,69229,124.7
08099,CO,Prowers County,7082,6673,409,5.8,36563,65.8
08101,CO,Pueblo County,76538,68697,7841,10.2,40764,73.4
08103,CO,Rio Blanco County,4338,4100,238,5.5,60022,108.1
08105,CO,Rio Grande County,6620,6006,614,9.3,37472,67.5
08107,CO,Routt County,14227,13063,1164,8.2,64998,117.1
08109,CO,Saguache County,3322,2989,333,10,30746,55.4
08111,CO,San Juan County,564,506,58,10.3,36547,65.8
08113,CO,San Miguel County,5110,4728,382,7.5,57578,103.7
08115,CO,Sedgwick County,1638,1552,86,5.3,37511,67.6
08117,CO,Summit County,16804,15548,1256,7.5,60397,108.8
08119,CO,Teller County,12646,11543,1103,8.7,60313,108.6
08121,CO,Washington County,2933,2792,141,4.8,43433,78.2
08123,CO,Weld County,122877,111434,11443,9.3,51773,93.2
08125,CO,Yuma County,6856,6594,262,3.8,46246,83.3
09000,CT,Connecticut,1918127,1749478,168649,8.8,65822,100
09001,CT,Fairfield County,481769,443195,38574,8,77065,117.1
09003,CT,Hartford County,472551,429025,43526,9.2,60947,92.6
09005,CT,Litchfield County,106511,97753,8758,8.2,68658,104.3
09007,CT,Middlesex County,95001,87800,7201,7.6,75714,115
09009,CT,New Haven County,457666,413073,44593,9.7,58985,89.6
09011,CT,New London County,151676,138557,13119,8.6,63561,96.6
09013,CT,Tolland County,87640,81173,6467,7.4,77124,117.2
09015,CT,Windham County,65312,58902,6410,9.8,54911,83.4
10000,DE,Delaware,439181,406952,32229,7.3,58159,100
10001,DE,Kent County,75580,70021,5559,7.4,51045,87.8
10003,DE,New Castle County,271024,251111,19913,7.3,63087,108.5
10005,DE,Sussex County,92577,85820,6757,7.3,51060,87.8
11000,DC,District of Columbia,344333,309060,35273,10.2,62087,100
11001,DC,District of Columbia,344333,309060,35273,10.2,62087,100
12000,FL,Florida,9248503,8278141,970362,10.5,44250,100
12001,FL,Alachua County,130699,120631,10068,7.7,39825,90
12003,FL,Baker County,12494,11255,1239,9.9,42564,96.2
12005,FL,Bay County,91028,81886,9142,10,44204,99.9
12007,FL,Bradford County,12470,11374,1096,8.8,39271,88.7
12009,FL,Brevard County,267597,237935,29662,11.1,46494,105.1
12011,FL,Broward County,990714,899611,91103,9.2,48478,109.6
12013,FL,Calhoun County,5882,5321,561,9.5,32395,73.2
12015,FL,Charlotte County,70507,62920,7587,10.8,41346,93.4
12017,FL,Citrus County,56154,49466,6688,11.9,35041,79.2
12019,FL,Clay County,97148,88138,9010,9.3,54867,124
12021,FL,Collier County,148485,133216,15269,10.3,51039,115.3
12023,FL,Columbia County,31641,28581,3060,9.7,36452,82.4
12027,FL,DeSoto County,15208,13696,1512,9.9,32753,74
12029,FL,Dixie County,5739,5007,732,12.8,30801,69.6
12031,FL,Duval County,447217,399669,47548,10.6,45995,103.9
12033,FL,Escambia County,140603,126409,14194,10.1,41072,92.8
12035,FL,Flagler County,34022,29209,4813,14.1,47095,106.4
12037,FL,Franklin County,5497,5062,435,7.9,35288,79.7
12039,FL,Gadsden County,20240,18148,2092,10.3,34154,77.2
12041,FL,Gilchrist County,7557,6819,738,9.8,38046,86
12043,FL,Glades County,5201,4668,533,10.2,38825,87.7
12045,FL,Gulf County,6235,5598,637,10.2,37373,84.5
12047,FL,Hamilton County,4855,4304,551,11.3,29482,66.6
12049,FL,Hardee County,12329,11054,1275,10.3,33058,74.7
12051,FL,Hendry County,17894,15310,2584,14.4,35247,79.7
12053,FL,Hernando County,62857,54408,8449,13.4,40411,91.3
12055,FL,Highlands County,43193,38689,4504,10.4,33692,76.1
12057,FL,Hillsborough County,611769,547463,64306,10.5,46592,105.3
12059,FL,Holmes County,8871,8145,726,8.2,33211,75.1
12061,FL,Indian River County,63290,55313,7977,12.6,42053,95
12063,FL,Jackson County,22476,20665,1811,8.1,35193,79.5
12065,FL,Jefferson County,6816,6210,606,8.9,40252,91
12067,FL,Lafayette County,3159,2903,256,8.1,40407,91.3
12069,FL,Lake County,127861,113542,14319,11.2,44965,101.6
12071,FL,Lee County,282699,251298,31401,11.1,45515,102.9
12073,FL,Leon County,149905,137899,12006,8,44857,101.4
12075,FL,Levy County,16995,15082,1913,11.3,33544,75.8
12077,FL,Liberty County,3332,3054,278,8.3,37446,84.6
12079,FL,Madison County,7217,6386,831,11.5,31578,71.4
12081,FL,Manatee County,140040,124855,15185,10.8,43494,98.3
12083,FL,Marion County,133997,117482,16515,12.3,36425,82.3
12085,FL,Martin County,64157,57234,6923,10.8,50417,113.9
12086,FL,Miami-Dade County,1264608,1121772,142836,11.3,40476,91.5
12087,FL,Monroe County,46909,43908,3001,6.4,51152,115.6
12089,FL,Nassau County,37412,33826,3586,9.6,54706,123.6
12091,FL,Okaloosa County,99218,91905,7313,7.4,49643,112.2
12093,FL,Okeechobee County,19208,16958,2250,11.7,33806,76.4
12095,FL,Orange County,620155,556343,63812,10.3,44635,100.9
12097,FL,Osceola County,136698,120866,15832,11.6,40933,92.5
12099,FL,Palm Beach County,620081,553380,66701,10.8,48973,110.7
12101,FL,Pasco County,192126,169038,23088,12,40859,92.3
12103,FL,Pinellas County,441627,395412,46215,10.5,42761,96.6
12105,FL,Polk County,274561,242803,31758,11.6,40258,91
12107,FL,Putnam County,32635,28752,3883,11.9,31150,70.4
12109,FL,St. Johns County,98258,89947,8311,8.5,63299,143
12111,FL,St. Lucie County,125291,108959,16332,13,41384,93.5
12113,FL,Santa Rosa County,73088,66445,6643,9.1,53155,120.1
12115,FL,Sarasota County,161271,143977,17294,10.7,47457,107.2
12117,FL,Seminole County,237758,214784,22974,9.7,54449,123
12119,FL,Sumter County,36107,33029,3078,8.5,44595,100.8
12121,FL,Suwannee County,19169,17390,1779,9.3,34947,79
12123,FL,Taylor County,9274,8263,1011,10.9,33863,76.5
12125,FL,Union County,5444,4995,449,8.2,40743,92.1
12127,FL,Volusia County,254098,226670,27428,10.8,38477,87
12129,FL,Wakulla County,15429,14142,1287,8.3,50716,114.6
12131,FL,Walton County,31849,29584,2265,7.1,44763,101.2
12133,FL,Washington County,10189,9081,1108,10.9,35004,79.1
13000,GA,Georgia,4725104,4262175,462929,9.8,45886,100
13001,GA,Appling County,9542,8497,1045,11,35164,76.6
13003,GA,Atkinson County,3011,2588,423,14,28693,62.5
13005,GA,Bacon County,4591,4138,453,9.9,31301,68.2
13007,GA,Baker County,1509,1375,134,8.9,33347,72.7
13009,GA,Baldwin County,18018,15544,2474,13.7,34304,74.8
13011,GA,Banks County,10201,9460,741,7.3,42673,93
13013,GA,Barrow County,34424,31224,3200,9.3,50996,111.1
13015,GA,Bartow County,48802,43831,4971,10.2,45072,98.2
13017,GA,Ben Hill County,6810,5868,942,13.8,29425,64.1
13019,GA,Berrien County,7816,6879,937,12,33417,72.8
13021,GA,Bibb County,73237,65551,7686,10.5,35125,76.5
13023,GA,Bleckley County,5225,4508,717,13.7,39461,86
13025,GA,Brantley County,7651,6772,879,11.5,36300,79.1
13027,GA,Brooks County,7436,6736,700,9.4,29282,63.8
13029,GA,Bryan County,15459,14181,1278,8.3,63092,137.5
13031,GA,Bulloch County,32213,28922,3291,10.2,33192,72.3
13033,GA,Burke County,9645,8544,1101,11.4,31188,68
13035,GA,Butts County,10120,8928,1192,11.8,40330,87.9
13037,GA,Calhoun County,2273,2027,246,10.8,27860,60.7
13039,GA,Camden County,20243,18272,1971,9.7,50207,109.4
13043,GA,Candler County,4414,3957,457,10.4,28263,61.6
13045,GA,Carroll County,52170,46608,5562,10.7,43237,94.2
13047,GA,Catoosa County,34216,31562,2654,7.8,47111,102.7
13049,GA,Charlton County,4477,3961,516,11.5,31582,68.8
13051,GA,Chatham County,132848,120617,12231,9.2,42383,92.4
13053,GA,Chattahoochee County,2090,1717,373,17.8,41476,90.4
13055,GA,Chattooga County,10316,9158,1158,11.2,33350,72.7
13057,GA,Cherokee County,112406,103304,9102,8.1,65769,143.3
13059,GA,Clarke County,65606,60677,4929,7.5,30880,67.3
13061,GA,Clay County,1432,1307,125,8.7,26420,57.6
13063,GA,Clayton County,129784,114008,15776,12.2,37706,82.2
13065,GA,Clinch County,2635,2332,303,11.5,30237,65.9
13067,GA,Cobb County,371429,338369,33060,8.9,57995,126.4
13069,GA,Coffee County,15257,13055,2202,14.4,32351,70.5
13071,GA,Colquitt County,19996,18063,1933,9.7,30669,66.8
13073,GA,Columbia County,63896,59359,4537,7.1,62717,136.7
13075,GA,Cook County,6487,5662,825,12.7,32812,71.5
13077,GA,Coweta County,62717,57144,5573,8.9,56878,124
13079,GA,Crawford County,6108,5504,604,9.9,41855,91.2
13081,GA,Crisp County,9149,7935,1214,13.3,29134,63.5
13083,GA,Dade County,8168,7504,664,8.1,41233,89.9
13085,GA,Dawson County,11358,10352,1006,8.9,53339,116.2
13087,GA,Decatur County,10834,9422,1412,13,31054,67.7
13089,GA,DeKalb County,364638,327792,36846,10.1,48293,105.2
13091,GA,Dodge County,8868,7829,1039,11.7,31696,69.1
13093,GA,Dooly County,4551,3907,644,14.2,29970,65.3
13095,GA,Dougherty County,42318,37514,4804,11.4,30186,65.8
13097,GA,Douglas County,68214,61232,6982,10.2,50501,110.1
13099,GA,Early County,5341,4846,495,9.3,31345,68.3
13101,GA,Echols County,1954,1811,143,7.3,32543,70.9
13103,GA,Effingham County,27262,24992,2270,8.3,59502,129.7
13105,GA,Elbert County,9380,8013,1367,14.6,33068,72.1
13107,GA,Emanuel County,10165,8987,1178,11.6,30459,66.4
13109,GA,Evans County,4892,4458,434,8.9,30858,67.2
13111,GA,Fannin County,10759,9654,1105,10.3,34775,75.8
13113,GA,Fayette County,52392,48162,4230,8.1,74476,162.3
13115,GA,Floyd County,47590,42401,5189,10.9,40159,87.5
13117,GA,Forsyth County,90431,83856,6575,7.3,85137,185.5
13119,GA,Franklin County,10191,9059,1132,11.1,31042,67.7
13121,GA,Fulton County,449190,402210,46980,10.5,54893,119.6
13123,GA,Gilmer County,12729,11340,1389,10.9,36359,79.2
13125,GA,Glascock County,1144,1008,136,11.9,35247,76.8
13127,GA,Glynn County,36684,32827,3857,10.5,44238,96.4
13129,GA,Gordon County,25425,22501,2924,11.5,42253,92.1
13131,GA,Grady County,11899,10921,978,8.2,33368,72.7
13133,GA,Greene County,7304,6561,743,10.2,39209,85.4
13135,GA,Gwinnett County,427058,390792,36266,8.5,56944,124.1
13137,GA,Habersham County,19379,17528,1851,9.6,39879,86.9
13139,GA,Hall County,89960,82630,7330,8.1,49324,107.5
13141,GA,Hancock County,2985,2385,600,20.1,26107,56.9
13143,GA,Haralson County,12783,11452,1331,10.4,37495,81.7
13145,GA,Harris County,17080,15890,1190,7,64048,139.6
13147,GA,Hart County,10362,9164,1198,11.6,36327,79.2
13149,GA,Heard County,5058,4507,551,10.9,35765,77.9
13151,GA,Henry County,104821,94646,10175,9.7,58130,126.7
13153,GA,Houston County,70292,64824,5468,7.8,51851,113
13155,GA,Irwin County,3578,3106,472,13.2,33602,73.2
13157,GA,Jackson County,27249,24602,2647,9.7,49400,107.7
13159,GA,Jasper County,6498,5808,690,10.6,43618,95.1
13161,GA,Jeff Davis County,5485,4702,783,14.3,32248,70.3
13163,GA,Jefferson County,6847,5930,917,13.4,27703,60.4
13165,GA,Jenkins County,2459,2009,450,18.3,25807,56.2
13167,GA,Johnson County,3595,3137,458,12.7,28585,62.3
13169,GA,Jones County,14496,13209,1287,8.9,48792,106.3
13171,GA,Lamar County,8584,7571,1013,11.8,40769,88.8
13173,GA,Lanier County,4553,4165,388,8.5,33929,73.9
13175,GA,Laurens County,21253,18532,2721,12.8,34865,76
13177,GA,Lee County,15180,13967,1213,8,60652,132.2
13179,GA,Liberty County,25750,23251,2499,9.7,40659,88.6
13181,GA,Lincoln County,3728,3324,404,10.8,35183,76.7
13183,GA,Long County,7587,7042,545,7.2,35244,76.8
13185,GA,Lowndes County,51446,46659,4787,9.3,34252,74.6
13187,GA,Lumpkin County,12844,11479,1365,10.6,44422,96.8
13189,GA,McDuffie County,10262,9124,1138,11.1,36907,80.4
13191,GA,McIntosh County,5725,5112,613,10.7,34787,75.8
13193,GA,Macon County,5074,4329,745,14.7,27934,60.9
13195,GA,Madison County,15911,14660,1251,7.9,40123,87.4
13197,GA,Marion County,4018,3669,349,8.7,34189,74.5
13199,GA,Meriwether County,9311,8115,1196,12.8,34589,75.4
13201,GA,Miller County,3483,3243,240,6.9,33063,72.1
13205,GA,Mitchell County,9762,8803,959,9.8,31088,67.8
13207,GA,Monroe County,13959,12727,1232,8.8,51747,112.8
13209,GA,Montgomery County,4680,4163,517,11,34379,74.9
13211,GA,Morgan County,9139,8312,827,9,46176,100.6
13213,GA,Murray County,17614,15317,2297,13,36230,79
13215,GA,Muscogee County,84680,76720,7960,9.4,37564,81.9
13217,GA,Newton County,48196,42653,5543,11.5,44089,96.1
13219,GA,Oconee County,18671,17528,1143,6.1,71707,156.3
13221,GA,Oglethorpe County,8230,7598,632,7.7,44066,96
13223,GA,Paulding County,72416,65619,6797,9.4,59185,129
13225,GA,Peach County,12223,10793,1430,11.7,39838,86.8
13227,GA,Pickens County,14519,13110,1409,9.7,50282,109.6
13229,GA,Pierce County,8400,7575,825,9.8,38290,83.4
13231,GA,Pike County,8292,7482,810,9.8,54457,118.7
13233,GA,Polk County,20351,18295,2056,10.1,36326,79.2
13235,GA,Pulaski County,4392,3951,441,10,34820,75.9
13237,GA,Putnam County,9335,8264,1071,11.5,40168,87.5
13239,GA,Quitman County,946,830,116,12.3,28621,62.4
13241,GA,Rabun County,6599,5773,826,12.5,34394,75
13243,GA,Randolph County,2839,2475,364,12.8,27950,60.9
13245,GA,Richmond County,87830,78540,9290,10.6,37352,81.4
13247,GA,Rockdale County,41592,37095,4497,10.8,51106,111.4
13249,GA,Schley County,1915,1697,218,11.4,37669,82.1
13251,GA,Screven County,7049,6156,893,12.7,32990,71.9
13253,GA,Seminole County,3793,3402,391,10.3,30489,66.4
13255,GA,Spalding County,28965,25335,3630,12.5,38914,84.8
13257,GA,Stephens County,13244,11909,1335,10.1,37536,81.8
13259,GA,Stewart County,2259,2005,254,11.2,26010,56.7
13261,GA,Sumter County,13025,11297,1728,13.3,31937,69.6
13263,GA,Talbot County,3150,2856,294,9.3,31290,68.2
13265,GA,Taliaferro County,741,648,93,12.6,25807,56.2
13267,GA,Tattnall County,9585,8639,946,9.9,31531,68.7
13269,GA,Taylor County,3440,2986,454,13.2,30308,66.1
13271,GA,Telfair County,4436,3700,736,16.6,27831,60.7
13273,GA,Terrell County,4071,3620,451,11.1,30552,66.6
13275,GA,Thomas County,21956,20143,1813,8.3,34611,75.4
13277,GA,Tift County,18567,16427,2140,11.5,33669,73.4
13279,GA,Toombs County,13748,12184,1564,11.4,32915,71.7
13281,GA,Towns County,5701,5191,510,8.9,36858,80.3
13283,GA,Treutlen County,2926,2537,389,13.3,30131,65.7
13285,GA,Troup County,33256,29619,3637,10.9,39370,85.8
13287,GA,Turner County,4317,3816,501,11.6,25480,55.5
13289,GA,Twiggs County,4119,3605,514,12.5,34528,75.2
13291,GA,Union County,10838,9923,915,8.4,39206,85.4
13293,GA,Upson County,11674,10332,1342,11.5,33483,73
13295,GA,Walker County,33160,30272,2888,8.7,37813,82.4
13297,GA,Walton County,40825,36791,4034,9.9,52747,115
13299,GA,Ware County,14509,12807,1702,11.7,31529,68.7
13301,GA,Warren County,2520,2097,423,16.8,30946,67.4
13303,GA,Washington County,7616,6587,1029,13.5,34117,74.4
13305,GA,Wayne County,11996,10501,1495,12.5,37082,80.8
13307,GA,Webster County,1257,1141,116,9.2,32842,71.6
13309,GA,Wheeler County,2963,2658,305,10.3,29780,64.9
13311,GA,White County,12546,11315,1231,9.8,39753,86.6
13313,GA,Whitfield County,43878,38550,5328,12.1,38452,83.8
13315,GA,Wilcox County,3065,2664,401,13.1,30500,66.5
13317,GA,Wilkes County,4450,3918,532,12,30720,66.9
13319,GA,Wilkinson County,4435,3969,466,10.5,38570,84.1
13321,GA,Worth County,10426,9398,1028,9.9,35308,76.9
15000,HI,Hawaii,660694,616453,44241,6.7,62071,100
15001,HI,Hawaii County,87598,78948,8650,9.9,46186,74.4
15003,HI,Honolulu County,460643,434211,26432,5.7,65489,105.5
15007,HI,Kauai County,33688,30730,2958,8.8,56937,91.7
15009,HI,Maui County,78766,72565,6201,7.9,57789,93.1
16000,ID,Idaho,770639,703562,67077,8.7,43345,100
16001,ID,Ada County,199770,183876,15894,8,50701,117
16003,ID,Adams County,1850,1530,320,17.3,32752,75.6
16005,ID,Bannock County,40635,37394,3241,8,41749,96.3
16007,ID,Bear Lake County,3326,3144,182,5.5,42327,97.7
16009,ID,Benewah County,4219,3642,577,13.7,39327,90.7
16011,ID,Bingham County,23400,21700,1700,7.3,45354,104.6
16013,ID,Blaine County,12575,11471,1104,8.8,60448,139.5
16015,ID,Boise County,3240,2930,310,9.6,48657,112.3
16017,ID,Bonner County,19992,17536,2456,12.3,41255,95.2
16019,ID,Bonneville County,51005,47390,3615,7.1,49748,114.8
16021,ID,Boundary County,4923,4271,652,13.2,33655,77.6
16023,ID,Butte County,1500,1393,107,7.1,41026,94.6
16025,ID,Camas County,595,528,67,11.3,45439,104.8
16027,ID,Canyon County,86516,77165,9351,10.8,39132,90.3
16029,ID,Caribou County,3973,3673,300,7.6,49665,114.6
16031,ID,Cassia County,11494,10714,780,6.8,41393,95.5
16033,ID,Clark County,559,512,47,8.4,40581,93.6
16035,ID,Clearwater County,3312,2818,494,14.9,38086,87.9
16037,ID,Custer County,2549,2363,186,7.3,44234,102.1
16039,ID,Elmore County,11147,10148,999,9,43120,99.5
16041,ID,Franklin County,6223,5870,353,5.7,46348,106.9
16043,ID,Fremont County,6140,5636,504,8.2,41411,95.5
16045,ID,Gem County,7133,6322,811,11.4,40541,93.5
16047,ID,Gooding County,8992,8401,591,6.6,39670,91.5
16049,ID,Idaho County,7414,6569,845,11.4,39184,90.4
16051,ID,Jefferson County,11942,11079,863,7.2,47827,110.3
16053,ID,Jerome County,10713,9875,838,7.8,39454,91
16055,ID,Kootenai County,72048,64634,7414,10.3,47026,108.5
16057,ID,Latah County,17548,16280,1268,7.2,42350,97.7
16059,ID,Lemhi County,3645,3249,396,10.9,36319,83.8
16061,ID,Lewis County,1741,1632,109,6.3,40149,92.6
16063,ID,Lincoln County,2661,2332,329,12.4,40460,93.3
16065,ID,Madison County,16743,15712,1031,6.2,33343,76.9
16067,ID,Minidoka County,10537,9773,764,7.3,40779,94.1
16069,ID,Nez Perce County,19120,17797,1323,6.9,43662,100.7
16071,ID,Oneida County,2336,2217,119,5.1,43519,100.4
16073,ID,Owyhee County,4506,4279,227,5,33518,77.3
16075,ID,Payette County,10866,9827,1039,9.6,39031,90
16077,ID,Power County,3634,3300,334,9.2,39288,90.6
16079,ID,Shoshone County,6138,5295,843,13.7,36811,84.9
16081,ID,Teton County,5410,5045,365,6.7,51561,119
16083,ID,Twin Falls County,39135,35991,3144,8,41942,96.8
16085,ID,Valley County,4609,3910,699,15.2,46210,106.6
16087,ID,Washington County,4831,4341,490,10.1,35232,81.3
17000,IL,Illinois,6565504,5925154,640350,9.8,53271,100
17001,IL,Adams County,37424,34939,2485,6.6,43293,81.3
17003,IL,Alexander County,3035,2637,398,13.1,28499,53.5
17005,IL,Bond County,8435,7655,780,9.2,44945,84.4
17007,IL,Boone County,26123,22604,3519,13.5,57708,108.3
17009,IL,Brown County,3606,3438,168,4.7,46160,86.7
17011,IL,Bureau County,19105,17126,1979,10.4,48474,91
17013,IL,Calhoun County,2548,2284,264,10.4,47218,88.6
17015,IL,Carroll County,8080,7290,790,9.8,42996,80.7
17017,IL,Cass County,7620,7035,585,7.7,40065,75.2
17019,IL,Champaign County,100761,92289,8472,8.4,42847,80.4
17021,IL,Christian County,17761,16088,1673,9.4,46401,87.1
17023,IL,Clark County,7917,7033,884,11.2,48940,91.9
17025,IL,Clay County,6524,5781,743,11.4,38445,72.2
17027,IL,Clinton County,19416,17866,1550,8,59711,112.1
17029,IL,Coles County,26321,23886,2435,9.3,40623,76.3
17031,IL,Cook County,2575226,2307751,267475,10.4,50806,95.4
17033,IL,Crawford County,9854,9014,840,8.5,46307,86.9
17035,IL,Cumberland County,5304,4749,555,10.5,47145,88.5
17037,IL,DeKalb County,58924,53532,5392,9.2,52091,97.8
17039,IL,De Witt County,9017,8254,763,8.5,56147,105.4
17041,IL,Douglas County,9986,9114,872,8.7,48671,91.4
17043,IL,DuPage County,517675,476382,41293,8,74122,139.1
17045,IL,Edgar County,10071,9043,1028,10.2,43596,81.8
17047,IL,Edwards County,3138,2868,270,8.6,41239,77.4
17049,IL,Effingham County,18297,16962,1335,7.3,48632,91.3
17051,IL,Fayette County,10420,9316,1104,10.6,40247,75.6
17053,IL,Ford County,6646,6020,626,9.4,49206,92.4
17055,IL,Franklin County,17625,15571,2054,11.7,36610,68.7
17057,IL,Fulton County,18404,16501,1903,10.3,46539,87.4
17059,IL,Gallatin County,2635,2395,240,9.1,36722,68.9
17061,IL,Greene County,6804,6183,621,9.1,42534,79.8
17063,IL,Grundy County,27700,24430,3270,11.8,63685,119.5
17065,IL,Hamilton County,4278,3914,364,8.5,41446,77.8
17067,IL,Hancock County,9355,8427,928,9.9,43850,82.3
17069,IL,Hardin County,1764,1564,200,11.3,35596,66.8
17071,IL,Henderson County,3853,3549,304,7.9,45217,84.9
17073,IL,Henry County,27073,24999,2074,7.7,50445,94.7
17075,IL,Iroquois County,16732,15218,1514,9,48577,91.2
17077,IL,Jackson County,32152,29678,2474,7.7,32868,61.7
17079,IL,Jasper County,4940,4524,416,8.4,46257,86.8
17081,IL,Jefferson County,20191,18428,1763,8.7,42601,80
17083,IL,Jersey County,11846,10741,1105,9.3,55416,104
17085,IL,Jo Daviess County,12847,11850,997,7.8,51293,96.3
17087,IL,Johnson County,5169,4636,533,10.3,42357,79.5
17089,IL,Kane County,273903,246770,27133,9.9,66133,124.1
17091,IL,Kankakee County,55889,49083,6806,12.2,46046,86.4
17093,IL,Kendall County,65541,59853,5688,8.7,82934,155.7
17095,IL,Knox County,25687,23443,2244,8.7,39803,74.7
17097,IL,Lake County,358515,324658,33857,9.4,74306,139.5
17099,IL,La Salle County,58901,52110,6791,11.5,48945,91.9
17101,IL,Lawrence County,8102,7387,715,8.8,38934,73.1
17103,IL,Lee County,17988,16183,1805,10,48388,90.8
17105,IL,Livingston County,18764,17095,1669,8.9,53249,100
17107,IL,Logan County,13404,12284,1120,8.4,46863,88
17109,IL,McDonough County,16588,15322,1266,7.6,38916,73.1
17111,IL,McHenry County,174781,158424,16357,9.4,70682,132.7
17113,IL,McLean County,91405,84830,6575,7.2,58791,110.4
17115,IL,Macon County,54585,48818,5767,10.6,44725,84
17117,IL,Macoupin County,24013,21617,2396,10,46525,87.3
17119,IL,Madison County,138131,125637,12494,9,50462,94.7
17121,IL,Marion County,18169,16146,2023,11.1,40056,75.2
17123,IL,Marshall County,7046,6429,617,8.8,50571,94.9
17125,IL,Mason County,7445,6650,795,10.7,43515,81.7
17127,IL,Massac County,7586,6907,679,9,39308,73.8
17129,IL,Menard County,7076,6543,533,7.5,58900,110.6
17131,IL,Mercer County,8637,7866,771,8.9,52351,98.3
17133,IL,Monroe County,18315,16960,1355,7.4,71766,134.7
17135,IL,Montgomery County,13560,11857,1703,12.6,49430,92.8
17137,IL,Morgan County,17250,15697,1553,9,43210,81.1
17139,IL,Moultrie County,7805,7207,598,7.7,49994,93.8
17141,IL,Ogle County,27063,23781,3282,12.1,55009,103.3
17143,IL,Peoria County,97714,88470,9244,9.5,49022,92
17145,IL,Perry County,9436,8422,1014,10.7,40167,75.4
17147,IL,Piatt County,8364,7687,677,8.1,61339,115.1
17149,IL,Pike County,8568,7895,673,7.9,38743,72.7
17151,IL,Pope County,1873,1674,199,10.6,37757,70.9
17153,IL,Pulaski County,2749,2453,296,10.8,31742,59.6
17155,IL,Putnam County,3155,2815,340,10.8,51518,96.7
17157,IL,Randolph County,15301,14013,1288,8.4,44708,83.9
17159,IL,Richland County,7232,6548,684,9.5,42369,79.5
17161,IL,Rock Island County,77750,71319,6431,8.3,44487,83.5
17163,IL,St. Clair County,127085,114029,13056,10.3,49634,93.2
17165,IL,Saline County,12718,11533,1185,9.3,35999,67.6
17167,IL,Sangamon County,109491,101041,8450,7.7,51537,96.7
17169,IL,Schuyler County,4268,3951,317,7.4,43744,82.1
17171,IL,Scott County,2709,2457,252,9.3,48361,90.8
17173,IL,Shelby County,11058,10011,1047,9.5,45964,86.3
17175,IL,Stark County,2844,2568,276,9.7,46913,88.1
17177,IL,Stephenson County,24173,21675,2498,10.3,42661,80.1
17179,IL,Tazewell County,73955,67813,6142,8.3,50106,94.1
17181,IL,Union County,8031,7088,943,11.7,39099,73.4
17183,IL,Vermilion County,36594,32703,3891,10.6,40742,76.5
17185,IL,Wabash County,6017,5463,554,9.2,48154,90.4
17187,IL,Warren County,9189,8476,713,7.8,44818,84.1
17189,IL,Washington County,8259,7651,608,7.4,50465,94.7
17191,IL,Wayne County,7998,7298,700,8.8,40178,75.4
17193,IL,White County,7659,7036,623,8.1,41706,78.3
17195,IL,Whiteside County,29733,26794,2939,9.9,45930,86.2
17197,IL,Will County,364982,327954,37028,10.1,72053,135.3
17199,IL,Williamson County,34754,31663,3091,8.9,41079,77.1
17201,IL,Winnebago County,141944,123863,18081,12.7,44353,83.3
17203,IL,Woodford County,21158,19672,1486,7,65998,123.9
18000,IN,Indiana,3188260,2901084,287176,9,46410,100
18001,IN,Adams County,15014,13755,1259,8.4,47265,101.8
18003,IN,Allen County,179007,162753,16254,9.1,47411,102.2
18005,IN,Bartholomew County,39998,37026,2972,7.4,52669,113.5
18007,IN,Benton County,4267,3895,372,8.7,45644,98.3
18009,IN,Blackford County,6284,5595,689,11,38346,82.6
18011,IN,Boone County,28272,26213,2059,7.3,63717,137.3
18013,IN,Brown County,7663,7021,642,8.4,53376,115
18015,IN,Carroll County,9781,8976,805,8.2,49216,106
18017,IN,Cass County,18457,16747,1710,9.3,41271,88.9
18019,IN,Clark County,55899,51032,4867,8.7,49778,107.3
18021,IN,Clay County,12736,11368,1368,10.7,44574,96
18023,IN,Clinton County,16651,15200,1451,8.7,45712,98.5
18025,IN,Crawford County,5263,4690,573,10.9,37674,81.2
18027,IN,Daviess County,15093,14188,905,6,43800,94.4
18029,IN,Dearborn County,25903,23594,2309,8.9,56344,121.4
18031,IN,Decatur County,12425,11160,1265,10.2,44574,96
18033,IN,DeKalb County,19980,18005,1975,9.9,46262,99.7
18035,IN,Delaware County,53755,48297,5458,10.2,37208,80.2
18037,IN,Dubois County,21811,20488,1323,6.1,51936,111.9
18039,IN,Elkhart County,91753,81506,10247,11.2,44354,95.6
18041,IN,Fayette County,9418,8221,1197,12.7,37292,80.4
18043,IN,Floyd County,37209,34226,2983,8,51210,110.3
18045,IN,Fountain County,8561,7735,826,9.6,44508,95.9
18047,IN,Franklin County,11529,10475,1054,9.1,51106,110.1
18049,IN,Fulton County,10137,9197,940,9.3,42417,91.4
18051,IN,Gibson County,17078,15711,1367,8,46570,100.3
18053,IN,Grant County,32880,29437,3443,10.5,38745,83.5
18055,IN,Greene County,15475,14069,1406,9.1,42877,92.4
18057,IN,Hamilton County,141952,133051,8901,6.3,84939,183
18059,IN,Hancock County,36661,33775,2886,7.9,61540,132.6
18061,IN,Harrison County,19930,18275,1655,8.3,49551,106.8
18063,IN,Hendricks County,75739,70273,5466,7.2,64239,138.4
18065,IN,Henry County,21857,19376,2481,11.4,36572,78.8
18067,IN,Howard County,35470,31757,3713,10.5,40997,88.3
18069,IN,Huntington County,19882,18009,1873,9.4,47326,102
18071,IN,Jackson County,21543,19771,1772,8.2,44921,96.8
18073,IN,Jasper County,15652,14271,1381,8.8,52559,113.2
18075,IN,Jay County,11363,10433,930,8.2,38572,83.1
18077,IN,Jefferson County,16233,14742,1491,9.2,40386,87
18079,IN,Jennings County,13904,12369,1535,11,42577,91.7
18081,IN,Johnson County,72965,67356,5609,7.7,61477,132.5
18083,IN,Knox County,20552,19178,1374,6.7,38653,83.3
18085,IN,Kosciusko County,41500,38065,3435,8.3,50823,109.5
18087,IN,LaGrange County,15562,14074,1488,9.6,42119,90.8
18089,IN,Lake County,223334,201250,22084,9.9,48143,103.7
18091,IN,LaPorte County,51040,45709,5331,10.4,47201,101.7
18093,IN,Lawrence County,21754,19350,2404,11.1,43471,93.7
18095,IN,Madison County,61156,54724,6432,10.5,41766,90
18097,IN,Marion County,463504,420002,43502,9.4,39957,86.1
18099,IN,Marshall County,22468,20254,2214,9.9,46301,99.8
18101,IN,Martin County,5629,5212,417,7.4,44715,96.3
18103,IN,Miami County,15653,13933,1720,11,43287,93.3
18105,IN,Monroe County,70592,65564,5028,7.1,40262,86.8
18107,IN,Montgomery County,18773,17127,1646,8.8,43176,93
18109,IN,Morgan County,35449,32277,3172,8.9,53690,115.7
18111,IN,Newton County,6908,6249,659,9.5,44418,95.7
18113,IN,Noble County,22036,19704,2332,10.6,46690,100.6
18115,IN,Ohio County,3171,2891,280,8.8,48788,105.1
18117,IN,Orange County,10351,9317,1034,10,37910,81.7
18119,IN,Owen County,10527,9477,1050,10,41004,88.4
18121,IN,Parke County,7913,7138,775,9.8,40790,87.9
18123,IN,Perry County,9690,8856,834,8.6,45234,97.5
18125,IN,Pike County,5965,5502,463,7.8,42482,91.5
18127,IN,Porter County,82653,76141,6512,7.9,62178,134
18129,IN,Posey County,13064,12125,939,7.2,55488,119.6
18131,IN,Pulaski County,6858,6347,511,7.5,43971,94.7
18133,IN,Putnam County,17600,15783,1817,10.3,46961,101.2
18135,IN,Randolph County,12969,11619,1350,10.4,39611,85.4
18137,IN,Ripley County,14532,13137,1395,9.6,47900,103.2
18139,IN,Rush County,9075,8266,809,8.9,44416,95.7
18141,IN,St. Joseph County,128868,115815,13053,10.1,43813,94.4
18143,IN,Scott County,10956,9726,1230,11.2,38826,83.7
18145,IN,Shelby County,23131,21113,2018,8.7,49437,106.5
18147,IN,Spencer County,10540,9702,838,8,53464,115.2
18149,IN,Starke County,10718,9546,1172,10.9,42088,90.7
18151,IN,Steuben County,16276,14617,1659,10.2,45957,99
18153,IN,Sullivan County,8744,7865,879,10.1,42608,91.8
18155,IN,Switzerland County,5692,5292,400,7,42285,91.1
18157,IN,Tippecanoe County,83860,77401,6459,7.7,43339,93.4
18159,IN,Tipton County,7356,6596,760,10.3,53300,114.8
18161,IN,Union County,3559,3235,324,9.1,43540,93.8
18163,IN,Vanderburgh County,92879,85645,7234,7.8,43570,93.9
18165,IN,Vermillion County,7738,6830,908,11.7,45089,97.2
18167,IN,Vigo County,50483,45314,5169,10.2,39173,84.4
18169,IN,Wabash County,16293,14790,1503,9.2,42032,90.6
18171,IN,Warren County,4784,4443,341,7.1,49095,105.8
18173,IN,Warrick County,31648,29426,2222,7,63543,136.9
18175,IN,Washington County,13679,12390,1289,9.4,39085,84.2
18177,IN,Wayne County,30924,27467,3457,11.2,36559,78.8
18179,IN,Wells County,14120,12943,1177,8.3,49234,106.1
18181,IN,White County,12688,11558,1130,8.9,45748,98.6
18183,IN,Whitley County,17607,16067,1540,8.7,48192,103.8
19000,IA,Iowa,1663592,1565623,97969,5.9,49545,100
19001,IA,Adair County,4359,4149,210,4.8,44701,90.2
19003,IA,Adams County,2099,1993,106,5.1,42062,84.9
19005,IA,Allamakee County,7847,7232,615,7.8,39670,80.1
19007,IA,Appanoose County,6042,5598,444,7.3,36693,74.1
19009,IA,Audubon County,3269,3064,205,6.3,45310,91.5
19011,IA,Benton County,14066,13209,857,6.1,54222,109.4
19013,IA,Black Hawk County,74736,70330,4406,5.9,43037,86.9
19015,IA,Boone County,15321,14542,779,5.1,50758,102.4
19017,IA,Bremer County,14034,13392,642,4.6,57646,116.4
19019,IA,Buchanan County,11012,10335,677,6.1,52930,106.8
19021,IA,Buena Vista County,10918,10371,547,5,46356,93.6
19023,IA,Butler County,8603,8168,435,5.1,51560,104.1
19025,IA,Calhoun County,5104,4841,263,5.2,46171,93.2
19027,IA,Carroll County,12372,11878,494,4,49251,99.4
19029,IA,Cass County,7589,7118,471,6.2,41329,83.4
19031,IA,Cedar County,11365,10766,599,5.3,57890,116.8
19033,IA,Cerro Gordo County,24832,23253,1579,6.4,43120,87
19035,IA,Cherokee County,6799,6448,351,5.2,47570,96
19037,IA,Chickasaw County,6805,6379,426,6.3,47311,95.5
19039,IA,Clarke County,4782,4432,350,7.3,42670,86.1
19041,IA,Clay County,9712,9180,532,5.5,50289,101.5
19043,IA,Clayton County,10141,9440,701,6.9,44370,89.6
19045,IA,Clinton County,27424,25608,1816,6.6,45600,92
19047,IA,Crawford County,9465,9030,435,4.6,44197,89.2
19049,IA,Dallas County,36220,34495,1725,4.8,72146,145.6
19051,IA,Davis County,4022,3742,280,7,40673,82.1
19053,IA,Decatur County,4267,4031,236,5.5,33803,68.2
19055,IA,Delaware County,10846,10251,595,5.5,50231,101.4
19057,IA,Des Moines County,20926,19427,1499,7.2,43721,88.2
19059,IA,Dickinson County,9377,8765,612,6.5,52821,106.6
19061,IA,Dubuque County,53259,50326,2933,5.5,52084,105.1
19063,IA,Emmet County,5811,5458,353,6.1,44556,89.9
19065,IA,Fayette County,11174,10435,739,6.6,41547,83.9
19067,IA,Floyd County,8296,7731,565,6.8,44478,89.8
19069,IA,Franklin County,6012,5684,328,5.5,47396,95.7
19071,IA,Fremont County,3857,3612,245,6.4,51410,103.8
19073,IA,Greene County,5029,4727,302,6,44271,89.4
19075,IA,Grundy County,6955,6600,355,5.1,54645,110.3
19077,IA,Guthrie County,5630,5276,354,6.3,47762,96.4
19079,IA,Hamilton County,7230,6512,718,9.9,45331,91.5
19081,IA,Hancock County,5672,5314,358,6.3,50935,102.8
19083,IA,Hardin County,9153,8580,573,6.3,46708,94.3
19085,IA,Harrison County,7252,6877,375,5.2,49921,100.8
19087,IA,Henry County,9510,8787,723,7.6,45330,91.5
19089,IA,Howard County,5051,4746,305,6,45450,91.7
19091,IA,Humboldt County,5093,4822,271,5.3,48954,98.8
19093,IA,Ida County,3916,3720,196,5,48480,97.9
19095,IA,Iowa County,8390,7886,504,6,53984,109
19097,IA,Jackson County,11093,10333,760,6.9,44840,90.5
19099,IA,Jasper County,17162,15812,1350,7.9,50469,101.9
19101,IA,Jefferson County,7878,7306,572,7.3,39743,80.2
19103,IA,Johnson County,79970,76558,3412,4.3,53570,108.1
19105,IA,Jones County,10599,9939,660,6.2,50929,102.8
19107,IA,Keokuk County,5567,5209,358,6.4,42766,86.3
19109,IA,Kossuth County,9038,8651,387,4.3,46294,93.4
19111,IA,Lee County,17112,15525,1587,9.3,43110,87
19113,IA,Linn County,121783,114533,7250,6,55772,112.6
19115,IA,Louisa County,5878,5508,370,6.3,48372,97.6
19117,IA,Lucas County,4581,4346,235,5.1,38422,77.5
19119,IA,Lyon County,7584,7360,224,3,53398,107.8
19121,IA,Madison County,8153,7630,523,6.4,56258,113.5
19123,IA,Mahaska County,11725,10985,740,6.3,47598,96.1
19125,IA,Marion County,16923,15930,993,5.9,51996,104.9
19127,IA,Marshall County,20300,18888,1412,7,47535,95.9
19129,IA,Mills County,7696,7356,340,4.4,57331,115.7
19131,IA,Mitchell County,5649,5364,285,5,45430,91.7
19133,IA,Monona County,4629,4273,356,7.7,41458,83.7
19135,IA,Monroe County,4289,4007,282,6.6,42961,86.7
19137,IA,Montgomery County,4960,4644,316,6.4,41437,83.6
19139,IA,Muscatine County,22987,21513,1474,6.4,47966,96.8
19141,IA,O'Brien County,7982,7617,365,4.6,46233,93.3
19143,IA,Osceola County,3237,3067,170,5.3,47890,96.7
19145,IA,Page County,7432,6999,433,5.8,42449,85.7
19147,IA,Palo Alto County,5176,4935,241,4.7,43557,87.9
19149,IA,Plymouth County,14544,13871,673,4.6,55047,111.1
19151,IA,Pocahontas County,4198,4007,191,4.5,43766,88.3
19153,IA,Polk County,237599,223088,14511,6.1,56091,113.2
19155,IA,Pottawattamie County,48223,45716,2507,5.2,48589,98.1
19157,IA,Poweshiek County,10261,9654,607,5.9,49853,100.6
19159,IA,Ringgold County,2515,2387,128,5.1,38547,77.8
19161,IA,Sac County,5857,5591,266,4.5,49480,99.9
19163,IA,Scott County,88276,82156,6120,6.9,50007,100.9
19165,IA,Shelby County,7657,7346,311,4.1,48572,98
19167,IA,Sioux County,20014,19256,758,3.8,57027,115.1
19169,IA,Story County,48571,46380,2191,4.5,48121,97.1
19171,IA,Tama County,8864,8256,608,6.9,50034,101
19173,IA,Taylor County,3370,3214,156,4.6,43155,87.1
19175,IA,Union County,7085,6698,387,5.5,39922,80.6
19177,IA,Van Buren County,3768,3501,267,7.1,39719,80.2
19179,IA,Wapello County,18403,17003,1400,7.6,39999,80.7
19181,IA,Warren County,25604,24192,1412,5.5,62688,126.5
19183,IA,Washington County,12022,11424,598,5,50559,102
19185,IA,Wayne County,3274,3106,168,5.1,37662,76
19187,IA,Webster County,18859,17354,1505,8,42178,85.1
19189,IA,Winnebago County,5471,5110,361,6.6,47692,96.3
19191,IA,Winneshiek County,12275,11636,639,5.2,49148,99.2
19193,IA,Woodbury County,54539,51299,3240,5.9,43362,87.5
19195,IA,Worth County,4336,4051,285,6.6,48718,98.3
19197,IA,Wright County,6988,6482,506,7.2,43647,88.1
20000,KS,Kansas,1505043,1404339,100704,6.7,48844,100
20001,KS,Allen County,7716,7142,574,7.4,37353,76.5
20003,KS,Anderson County,4415,4062,353,8,42185,86.4
20005,KS,Atchison County,8649,7932,717,8.3,42131,86.3
20007,KS,Barber County,2849,2736,113,4,44118,90.3
20009,KS,Barton County,16161,15355,806,5,42183,86.4
20011,KS,Bourbon County,8409,7775,634,7.5,35805,73.3
20013,KS,Brown County,5621,5306,315,5.6,40424,82.8
20015,KS,Butler County,31763,29331,2432,7.7,53217,109
20017,KS,Chase County,1503,1432,71,4.7,41217,84.4
20019,KS,Chautauqua County,1877,1731,146,7.8,35952,73.6
20021,KS,Cherokee County,11621,10718,903,7.8,39696,81.3
20023,KS,Cheyenne County,1513,1453,60,4,36835,75.4
20025,KS,Clark County,1284,1236,48,3.7,42171,86.3
20027,KS,Clay County,5140,4892,248,4.8,47692,97.6
20029,KS,Cloud County,5752,5493,259,4.5,39763,81.4
20031,KS,Coffey County,5495,5125,370,6.7,54644,111.9
20033,KS,Comanche County,1059,1014,45,4.2,39975,81.8
20035,KS,Cowley County,18235,17006,1229,6.7,40248,82.4
20037,KS,Crawford County,20198,18640,1558,7.7,34080,69.8
20039,KS,Decatur County,1641,1569,72,4.4,37986,77.8
20041,KS,Dickinson County,10811,10125,686,6.3,46894,96
20043,KS,Doniphan County,4765,4382,383,8,43092,88.2
20045,KS,Douglas County,61838,58133,3705,6,47930,98.1
20047,KS,Edwards County,1822,1746,76,4.2,43874,89.8
20049,KS,Elk County,1530,1422,108,7.1,34174,70
20051,KS,Ellis County,19189,18537,652,3.4,41774,85.5
20053,KS,Ellsworth County,4072,3912,160,3.9,46927,96.1
20055,KS,Finney County,20915,20009,906,4.3,44888,91.9
20057,KS,Ford County,20269,19462,807,4,47542,97.3
20059,KS,Franklin County,13447,12299,1148,8.5,46553,95.3
20061,KS,Geary County,14754,13565,1189,8.1,40224,82.4
20063,KS,Gove County,1653,1601,52,3.1,44351,90.8
20065,KS,Graham County,1511,1451,60,4,40903,83.7
20067,KS,Grant County,4116,3944,172,4.2,54230,111
20069,KS,Gray County,3704,3587,117,3.2,59278,121.4
20071,KS,Greeley County,769,742,27,3.5,48606,99.5
20073,KS,Greenwood County,3498,3260,238,6.8,35765,73.2
20075,KS,Hamilton County,1263,1213,50,4,43679,89.4
20077,KS,Harper County,3493,3320,173,5,39104,80.1
20079,KS,Harvey County,17311,16183,1128,6.5,50958,104.3
20081,KS,Haskell County,2368,2280,88,3.7,54340,111.3
20083,KS,Hodgeman County,1025,984,41,4,45992,94.2
20085,KS,Jackson County,6856,6388,468,6.8,50342,103.1
20087,KS,Jefferson County,9915,9175,740,7.5,57046,116.8
20089,KS,Jewell County,1897,1823,74,3.9,38865,79.6
20091,KS,Johnson County,301113,283223,17890,5.9,70665,144.7
20093,KS,Kearny County,2278,2180,98,4.3,51413,105.3
20095,KS,Kingman County,4658,4414,244,5.2,47684,97.6
20097,KS,Kiowa County,1551,1493,58,3.7,39796,81.5
20099,KS,Labette County,11143,10121,1022,9.2,37879,77.6
20101,KS,Lane County,1135,1091,44,3.9,47900,98.1
20103,KS,Leavenworth County,33526,30755,2771,8.3,61567,126
20105,KS,Lincoln County,1954,1854,100,5.1,39317,80.5
20107,KS,Linn County,4703,4199,504,10.7,44615,91.3
20109,KS,Logan County,1808,1748,60,3.3,43315,88.7
20111,KS,Lyon County,18357,17199,1158,6.3,39346,80.6
20113,KS,McPherson County,16985,16201,784,4.6,48130,98.5
20115,KS,Marion County,6603,6227,376,5.7,43749,89.6
20117,KS,Marshall County,6320,6030,290,4.6,42619,87.3
20119,KS,Meade County,2585,2490,95,3.7,49141,100.6
20121,KS,Miami County,17131,15822,1309,7.6,57580,117.9
20123,KS,Mitchell County,3680,3534,146,4,43511,89.1
20125,KS,Montgomery County,18651,16779,1872,10,36670,75.1
20127,KS,Morris County,3052,2828,224,7.3,44227,90.5
20129,KS,Morton County,1676,1611,65,3.9,47006,96.2
20131,KS,Nemaha County,6202,5970,232,3.7,47971,98.2
20133,KS,Neosho County,8666,7981,685,7.9,40651,83.2
20135,KS,Ness County,1852,1796,56,3,44774,91.7
20137,KS,Norton County,2909,2785,124,4.3,41396,84.8
20139,KS,Osage County,8466,7744,722,8.5,47318,96.9
20141,KS,Osborne County,2375,2270,105,4.4,37949,77.7
20143,KS,Ottawa County,3306,3109,197,6,48998,100.3
20145,KS,Pawnee County,3841,3665,176,4.6,41623,85.2
20147,KS,Phillips County,3185,3029,156,4.9,40746,83.4
20149,KS,Pottawatomie County,11451,10845,606,5.3,53339,109.2
20151,KS,Pratt County,6218,5935,283,4.6,44964,92.1
20153,KS,Rawlins County,1375,1321,54,3.9,40908,83.8
20155,KS,Reno County,35066,32983,2083,5.9,41892,85.8
20157,KS,Republic County,2925,2818,107,3.7,38132,78.1
20159,KS,Rice County,6055,5768,287,4.7,45420,93
20161,KS,Riley County,36092,34290,1802,5,45558,93.3
20163,KS,Rooks County,2656,2507,149,5.6,39326,80.5
20165,KS,Rush County,1831,1745,86,4.7,37693,77.2
20167,KS,Russell County,3721,3539,182,4.9,37140,76
20169,KS,Saline County,31023,29096,1927,6.2,45359,92.9
20171,KS,Scott County,2932,2828,104,3.5,49066,100.5
20173,KS,Sedgwick County,247445,227061,20384,8.2,49526,101.4
20175,KS,Seward County,11049,10536,513,4.6,47855,98
20177,KS,Shawnee County,93298,86759,6539,7,44816,91.8
20179,KS,Sheridan County,1681,1631,50,3,48498,99.3
20181,KS,Sherman County,3963,3831,132,3.3,39660,81.2
20183,KS,Smith County,2247,2154,93,4.1,38090,78
20185,KS,Stafford County,2225,2107,118,5.3,44133,90.4
20187,KS,Stanton County,1215,1171,44,3.6,49115,100.6
20189,KS,Stevens County,2277,2163,114,5,58901,120.6
20191,KS,Sumner County,11131,10307,824,7.4,47460,97.2
20193,KS,Thomas County,4385,4213,172,3.9,47892,98.1
20195,KS,Trego County,1905,1831,74,3.9,45683,93.5
20197,KS,Wabaunsee County,3742,3499,243,6.5,51666,105.8
20199,KS,Wallace County,893,846,47,5.3,47573,97.4
20201,KS,Washington County,3407,3255,152,4.5,39962,81.8
20203,KS,Wichita County,1244,1193,51,4.1,46929,96.1
20205,KS,Wilson County,4786,4295,491,10.3,37002,75.8
20207,KS,Woodson County,1780,1659,121,6.8,33221,68
20209,KS,Wyandotte County,71625,64515,7110,9.9,38016,77.8
21000,KY,Kentucky,2067527,1870546,196981,9.5,41141,100
21001,KY,Adair County,9371,8446,925,9.9,30147,73.3
21003,KY,Allen County,8578,7674,904,10.5,35614,86.6
21005,KY,Anderson County,10828,9873,955,8.8,51817,125.9
21007,KY,Ballard County,4221,3819,402,9.5,40790,99.1
21009,KY,Barren County,19421,17438,1983,10.2,36298,88.2
21011,KY,Bath County,4990,4380,610,12.2,29857,72.6
21013,KY,Bell County,9464,8214,1250,13.2,24940,60.6
21015,KY,Boone County,64737,59519,5218,8.1,64055,155.7
21017,KY,Bourbon County,9422,8577,845,9,40350,98.1
21019,KY,Boyd County,22808,20760,2048,9,38783,94.3
21021,KY,Boyle County,12540,11106,1434,11.4,39663,96.4
21023,KY,Bracken County,4243,3786,457,10.8,41974,102
21025,KY,Breathitt County,5520,4946,574,10.4,26829,65.2
21027,KY,Breckinridge County,9630,8662,968,10.1,38028,92.4
21029,KY,Bullitt County,38439,34365,4074,10.6,52033,126.5
21031,KY,Butler County,5623,5028,595,10.6,32483,79
21033,KY,Caldwell County,6686,6140,546,8.2,36650,89.1
21035,KY,Calloway County,18119,16678,1441,8,39599,96.3
21037,KY,Campbell County,46317,42023,4294,9.3,53501,130
21039,KY,Carlisle County,2451,2244,207,8.4,38075,92.5
21041,KY,Carroll County,5574,4943,631,11.3,40685,98.9
21043,KY,Carter County,13470,11830,1640,12.2,35471,86.2
21045,KY,Casey County,7363,6653,710,9.6,28034,68.1
21047,KY,Christian County,26470,23379,3091,11.7,35779,87
21049,KY,Clark County,16765,15111,1654,9.9,44494,108.2
21051,KY,Clay County,7082,6115,967,13.7,22289,54.2
21053,KY,Clinton County,4851,4358,493,10.2,27095,65.9
21055,KY,Crittenden County,4255,3891,364,8.6,33570,81.6
21057,KY,Cumberland County,3123,2771,352,11.3,27570,67
21059,KY,Daviess County,49493,45458,4035,8.2,44379,107.9
21061,KY,Edmonson County,5148,4556,592,11.5,36321,88.3
21063,KY,Elliott County,3223,2831,392,12.2,29339,71.3
21065,KY,Estill County,6329,5626,703,11.1,28961,70.4
21067,KY,Fayette County,152998,141601,11397,7.4,47211,114.8
21069,KY,Fleming County,6387,5695,692,10.8,34736,84.4
21071,KY,Floyd County,15131,13584,1547,10.2,28459,69.2
21073,KY,Franklin County,24768,22764,2004,8.1,44241,107.5
21075,KY,Fulton County,2549,2178,371,14.6,28340,68.9
21077,KY,Gallatin County,4154,3760,394,9.5,41084,99.9
21079,KY,Garrard County,7684,6894,790,10.3,40292,97.9
21081,KY,Grant County,12380,11136,1244,10,44158,107.3
21083,KY,Graves County,16649,15095,1554,9.3,35690,86.8
21085,KY,Grayson County,11487,10069,1418,12.3,32793,79.7
21087,KY,Green County,5610,5078,532,9.5,31189,75.8
21089,KY,Greenup County,16984,15359,1625,9.6,41676,101.3
21091,KY,Hancock County,4232,3884,348,8.2,52049,126.5
21093,KY,Hardin County,49693,45242,4451,9,47423,115.3
21095,KY,Harlan County,10680,9549,1131,10.6,28721,69.8
21097,KY,Harrison County,9328,8414,914,9.8,39160,95.2
21099,KY,Hart County,8569,7755,814,9.5,31677,77
21101,KY,Henderson County,23348,21319,2029,8.7,41244,100.3
21103,KY,Henry County,7447,6761,686,9.2,42614,103.6
21105,KY,Hickman County,2169,1957,212,9.8,38446,93.4
21107,KY,Hopkins County,23019,21169,1850,8,39733,96.6
21109,KY,Jackson County,4493,3766,727,16.2,26344,64
21111,KY,Jefferson County,364514,327926,36588,10,45407,110.4
21113,KY,Jessamine County,23352,21402,1950,8.4,47280,114.9
21115,KY,Johnson County,9583,8621,962,10,35068,85.2
21117,KY,Kenton County,85017,77423,7594,8.9,50150,121.9
21119,KY,Knott County,6311,5608,703,11.1,29791,72.4
21121,KY,Knox County,12248,10823,1425,11.6,25536,62.1
21123,KY,Larue County,6983,6382,601,8.6,39174,95.2
21125,KY,Laurel County,26672,23786,2886,10.8,35147,85.4
21127,KY,Lawrence County,6165,5477,688,11.2,32275,78.4
21129,KY,Lee County,2809,2460,349,12.4,24770,60.2
21131,KY,Leslie County,3767,3298,469,12.5,29645,72.1
21133,KY,Letcher County,8922,8012,910,10.2,31146,75.7
21135,KY,Lewis County,5522,4761,761,13.8,28740,69.9
21137,KY,Lincoln County,10417,9119,1298,12.5,35530,86.4
21139,KY,Livingston County,4811,4369,442,9.2,38724,94.1
21141,KY,Logan County,12665,11537,1128,8.9,39304,95.5
21143,KY,Lyon County,3483,3141,342,9.8,40006,97.2
21145,KY,McCracken County,31994,29289,2705,8.5,44115,107.2
21147,KY,McCreary County,6025,5146,879,14.6,24640,59.9
21149,KY,McLean County,4573,4148,425,9.3,38984,94.8
21151,KY,Madison County,44467,41047,3420,7.7,40034,97.3
21153,KY,Magoffin County,4378,3651,727,16.6,28004,68.1
21155,KY,Marion County,10305,9287,1018,9.9,36168,87.9
21157,KY,Marshall County,15001,13538,1463,9.8,45163,109.8
21159,KY,Martin County,3786,3396,390,10.3,28880,70.2
21161,KY,Mason County,8640,7872,768,8.9,39837,96.8
21163,KY,Meade County,12208,10656,1552,12.7,48485,117.9
21165,KY,Menifee County,2488,2110,378,15.2,28773,69.9
21167,KY,Mercer County,10044,8978,1066,10.6,40502,98.4
21169,KY,Metcalfe County,4299,3785,514,12,29852,72.6
21171,KY,Monroe County,4783,4340,443,9.3,27480,66.8
21173,KY,Montgomery County,12717,11365,1352,10.6,40267,97.9
21175,KY,Morgan County,5245,4614,631,12,29078,70.7
21177,KY,Muhlenberg County,13969,12596,1373,9.8,35316,85.8
21179,KY,Nelson County,21570,19075,2495,11.6,42271,102.7
21181,KY,Nicholas County,3209,2880,329,10.3,34886,84.8
21183,KY,Ohio County,12772,11756,1016,8,37545,91.3
21185,KY,Oldham County,28511,26335,2176,7.6,80872,196.6
21187,KY,Owen County,5339,4896,443,8.3,40909,99.4
21189,KY,Owsley County,1582,1398,184,11.6,21865,53.1
21191,KY,Pendleton County,7278,6492,786,10.8,44853,109
21193,KY,Perry County,11484,10314,1170,10.2,31623,76.9
21195,KY,Pike County,25797,23546,2251,8.7,33881,82.4
21197,KY,Powell County,5556,4880,676,12.2,31129,75.7
21199,KY,Pulaski County,28066,25078,2988,10.6,34695,84.3
21201,KY,Robertson County,1046,946,100,9.6,35504,86.3
21203,KY,Rockcastle County,7733,6832,901,11.7,30323,73.7
21205,KY,Rowan County,12741,11667,1074,8.4,34920,84.9
21207,KY,Russell County,8468,7603,865,10.2,30691,74.6
21209,KY,Scott County,23580,21581,1999,8.5,58164,141.4
21211,KY,Shelby County,21406,19606,1800,8.4,51021,124
21213,KY,Simpson County,9123,8158,965,10.6,40583,98.6
21215,KY,Spencer County,8758,7909,849,9.7,62347,151.5
21217,KY,Taylor County,14203,12898,1305,9.2,33348,81.1
21219,KY,Todd County,5516,5008,508,9.2,37325,90.7
21221,KY,Trigg County,6504,5848,656,10.1,41699,101.4
21223,KY,Trimble County,4146,3728,418,10.1,43677,106.2
21225,KY,Union County,8273,7604,669,8.1,42059,102.2
21227,KY,Warren County,59076,54200,4876,8.3,40026,97.3
21229,KY,Washington County,5500,4923,577,10.5,39023,94.9
21231,KY,Wayne County,8405,7262,1143,13.6,27637,67.2
21233,KY,Webster County,6358,5841,517,8.1,40454,98.3
21235,KY,Whitley County,15695,13995,1700,10.8,29870,72.6
21237,KY,Wolfe County,2390,2066,324,13.6,25950,63.1
21239,KY,Woodford County,12975,12032,943,7.3,54930,133.5
22000,LA,Louisiana,2060635,1909303,151332,7.3,41804,100
22001,LA,Acadia Parish,25644,24024,1620,6.3,34960,83.6
22003,LA,Allen Parish,8739,7876,863,9.9,34586,82.7
22005,LA,Ascension Parish,51388,47900,3488,6.8,63305,151.4
22007,LA,Assumption Parish,10315,9187,1128,10.9,44770,107.1
22009,LA,Avoyelles Parish,16450,15080,1370,8.3,31733,75.9
22011,LA,Beauregard Parish,14054,12945,1109,7.9,43673,104.5
22013,LA,Bienville Parish,6400,5833,567,8.9,30750,73.6
22015,LA,Bossier Parish,55494,52345,3149,5.7,50577,121
22017,LA,Caddo Parish,116978,108574,8404,7.2,39948,95.6
22019,LA,Calcasieu Parish,89641,83466,6175,6.9,41065,98.2
22021,LA,Caldwell Parish,4485,4085,400,8.9,36415,87.1
22023,LA,Cameron Parish,3044,2867,177,5.8,54318,129.9
22025,LA,Catahoula Parish,4242,3818,424,10,31678,75.8
22027,LA,Claiborne Parish,6624,6036,588,8.9,34084,81.5
22029,LA,Concordia Parish,7522,6688,834,11.1,29373,70.3
22031,LA,De Soto Parish,11291,10414,877,7.8,40507,96.9
22033,LA,East Baton Rouge Parish,215626,200108,15518,7.2,45924,109.9
22035,LA,East Carroll Parish,2819,2383,436,15.5,25163,60.2
22037,LA,East Feliciana Parish,7696,7024,672,8.7,38478,92
22039,LA,Evangeline Parish,12757,11696,1061,8.3,32099,76.8
22041,LA,Franklin Parish,7593,6693,900,11.9,29025,69.4
22043,LA,Grant Parish,9418,8731,687,7.3,39539,94.6
22045,LA,Iberia Parish,33265,30927,2338,7,40626,97.2
22047,LA,Iberville Parish,12523,11167,1356,10.8,37832,90.5
22049,LA,Jackson Parish,6513,5991,522,8,36032,86.2
22051,LA,Jefferson Parish,211904,197570,14334,6.8,43700,104.5
22053,LA,Jefferson Davis Parish,14329,13453,876,6.1,38723,92.6
22055,LA,Lafayette Parish,114282,107967,6315,5.5,47138,112.8
22057,LA,Lafourche Parish,48254,45695,2559,5.3,48180,115.3
22059,LA,La Salle Parish,7072,6692,380,5.4,42576,101.8
22061,LA,Lincoln Parish,19474,17783,1691,8.7,34497,82.5
22063,LA,Livingston Parish,59711,55530,4181,7,53700,128.5
22065,LA,Madison Parish,4441,3939,502,11.3,23240,55.6
22067,LA,Morehouse Parish,11250,9713,1537,13.7,26778,64.1
22069,LA,Natchitoches Parish,16546,15150,1396,8.4,32203,77
22071,LA,Orleans Parish,146037,133177,12860,8.8,34851,83.4
22073,LA,Ouachita Parish,70769,65312,5457,7.7,35770,85.6
22075,LA,Plaquemines Parish,9019,8398,621,6.9,52259,125
22077,LA,Pointe Coupee Parish,9554,8693,861,9,40052,95.8
22079,LA,Rapides Parish,58388,54215,4173,7.1,36624,87.6
22081,LA,Red River Parish,3753,3450,303,8.1,32650,78.1
22083,LA,Richland Parish,9159,8238,921,10.1,32148,76.9
22085,LA,Sabine Parish,9486,8842,644,6.8,37875,90.6
22087,LA,St. Bernard Parish,16620,15319,1301,7.8,38068,91.1
22089,LA,St. Charles Parish,24899,23230,1669,6.7,54860,131.2
22091,LA,St. Helena Parish,4439,3872,567,12.8,33721,80.7
22093,LA,St. James Parish,9314,8188,1126,12.1,43701,104.5
22095,LA,St. John the Baptist Parish,20567,18661,1906,9.3,50483,120.8
22097,LA,St. Landry Parish,37739,34788,2951,7.8,33750,80.7
22099,LA,St. Martin Parish,23092,21496,1596,6.9,38826,92.9
22101,LA,St. Mary Parish,22930,20815,2115,9.2,38813,92.8
22103,LA,St. Tammany Parish,111724,105346,6378,5.7,57694,138
22105,LA,Tangipahoa Parish,53139,48372,4767,9,38420,91.9
22107,LA,Tensas Parish,1911,1652,259,13.6,26903,64.4
22109,LA,Terrebonne Parish,53749,50847,2902,5.4,45054,107.8
22111,LA,Union Parish,9791,9012,779,8,34958,83.6
22113,LA,Vermilion Parish,23858,22278,1580,6.6,39091,93.5
22115,LA,Vernon Parish,21667,20007,1660,7.7,45726,109.4
22117,LA,Washington Parish,15249,13745,1504,9.9,29443,70.4
22119,LA,Webster Parish,19585,18047,1538,7.9,35908,85.9
22121,LA,West Baton Rouge Parish,10947,10091,856,7.8,46155,110.4
22123,LA,West Carroll Parish,4246,3576,670,15.8,27781,66.5
22125,LA,West Feliciana Parish,4799,4400,399,8.3,49479,118.4
22127,LA,Winn Parish,6431,5892,539,8.4,33875,81
23000,ME,Maine,704078,651038,53040,7.5,46160,100
23001,ME,Androscoggin County,58281,53889,4392,7.5,43899,95.1
23003,ME,Aroostook County,34542,31266,3276,9.5,35572,77.1
23005,ME,Cumberland County,159436,149870,9566,6,55459,120.1
23007,ME,Franklin County,14280,12931,1349,9.4,39405,85.4
23009,ME,Hancock County,30159,27561,2598,8.6,44599,96.6
23011,ME,Kennebec County,64071,59501,4570,7.1,44441,96.3
23013,ME,Knox County,20901,19430,1471,7,46366,100.4
23015,ME,Lincoln County,18307,17008,1299,7.1,45674,98.9
23017,ME,Oxford County,28680,25912,2768,9.7,37915,82.1
23019,ME,Penobscot County,78770,72377,6393,8.1,41199,89.3
23021,ME,Piscataquis County,7471,6691,780,10.4,36017,78
23023,ME,Sagadahoc County,19114,17858,1256,6.6,52187,113.1
23025,ME,Somerset County,24936,22353,2583,10.4,37339,80.9
23027,ME,Waldo County,19521,17884,1637,8.4,41888,90.7
23029,ME,Washington County,14497,12949,1548,10.7,33637,72.9
23031,ME,York County,111121,103562,7559,6.8,55725,120.7
24000,MD,Maryland,3072246,2855735,216511,7,70075,100
24001,MD,Allegany County,36996,33862,3134,8.5,38504,54.9
24003,MD,Anne Arundel County,296863,277929,18934,6.4,82980,118.4
24005,MD,Baltimore County,443127,409881,33246,7.5,62309,88.9
24009,MD,Calvert County,47773,44953,2820,5.9,88406,126.2
24011,MD,Caroline County,16877,15381,1496,8.9,50809,72.5
24013,MD,Carroll County,93855,87879,5976,6.4,82553,117.8
24015,MD,Cecil County,50711,46188,4523,8.9,61191,87.3
24017,MD,Charles County,79036,74293,4743,6,88575,126.4
24019,MD,Dorchester County,17419,15591,1828,10.5,41936,59.8
24021,MD,Frederick County,127744,119923,7821,6.1,77872,111.1
24023,MD,Garrett County,17385,16075,1310,7.5,41829,59.7
24025,MD,Harford County,136434,126642,9792,7.2,77095,110
24027,MD,Howard County,167369,158755,8614,5.1,99040,141.3
24029,MD,Kent County,11114,10264,850,7.6,49795,71.1
24031,MD,Montgomery County,525157,498057,27100,5.2,92288,131.7
24033,MD,Prince George's County,464524,432133,32391,7,70114,100.1
24035,MD,Queen Anne's County,27128,25251,1877,6.9,75158,107.3
24037,MD,St. Mary's County,55648,52320,3328,6,80943,115.5
24039,MD,Somerset County,11004,9865,1139,10.4,35426,50.6
24041,MD,Talbot County,18937,17487,1450,7.7,55145,78.7
24043,MD,Washington County,69463,62668,6795,9.8,52028,74.2
24045,MD,Wicomico County,53239,48473,4766,9,45788,65.3
24047,MD,Worcester County,28375,24912,3463,12.2,48472,69.2
24510,MD,Baltimore city,276072,246957,29115,10.5,38478,54.9
25000,MA,Massachusetts,3456442,3202251,254191,7.4,63126,100
25001,MA,Barnstable County,120515,110635,9880,8.2,56167,89
25003,MA,Berkshire County,72397,67146,5251,7.3,43813,69.4
25005,MA,Bristol County,290171,261011,29160,10,53307,84.4
25007,MA,Dukes County,11501,10662,839,7.3,58133,92.1
25009,MA,Essex County,381493,351833,29660,7.8,64303,101.9
25011,MA,Franklin County,38558,35960,2598,6.7,49940,79.1
25013,MA,Hampden County,223728,203170,20558,9.2,47313,75
25015,MA,Hampshire County,87865,82673,5192,5.9,54548,86.4
25017,MA,Middlesex County,828752,780236,48516,5.9,76803,121.7
25019,MA,Nantucket County,7746,7217,529,6.8,68431,108.4
25021,MA,Norfolk County,363147,340517,22630,6.2,81590,129.2
25023,MA,Plymouth County,262513,242112,20401,7.8,67989,107.7
25025,MA,Suffolk County,363962,337609,26353,7.2,49033,77.7
25027,MA,Worcester County,404098,371471,32627,8.1,60396,95.7
26000,MI,Michigan,4658108,4178121,479987,10.3,45931,100
26001,MI,Alcona County,3789,3272,517,13.6,35407,77.1
26003,MI,Alger County,3881,3437,444,11.4,39486,86
26005,MI,Allegan County,52872,48269,4603,8.7,50559,110.1
26007,MI,Alpena County,13362,11954,1408,10.5,37270,81.1
26009,MI,Antrim County,10233,8975,1258,12.3,41679,90.7
26011,MI,Arenac County,7047,6115,932,13.2,38008,82.8
26013,MI,Baraga County,3833,3115,718,18.7,38119,83
26015,MI,Barry County,28415,26327,2088,7.3,50051,109
26017,MI,Bay County,51715,46711,5004,9.7,43068,93.8
26019,MI,Benzie County,8367,7360,1007,12,45998,100.1
26021,MI,Berrien County,73088,65673,7415,10.1,40946,89.1
26023,MI,Branch County,19685,17727,1958,9.9,40462,88.1
26025,MI,Calhoun County,64509,58781,5728,8.9,39694,86.4
26027,MI,Cass County,25386,23163,2223,8.8,44626,97.2
26029,MI,Charlevoix County,12685,11162,1523,12,44061,95.9
26031,MI,Cheboygan County,10696,9534,1162,10.9,37133,80.8
26033,MI,Chippewa County,16269,14405,1864,11.5,39520,86
26035,MI,Clare County,12174,10598,1576,12.9,31443,68.5
26037,MI,Clinton County,37511,34920,2591,6.9,58286,126.9
26039,MI,Crawford County,5874,5216,658,11.2,37874,82.5
26041,MI,Delta County,18424,16573,1851,10,40982,89.2
26043,MI,Dickinson County,13390,12204,1186,8.9,44262,96.4
26045,MI,Eaton County,54634,50623,4011,7.3,51428,112
26047,MI,Emmet County,18629,16280,2349,12.6,48285,105.1
26049,MI,Genesee County,184435,164302,20133,10.9,40854,88.9
26051,MI,Gladwin County,9595,8278,1317,13.7,34747,75.7
26053,MI,Gogebic County,7141,6340,801,11.2,33382,72.7
26055,MI,Grand Traverse County,45830,41442,4388,9.6,46786,101.9
26057,MI,Gratiot County,18731,16795,1936,10.3,40359,87.9
26059,MI,Hillsdale County,19091,16987,2104,11,41030,89.3
26061,MI,Houghton County,16739,15158,1581,9.4,35425,77.1
26063,MI,Huron County,15404,13870,1534,10,39726,86.5
26065,MI,Ingham County,143240,130735,12505,8.7,42371,92.2
26067,MI,Ionia County,28634,25781,2853,10,46072,100.3
26069,MI,Iosco County,9933,8643,1290,13,33487,72.9
26071,MI,Iron County,5380,4856,524,9.7,34384,74.9
26073,MI,Isabella County,36455,33723,2732,7.5,38154,83.1
26075,MI,Jackson County,71149,64075,7074,9.9,42379,92.3
26077,MI,Kalamazoo County,124194,114013,10181,8.2,45798,99.7
26079,MI,Kalkaska County,8005,7100,905,11.3,38053,82.8
26081,MI,Kent County,302511,278213,24298,8,50651,110.3
26083,MI,Keweenaw County,968,840,128,13.2,38207,83.2
26085,MI,Lake County,3957,3445,512,12.9,28971,63.1
26087,MI,Lapeer County,40542,35225,5317,13.1,50232,109.4
26089,MI,Leelanau County,10343,9463,880,8.5,51267,111.6
26091,MI,Lenawee County,44359,39535,4824,10.9,45512,99.1
26093,MI,Livingston County,88278,79924,8354,9.5,68895,150
26095,MI,Luce County,2463,2161,302,12.3,36515,79.5
26097,MI,Mackinac County,5741,5067,674,11.7,36393,79.2
26099,MI,Macomb County,402745,356457,46288,11.5,50958,110.9
26101,MI,Manistee County,10836,9617,1219,11.2,39347,85.7
26103,MI,Marquette County,34139,31230,2909,8.5,41663,90.7
26105,MI,Mason County,14153,12673,1480,10.5,39101,85.1
26107,MI,Mecosta County,19690,17610,2080,10.6,38634,84.1
26109,MI,Menominee County,12118,11147,971,8,39292,85.5
26111,MI,Midland County,41488,38281,3207,7.7,53764,117.1
26113,MI,Missaukee County,5779,5057,722,12.5,37616,81.9
26115,MI,Monroe County,68733,62032,6701,9.7,54174,117.9
26117,MI,Montcalm County,25310,22217,3093,12.2,41023,89.3
26119,MI,Montmorency County,3556,2939,617,17.4,32241,70.2
26121,MI,Muskegon County,82113,73756,8357,10.2,38482,83.8
26123,MI,Newaygo County,21292,19093,2199,10.3,41674,90.7
26125,MI,Oakland County,587577,528805,58772,10,61961,134.9
26127,MI,Oceana County,13381,11695,1686,12.6,39670,86.4
26129,MI,Ogemaw County,9099,8061,1038,11.4,33222,72.3
26131,MI,Ontonagon County,2770,2326,444,16,34352,74.8
26133,MI,Osceola County,9004,7944,1060,11.8,38404,83.6
26135,MI,Oscoda County,3482,2900,582,16.7,31579,68.8
26137,MI,Otsego County,10907,9550,1357,12.4,44129,96.1
26139,MI,Ottawa County,128202,117606,10596,8.3,54461,118.6
26141,MI,Presque Isle County,5507,4627,880,16,39483,86
26143,MI,Roscommon County,9518,8305,1213,12.7,32742,71.3
26145,MI,Saginaw County,90082,81393,8689,9.6,40411,88
26147,MI,St. Clair County,76163,66164,9999,13.1,45858,99.8
26149,MI,St. Joseph County,27594,24768,2826,10.2,40796,88.8
26151,MI,Sanilac County,19099,16843,2256,11.8,37465,81.6
26153,MI,Schoolcraft County,3797,3309,488,12.9,38366,83.5
26155,MI,Shiawassee County,33030,29536,3494,10.6,46811,101.9
26157,MI,Tuscola County,25707,22790,2917,11.3,42808,93.2
26159,MI,Van Buren County,36017,32062,3955,11,41600,90.6
26161,MI,Washtenaw County,179685,168067,11618,6.5,56789,123.6
26163,MI,Wayne County,822523,719175,103348,12.6,38479,83.8
26165,MI,Wexford County,13433,11725,1708,12.7,43873,95.5
27000,MN,Minnesota,2977919,2787131,190788,6.4,56944,100
27001,MN,Aitkin County,7609,6929,680,8.9,39958,70.2
27003,MN,Anoka County,190498,177897,12601,6.6,63799,112
27005,MN,Becker County,18067,16810,1257,7,47819,84
27007,MN,Beltrami County,22597,20757,1840,8.1,40657,71.4
27009,MN,Benton County,22363,20767,1596,7.1,49482,86.9
27011,MN,Big Stone County,2984,2801,183,6.1,43190,75.8
27013,MN,Blue Earth County,38765,36700,2065,5.3,46280,81.3
27015,MN,Brown County,15863,14986,877,5.5,50339,88.4
27017,MN,Carlton County,17981,16586,1395,7.8,49357,86.7
27019,MN,Carver County,50425,47583,2842,5.6,85314,149.8
27021,MN,Cass County,14591,13192,1399,9.6,42297,74.3
27023,MN,Chippewa County,7422,6975,447,6,46545,81.7
27025,MN,Chisago County,29087,26836,2251,7.7,62776,110.2
27027,MN,Clay County,34004,32318,1686,5,52731,92.6
27029,MN,Clearwater County,4398,3860,538,12.2,36697,64.4
27031,MN,Cook County,3265,3060,205,6.3,44278,77.8
27033,MN,Cottonwood County,6871,6498,373,5.4,44504,78.2
27035,MN,Crow Wing County,34140,31369,2771,8.1,45514,79.9
27037,MN,Dakota County,232031,218093,13938,6,70064,123
27039,MN,Dodge County,11116,10439,677,6.1,65423,114.9
27041,MN,Douglas County,21333,20135,1198,5.6,49161,86.3
27043,MN,Faribault County,7827,7278,549,7,43567,76.5
27045,MN,Fillmore County,11484,10745,739,6.4,47241,83
27047,MN,Freeborn County,17009,15884,1125,6.6,44352,77.9
27049,MN,Goodhue County,26368,24750,1618,6.1,54724,96.1
27051,MN,Grant County,3253,3021,232,7.1,43715,76.8
27053,MN,Hennepin County,657760,618156,39604,6,60811,106.8
27055,MN,Houston County,10869,10105,764,7,52325,91.9
27057,MN,Hubbard County,9436,8568,868,9.2,41196,72.3
27059,MN,Isanti County,20924,19208,1716,8.2,57860,101.6
27061,MN,Itasca County,23809,21765,2044,8.6,44957,78.9
27063,MN,Jackson County,7296,6972,324,4.4,47337,83.1
27065,MN,Kanabec County,8195,7314,881,10.8,44691,78.5
27067,MN,Kandiyohi County,25325,23898,1427,5.6,49543,87
27069,MN,Kittson County,2667,2495,172,6.4,45740,80.3
27071,MN,Koochiching County,6870,6302,568,8.3,42771,75.1
27073,MN,Lac qui Parle County,4348,4116,232,5.3,49670,87.2
27075,MN,Lake County,6466,6059,407,6.3,48683,85.5
27077,MN,Lake of the Woods County,2346,2206,140,6,39777,69.9
27079,MN,Le Sueur County,14600,13410,1190,8.2,54468,95.7
27081,MN,Lincoln County,3564,3391,173,4.9,43996,77.3
27083,MN,Lyon County,15377,14577,800,5.2,50031,87.9
27085,MN,McLeod County,20563,19015,1548,7.5,52120,91.5
27087,MN,Mahnomen County,2591,2400,191,7.4,37593,66
27089,MN,Marshall County,5666,5209,457,8.1,47760,83.9
27091,MN,Martin County,11960,11235,725,6.1,44597,78.3
27093,MN,Meeker County,12892,11909,983,7.6,51299,90.1
27095,MN,Mille Lacs County,12318,11052,1266,10.3,43155,75.8
27097,MN,Morrison County,18572,17053,1519,8.2,44928,78.9
27099,MN,Mower County,21839,20661,1178,5.4,46860,82.3
27101,MN,Murray County,6117,5812,305,5,52281,91.8
27103,MN,Nicollet County,19791,18775,1016,5.1,55360,97.2
27105,MN,Nobles County,11977,11379,598,5,44945,78.9
27107,MN,Norman County,3826,3594,232,6.1,44576,78.3
27109,MN,Olmsted County,80730,76458,4272,5.3,66997,117.7
27111,MN,Otter Tail County,31536,29562,1974,6.3,47879,84.1
27113,MN,Pennington County,9533,8880,653,6.8,41986,73.7
27115,MN,Pine County,14913,13545,1368,9.2,42447,74.5
27117,MN,Pipestone County,5796,5472,324,5.6,43726,76.8
27119,MN,Polk County,17865,16775,1090,6.1,49978,87.8
27121,MN,Pope County,6432,6087,345,5.4,47165,82.8
27123,MN,Ramsey County,274023,255975,18048,6.6,51719,90.8
27125,MN,Red Lake County,2461,2268,193,7.8,44658,78.4
27127,MN,Redwood County,8537,8039,498,5.8,47858,84
27129,MN,Renville County,9101,8501,600,6.6,49153,86.3
27131,MN,Rice County,33615,31297,2318,6.9,56781,99.7
27133,MN,Rock County,5540,5304,236,4.3,49920,87.7
27135,MN,Roseau County,9263,8812,451,4.9,48344,84.9
27137,MN,St. Louis County,104859,97221,7638,7.3,44475,78.1
27139,MN,Scott County,74145,69751,4394,5.9,81586,143.3
27141,MN,Sherburne County,49573,46026,3547,7.2,68646,120.6
27143,MN,Sibley County,9779,9217,562,5.7,53047,93.2
27145,MN,Stearns County,85557,80205,5352,6.3,52053,91.4
27147,MN,Steele County,21516,20157,1359,6.3,54971,96.5
27149,MN,Stevens County,6471,6202,269,4.2,46892,82.3
27151,MN,Swift County,5478,5112,366,6.7,46209,81.1
27153,MN,Todd County,13004,12119,885,6.8,41290,72.5
27155,MN,Traverse County,1871,1765,106,5.7,45815,80.5
27157,MN,Wabasha County,11888,11183,705,5.9,53899,94.7
27159,MN,Wadena County,6525,5951,574,8.8,36097,63.4
27161,MN,Waseca County,10739,10067,672,6.3,52660,92.5
27163,MN,Washington County,133172,125083,8089,6.1,77069,135.3
27165,MN,Watonwan County,5626,5244,382,6.8,46565,81.8
27167,MN,Wilkin County,3778,3595,183,4.8,50004,87.8
27169,MN,Winona County,29965,28248,1717,5.7,46028,80.8
27171,MN,Wright County,69526,64636,4890,7,68387,120.1
27173,MN,Yellow Medicine County,5795,5472,323,5.6,46586,81.8
28000,MS,Mississippi,1343846,1200710,143136,10.7,36963,100
28001,MS,Adams County,13227,11820,1407,10.6,26722,72.3
28003,MS,Alcorn County,15819,13947,1872,11.8,31496,85.2
28005,MS,Amite County,5028,4436,592,11.8,32824,88.8
28007,MS,Attala County,7231,6282,949,13.1,30108,81.5
28009,MS,Benton County,3082,2636,446,14.5,27834,75.3
28011,MS,Bolivar County,15657,13842,1815,11.6,28802,77.9
28013,MS,Calhoun County,6406,5747,659,10.3,32453,87.8
28015,MS,Carroll County,5061,4551,510,10.1,34876,94.4
28017,MS,Chickasaw County,7845,6838,1007,12.8,30524,82.6
28019,MS,Choctaw County,3479,3074,405,11.6,31177,84.3
28021,MS,Claiborne County,3925,3314,611,15.6,22802,61.7
28023,MS,Clarke County,6751,5910,841,12.5,32875,88.9
28025,MS,Clay County,7412,6028,1384,18.7,30243,81.8
28027,MS,Coahoma County,10583,9000,1583,15,27349,74
28029,MS,Copiah County,12801,11281,1520,11.9,31971,86.5
28031,MS,Covington County,9047,8204,843,9.3,31655,85.6
28033,MS,DeSoto County,80735,74295,6440,8,54495,147.4
28035,MS,Forrest County,35533,31824,3709,10.4,33203,89.8
28037,MS,Franklin County,3186,2810,376,11.8,33979,91.9
28039,MS,George County,9425,8208,1217,12.9,42596,115.2
28041,MS,Greene County,5086,4412,674,13.3,37362,101.1
28043,MS,Grenada County,9484,8419,1065,11.2,32543,88
28045,MS,Hancock County,19904,17890,2014,10.1,39996,108.2
28047,MS,Harrison County,89680,80983,8697,9.7,39046,105.6
28049,MS,Hinds County,120650,109210,11440,9.5,35014,94.7
28051,MS,Holmes County,7098,5785,1313,18.5,22623,61.2
28053,MS,Humphreys County,4220,3541,679,16.1,23169,62.7
28055,MS,Issaquena County,674,559,115,17.1,27683,74.9
28057,MS,Itawamba County,10360,9213,1147,11.1,37848,102.4
28059,MS,Jackson County,64228,57033,7195,11.2,47672,129
28061,MS,Jasper County,7219,6347,872,12.1,30574,82.7
28063,MS,Jefferson County,3056,2546,510,16.7,23420,63.4
28065,MS,Jefferson Davis County,5103,4430,673,13.2,27808,75.2
28067,MS,Jones County,31362,28709,2653,8.5,37030,100.2
28069,MS,Kemper County,4269,3672,597,14,27601,74.7
28071,MS,Lafayette County,23386,21327,2059,8.8,39853,107.8
28073,MS,Lamar County,27345,25163,2182,8,49250,133.2
28075,MS,Lauderdale County,34239,30395,3844,11.2,36420,98.5
28077,MS,Lawrence County,5121,4484,637,12.4,35396,95.8
28079,MS,Leake County,8544,7539,1005,11.8,33007,89.3
28081,MS,Lee County,39699,35476,4223,10.6,40983,110.9
28083,MS,Leflore County,12936,11087,1849,14.3,24839,67.2
28085,MS,Lincoln County,14215,12663,1552,10.9,36112,97.7
28087,MS,Lowndes County,26574,23503,3071,11.6,38605,104.4
28089,MS,Madison County,49578,45885,3693,7.4,55692,150.7
28091,MS,Marion County,11117,9858,1259,11.3,31368,84.9
28093,MS,Marshall County,15486,13469,2017,13,32943,89.1
28095,MS,Monroe County,16419,14283,2136,13,34735,94
28097,MS,Montgomery County,4680,4057,623,13.3,31249,84.5
28099,MS,Neshoba County,13571,12395,1176,8.7,34658,93.8
28101,MS,Newton County,9431,8489,942,10,34269,92.7
28103,MS,Noxubee County,3789,3112,677,17.9,21929,59.3
28105,MS,Oktibbeha County,21309,18993,2316,10.9,31937,86.4
28107,MS,Panola County,15691,13632,2059,13.1,32764,88.6
28109,MS,Pearl River County,22170,19738,2432,11,38276,103.6
28111,MS,Perry County,5134,4530,604,11.8,34766,94.1
28113,MS,Pike County,15587,13642,1945,12.5,31142,84.3
28115,MS,Pontotoc County,13835,12460,1375,9.9,38348,103.7
28117,MS,Prentiss County,11142,9841,1301,11.7,32927,89.1
28119,MS,Quitman County,3483,2984,499,14.3,24786,67.1
28121,MS,Rankin County,76567,71476,5091,6.6,59890,162
28123,MS,Scott County,14111,12881,1230,8.7,32004,86.6
28125,MS,Sharkey County,2426,2109,317,13.1,26439,71.5
28127,MS,Simpson County,11900,10761,1139,9.6,32348,87.5
28129,MS,Smith County,6540,5885,655,10,36311,98.2
28131,MS,Stone County,8471,7656,815,9.6,39113,105.8
28133,MS,Sunflower County,10528,8817,1711,16.3,26788,72.5
28135,MS,Tallahatchie County,6451,5619,832,12.9,27789,75.2
28137,MS,Tate County,12551,11143,1408,11.2,42201,114.2
28139,MS,Tippah County,8706,7536,1170,13.4,33701,91.2
28141,MS,Tishomingo County,7994,6960,1034,12.9,33494,90.6
28143,MS,Tunica County,4807,3892,915,19,29631,80.2
28145,MS,Union County,12810,11407,1403,11,36925,99.9
28147,MS,Walthall County,5771,5024,747,12.9,28707,77.7
28149,MS,Warren County,22062,19501,2561,11.6,38874,105.2
28151,MS,Washington County,22379,18879,3500,15.6,29287,79.2
28153,MS,Wayne County,8657,7598,1059,12.2,32817,88.8
28155,MS,Webster County,3590,3093,497,13.8,32935,89.1
28157,MS,Wilkinson County,4140,3627,513,12.4,26738,72.3
28159,MS,Winston County,7684,6527,1157,15.1,31826,86.1
28161,MS,Yalobusha County,5794,5107,687,11.9,32022,86.6
28163,MS,Yazoo County,10809,9420,1389,12.9,28449,77
29000,MO,Missouri,3046302,2785797,260505,8.6,45231,100
29001,MO,Adair County,12611,11665,946,7.5,33639,74.4
29003,MO,Andrew County,10633,9968,665,6.3,54994,121.6
29005,MO,Atchison County,3156,2925,231,7.3,43322,95.8
29007,MO,Audrain County,11959,11006,953,8,39554,87.4
29009,MO,Barry County,16838,15480,1358,8.1,36546,80.8
29011,MO,Barton County,5035,4535,500,9.9,36076,79.8
29013,MO,Bates County,7971,7141,830,10.4,40376,89.3
29015,MO,Benton County,7954,7154,800,10.1,36255,80.2
29017,MO,Bollinger County,5652,5151,501,8.9,38565,85.3
29019,MO,Boone County,91749,86410,5339,5.8,46769,103.4
29021,MO,Buchanan County,51005,47208,3797,7.4,42031,92.9
29023,MO,Butler County,20721,19018,1703,8.2,33480,74
29025,MO,Caldwell County,4419,4026,393,8.9,44206,97.7
29027,MO,Callaway County,22877,21155,1722,7.5,45968,101.6
29029,MO,Camden County,19978,17831,2147,10.7,40370,89.3
29031,MO,Cape Girardeau County,38879,36118,2761,7.1,41755,92.3
29033,MO,Carroll County,4880,4386,494,10.1,40035,88.5
29035,MO,Carter County,3172,2876,296,9.3,27878,61.6
29037,MO,Cass County,51229,46833,4396,8.6,54969,121.5
29039,MO,Cedar County,6477,5952,525,8.1,30536,67.5
29041,MO,Chariton County,4268,3897,371,8.7,41409,91.6
29043,MO,Christian County,41095,38009,3086,7.5,50426,111.5
29045,MO,Clark County,3539,3222,317,9,38597,85.3
29047,MO,Clay County,121143,111789,9354,7.7,59039,130.5
29049,MO,Clinton County,10422,9384,1038,10,49202,108.8
29051,MO,Cole County,40532,37926,2606,6.4,54396,120.3
29053,MO,Cooper County,8674,7927,747,8.6,42482,93.9
29055,MO,Crawford County,11813,10629,1184,10,38215,84.5
29057,MO,Dade County,3558,3237,321,9,33754,74.6
29059,MO,Dallas County,7395,6642,753,10.2,33535,74.1
29061,MO,Daviess County,4001,3645,356,8.9,38820,85.8
29063,MO,DeKalb County,5686,5215,471,8.3,41264,91.2
29065,MO,Dent County,6669,6040,629,9.4,34288,75.8
29067,MO,Douglas County,6289,5743,546,8.7,30071,66.5
29069,MO,Dunklin County,14210,12727,1483,10.4,32018,70.8
29071,MO,Franklin County,53501,48502,4999,9.3,47663,105.4
29073,MO,Gasconade County,7381,6702,679,9.2,39751,87.9
29075,MO,Gentry County,3625,3383,242,6.7,37314,82.5
29077,MO,Greene County,143790,133086,10704,7.4,40423,89.4
29079,MO,Grundy County,4868,4516,352,7.2,35002,77.4
29081,MO,Harrison County,4347,4005,342,7.9,35626,78.8
29083,MO,Henry County,10449,9469,980,9.4,38410,84.9
29085,MO,Hickory County,3433,3008,425,12.4,30049,66.4
29087,MO,Holt County,2819,2625,194,6.9,41054,90.8
29089,MO,Howard County,5244,4848,396,7.6,42733,94.5
29091,MO,Howell County,18969,17371,1598,8.4,31645,70
29093,MO,Iron County,4971,4459,512,10.3,32173,71.1
29095,MO,Jackson County,339642,307176,32466,9.6,44508,98.4
29097,MO,Jasper County,58462,53978,4484,7.7,39359,87
29099,MO,Jefferson County,117808,107368,10440,8.9,51008,112.8
29101,MO,Johnson County,25418,23280,2138,8.4,48483,107.2
29103,MO,Knox County,2245,2103,142,6.3,33381,73.8
29105,MO,Laclede County,16290,14485,1805,11.1,37442,82.8
29107,MO,Lafayette County,16947,15281,1666,9.8,47604,105.2
29109,MO,Lawrence County,18788,17338,1450,7.7,41128,90.9
29111,MO,Lewis County,5628,5264,364,6.5,40795,90.2
29113,MO,Lincoln County,27224,24453,2771,10.2,50523,111.7
29115,MO,Linn County,5867,5232,635,10.8,35697,78.9
29117,MO,Livingston County,6944,6385,559,8.1,37956,83.9
29119,MO,McDonald County,11052,10203,849,7.7,34497,76.3
29121,MO,Macon County,7953,7334,619,7.8,36969,81.7
29123,MO,Madison County,6119,5557,562,9.2,32734,72.4
29125,MO,Maries County,4768,4419,349,7.3,38568,85.3
29127,MO,Marion County,14555,13463,1092,7.5,36120,79.9
29129,MO,Mercer County,1919,1797,122,6.4,35483,78.4
29131,MO,Miller County,11609,10294,1315,11.3,35573,78.6
29133,MO,Mississippi County,5946,5321,625,10.5,29533,65.3
29135,MO,Moniteau County,7584,7006,578,7.6,43931,97.1
29137,MO,Monroe County,3931,3515,416,10.6,40176,88.8
29139,MO,Montgomery County,5901,5307,594,10.1,38722,85.6
29141,MO,Morgan County,8767,7761,1006,11.5,34885,77.1
29143,MO,New Madrid County,8485,7683,802,9.5,35522,78.5
29145,MO,Newton County,29312,26995,2317,7.9,41722,92.2
29147,MO,Nodaway County,11860,11059,801,6.8,38478,85.1
29149,MO,Oregon County,4556,4159,397,8.7,27616,61.1
29151,MO,Osage County,7409,6939,470,6.3,49977,110.5
29153,MO,Ozark County,4389,3969,420,9.6,30284,67
29155,MO,Pemiscot County,8258,7418,840,10.2,26647,58.9
29157,MO,Perry County,10663,10007,656,6.2,43899,97.1
29159,MO,Pettis County,20007,18337,1670,8.3,38026,84.1
29161,MO,Phelps County,21855,20175,1680,7.7,39880,88.2
29163,MO,Pike County,9024,8291,733,8.1,38595,85.3
29165,MO,Platte County,50837,47191,3646,7.2,63676,140.8
29167,MO,Polk County,14525,13227,1298,8.9,38112,84.3
29169,MO,Pulaski County,20984,19154,1830,8.7,49600,109.7
29171,MO,Putnam County,2385,2224,161,6.8,34146,75.5
29173,MO,Ralls County,5725,5303,422,7.4,46640,103.1
29175,MO,Randolph County,12641,11459,1182,9.4,36590,80.9
29177,MO,Ray County,11568,10426,1142,9.9,52222,115.5
29179,MO,Reynolds County,2393,2085,308,12.9,29475,65.2
29181,MO,Ripley County,6673,6020,653,9.8,27794,61.4
29183,MO,St. Charles County,201263,186554,14709,7.3,67074,148.3
29185,MO,St. Clair County,4005,3593,412,10.3,30098,66.5
29186,MO,Ste. Genevieve County,9776,8918,858,8.8,48217,106.6
29187,MO,St. Francois County,29264,26197,3067,10.5,35252,77.9
29189,MO,St. Louis County,526916,483699,43217,8.2,55131,121.9
29195,MO,Saline County,11458,10564,894,7.8,38379,84.9
29197,MO,Schuyler County,2080,1925,155,7.5,33322,73.7
29199,MO,Scotland County,2423,2268,155,6.4,36804,81.4
29201,MO,Scott County,20072,18351,1721,8.6,37793,83.6
29203,MO,Shannon County,3132,2728,404,12.9,25684,56.8
29205,MO,Shelby County,3162,2927,235,7.4,35321,78.1
29207,MO,Stoddard County,15284,13909,1375,9,35916,79.4
29209,MO,Stone County,15117,13330,1787,11.8,37302,82.5
29211,MO,Sullivan County,3451,3216,235,6.8,36039,79.7
29213,MO,Taney County,27989,24589,3400,12.1,36176,80
29215,MO,Texas County,11168,10185,983,8.8,32193,71.2
29217,MO,Vernon County,9670,8970,700,7.2,37342,82.6
29219,MO,Warren County,17269,15714,1555,9,50773,112.3
29221,MO,Washington County,10424,9168,1256,12,30896,68.3
29223,MO,Wayne County,5911,5346,565,9.6,28066,62.1
29225,MO,Webster County,16726,15327,1399,8.4,39261,86.8
29227,MO,Worth County,1183,1110,73,6.2,38105,84.2
29229,MO,Wright County,8190,7445,745,9.1,27007,59.7
29510,MO,St. Louis city,145576,128480,17096,11.7,32576,72
30000,MT,Montana,504410,469891,34519,6.8,44011,100
30001,MT,Beaverhead County,5162,4867,295,5.7,38632,87.8
30003,MT,Big Horn County,5446,4773,673,12.4,34778,79
30005,MT,Blaine County,2690,2520,170,6.3,36562,83.1
30007,MT,Broadwater County,2334,2152,182,7.8,44183,100.4
30009,MT,Carbon County,5321,5020,301,5.7,43223,98.2
30011,MT,Carter County,712,685,27,3.8,35540,80.8
30013,MT,Cascade County,40672,38212,2460,6,42525,96.6
30015,MT,Chouteau County,2585,2466,119,4.6,38106,86.6
30017,MT,Custer County,6247,5983,264,4.2,42308,96.1
30019,MT,Daniels County,779,747,32,4.1,43585,99
30021,MT,Dawson County,4344,4176,168,3.9,44925,102.1
30023,MT,Deer Lodge County,4079,3752,327,8,34375,78.1
30025,MT,Fallon County,2016,1967,49,2.4,50823,115.5
30027,MT,Fergus County,6000,5641,359,6,38912,88.4
30029,MT,Flathead County,44103,39390,4713,10.7,44006,100
30031,MT,Gallatin County,49513,46478,3035,6.1,50042,113.7
30033,MT,Garfield County,637,609,28,4.4,34607,78.6
30035,MT,Glacier County,5718,5043,675,11.8,33766,76.7
30037,MT,Golden Valley County,526,505,21,4,32236,73.2
30039,MT,Granite County,1289,1155,134,10.4,38140,86.7
30041,MT,Hill County,8416,7924,492,5.8,42014,95.5
30043,MT,Jefferson County,5948,5616,332,5.6,56280,127.9
30045,MT,Judith Basin County,1069,1012,57,5.3,37657,85.6
30047,MT,Lake County,11471,10299,1172,10.2,37135,84.4
30049,MT,Lewis and Clark County,34785,32953,1832,5.3,52510,119.3
30051,MT,Liberty County,759,720,39,5.1,35769,81.3
30053,MT,Lincoln County,7733,6524,1209,15.6,33656,76.5
30055,MT,McCone County,1064,1032,32,3,39993,90.9
30057,MT,Madison County,4146,3861,285,6.9,42799,97.2
30059,MT,Meagher County,832,762,70,8.4,35591,80.9
30061,MT,Mineral County,1997,1779,218,10.9,33416,75.9
30063,MT,Missoula County,59394,55477,3917,6.6,42082,95.6
30065,MT,Musselshell County,2314,2162,152,6.6,37495,85.2
30067,MT,Park County,8535,7899,636,7.5,42332,96.2
30069,MT,Petroleum County,240,225,15,6.3,31891,72.5
30071,MT,Phillips County,2168,2029,139,6.4,37217,84.6
30073,MT,Pondera County,2548,2376,172,6.8,37218,84.6
30075,MT,Powder River County,934,887,47,5,38756,88.1
30077,MT,Powell County,2817,2570,247,8.8,37850,86
30079,MT,Prairie County,555,526,29,5.2,35144,79.9
30081,MT,Ravalli County,17925,16263,1662,9.3,39421,89.6
30083,MT,Richland County,6243,6060,183,2.9,55593,126.3
30085,MT,Roosevelt County,4196,3866,330,7.9,33152,75.3
30087,MT,Rosebud County,4201,3888,313,7.5,45838,104.2
30089,MT,Sanders County,4282,3636,646,15.1,30703,69.8
30091,MT,Sheridan County,1846,1779,67,3.6,44992,102.2
30093,MT,Silver Bow County,17804,16688,1116,6.3,39586,89.9
30095,MT,Stillwater County,4339,4093,246,5.7,49185,111.8
30097,MT,Sweet Grass County,2338,2262,76,3.3,40859,92.8
30099,MT,Teton County,2950,2790,160,5.4,39479,89.7
30101,MT,Toole County,2463,2346,117,4.8,38983,88.6
30103,MT,Treasure County,372,352,20,5.4,42837,97.3
30105,MT,Valley County,3670,3488,182,5,46566,105.8
30107,MT,Wheatland County,1013,950,63,6.2,28086,63.8
30109,MT,Wibaux County,586,564,22,3.8,38722,88
30111,MT,Yellowstone County,82291,78094,4197,5.1,47346,107.6
31000,NE,Nebraska,1005455,960830,44625,4.4,50281,100
31001,NE,Adams County,16893,16186,707,4.2,46321,92.1
31003,NE,Antelope County,3874,3746,128,3.3,44394,88.3
31005,NE,Arthur County,258,244,14,5.4,37578,74.7
31007,NE,Banner County,374,358,16,4.3,29383,58.4
31009,NE,Blaine County,268,253,15,5.6,35133,69.9
31011,NE,Boone County,3515,3404,111,3.2,46354,92.2
31013,NE,Box Butte County,5511,5255,256,4.6,50430,100.3
31015,NE,Boyd County,1181,1139,42,3.6,36181,72
31017,NE,Brown County,1858,1794,64,3.4,38011,75.6
31019,NE,Buffalo County,28282,27352,930,3.3,50346,100.1
31021,NE,Burt County,3985,3777,208,5.2,46434,92.3
31023,NE,Butler County,5016,4837,179,3.6,53928,107.3
31025,NE,Cass County,13553,12836,717,5.3,60091,119.5
31027,NE,Cedar County,5030,4875,155,3.1,48085,95.6
31029,NE,Chase County,2170,2113,57,2.6,45885,91.3
31031,NE,Cherry County,3680,3577,103,2.8,39638,78.8
31033,NE,Cheyenne County,5086,4911,175,3.4,49065,97.6
31035,NE,Clay County,3289,3147,142,4.3,47339,94.1
31037,NE,Colfax County,5905,5683,222,3.8,45936,91.4
31039,NE,Cuming County,5251,5081,170,3.2,47273,94
31041,NE,Custer County,6350,6143,207,3.3,40928,81.4
31043,NE,Dakota County,11470,10744,726,6.3,47067,93.6
31045,NE,Dawes County,4965,4756,209,4.2,38824,77.2
31047,NE,Dawson County,12601,11948,653,5.2,43224,86
31049,NE,Deuel County,1077,1040,37,3.4,40417,80.4
31051,NE,Dixon County,3207,3054,153,4.8,46524,92.5
31053,NE,Dodge County,19905,18981,924,4.6,46246,92
31055,NE,Douglas County,276984,262848,14136,5.1,51741,102.9
31057,NE,Dundy County,1130,1094,36,3.2,40556,80.7
31059,NE,Fillmore County,3266,3148,118,3.6,48192,95.8
31061,NE,Franklin County,1823,1755,68,3.7,41551,82.6
31063,NE,Frontier County,1672,1616,56,3.3,50811,101.1
31065,NE,Furnas County,2662,2565,97,3.6,39868,79.3
31067,NE,Gage County,11901,11259,642,5.4,45221,89.9
31069,NE,Garden County,1117,1070,47,4.2,36486,72.6
31071,NE,Garfield County,1140,1098,42,3.7,36318,72.2
31073,NE,Gosper County,1103,1063,40,3.6,48409,96.3
31075,NE,Grant County,434,419,15,3.5,40482,80.5
31077,NE,Greeley County,1397,1344,53,3.8,37241,74.1
31079,NE,Hall County,32922,31499,1423,4.3,45043,89.6
31081,NE,Hamilton County,5602,5422,180,3.2,58591,116.5
31083,NE,Harlan County,1867,1806,61,3.3,44467,88.4
31085,NE,Hayes County,550,529,21,3.8,43321,86.2
31087,NE,Hitchcock County,1484,1421,63,4.2,39411,78.4
31089,NE,Holt County,6504,6301,203,3.1,42844,85.2
31091,NE,Hooker County,497,476,21,4.2,38397,76.4
31093,NE,Howard County,3531,3399,132,3.7,45336,90.2
31095,NE,Jefferson County,4541,4349,192,4.2,42418,84.4
31097,NE,Johnson County,2818,2700,118,4.2,43526,86.6
31099,NE,Kearney County,3683,3563,120,3.3,53129,105.7
31101,NE,Keith County,4683,4497,186,4,40154,79.9
31103,NE,Keya Paha County,423,405,18,4.3,35436,70.5
31105,NE,Kimball County,2021,1940,81,4,40240,80
31107,NE,Knox County,4968,4783,185,3.7,39178,77.9
31109,NE,Lancaster County,163271,156755,6516,4,49046,97.5
31111,NE,Lincoln County,21847,20993,854,3.9,49139,97.7
31113,NE,Logan County,467,455,12,2.6,46820,93.1
31115,NE,Loup County,348,331,17,4.9,37885,75.3
31117,NE,McPherson County,315,307,8,2.5,38097,75.8
31119,NE,Madison County,19292,18498,794,4.1,46363,92.2
31121,NE,Merrick County,4285,4109,176,4.1,50714,100.9
31123,NE,Morrill County,2845,2754,91,3.2,42378,84.3
31125,NE,Nance County,2228,2154,74,3.3,42629,84.8
31127,NE,Nemaha County,3542,3341,201,5.7,44705,88.9
31129,NE,Nuckolls County,2409,2321,88,3.7,36861,73.3
31131,NE,Otoe County,9079,8649,430,4.7,48509,96.5
31133,NE,Pawnee County,1689,1627,62,3.7,37006,73.6
31135,NE,Perkins County,1744,1692,52,3,50293,100
31137,NE,Phelps County,5237,5054,183,3.5,51371,102.2
31139,NE,Pierce County,3948,3801,147,3.7,51019,101.5
31141,NE,Platte County,18565,17825,740,4,51209,101.8
31143,NE,Polk County,3062,2960,102,3.3,51981,103.4
31145,NE,Red Willow County,6108,5882,226,3.7,44250,88
31147,NE,Richardson County,4320,4044,276,6.4,37197,74
31149,NE,Rock County,920,895,25,2.7,37225,74
31151,NE,Saline County,8472,8133,339,4,47635,94.7
31153,NE,Sarpy County,82002,78230,3772,4.6,65801,130.9
31155,NE,Saunders County,11112,10598,514,4.6,56466,112.3
31157,NE,Scotts Bluff County,19472,18509,963,4.9,41905,83.3
31159,NE,Seward County,8995,8663,332,3.7,58898,117.1
31161,NE,Sheridan County,3088,2986,102,3.3,37695,75
31163,NE,Sherman County,2012,1936,76,3.8,40734,81
31165,NE,Sioux County,729,701,28,3.8,38646,76.9
31167,NE,Stanton County,3409,3285,124,3.6,48448,96.4
31169,NE,Thayer County,2959,2863,96,3.2,44818,89.1
31171,NE,Thomas County,375,354,21,5.6,41783,83.1
31173,NE,Thurston County,3232,2923,309,9.6,38786,77.1
31175,NE,Valley County,2577,2491,86,3.3,40802,81.1
31177,NE,Washington County,11259,10760,499,4.4,68406,136
31179,NE,Wayne County,5488,5294,194,3.5,46980,93.4
31181,NE,Webster County,1839,1759,80,4.4,39018,77.6
31183,NE,Wheeler County,443,427,16,3.6,38340,76.3
31185,NE,York County,7200,6873,327,4.5,47936,95.3
32000,NV,Nevada,1385872,1198140,187732,13.5,49099,100
32001,NV,Churchill County,13426,11954,1472,11,50620,103.1
32003,NV,Clark County,994152,856174,137978,13.9,48343,98.5
32005,NV,Douglas County,22354,19130,3224,14.4,54640,111.3
32007,NV,Elko County,30318,28173,2145,7.1,62937,128.2
32009,NV,Esmeralda County,561,526,35,6.2,40536,82.6
32011,NV,Eureka County,1115,1048,67,6,58985,120.1
32013,NV,Humboldt County,9960,9241,719,7.2,61680,125.6
32015,NV,Lander County,4458,4165,293,6.6,64392,131.1
32017,NV,Lincoln County,1812,1564,248,13.7,42724,87
32019,NV,Lyon County,23157,19113,4044,17.5,44877,91.4
32021,NV,Mineral County,2513,2180,333,13.3,39705,80.9
32023,NV,Nye County,18138,15147,2991,16.5,41150,83.8
32027,NV,Pershing County,2775,2450,325,11.7,46683,95.1
32029,NV,Storey County,2237,1923,314,14,54582,111.2
32031,NV,Washoe County,224278,194984,29294,13.1,50910,103.7
32033,NV,White Pine County,5859,5367,492,8.4,52014,105.9
32510,NV,Carson City,28761,25001,3760,13.1,50689,103.2
33000,NH,New Hampshire,738281,698290,39991,5.4,62436,100
33001,NH,Belknap County,31669,29907,1762,5.6,55422,88.8
33003,NH,Carroll County,25487,24114,1373,5.4,45582,73
33005,NH,Cheshire County,40744,38602,2142,5.3,53111,85.1
33007,NH,Coos County,15945,14728,1217,7.6,37677,60.3
33009,NH,Grafton County,48737,46563,2174,4.5,51289,82.1
33011,NH,Hillsborough County,228370,215701,12669,5.5,66275,106.1
33013,NH,Merrimack County,79841,75903,3938,4.9,64482,103.3
33015,NH,Rockingham County,174943,165027,9916,5.7,75285,120.6
33017,NH,Strafford County,70149,66452,3697,5.3,54783,87.7
33019,NH,Sullivan County,22399,21294,1105,4.9,47868,76.7
34000,NJ,New Jersey,4556186,4131832,424354,9.3,67574,100
34001,NJ,Atlantic County,135813,118346,17467,12.9,49983,74
34003,NJ,Bergen County,479131,441277,37854,7.9,79037,117
34005,NJ,Burlington County,241524,220072,21452,8.9,72594,107.4
34007,NJ,Camden County,267945,239569,28376,10.6,57261,84.7
34009,NJ,Cape May County,58268,51008,7260,12.5,52312,77.4
34011,NJ,Cumberland County,70761,61294,9467,13.4,49465,73.2
34013,NJ,Essex County,370417,330337,40080,10.8,51021,75.5
34015,NJ,Gloucester County,157955,142463,15492,9.8,70618,104.5
34017,NJ,Hudson County,312467,280302,32165,10.3,55133,81.6
34019,NJ,Hunterdon County,71262,66363,4899,6.9,99216,146.8
34021,NJ,Mercer County,208203,192143,16060,7.7,71926,106.4
34023,NJ,Middlesex County,436228,399546,36682,8.4,73914,109.4
34025,NJ,Monmouth County,329571,301254,28317,8.6,79062,117
34027,NJ,Morris County,272849,253719,19130,7,91420,135.3
34029,NJ,Ocean County,267070,240280,26790,10,56851,84.1
34031,NJ,Passaic County,246012,218724,27288,11.1,52243,77.3
34033,NJ,Salem County,31654,28249,3405,10.8,53890,79.7
34035,NJ,Somerset County,180007,167144,12863,7.1,95915,141.9
34037,NJ,Sussex County,83728,76076,7652,9.1,82987,122.8
34039,NJ,Union County,275886,249244,26642,9.7,65178,96.5
34041,NJ,Warren County,59440,54424,5016,8.4,66857,98.9
35000,NM,New Mexico,927785,858951,68834,7.4,42097,100
35001,NM,Bernalillo County,304106,281095,23011,7.6,44224,105.1
35003,NM,Catron County,1510,1384,126,8.3,37277,88.6
35005,NM,Chaves County,26489,24625,1864,7,35894,85.3
35006,NM,Cibola County,12052,11202,850,7.1,33345,79.2
35007,NM,Colfax County,6506,5987,519,8,33988,80.7
35009,NM,Curry County,21270,20204,1066,5,38423,91.3
35011,NM,De Baca County,797,758,39,4.9,30017,71.3
35013,NM,Dona Ana County,92186,85300,6886,7.5,36136,85.8
35015,NM,Eddy County,29118,27800,1318,4.5,46212,109.8
35017,NM,Grant County,11567,10652,915,7.9,34686,82.4
35019,NM,Guadalupe County,1741,1557,184,10.6,29187,69.3
35021,NM,Harding County,386,367,19,4.9,32983,78.4
35023,NM,Hidalgo County,2636,2465,171,6.5,30978,73.6
35025,NM,Lea County,29039,27531,1508,5.2,46176,109.7
35027,NM,Lincoln County,10475,9880,595,5.7,40096,95.2
35028,NM,Los Alamos County,9880,9567,313,3.2,110204,261.8
35029,NM,Luna County,12611,10385,2226,17.7,27566,65.5
35031,NM,McKinley County,26287,23876,2411,9.2,29320,69.6
35033,NM,Mora County,1918,1632,286,14.9,28911,68.7
35035,NM,Otero County,25551,23899,1652,6.5,35894,85.3
35037,NM,Quay County,3791,3473,318,8.4,29895,71
35039,NM,Rio Arriba County,18940,17373,1567,8.3,37101,88.1
35041,NM,Roosevelt County,9002,8541,461,5.1,34055,80.9
35043,NM,Sandoval County,55982,51156,4826,8.6,53948,128.2
35045,NM,San Juan County,55307,50988,4319,7.8,46370,110.2
35047,NM,San Miguel County,12900,11872,1028,8,31983,76
35049,NM,Santa Fe County,74644,70155,4489,6,50234,119.3
35051,NM,Sierra County,5852,5475,377,6.4,28544,67.8
35053,NM,Socorro County,9117,8612,505,5.5,31371,74.5
35055,NM,Taos County,16828,15250,1578,9.4,32521,77.3
35057,NM,Torrance County,6531,5913,618,9.5,33193,78.8
35059,NM,Union County,1903,1807,96,5,33206,78.9
35061,NM,Valencia County,30866,28174,2692,8.7,41983,99.7
36000,NY,New York,9504239,8729587,774652,8.2,55147,100
36001,NY,Albany County,153850,143048,10802,7,56602,102.6
36003,NY,Allegany County,23757,21681,2076,8.7,39900,72.4
36005,NY,Bronx County,545178,478381,66797,12.3,32137,58.3
36007,NY,Broome County,93831,85894,7937,8.5,43756,79.3
36009,NY,Cattaraugus County,40323,36812,3511,8.7,39368,71.4
36011,NY,Cayuga County,40577,37438,3139,7.7,47875,86.8
36013,NY,Chautauqua County,63007,58013,4994,7.9,40782,74
36015,NY,Chemung County,40015,36852,3163,7.9,47994,87
36017,NY,Chenango County,24605,22557,2048,8.3,40693,73.8
36019,NY,Clinton County,38057,34303,3754,9.9,49025,88.9
36021,NY,Columbia County,30334,28093,2241,7.4,53500,97
36023,NY,Cortland County,24064,22041,2023,8.4,44397,80.5
36025,NY,Delaware County,21469,19613,1856,8.6,38208,69.3
36027,NY,Dutchess County,145180,134385,10795,7.4,67153,121.8
36029,NY,Erie County,460282,424091,36191,7.9,47505,86.1
36031,NY,Essex County,17672,16041,1631,9.2,45668,82.8
36033,NY,Franklin County,22340,20318,2022,9.1,40092,72.7
36035,NY,Fulton County,26777,24099,2678,10,46976,85.2
36037,NY,Genesee County,32015,29607,2408,7.5,45955,83.3
36039,NY,Greene County,23683,21626,2057,8.7,45127,81.8
36041,NY,Hamilton County,2999,2765,234,7.8,43104,78.2
36043,NY,Herkimer County,30880,28273,2607,8.4,40504,73.4
36045,NY,Jefferson County,48996,44229,4767,9.7,45292,82.1
36047,NY,Kings County,1119975,1011289,108686,9.7,42437,77
36049,NY,Lewis County,12521,11329,1192,9.5,44097,80
36051,NY,Livingston County,32087,29582,2505,7.8,50186,91
36053,NY,Madison County,35890,32969,2921,8.1,49326,89.4
36055,NY,Monroe County,366795,339012,27783,7.6,50247,91.1
36057,NY,Montgomery County,23757,21430,2327,9.8,40160,72.8
36059,NY,Nassau County,681544,635868,45676,6.7,91162,165.3
36061,NY,New York County,921593,853276,68317,7.4,65833,119.4
36063,NY,Niagara County,110239,100948,9291,8.4,45291,82.1
36065,NY,Oneida County,107753,99009,8744,8.1,45957,83.3
36067,NY,Onondaga County,227249,209775,17474,7.7,53394,96.8
36069,NY,Ontario County,56182,52132,4050,7.2,52367,95
36071,NY,Orange County,173955,160349,13606,7.8,65648,119
36073,NY,Orleans County,19056,17309,1747,9.2,43870,79.6
36075,NY,Oswego County,57464,51555,5909,10.3,43687,79.2
36077,NY,Otsego County,31269,28877,2392,7.6,43563,79
36079,NY,Putnam County,54048,50503,3545,6.6,87248,158.2
36081,NY,Queens County,1118132,1027958,90174,8.1,53124,96.3
36083,NY,Rensselaer County,81504,75366,6138,7.5,57059,103.5
36085,NY,Richmond County,241002,221262,19740,8.2,69436,125.9
36087,NY,Rockland County,155845,145709,10136,6.5,80765,146.5
36089,NY,St. Lawrence County,48487,43445,5042,10.4,42609,77.3
36091,NY,Saratoga County,115570,107978,7592,6.6,64298,116.6
36093,NY,Schenectady County,74090,68537,5553,7.5,52283,94.8
36095,NY,Schoharie County,15402,13960,1442,9.4,49610,90
36097,NY,Schuyler County,9982,9203,779,7.8,45986,83.4
36099,NY,Seneca County,16799,15508,1291,7.7,45951,83.3
36101,NY,Steuben County,44339,40178,4161,9.4,43834,79.5
36103,NY,Suffolk County,778671,721298,57373,7.4,83360,151.2
36105,NY,Sullivan County,34294,31167,3127,9.1,46161,83.7
36107,NY,Tioga County,24939,22926,2013,8.1,51675,93.7
36109,NY,Tompkins County,55331,52098,3233,5.8,49639,90
36111,NY,Ulster County,87658,80511,7147,8.2,54060,98
36113,NY,Warren County,35604,32694,2910,8.2,50807,92.1
36115,NY,Washington County,32086,29675,2411,7.5,48555,88
36117,NY,Wayne County,47218,43386,3832,8.1,51901,94.1
36119,NY,Westchester County,472700,440645,32055,6.8,76728,139.1
36121,NY,Wyoming County,20201,18498,1703,8.4,50192,91
36123,NY,Yates County,13124,12216,908,6.9,44776,81.2
37000,NC,North Carolina,4653911,4164814,489097,10.5,44028,100
37001,NC,Alamance County,72415,64689,7726,10.7,41371,94
37003,NC,Alexander County,17964,15920,2044,11.4,39812,90.4
37005,NC,Alleghany County,4631,4055,576,12.4,32673,74.2
37007,NC,Anson County,10869,9539,1330,12.2,32341,73.5
37009,NC,Ashe County,12339,10834,1505,12.2,34333,78
37011,NC,Avery County,7939,7016,923,11.6,36071,81.9
37013,NC,Beaufort County,21482,19007,2475,11.5,38248,86.9
37015,NC,Bertie County,8844,7718,1126,12.7,29615,67.3
37017,NC,Bladen County,15720,13658,2062,13.1,33138,75.3
37019,NC,Brunswick County,50489,44688,5801,11.5,42685,96.9
37021,NC,Buncombe County,125456,115111,10345,8.2,41551,94.4
37023,NC,Burke County,39821,34763,5058,12.7,37719,85.7
37025,NC,Cabarrus County,89557,80499,9058,10.1,50298,114.2
37027,NC,Caldwell County,38814,33693,5121,13.2,34876,79.2
37029,NC,Camden County,4568,4179,389,8.5,57113,129.7
37031,NC,Carteret County,32734,29652,3082,9.4,45507,103.4
37033,NC,Caswell County,11068,9860,1208,10.9,38495,87.4
37035,NC,Catawba County,74017,64648,9369,12.7,44339,100.7
37037,NC,Chatham County,33055,30410,2645,8,53564,121.7
37039,NC,Cherokee County,10347,8914,1433,13.8,32913,74.8
37041,NC,Chowan County,6258,5532,726,11.6,36231,82.3
37043,NC,Clay County,4575,4085,490,10.7,35143,79.8
37045,NC,Cleveland County,49745,43856,5889,11.8,37390,84.9
37047,NC,Columbus County,24167,20814,3353,13.9,31282,71.1
37049,NC,Craven County,42846,38124,4722,11,47291,107.4
37051,NC,Cumberland County,142251,127739,14512,10.2,43863,99.6
37053,NC,Currituck County,12542,11659,883,7,55665,126.4
37055,NC,Dare County,22591,19916,2675,11.8,50393,114.5
37057,NC,Davidson County,78070,68980,9090,11.6,41631,94.6
37059,NC,Davie County,20793,18670,2123,10.2,50110,113.8
37061,NC,Duplin County,25737,23202,2535,9.8,35587,80.8
37063,NC,Durham County,140993,129003,11990,8.5,47007,106.8
37065,NC,Edgecombe County,25620,21556,4064,15.9,32901,74.7
37067,NC,Forsyth County,178667,160810,17857,10,43804,99.5
37069,NC,Franklin County,28622,25683,2939,10.3,42269,96
37071,NC,Gaston County,98998,87525,11473,11.6,40969,93.1
37073,NC,Gates County,4842,4463,379,7.8,41437,94.1
37075,NC,Graham County,3996,3315,681,17,33041,75
37077,NC,Granville County,26518,23746,2772,10.5,48596,110.4
37079,NC,Greene County,9218,8231,987,10.7,35013,79.5
37081,NC,Guilford County,254303,226732,27571,10.8,44229,100.5
37083,NC,Halifax County,23459,20154,3305,14.1,31084,70.6
37085,NC,Harnett County,49297,43565,5732,11.6,40617,92.3
37087,NC,Haywood County,28845,26003,2842,9.9,40114,91.1
37089,NC,Henderson County,50897,46726,4171,8.2,46165,104.9
37091,NC,Hertford County,9849,8753,1096,11.1,31581,71.7
37093,NC,Hoke County,22292,20096,2196,9.9,42908,97.5
37095,NC,Hyde County,2901,2596,305,10.5,35810,81.3
37097,NC,Iredell County,81667,72423,9244,11.3,49902,113.3
37099,NC,Jackson County,21508,19424,2084,9.7,37388,84.9
37101,NC,Johnston County,80490,72760,7730,9.6,48380,109.9
37103,NC,Jones County,4462,3976,486,10.9,38230,86.8
37105,NC,Lee County,26938,23456,3482,12.9,41506,94.3
37107,NC,Lenoir County,28049,24923,3126,11.1,32602,74
37109,NC,Lincoln County,38972,34368,4604,11.8,50746,115.3
37111,NC,McDowell County,20559,17888,2671,13,35627,80.9
37113,NC,Macon County,16025,14212,1813,11.3,36953,83.9
37115,NC,Madison County,10110,9117,993,9.8,36724,83.4
37117,NC,Martin County,11604,10232,1372,11.8,33305,75.6
37119,NC,Mecklenburg County,483667,432152,51515,10.7,52111,118.4
37121,NC,Mitchell County,7060,6232,828,11.7,35322,80.2
37123,NC,Montgomery County,11301,9878,1423,12.6,30684,69.7
37125,NC,Moore County,38150,34532,3618,9.5,47301,107.4
37127,NC,Nash County,46579,40510,6069,13,43151,98
37129,NC,New Hanover County,107246,96580,10666,9.9,45890,104.2
37131,NC,Northampton County,9183,8067,1116,12.2,31668,71.9
37133,NC,Onslow County,67410,61262,6148,9.1,45434,103.2
37135,NC,Orange County,71395,66589,4806,6.7,56792,129
37137,NC,Pamlico County,5379,4824,555,10.3,41325,93.9
37139,NC,Pasquotank County,17500,15610,1890,10.8,40261,91.4
37141,NC,Pender County,24318,21390,2928,12,44171,100.3
37143,NC,Perquimans County,5446,4879,567,10.4,39112,88.8
37145,NC,Person County,19560,17496,2064,10.6,42303,96.1
37147,NC,Pitt County,84238,75164,9074,10.8,39713,90.2
37149,NC,Polk County,9411,8611,800,8.5,41851,95.1
37151,NC,Randolph County,73516,65692,7824,10.6,40749,92.6
37153,NC,Richmond County,20166,17364,2802,13.9,32932,74.8
37155,NC,Robeson County,56969,49049,7920,13.9,30931,70.3
37157,NC,Rockingham County,44326,38897,5429,12.2,36746,83.5
37159,NC,Rowan County,70765,62548,8217,11.6,40267,91.5
37161,NC,Rutherford County,27198,23169,4029,14.8,35208,80
37163,NC,Sampson County,32892,29932,2960,9,36471,82.8
37165,NC,Scotland County,13797,11376,2421,17.5,32999,75
37167,NC,Stanly County,30702,27170,3532,11.5,38301,87
37169,NC,Stokes County,24309,21940,2369,9.7,42147,95.7
37171,NC,Surry County,33438,29664,3774,11.3,35269,80.1
37173,NC,Swain County,6908,5888,1020,14.8,36167,82.1
37175,NC,Transylvania County,12811,11498,1313,10.2,41578,94.4
37177,NC,Tyrrell County,2422,2157,265,10.9,33430,75.9
37179,NC,Union County,98873,89681,9192,9.3,61515,139.7
37181,NC,Vance County,19674,16782,2892,14.7,33377,75.8
37183,NC,Wake County,473573,434219,39354,8.3,62436,141.8
37185,NC,Warren County,8017,6906,1111,13.9,33218,75.4
37187,NC,Washington County,6496,5621,875,13.5,33278,75.6
37189,NC,Watauga County,24457,22261,2196,9,39168,89
37191,NC,Wayne County,54656,49512,5144,9.4,39089,88.8
37193,NC,Wilkes County,30732,26921,3811,12.4,34954,79.4
37195,NC,Wilson County,40921,35403,5518,13.5,37166,84.4
37197,NC,Yadkin County,19285,17390,1895,9.8,40983,93.1
37199,NC,Yancey County,7766,6834,932,12,36440,82.8
38000,ND,North Dakota,382944,369579,13365,3.5,52135,100
38001,ND,Adams County,1167,1136,31,2.7,43836,84.1
38003,ND,Barnes County,6021,5791,230,3.8,47839,91.8
38005,ND,Benson County,2900,2731,169,5.8,38299,73.5
38007,ND,Billings County,570,555,15,2.6,60506,116.1
38009,ND,Bottineau County,3688,3561,127,3.4,52360,100.4
38011,ND,Bowman County,1862,1820,42,2.3,56721,108.8
38013,ND,Burke County,1261,1233,28,2.2,49481,94.9
38015,ND,Burleigh County,47427,45943,1484,3.1,57519,110.3
38017,ND,Cass County,87225,84218,3007,3.4,52444,100.6
38019,ND,Cavalier County,1985,1922,63,3.2,50572,97
38021,ND,Dickey County,2686,2605,81,3,47501,91.1
38023,ND,Divide County,1064,1036,28,2.6,51219,98.2
38025,ND,Dunn County,2914,2857,57,2,55353,106.2
38027,ND,Eddy County,1152,1093,59,5.1,43249,83
38029,ND,Emmons County,1609,1502,107,6.7,40749,78.2
38031,ND,Foster County,1690,1635,55,3.3,49333,94.6
38033,ND,Golden Valley County,861,836,25,2.9,44706,85.8
38035,ND,Grand Forks County,37068,35614,1454,3.9,45541,87.4
38037,ND,Grant County,1209,1160,49,4.1,35744,68.6
38039,ND,Griggs County,1233,1196,37,3,45283,86.9
38041,ND,Hettinger County,1203,1161,42,3.5,46793,89.8
38043,ND,Kidder County,1028,962,66,6.4,39857,76.4
38045,ND,LaMoure County,2197,2131,66,3,52242,100.2
38047,ND,Logan County,963,934,29,3,40760,78.2
38049,ND,McHenry County,2645,2518,127,4.8,44981,86.3
38051,ND,McIntosh County,1264,1215,49,3.9,36642,70.3
38053,ND,McKenzie County,4433,4357,76,1.7,59000,113.2
38055,ND,McLean County,4776,4555,221,4.6,50902,97.6
38057,ND,Mercer County,4426,4205,221,5,67449,129.4
38059,ND,Morton County,15229,14614,615,4,58330,111.9
38061,ND,Mountrail County,5500,5366,134,2.4,52930,101.5
38063,ND,Nelson County,1625,1557,68,4.2,43426,83.3
38065,ND,Oliver County,1163,1108,55,4.7,59262,113.7
38067,ND,Pembina County,3738,3474,264,7.1,54584,104.7
38069,ND,Pierce County,1762,1671,91,5.2,41419,79.4
38071,ND,Ramsey County,5741,5515,226,3.9,46624,89.4
38073,ND,Ransom County,2905,2804,101,3.5,51812,99.4
38075,ND,Renville County,1300,1258,42,3.2,52982,101.6
38077,ND,Richland County,8552,8202,350,4.1,51860,99.5
38079,ND,Rolette County,4811,4264,547,11.4,33024,63.3
38081,ND,Sargent County,2095,2032,63,3,58138,111.5
38083,ND,Sheridan County,662,631,31,4.7,40593,77.9
38085,ND,Sioux County,1318,1249,69,5.2,30271,58.1
38087,ND,Slope County,727,718,9,1.2,52493,100.7
38089,ND,Stark County,17136,16793,343,2,55492,106.4
38091,ND,Steele County,1142,1114,28,2.5,54695,104.9
38093,ND,Stutsman County,11250,10844,406,3.6,49519,95
38095,ND,Towner County,1027,989,38,3.7,52376,100.5
38097,ND,Traill County,4076,3910,166,4.1,53388,102.4
38099,ND,Walsh County,5242,4955,287,5.5,46802,89.8
38101,ND,Ward County,30577,29550,1027,3.4,53157,102
38103,ND,Wells County,1996,1899,97,4.9,47328,90.8
38105,ND,Williams County,24848,24581,267,1.1,67492,129.5
39000,OH,Ohio,5806467,5305348,501119,8.6,45803,100
39001,OH,Adams County,12637,11034,1603,12.7,34778,75.9
39003,OH,Allen County,49954,45150,4804,9.6,41453,90.5
39005,OH,Ashland County,26344,23869,2475,9.4,44950,98.1
39007,OH,Ashtabula County,47509,42454,5055,10.6,38683,84.5
39009,OH,Athens County,29608,26978,2630,8.9,33943,74.1
39011,OH,Auglaize County,25548,23668,1880,7.4,51130,111.6
39013,OH,Belmont County,33650,30742,2908,8.6,40300,88
39015,OH,Brown County,21863,19546,2317,10.6,43181,94.3
39017,OH,Butler County,191310,174879,16431,8.6,53275,116.3
39019,OH,Carroll County,13792,12435,1357,9.8,40896,89.3
39021,OH,Champaign County,19440,17654,1786,9.2,48063,104.9
39023,OH,Clark County,68988,62836,6152,8.9,41704,91.1
39025,OH,Clermont County,105587,96557,9030,8.6,55963,122.2
39027,OH,Clinton County,17968,15612,2356,13.1,44905,98
39029,OH,Columbiana County,51351,46138,5213,10.2,42091,91.9
39031,OH,Coshocton County,16675,14807,1868,11.2,40282,87.9
39033,OH,Crawford County,20772,18442,2330,11.2,39679,86.6
39035,OH,Cuyahoga County,644854,593380,51474,8,41609,90.8
39037,OH,Darke County,27206,24826,2380,8.7,42065,91.8
39039,OH,Defiance County,19153,17369,1784,9.3,49360,107.8
39041,OH,Delaware County,92550,86922,5628,6.1,86980,189.9
39043,OH,Erie County,42202,38537,3665,8.7,46613,101.8
39045,OH,Fairfield County,74248,68567,5681,7.7,58829,128.4
39047,OH,Fayette County,15759,14295,1464,9.3,36684,80.1
39049,OH,Franklin County,616711,570065,46646,7.6,47174,103
39051,OH,Fulton County,22104,20025,2079,9.4,49038,107.1
39053,OH,Gallia County,13485,12108,1377,10.2,36441,79.6
39055,OH,Geauga County,50487,47220,3267,6.5,66088,144.3
39057,OH,Greene County,79329,72690,6639,8.4,56683,123.8
39059,OH,Guernsey County,18960,17028,1932,10.2,36348,79.4
39061,OH,Hamilton County,406861,371982,34879,8.6,46125,100.7
39063,OH,Hancock County,39549,36551,2998,7.6,48258,105.4
39065,OH,Hardin County,14744,13351,1393,9.4,37914,82.8
39067,OH,Harrison County,7059,6332,727,10.3,37057,80.9
39069,OH,Henry County,15312,13824,1488,9.7,48859,106.7
39071,OH,Highland County,18623,16263,2360,12.7,36865,80.5
39073,OH,Hocking County,13943,12602,1341,9.6,42491,92.8
39075,OH,Holmes County,19708,18512,1196,6.1,45893,100.2
39077,OH,Huron County,27836,24576,3260,11.7,48196,105.2
39079,OH,Jackson County,14843,13237,1606,10.8,39144,85.5
39081,OH,Jefferson County,31022,27533,3489,11.2,41103,89.7
39083,OH,Knox County,29592,27030,2562,8.7,50929,111.2
39085,OH,Lake County,131619,122291,9328,7.1,53016,115.7
39087,OH,Lawrence County,28206,25835,2371,8.4,37666,82.2
39089,OH,Licking County,84426,77638,6788,8,54298,118.5
39091,OH,Logan County,23112,20959,2153,9.3,48011,104.8
39093,OH,Lorain County,158978,146187,12791,8,48733,106.4
39095,OH,Lucas County,211690,191261,20429,9.7,38798,84.7
39097,OH,Madison County,20079,18421,1658,8.3,54107,118.1
39099,OH,Mahoning County,112938,102106,10832,9.6,39447,86.1
39101,OH,Marion County,30294,27365,2929,9.7,42141,92
39103,OH,Medina County,96880,90408,6472,6.7,60527,132.1
39105,OH,Meigs County,9298,8042,1256,13.5,33520,73.2
39107,OH,Mercer County,24515,23073,1442,5.9,47994,104.8
39109,OH,Miami County,53518,48880,4638,8.7,48103,105
39111,OH,Monroe County,5621,4990,631,11.2,39007,85.2
39113,OH,Montgomery County,257589,233278,24311,9.4,41001,89.5
39115,OH,Morgan County,5867,5117,750,12.8,35173,76.8
39117,OH,Morrow County,17424,15817,1607,9.2,47431,103.6
39119,OH,Muskingum County,37574,33213,4361,11.6,38652,84.4
39121,OH,Noble County,5625,4923,702,12.5,41151,89.8
39123,OH,Ottawa County,21132,18623,2509,11.9,51641,112.7
39125,OH,Paulding County,10011,9121,890,8.9,41946,91.6
39127,OH,Perry County,16351,14521,1830,11.2,39383,86
39129,OH,Pickaway County,24341,22049,2292,9.4,52161,113.9
39131,OH,Pike County,10627,8999,1628,15.3,37802,82.5
39133,OH,Portage County,91043,83474,7569,8.3,49712,108.5
39135,OH,Preble County,21157,19073,2084,9.9,45920,100.3
39137,OH,Putnam County,17848,16460,1388,7.8,57039,124.5
39139,OH,Richland County,59341,53219,6122,10.3,40424,88.3
39141,OH,Ross County,34584,31149,3435,9.9,43118,94.1
39143,OH,Sandusky County,32815,29913,2902,8.8,44016,96.1
39145,OH,Scioto County,32321,28400,3921,12.1,31987,69.8
39147,OH,Seneca County,28891,26193,2698,9.3,38349,83.7
39149,OH,Shelby County,24639,22378,2261,9.2,53028,115.8
39151,OH,Stark County,185923,168787,17136,9.2,42190,92.1
39153,OH,Summit County,283054,259018,24036,8.5,46595,101.7
39155,OH,Trumbull County,101706,91948,9758,9.6,40913,89.3
39157,OH,Tuscarawas County,46460,42393,4067,8.8,41857,91.4
39159,OH,Union County,26864,24942,1922,7.2,64403,140.6
39161,OH,Van Wert County,14134,12827,1307,9.2,46717,102
39163,OH,Vinton County,5711,5051,660,11.6,35298,77.1
39165,OH,Warren County,109211,100940,8271,7.6,70245,153.4
39167,OH,Washington County,31934,29319,2615,8.2,42211,92.2
39169,OH,Wayne County,56561,52180,4381,7.7,46842,102.3
39171,OH,Williams County,18859,16872,1987,10.5,40856,89.2
39173,OH,Wood County,65549,60141,5408,8.3,50624,110.5
39175,OH,Wyandot County,11015,9932,1083,9.8,45555,99.5
40000,OK,Oklahoma,1770822,1661839,108983,6.2,43232,100
40001,OK,Adair County,11041,10235,806,7.3,31680,73.3
40003,OK,Alfalfa County,2528,2407,121,4.8,38852,89.9
40005,OK,Atoka County,6107,5661,446,7.3,34673,80.2
40007,OK,Beaver County,3365,3255,110,3.3,49683,114.9
40009,OK,Beckham County,13099,12638,461,3.5,43565,100.8
40011,OK,Blaine County,4651,4336,315,6.8,37882,87.6
40013,OK,Bryan County,20006,18927,1079,5.4,34276,79.3
40015,OK,Caddo County,12825,11955,870,6.8,38583,89.2
40017,OK,Canadian County,55793,53007,2786,5,60225,139.3
40019,OK,Carter County,27835,26413,1422,5.1,40122,92.8
40021,OK,Cherokee County,24772,23355,1417,5.7,34968,80.9
40023,OK,Choctaw County,7300,6746,554,7.6,28598,66.2
40025,OK,Cimarron County,1230,1183,47,3.8,38572,89.2
40027,OK,Cleveland County,125178,118846,6332,5.1,51900,120
40029,OK,Coal County,2686,2484,202,7.5,35475,82.1
40031,OK,Comanche County,48139,44927,3212,6.7,44286,102.4
40033,OK,Cotton County,3470,3286,184,5.3,38129,88.2
40035,OK,Craig County,6992,6572,420,6,36996,85.6
40037,OK,Creek County,30199,28019,2180,7.2,41000,94.8
40039,OK,Custer County,16344,15703,641,3.9,41678,96.4
40041,OK,Delaware County,19684,18410,1274,6.5,35936,83.1
40043,OK,Dewey County,2986,2887,99,3.3,41124,95.1
40045,OK,Ellis County,2539,2456,83,3.3,42728,98.8
40047,OK,Garfield County,32984,31638,1346,4.1,42800,99
40049,OK,Garvin County,14894,14162,732,4.9,37878,87.6
40051,OK,Grady County,23180,21833,1347,5.8,45688,105.7
40053,OK,Grant County,2665,2567,98,3.7,40691,94.1
40055,OK,Greer County,2011,1856,155,7.7,34350,79.5
40057,OK,Harmon County,1376,1303,73,5.3,30329,70.2
40059,OK,Harper County,1974,1903,71,3.6,43198,99.9
40061,OK,Haskell County,5887,5465,422,7.2,33309,77
40063,OK,Hughes County,5675,5131,544,9.6,30888,71.4
40065,OK,Jackson County,12282,11631,651,5.3,40129,92.8
40067,OK,Jefferson County,2454,2283,171,7,30755,71.1
40069,OK,Johnston County,4909,4595,314,6.4,35713,82.6
40071,OK,Kay County,22596,20959,1637,7.2,39152,90.6
40073,OK,Kingfisher County,7940,7639,301,3.8,53050,122.7
40075,OK,Kiowa County,4068,3838,230,5.7,35977,83.2
40077,OK,Latimer County,4312,3841,471,10.9,35660,82.5
40079,OK,Le Flore County,19828,17964,1864,9.4,34131,78.9
40081,OK,Lincoln County,14556,13700,856,5.9,38058,88
40083,OK,Logan County,19160,18167,993,5.2,48346,111.8
40085,OK,Love County,5720,5460,260,4.5,40853,94.5
40087,OK,McClain County,15691,14867,824,5.3,54633,126.4
40089,OK,McCurtain County,15737,14186,1551,9.9,29614,68.5
40091,OK,McIntosh County,8950,8150,800,8.9,32487,75.1
40093,OK,Major County,4393,4226,167,3.8,45374,105
40095,OK,Marshall County,6596,6183,413,6.3,35610,82.4
40097,OK,Mayes County,18308,16871,1437,7.8,40229,93.1
40099,OK,Murray County,9218,8883,335,3.6,39914,92.3
40101,OK,Muskogee County,31714,29354,2360,7.4,35443,82
40103,OK,Noble County,5882,5597,285,4.8,43416,100.4
40105,OK,Nowata County,5132,4728,404,7.9,38453,88.9
40107,OK,Okfuskee County,4739,4336,403,8.5,31150,72.1
40109,OK,Oklahoma County,326227,306412,19815,6.1,43101,99.7
40111,OK,Okmulgee County,16005,14448,1557,9.7,35226,81.5
40113,OK,Osage County,20308,18802,1506,7.4,43170,99.9
40115,OK,Ottawa County,18326,17218,1108,6,34431,79.6
40117,OK,Pawnee County,7217,6627,590,8.2,41938,97
40119,OK,Payne County,35392,33473,1919,5.4,37075,85.8
40121,OK,Pittsburg County,23805,22354,1451,6.1,41712,96.5
40123,OK,Pontotoc County,20939,19911,1028,4.9,40378,93.4
40125,OK,Pottawatomie County,34228,32264,1964,5.7,41520,96
40127,OK,Pushmataha County,5580,5160,420,7.5,30190,69.8
40129,OK,Roger Mills County,2169,2107,62,2.9,50761,117.4
40131,OK,Rogers County,40030,37447,2583,6.5,54974,127.2
40133,OK,Seminole County,11140,10276,864,7.8,32976,76.3
40135,OK,Sequoyah County,17099,15449,1650,9.6,36080,83.5
40137,OK,Stephens County,22675,21431,1244,5.5,43765,101.2
40139,OK,Texas County,7786,7388,398,5.1,44550,103
40141,OK,Tillman County,3448,3243,205,5.9,32122,74.3
40143,OK,Tulsa County,289483,269996,19487,6.7,46497,107.6
40145,OK,Wagoner County,33988,31791,2197,6.5,52717,121.9
40147,OK,Washington County,28252,26812,1440,5.1,47645,110.2
40149,OK,Washita County,6238,5978,260,4.2,45374,105
40151,OK,Woods County,4698,4523,175,3.7,38688,89.5
40153,OK,Woodward County,12198,11709,489,4,48420,112
41000,OR,Oregon,1991873,1803602,188271,9.5,46876,100
41001,OR,Baker County,7584,6795,789,10.4,36877,78.7
41003,OR,Benton County,44859,41911,2948,6.6,46272,98.7
41005,OR,Clackamas County,202378,184796,17582,8.7,58496,124.8
41007,OR,Clatsop County,21028,19242,1786,8.5,43544,92.9
41009,OR,Columbia County,24818,22251,2567,10.3,55646,118.7
41011,OR,Coos County,28746,25462,3284,11.4,36567,78
41013,OR,Crook County,9135,7787,1348,14.8,37473,79.9
41015,OR,Curry County,9285,8173,1112,12,39958,85.2
41017,OR,Deschutes County,80216,70299,9917,12.4,47924,102.2
41019,OR,Douglas County,46209,40141,6068,13.1,38196,81.5
41021,OR,Gilliam County,1219,1130,89,7.3,49378,105.3
41023,OR,Grant County,3517,3047,470,13.4,37191,79.3
41025,OR,Harney County,3447,2950,497,14.4,35141,75
41027,OR,Hood River County,14546,13419,1127,7.7,48516,103.5
41029,OR,Jackson County,102261,90455,11806,11.5,40013,85.4
41031,OR,Jefferson County,9391,8152,1239,13.2,40910,87.3
41033,OR,Josephine County,34789,30394,4395,12.6,36269,77.4
41035,OR,Klamath County,30894,27124,3770,12.2,36765,78.4
41037,OR,Lake County,3777,3288,489,12.9,35517,75.8
41039,OR,Lane County,181183,164025,17158,9.5,40689,86.8
41041,OR,Lincoln County,23299,21003,2296,9.9,40507,86.4
41043,OR,Linn County,55713,49236,6477,11.6,42836,91.4
41045,OR,Malheur County,13555,12186,1369,10.1,36039,76.9
41047,OR,Marion County,158269,142165,16104,10.2,44275,94.5
41049,OR,Morrow County,5611,5133,478,8.5,46902,100.1
41051,OR,Multnomah County,407497,372756,34741,8.5,46989,100.2
41053,OR,Polk County,39265,35836,3429,8.7,52277,111.5
41055,OR,Sherman County,1085,987,98,9,52263,111.5
41057,OR,Tillamook County,12963,11809,1154,8.9,41246,88
41059,OR,Umatilla County,40009,36421,3588,9,43231,92.2
41061,OR,Union County,12623,11390,1233,9.8,41510,88.6
41063,OR,Wallowa County,3778,3356,422,11.2,38486,82.1
41065,OR,Wasco County,14437,13215,1222,8.5,41144,87.8
41067,OR,Washington County,294403,271803,22600,7.7,62463,133.3
41069,OR,Wheeler County,687,619,68,9.9,32491,69.3
41071,OR,Yamhill County,49399,44847,4552,9.2,52103,111.2
42000,PA,Pennsylvania,6385714,5878897,506817,7.9,50221,100
42001,PA,Adams County,54230,50602,3628,6.7,54460,108.4
42003,PA,Allegheny County,647620,602536,45084,7,49682,98.9
42005,PA,Armstrong County,33588,30782,2806,8.4,41934,83.5
42007,PA,Beaver County,89345,82705,6640,7.4,48140,95.9
42009,PA,Bedford County,24385,22076,2309,9.5,40240,80.1
42011,PA,Berks County,204817,187988,16829,8.2,52168,103.9
42013,PA,Blair County,64967,60437,4530,7,42488,84.6
42015,PA,Bradford County,34172,32162,2010,5.9,44265,88.1
42017,PA,Bucks County,339394,314627,24767,7.3,70735,140.8
42019,PA,Butler County,99627,92966,6661,6.7,54888,109.3
42021,PA,Cambria County,68352,62467,5885,8.6,39892,79.4
42023,PA,Cameron County,2326,2056,270,11.6,38451,76.6
42025,PA,Carbon County,31785,28554,3231,10.2,44062,87.7
42027,PA,Centre County,75959,71666,4293,5.7,47222,94
42029,PA,Chester County,264539,248392,16147,6.1,79508,158.3
42031,PA,Clarion County,19905,17960,1945,9.8,41423,82.5
42033,PA,Clearfield County,41018,37397,3621,8.8,41408,82.5
42035,PA,Clinton County,19851,18269,1582,8,40281,80.2
42037,PA,Columbia County,36688,33720,2968,8.1,40704,81
42039,PA,Crawford County,42932,39566,3366,7.8,40775,81.2
42041,PA,Cumberland County,122628,114472,8156,6.7,58722,116.9
42043,PA,Dauphin County,137838,127318,10520,7.6,51668,102.9
42045,PA,Delaware County,278454,256164,22290,8,59978,119.4
42047,PA,Elk County,17300,16045,1255,7.3,43181,86
42049,PA,Erie County,140259,128982,11277,8,41951,83.5
42051,PA,Fayette County,63799,57843,5956,9.3,36300,72.3
42053,PA,Forest County,2583,2375,208,8.1,32852,65.4
42055,PA,Franklin County,79788,74253,5535,6.9,49545,98.7
42057,PA,Fulton County,7753,6998,755,9.7,44287,88.2
42059,PA,Greene County,20396,18992,1404,6.9,42049,83.7
42061,PA,Huntingdon County,22595,20432,2163,9.6,41923,83.5
42063,PA,Indiana County,47651,44126,3525,7.4,41707,83
42065,PA,Jefferson County,23368,21454,1914,8.2,39051,77.8
42067,PA,Juniata County,12310,11432,878,7.1,44865,89.3
42069,PA,Lackawanna County,107096,97341,9755,9.1,43886,87.4
42071,PA,Lancaster County,266366,248139,18227,6.8,53408,106.3
42073,PA,Lawrence County,42856,39196,3660,8.5,41447,82.5
42075,PA,Lebanon County,73400,68581,4819,6.6,53625,106.8
42077,PA,Lehigh County,179433,163835,15598,8.7,50737,101
42079,PA,Luzerne County,160117,145040,15077,9.4,42584,84.8
42081,PA,Lycoming County,61373,56668,4705,7.7,42163,84
42083,PA,McKean County,21101,19306,1795,8.5,40338,80.3
42085,PA,Mercer County,53189,48519,4670,8.8,41535,82.7
42087,PA,Mifflin County,22499,20638,1861,8.3,38544,76.7
42089,PA,Monroe County,81044,73265,7779,9.6,52505,104.5
42091,PA,Montgomery County,429051,400171,28880,6.7,76172,151.7
42093,PA,Montour County,9390,8812,578,6.2,48800,97.2
42095,PA,Northampton County,151562,138580,12982,8.6,57284,114.1
42097,PA,Northumberland County,46263,42147,4116,8.9,39834,79.3
42099,PA,Perry County,24210,22317,1893,7.8,52788,105.1
42101,PA,Philadelphia County,643473,573813,69660,10.8,34433,68.6
42103,PA,Pike County,26308,23544,2764,10.5,56070,111.6
42105,PA,Potter County,7928,7208,720,9.1,40599,80.8
42107,PA,Schuylkill County,72745,65861,6884,9.5,45549,90.7
42109,PA,Snyder County,19005,17481,1524,8,45460,90.5
42111,PA,Somerset County,38945,35566,3379,8.7,39042,77.7
42113,PA,Sullivan County,3251,3030,221,6.8,38608,76.9
42115,PA,Susquehanna County,23137,21390,1747,7.6,46814,93.2
42117,PA,Tioga County,21843,20369,1474,6.7,41010,81.7
42119,PA,Union County,17422,16041,1381,7.9,46992,93.6
42121,PA,Venango County,26346,24359,1987,7.5,39608,78.9
42123,PA,Warren County,21446,19898,1548,7.2,40940,81.5
42125,PA,Washington County,107048,99254,7794,7.3,52384,104.3
42127,PA,Wayne County,25484,23576,1908,7.5,47824,95.2
42129,PA,Westmoreland County,190893,176680,14213,7.4,46630,92.8
42131,PA,Wyoming County,14291,12948,1343,9.4,49011,97.6
42133,PA,York County,224969,207507,17462,7.8,55521,110.6
44000,RI,Rhode Island,563413,500014,63399,11.3,53152,100
44001,RI,Bristol County,26966,24359,2607,9.7,68483,128.8
44003,RI,Kent County,94995,84815,10180,10.7,59880,112.7
44005,RI,Newport County,44817,40205,4612,10.3,65755,123.7
44007,RI,Providence County,324175,285051,39124,12.1,46385,87.3
44009,RI,Washington County,72461,65585,6876,9.5,68539,128.9
45000,SC,South Carolina,2157266,1935885,221381,10.3,42477,100
45001,SC,Abbeville County,10947,9671,1276,11.7,36787,86.6
45003,SC,Aiken County,76200,69506,6694,8.8,42826,100.8
45005,SC,Allendale County,3354,2757,597,17.8,24820,58.4
45007,SC,Anderson County,84118,75776,8342,9.9,41000,96.5
45009,SC,Bamberg County,6322,5353,969,15.3,28467,67
45011,SC,Barnwell County,8564,7231,1333,15.6,33178,78.1
45013,SC,Beaufort County,63623,58091,5532,8.7,55018,129.5
45015,SC,Berkeley County,84280,76629,7651,9.1,49516,116.6
45017,SC,Calhoun County,6781,5963,818,12.1,38417,90.4
45019,SC,Charleston County,176222,161629,14593,8.3,49432,116.4
45021,SC,Cherokee County,24990,21707,3283,13.1,36400,85.7
45023,SC,Chester County,14790,12425,2365,16,32857,77.4
45025,SC,Chesterfield County,18962,16382,2580,13.6,30378,71.5
45027,SC,Clarendon County,12567,10651,1916,15.2,30780,72.5
45029,SC,Colleton County,17208,14972,2236,13,32963,77.6
45031,SC,Darlington County,30561,26871,3690,12.1,36248,85.3
45033,SC,Dillon County,13364,11316,2048,15.3,26308,61.9
45035,SC,Dorchester County,68185,62656,5529,8.1,51332,120.8
45037,SC,Edgefield County,11268,10220,1048,9.3,42289,99.6
45039,SC,Fairfield County,10714,9382,1332,12.4,34700,81.7
45041,SC,Florence County,62342,55466,6876,11,39663,93.4
45043,SC,Georgetown County,29880,26621,3259,10.9,40864,96.2
45045,SC,Greenville County,225947,207581,18366,8.1,48480,114.1
45047,SC,Greenwood County,30995,27548,3447,11.1,37038,87.2
45049,SC,Hampton County,7835,6747,1088,13.9,31915,75.1
45051,SC,Horry County,129085,114186,14899,11.5,38807,91.4
45053,SC,Jasper County,10365,9353,1012,9.8,34927,82.2
45055,SC,Kershaw County,29519,26716,2803,9.5,41055,96.7
45057,SC,Lancaster County,31142,26828,4314,13.9,40372,95
45059,SC,Laurens County,30922,27718,3204,10.4,35839,84.4
45061,SC,Lee County,8222,7129,1093,13.3,28733,67.6
45063,SC,Lexington County,132547,122185,10362,7.8,50738,119.4
45065,SC,McCormick County,3403,2912,491,14.4,35209,82.9
45067,SC,Marion County,12206,9829,2377,19.5,27726,65.3
45069,SC,Marlboro County,11699,9639,2060,17.6,28662,67.5
45071,SC,Newberry County,18450,16622,1828,9.9,38024,89.5
45073,SC,Oconee County,31343,28162,3181,10.1,42142,99.2
45075,SC,Orangeburg County,41689,35646,6043,14.5,31605,74.4
45077,SC,Pickens County,58183,53087,5096,8.8,40221,94.7
45079,SC,Richland County,180783,164190,16593,9.2,45182,106.4
45081,SC,Saluda County,9021,8218,803,8.9,39689,93.4
45083,SC,Spartanburg County,132077,118309,13768,10.4,40687,95.8
45085,SC,Sumter County,44811,39693,5118,11.4,40858,96.2
45087,SC,Union County,11587,9714,1873,16.2,33981,80
45089,SC,Williamsburg County,16175,14030,2145,13.3,27750,65.3
45091,SC,York County,114022,98570,15452,13.6,51017,120.1
46000,SD,South Dakota,446254,425413,20841,4.7,48188,100
46003,SD,Aurora County,1578,1525,53,3.4,45615,94.7
46005,SD,Beadle County,9962,9602,360,3.6,43803,90.9
46007,SD,Bennett County,1413,1330,83,5.9,31712,65.8
46009,SD,Bon Homme County,3047,2872,175,5.7,41711,86.6
46011,SD,Brookings County,18996,18207,789,4.2,48233,100.1
46013,SD,Brown County,21241,20439,802,3.8,50115,104
46015,SD,Brule County,2924,2815,109,3.7,45265,93.9
46017,SD,Buffalo County,538,458,80,14.9,21269,44.1
46019,SD,Butte County,5343,5097,246,4.6,39615,82.2
46021,SD,Campbell County,842,807,35,4.2,40855,84.8
46023,SD,Charles Mix County,4237,4032,205,4.8,37448,77.7
46025,SD,Clark County,1901,1807,94,4.9,43496,90.3
46027,SD,Clay County,7522,7220,302,4,39870,82.7
46029,SD,Codington County,16216,15513,703,4.3,47282,98.1
46031,SD,Corson County,1427,1318,109,7.6,28770,59.7
46033,SD,Custer County,4819,4598,221,4.6,49649,103
46035,SD,Davison County,11479,11046,433,3.8,47100,97.7
46037,SD,Day County,2884,2686,198,6.9,39223,81.4
46039,SD,Deuel County,2703,2556,147,5.4,45700,94.8
46041,SD,Dewey County,2697,2357,340,12.6,33902,70.4
46043,SD,Douglas County,1852,1780,72,3.9,46224,95.9
46045,SD,Edmunds County,2008,1932,76,3.8,49467,102.7
46047,SD,Fall River County,3804,3611,193,5.1,39444,81.9
46049,SD,Faulk County,1161,1113,48,4.1,43030,89.3
46051,SD,Grant County,4192,3976,216,5.2,47131,97.8
46053,SD,Gregory County,2386,2286,100,4.2,33991,70.5
46055,SD,Haakon County,1154,1112,42,3.6,44653,92.7
46057,SD,Hamlin County,2949,2818,131,4.4,50864,105.6
46059,SD,Hand County,1867,1804,63,3.4,41975,87.1
46061,SD,Hanson County,1818,1727,91,5,57519,119.4
46063,SD,Harding County,845,813,32,3.8,42575,88.4
46065,SD,Hughes County,10351,9977,374,3.6,60677,125.9
46067,SD,Hutchinson County,3869,3721,148,3.8,46309,96.1
46069,SD,Hyde County,768,738,30,3.9,44997,93.4
46071,SD,Jackson County,1267,1173,94,7.4,31854,66.1
46073,SD,Jerauld County,1533,1488,45,2.9,47433,98.4
46075,SD,Jones County,732,711,21,2.9,40171,83.4
46077,SD,Kingsbury County,2989,2860,129,4.3,46244,96
46079,SD,Lake County,6571,6268,303,4.6,49893,103.5
46081,SD,Lawrence County,13285,12668,617,4.6,42495,88.2
46083,SD,Lincoln County,25427,24467,960,3.8,76907,159.6
46085,SD,Lyman County,2029,1909,120,5.9,37914,78.7
46087,SD,McCook County,2761,2635,126,4.6,51365,106.6
46089,SD,McPherson County,1135,1082,53,4.7,35473,73.6
46091,SD,Marshall County,2161,2018,143,6.6,44849,93.1
46093,SD,Meade County,12713,12071,642,5,50985,105.8
46095,SD,Mellette County,873,822,51,5.8,30170,62.6
46097,SD,Miner County,1153,1095,58,5,44316,92
46099,SD,Minnehaha County,97733,93222,4511,4.6,51431,106.7
46101,SD,Moody County,3992,3735,257,6.4,50307,104.4
46103,SD,Pennington County,54686,52123,2563,4.7,48083,99.8
46105,SD,Perkins County,1590,1523,67,4.2,37919,78.7
46107,SD,Potter County,1278,1212,66,5.2,45851,95.2
46109,SD,Roberts County,4878,4549,329,6.7,41407,85.9
46111,SD,Sanborn County,1387,1332,55,4,47141,97.8
46113,SD,Shannon County,3880,3357,523,13.5,27200,56.4
46115,SD,Spink County,3510,3365,145,4.1,44455,92.3
46117,SD,Stanley County,2056,1984,72,3.5,53986,112
46119,SD,Sully County,1019,986,33,3.2,55194,114.5
46121,SD,Todd County,3719,3437,282,7.6,26639,55.3
46123,SD,Tripp County,2942,2820,122,4.1,38360,79.6
46125,SD,Turner County,4321,4140,181,4.2,50469,104.7
46127,SD,Union County,8241,7848,393,4.8,66242,137.5
46129,SD,Walworth County,2722,2554,168,6.2,39920,82.8
46135,SD,Yankton County,11792,11255,537,4.6,48147,99.9
46137,SD,Ziebach County,1087,1013,74,6.8,28350,58.8
47000,TN,Tennessee,3132742,2845010,287732,9.2,41691,100
47001,TN,Anderson County,37413,34281,3132,8.4,41694,100
47003,TN,Bedford County,23028,20619,2409,10.5,39552,94.9
47005,TN,Benton County,7139,6300,839,11.8,32527,78
47007,TN,Bledsoe County,4901,4352,549,11.2,33500,80.4
47009,TN,Blount County,65513,60497,5016,7.7,45539,109.2
47011,TN,Bradley County,47867,43556,4311,9,38667,92.7
47013,TN,Campbell County,17018,15069,1949,11.5,29044,69.7
47015,TN,Cannon County,6679,6086,593,8.9,36959,88.6
47017,TN,Carroll County,14103,12388,1715,12.2,34979,83.9
47019,TN,Carter County,29021,26366,2655,9.1,33772,81
47021,TN,Cheatham County,20854,19072,1782,8.5,50225,120.5
47023,TN,Chester County,8274,7472,802,9.7,38749,92.9
47025,TN,Claiborne County,13440,11867,1573,11.7,34454,82.6
47027,TN,Clay County,3475,3096,379,10.9,27978,67.1
47029,TN,Cocke County,16752,14599,2153,12.9,30092,72.2
47031,TN,Coffee County,26527,24053,2474,9.3,35571,85.3
47033,TN,Crockett County,6756,5950,806,11.9,34474,82.7
47035,TN,Cumberland County,24217,21724,2493,10.3,35873,86
47037,TN,Davidson County,332843,305630,27213,8.2,43893,105.3
47039,TN,Decatur County,5851,5210,641,11,34400,82.5
47041,TN,DeKalb County,10159,9222,937,9.2,37136,89.1
47043,TN,Dickson County,25096,22691,2405,9.6,43019,103.2
47045,TN,Dyer County,17428,15139,2289,13.1,37766,90.6
47047,TN,Fayette County,18023,16122,1901,10.5,49760,119.4
47049,TN,Fentress County,8218,7341,877,10.7,28666,68.8
47051,TN,Franklin County,20136,18246,1890,9.4,39935,95.8
47053,TN,Gibson County,22069,19241,2828,12.8,36365,87.2
47055,TN,Giles County,13851,12362,1489,10.8,39916,95.7
47057,TN,Grainger County,9945,8745,1200,12.1,34077,81.7
47059,TN,Greene County,30490,26881,3609,11.8,32880,78.9
47061,TN,Grundy County,6061,5352,709,11.7,27428,65.8
47063,TN,Hamblen County,29458,26335,3123,10.6,35630,85.5
47065,TN,Hamilton County,167419,153688,13731,8.2,42609,102.2
47067,TN,Hancock County,2471,2123,348,14.1,25297,60.7
47069,TN,Hardeman County,10993,9635,1358,12.4,31437,75.4
47071,TN,Hardin County,12453,11118,1335,10.7,31281,75
47073,TN,Hawkins County,26617,24239,2378,8.9,37095,89
47075,TN,Haywood County,8974,7754,1220,13.6,32422,77.8
47077,TN,Henderson County,12684,11129,1555,12.3,36392,87.3
47079,TN,Henry County,13942,12323,1619,11.6,35270,84.6
47081,TN,Hickman County,10728,9589,1139,10.6,35094,84.2
47083,TN,Houston County,4123,3712,411,10,36858,88.4
47085,TN,Humphreys County,9366,8405,961,10.3,43987,105.5
47087,TN,Jackson County,5361,4803,558,10.4,31903,76.5
47089,TN,Jefferson County,24090,21399,2691,11.2,38174,91.6
47091,TN,Johnson County,7597,6689,908,12,30897,74.1
47093,TN,Knox County,237269,220779,16490,6.9,45149,108.3
47095,TN,Lake County,2821,2492,329,11.7,26587,63.8
47097,TN,Lauderdale County,10000,8577,1423,14.2,31770,76.2
47099,TN,Lawrence County,16975,14882,2093,12.3,33963,81.5
47101,TN,Lewis County,5389,4689,700,13,31877,76.5
47103,TN,Lincoln County,17629,16548,1081,6.1,39246,94.1
47105,TN,Loudon County,25283,23339,1944,7.7,50375,120.8
47107,TN,McMinn County,23818,21162,2656,11.2,37319,89.5
47109,TN,McNairy County,11246,10032,1214,10.8,33738,80.9
47111,TN,Macon County,10980,9903,1077,9.8,34262,82.2
47113,TN,Madison County,49280,44543,4737,9.6,39510,94.8
47115,TN,Marion County,12868,11555,1313,10.2,40136,96.3
47117,TN,Marshall County,12445,10739,1706,13.7,40214,96.5
47119,TN,Maury County,36510,32093,4417,12.1,42569,102.1
47121,TN,Meigs County,5257,4640,617,11.7,37129,89.1
47123,TN,Monroe County,18933,16609,2324,12.3,32720,78.5
47125,TN,Montgomery County,76363,69359,7004,9.2,47684,114.4
47127,TN,Moore County,3298,3025,273,8.3,48229,115.7
47129,TN,Morgan County,9099,8105,994,10.9,35266,84.6
47131,TN,Obion County,15096,13182,1914,12.7,38429,92.2
47133,TN,Overton County,10606,9532,1074,10.1,32308,77.5
47135,TN,Perry County,2617,2247,370,14.1,31505,75.6
47137,TN,Pickett County,1929,1641,288,14.9,31518,75.6
47139,TN,Polk County,7515,6630,885,11.8,35628,85.5
47141,TN,Putnam County,36879,33722,3157,8.6,35053,84.1
47143,TN,Rhea County,13894,12282,1612,11.6,34920,83.8
47145,TN,Roane County,28398,26124,2274,8,42542,102
47147,TN,Robertson County,34888,31992,2896,8.3,48179,115.6
47149,TN,Rutherford County,143619,132108,11511,8,52586,126.1
47151,TN,Scott County,8389,6730,1659,19.8,29882,71.7
47153,TN,Sequatchie County,6347,5780,567,8.9,38008,91.2
47155,TN,Sevier County,50692,45607,5085,10,40353,96.8
47157,TN,Shelby County,442972,399084,43888,9.9,44051,105.7
47159,TN,Smith County,9387,8504,883,9.4,42182,101.2
47161,TN,Stewart County,6043,5338,705,11.7,40508,97.2
47163,TN,Sullivan County,76009,70134,5875,7.7,39564,94.9
47165,TN,Sumner County,84228,77465,6763,8,52704,126.4
47167,TN,Tipton County,29092,26231,2861,9.8,50401,120.9
47169,TN,Trousdale County,3822,3420,402,10.5,38541,92.4
47171,TN,Unicoi County,8801,7943,858,9.7,33522,80.4
47173,TN,Union County,9172,8330,842,9.2,35740,85.7
47175,TN,Van Buren County,2344,2064,280,11.9,32896,78.9
47177,TN,Warren County,17786,15890,1896,10.7,32823,78.7
47179,TN,Washington County,64991,59895,5096,7.8,41358,99.2
47181,TN,Wayne County,6608,5802,806,12.2,29883,71.7
47183,TN,Weakley County,16212,14232,1980,12.2,34457,82.6
47185,TN,White County,11252,9973,1279,11.4,33094,79.4
47187,TN,Williamson County,96464,90490,5974,6.2,90207,216.4
47189,TN,Wilson County,61813,57100,4713,7.6,59769,143.4
48000,TX,Texas,12451504,11464525,986979,7.9,49390,100
48001,TX,Anderson County,21787,19807,1980,9.1,38558,78.1
48003,TX,Andrews County,7749,7361,388,5,52865,107
48005,TX,Angelina County,40296,37182,3114,7.7,39169,79.3
48007,TX,Aransas County,10969,10088,881,8,38887,78.7
48009,TX,Archer County,5013,4718,295,5.9,52318,105.9
48011,TX,Armstrong County,992,943,49,4.9,49501,100.2
48013,TX,Atascosa County,19758,18191,1567,7.9,41236,83.5
48015,TX,Austin County,14321,13151,1170,8.2,48209,97.6
48017,TX,Bailey County,3349,3106,243,7.3,35816,72.5
48019,TX,Bandera County,9870,9194,676,6.8,46652,94.5
48021,TX,Bastrop County,35777,32976,2801,7.8,51947,105.2
48023,TX,Baylor County,1950,1837,113,5.8,32208,65.2
48025,TX,Bee County,12554,11471,1083,8.6,36048,73
48027,TX,Bell County,136597,125615,10982,8,45797,92.7
48029,TX,Bexar County,804768,743879,60889,7.6,46589,94.3
48031,TX,Blanco County,5122,4808,314,6.1,54000,109.3
48033,TX,Borden County,533,512,21,3.9,51426,104.1
48035,TX,Bosque County,8331,7626,705,8.5,41361,83.7
48037,TX,Bowie County,45615,42103,3512,7.7,43364,87.8
48039,TX,Brazoria County,153299,140078,13221,8.6,62180,125.9
48041,TX,Brazos County,100643,94245,6398,6.4,37623,76.2
48043,TX,Brewster County,5178,4905,273,5.3,43896,88.9
48045,TX,Briscoe County,672,626,46,6.8,38372,77.7
48047,TX,Brooks County,3365,3064,301,8.9,24567,49.7
48049,TX,Brown County,18828,17475,1353,7.2,38466,77.9
48051,TX,Burleson County,8044,7495,549,6.8,42025,85.1
48053,TX,Burnet County,22973,21549,1424,6.2,44636,90.4
48055,TX,Caldwell County,16759,15334,1425,8.5,40721,82.4
48057,TX,Calhoun County,10395,9445,950,9.1,43339,87.7
48059,TX,Callahan County,7014,6577,437,6.2,43074,87.2
48061,TX,Cameron County,163010,143839,19171,11.8,31902,64.6
48063,TX,Camp County,5880,5363,517,8.8,36393,73.7
48065,TX,Carson County,3306,3142,164,5,55250,111.9
48067,TX,Cass County,13582,12139,1443,10.6,36061,73
48069,TX,Castro County,3605,3398,207,5.7,38175,77.3
48071,TX,Chambers County,17618,16042,1576,8.9,69378,140.5
48073,TX,Cherokee County,21466,19565,1901,8.9,35119,71.1
48075,TX,Childress County,3187,2991,196,6.1,33780,68.4
48077,TX,Clay County,5869,5511,358,6.1,48358,97.9
48079,TX,Cochran County,1385,1261,124,9,38276,77.5
48081,TX,Coke County,1312,1220,92,7,37441,75.8
48083,TX,Coleman County,4252,3948,304,7.1,31865,64.5
48085,TX,Collin County,435101,404549,30552,7,82765,167.6
48087,TX,Collingsworth County,1432,1356,76,5.3,34634,70.1
48089,TX,Colorado County,11336,10531,805,7.1,42451,86
48091,TX,Comal County,55012,51202,3810,6.9,59754,121
48093,TX,Comanche County,6708,6256,452,6.7,35296,71.5
48095,TX,Concho County,1367,1259,108,7.9,35514,71.9
48097,TX,Cooke County,23122,21863,1259,5.4,49127,99.5
48099,TX,Coryell County,24912,22632,2280,9.2,47864,96.9
48101,TX,Cottle County,770,721,49,6.4,32656,66.1
48103,TX,Crane County,1773,1644,129,7.3,49831,100.9
48105,TX,Crockett County,2166,2051,115,5.3,44331,89.8
48107,TX,Crosby County,2591,2350,241,9.3,32661,66.1
48109,TX,Culberson County,1691,1620,71,4.2,31186,63.1
48111,TX,Dallam County,3814,3640,174,4.6,43454,88
48113,TX,Dallas County,1176200,1077721,98479,8.4,47335,95.8
48115,TX,Dawson County,5570,5130,440,7.9,37492,75.9
48117,TX,Deaf Smith County,9345,8819,526,5.6,36073,73
48119,TX,Delta County,2325,2118,207,8.9,34968,70.8
48121,TX,Denton County,374542,348511,26031,7,70243,142.2
48123,TX,DeWitt County,9748,9092,656,6.7,38620,78.2
48125,TX,Dickens County,1034,893,141,13.6,32925,66.7
48127,TX,Dimmit County,5510,5126,384,7,30503,61.8
48129,TX,Donley County,1956,1837,119,6.1,34606,70.1
48131,TX,Duval County,5623,5114,509,9.1,31294,63.4
48133,TX,Eastland County,9319,8658,661,7.1,36761,74.4
48135,TX,Ector County,78567,73999,4568,5.8,47930,97
48137,TX,Edwards County,975,906,69,7.1,33102,67
48139,TX,Ellis County,74518,68479,6039,8.1,60931,123.4
48141,TX,El Paso County,326472,292790,33682,10.3,39116,79.2
48143,TX,Erath County,19523,18320,1203,6.2,38860,78.7
48145,TX,Falls County,6856,6202,654,9.5,33125,67.1
48147,TX,Fannin County,13779,12418,1361,9.9,39985,81
48149,TX,Fayette County,12652,11917,735,5.8,44862,90.8
48151,TX,Fisher County,2021,1892,129,6.4,38717,78.4
48153,TX,Floyd County,3076,2822,254,8.3,35399,71.7
48155,TX,Foard County,715,665,50,7,30488,61.7
48157,TX,Fort Bend County,300276,278237,22039,7.3,82147,166.3
48159,TX,Franklin County,5374,4982,392,7.3,40936,82.9
48161,TX,Freestone County,10327,9667,660,6.4,44564,90.2
48163,TX,Frio County,8035,7446,589,7.3,31996,64.8
48165,TX,Gaines County,7713,7282,431,5.6,43703,88.5
48167,TX,Galveston County,148320,134854,13466,9.1,58106,117.6
48169,TX,Garza County,2628,2441,187,7.1,39004,79
48171,TX,Gillespie County,14171,13498,673,4.7,51981,105.2
48173,TX,Glasscock County,681,651,30,4.4,59309,120.1
48175,TX,Goliad County,3615,3394,221,6.1,43737,88.6
48177,TX,Gonzales County,10313,9726,587,5.7,36408,73.7
48179,TX,Gray County,11607,10911,696,6,41488,84
48181,TX,Grayson County,58711,53840,4871,8.3,44080,89.2
48183,TX,Gregg County,66811,62297,4514,6.8,43356,87.8
48185,TX,Grimes County,12220,11252,968,7.9,41981,85
48187,TX,Guadalupe County,65583,61195,4388,6.7,57576,116.6
48189,TX,Hale County,17575,16304,1271,7.2,36142,73.2
48191,TX,Hall County,1417,1291,126,8.9,30376,61.5
48193,TX,Hamilton County,4419,4158,261,5.9,37936,76.8
48195,TX,Hansford County,2809,2683,126,4.5,50505,102.3
48197,TX,Hardeman County,2211,2081,130,5.9,34294,69.4
48199,TX,Hardin County,27705,25097,2608,9.4,52514,106.3
48201,TX,Harris County,2054025,1886265,167760,8.2,50924,103.1
48203,TX,Harrison County,34018,31365,2653,7.8,44427,90
48205,TX,Hartley County,2887,2741,146,5.1,60548,122.6
48207,TX,Haskell County,3280,3109,171,5.2,32493,65.8
48209,TX,Hays County,84267,78634,5633,6.7,56557,114.5
48211,TX,Hemphill County,2990,2908,82,2.7,58502,118.4
48213,TX,Henderson County,36458,33409,3049,8.4,38191,77.3
48215,TX,Hidalgo County,317612,279517,38095,12,31021,62.8
48217,TX,Hill County,16199,14860,1339,8.3,39184,79.3
48219,TX,Hockley County,12777,12043,734,5.7,42710,86.5
48221,TX,Hood County,26329,24442,1887,7.2,48622,98.4
48223,TX,Hopkins County,18236,16986,1250,6.9,40441,81.9
48225,TX,Houston County,8969,8034,935,10.4,31892,64.6
48227,TX,Howard County,14273,13250,1023,7.2,41125,83.3
48229,TX,Hudspeth County,1817,1706,111,6.1,28624,58
48231,TX,Hunt County,40392,36983,3409,8.4,47394,96
48233,TX,Hutchinson County,11706,10914,792,6.8,49038,99.3
48235,TX,Irion County,873,825,48,5.5,54628,110.6
48237,TX,Jack County,5382,5094,288,5.4,45069,91.3
48239,TX,Jackson County,7213,6739,474,6.6,43740,88.6
48241,TX,Jasper County,16024,14153,1871,11.7,38260,77.5
48243,TX,Jeff Davis County,1202,1134,68,5.7,48161,97.5
48245,TX,Jefferson County,120358,106744,13614,11.3,41363,83.7
48247,TX,Jim Hogg County,3104,2901,203,6.5,32179,65.2
48249,TX,Jim Wells County,23374,21815,1559,6.7,39120,79.2
48251,TX,Johnson County,73705,68106,5599,7.6,54412,110.2
48253,TX,Jones County,8065,7458,607,7.5,35709,72.3
48255,TX,Karnes County,5543,5079,464,8.4,35904,72.7
48257,TX,Kaufman County,50043,45875,4168,8.3,59986,121.5
48259,TX,Kendall County,16590,15556,1034,6.2,71388,144.5
48261,TX,Kenedy County,257,245,12,4.7,34851,70.6
48263,TX,Kent County,448,421,27,6,42829,86.7
48265,TX,Kerr County,23306,21809,1497,6.4,45621,92.4
48267,TX,Kimble County,2031,1893,138,6.8,35867,72.6
48269,TX,King County,211,199,12,5.7,51237,103.7
48271,TX,Kinney County,1521,1390,131,8.6,37917,76.8
48273,TX,Kleberg County,17589,16371,1218,6.9,36198,73.3
48275,TX,Knox County,1818,1708,110,6.1,33395,67.6
48277,TX,Lamar County,23974,21642,2332,9.7,38685,78.3
48279,TX,Lamb County,6891,6392,499,7.2,34524,69.9
48281,TX,Lampasas County,9789,9085,704,7.2,47135,95.4
48283,TX,La Salle County,3750,3505,245,6.5,29938,60.6
48285,TX,Lavaca County,10260,9673,587,5.7,44186,89.5
48287,TX,Lee County,10154,9557,597,5.9,44411,89.9
48289,TX,Leon County,8130,7509,621,7.6,43545,88.2
48291,TX,Liberty County,32490,29050,3440,10.6,45712,92.6
48293,TX,Limestone County,11381,10545,836,7.3,36887,74.7
48295,TX,Lipscomb County,1795,1715,80,4.5,52256,105.8
48297,TX,Live Oak County,6145,5804,341,5.5,41601,84.2
48299,TX,Llano County,8531,7889,642,7.5,42282,85.6
48301,TX,Loving County,41,37,4,9.8,46204,93.5
48303,TX,Lubbock County,145391,136473,8918,6.1,42978,87
48305,TX,Lynn County,2839,2632,207,7.3,36530,74
48307,TX,McCulloch County,4408,4150,258,5.9,35775,72.4
48309,TX,McLennan County,116392,107791,8601,7.4,40563,82.1
48311,TX,McMullen County,559,538,21,3.8,46528,94.2
48313,TX,Madison County,5551,5099,452,8.1,35218,71.3
48315,TX,Marion County,5128,4654,474,9.2,31922,64.6
48317,TX,Martin County,2474,2342,132,5.3,46680,94.5
48319,TX,Mason County,2372,2255,117,4.9,39080,79.1
48321,TX,Matagorda County,18048,15963,2085,11.6,40778,82.6
48323,TX,Maverick County,24054,20643,3411,14.2,28502,57.7
48325,TX,Medina County,20725,19190,1535,7.4,47191,95.5
48327,TX,Menard County,988,919,69,7,30349,61.4
48329,TX,Midland County,83714,80025,3689,4.4,54996,111.4
48331,TX,Milam County,11193,10121,1072,9.6,38244,77.4
48333,TX,Mills County,2349,2206,143,6.1,39185,79.3
48335,TX,Mitchell County,3567,3287,280,7.8,36724,74.4
48337,TX,Montague County,10838,10198,640,5.9,44768,90.6
48339,TX,Montgomery County,231457,214886,16571,7.2,61996,125.5
48341,TX,Moore County,12095,11523,572,4.7,45795,92.7
48343,TX,Morris County,6227,5512,715,11.5,35972,72.8
48345,TX,Motley County,669,629,40,6,34852,70.6
48347,TX,Nacogdoches County,32127,29958,2169,6.8,38046,77
48349,TX,Navarro County,21994,20050,1944,8.8,38057,77.1
48351,TX,Newton County,5852,5038,814,13.9,36492,73.9
48353,TX,Nolan County,8064,7535,529,6.6,34920,70.7
48355,TX,Nueces County,176671,163233,13438,7.6,43470,88
48357,TX,Ochiltree County,5891,5644,247,4.2,52866,107
48359,TX,Oldham County,1008,957,51,5.1,46597,94.3
48361,TX,Orange County,41439,36798,4641,11.2,44758,90.6
48363,TX,Palo Pinto County,14467,13394,1073,7.4,34574,70
48365,TX,Panola County,14236,13257,979,6.9,45212,91.5
48367,TX,Parker County,56913,52875,4038,7.1,63584,128.7
48369,TX,Parmer County,4676,4439,237,5.1,39195,79.4
48371,TX,Pecos County,8972,8492,480,5.3,42984,87
48373,TX,Polk County,18460,16673,1787,9.7,37665,76.3
48375,TX,Potter County,58103,54429,3674,6.3,35690,72.3
48377,TX,Presidio County,4072,3490,582,14.3,31297,63.4
48379,TX,Rains County,5128,4690,438,8.5,40595,82.2
48381,TX,Randall County,72559,69141,3418,4.7,57402,116.2
48383,TX,Reagan County,2756,2667,89,3.2,47493,96.2
48385,TX,Real County,1488,1380,108,7.3,30499,61.8
48387,TX,Red River County,5874,5179,695,11.8,32615,66
48389,TX,Reeves County,4781,4257,524,11,32839,66.5
48391,TX,Refugio County,4371,4112,259,5.9,38470,77.9
48393,TX,Roberts County,632,607,25,4,59699,120.9
48395,TX,Robertson County,7358,6712,646,8.8,38073,77.1
48397,TX,Rockwall County,40255,37375,2880,7.2,84763,171.6
48399,TX,Runnels County,4583,4205,378,8.2,34515,69.9
48401,TX,Rusk County,26473,24609,1864,7,41840,84.7
48403,TX,Sabine County,3424,2866,558,16.3,34605,70.1
48405,TX,San Augustine County,3699,3241,458,12.4,32543,65.9
48407,TX,San Jacinto County,11105,10034,1071,9.6,41650,84.3
48409,TX,San Patricio County,29488,26699,2789,9.5,45965,93.1
48411,TX,San Saba County,2336,2161,175,7.5,35978,72.8
48413,TX,Schleicher County,1582,1483,99,6.3,41533,84.1
48415,TX,Scurry County,8269,7806,463,5.6,45907,92.9
48417,TX,Shackelford County,2299,2203,96,4.2,42976,87
48419,TX,Shelby County,13045,12008,1037,7.9,36677,74.3
48421,TX,Sherman County,1457,1384,73,5,44699,90.5
48423,TX,Smith County,104089,95963,8126,7.8,44261,89.6
48425,TX,Somervell County,4532,4188,344,7.6,52134,105.6
48427,TX,Starr County,25656,21322,4334,16.9,24537,49.7
48429,TX,Stephens County,4826,4466,360,7.5,37167,75.3
48431,TX,Sterling County,769,737,32,4.2,50943,103.1
48433,TX,Stonewall County,824,783,41,5,37513,76
48435,TX,Sutton County,2920,2788,132,4.5,53501,108.3
48437,TX,Swisher County,3605,3371,234,6.5,35736,72.4
48439,TX,Tarrant County,934168,861103,73065,7.8,52882,107.1
48441,TX,Taylor County,68693,64369,4324,6.3,40718,82.4
48443,TX,Terrell County,406,371,35,8.6,39260,79.5
48445,TX,Terry County,6046,5606,440,7.3,31984,64.8
48447,TX,Throckmorton County,1046,987,59,5.6,39860,80.7
48449,TX,Titus County,14315,13180,1135,7.9,38221,77.4
48451,TX,Tom Green County,55246,51784,3462,6.3,41626,84.3
48453,TX,Travis County,576384,538234,38150,6.6,53303,107.9
48455,TX,Trinity County,5891,5351,540,9.2,32745,66.3
48457,TX,Tyler County,8514,7536,978,11.5,36384,73.7
48459,TX,Upshur County,20506,19026,1480,7.2,44434,90
48461,TX,Upton County,1947,1860,87,4.5,47854,96.9
48463,TX,Uvalde County,12071,10982,1089,9,32749,66.3
48465,TX,Val Verde County,21009,19114,1895,9,38574,78.1
48467,TX,Van Zandt County,26058,24131,1927,7.4,41452,83.9
48469,TX,Victoria County,46910,43911,2999,6.4,45687,92.5
48471,TX,Walker County,28007,25834,2173,7.8,38138,77.2
48473,TX,Waller County,20022,18371,1651,8.2,47180,95.5
48475,TX,Ward County,5289,4960,329,6.2,43871,88.8
48477,TX,Washington County,17470,16419,1051,6,44897,90.9
48479,TX,Webb County,100246,92135,8111,8.1,35295,71.5
48481,TX,Wharton County,21643,19851,1792,8.3,41911,84.9
48483,TX,Wheeler County,3534,3393,141,4,45848,92.8
48485,TX,Wichita County,62271,57736,4535,7.3,42738,86.5
48487,TX,Wilbarger County,7910,7467,443,5.6,35785,72.5
48489,TX,Willacy County,9435,8084,1351,14.3,28487,57.7
48491,TX,Williamson County,226856,211364,15492,6.8,70276,142.3
48493,TX,Wilson County,20138,18718,1420,7.1,56873,115.2
48495,TX,Winkler County,3597,3378,219,6.1,47674,96.5
48497,TX,Wise County,28493,26429,2064,7.2,55118,111.6
48499,TX,Wood County,18625,17131,1494,8,39514,80
48501,TX,Yoakum County,4548,4333,215,4.7,48521,98.2
48503,TX,Young County,9662,9038,624,6.5,40133,81.3
48505,TX,Zapata County,6311,5773,538,8.5,30104,61
48507,TX,Zavala County,4234,3581,653,15.4,22982,46.5
49000,UT,Utah,1338259,1248197,90062,6.7,55802,100
49001,UT,Beaver County,3370,3125,245,7.3,44126,79.1
49003,UT,Box Elder County,21148,19421,1727,8.2,51558,92.4
49005,UT,Cache County,60980,58036,2944,4.8,47589,85.3
49007,UT,Carbon County,9883,9160,723,7.3,47585,85.3
49009,UT,Daggett County,467,438,29,6.2,48751,87.4
49011,UT,Davis County,143526,134583,8943,6.2,68974,123.6
49013,UT,Duchesne County,9974,9422,552,5.5,54973,98.5
49015,UT,Emery County,4995,4617,378,7.6,48745,87.4
49017,UT,Garfield County,2895,2578,317,10.9,40762,73
49019,UT,Grand County,5333,4811,522,9.8,41410,74.2
49021,UT,Iron County,19628,18010,1618,8.2,41094,73.6
49023,UT,Juab County,4101,3738,363,8.9,51762,92.8
49025,UT,Kane County,3404,3140,264,7.8,42515,76.2
49027,UT,Millard County,6356,6029,327,5.1,47316,84.8
49029,UT,Morgan County,4193,3955,238,5.7,74509,133.5
49031,UT,Piute County,772,720,52,6.7,34079,61.1
49033,UT,Rich County,1271,1201,70,5.5,50980,91.4
49035,UT,Salt Lake County,546055,510310,35745,6.5,56166,100.7
49037,UT,San Juan County,5221,4622,599,11.5,37444,67.1
49039,UT,Sanpete County,10556,9638,918,8.7,45231,81.1
49041,UT,Sevier County,9579,8872,707,7.4,43190,77.4
49043,UT,Summit County,21537,20231,1306,6.1,85221,152.7
49045,UT,Tooele County,27754,25837,1917,6.9,61719,110.6
49047,UT,Uintah County,17550,16651,899,5.1,58936,105.6
49049,UT,Utah County,218763,204526,14237,6.5,58077,104.1
49051,UT,Wasatch County,9879,9096,783,7.9,56379,101
49053,UT,Washington County,56873,51966,4907,8.6,46001,82.4
49055,UT,Wayne County,1368,1225,143,10.5,39593,71
49057,UT,Weber County,110832,102240,8592,7.8,52183,93.5
50000,VT,Vermont,359166,339077,20089,5.6,52033,100
50001,VT,Addison County,21589,20517,1072,5,54542,104.8
50003,VT,Bennington County,20093,18785,1308,6.5,44701,85.9
50005,VT,Caledonia County,16605,15519,1086,6.5,42452,81.6
50007,VT,Chittenden County,92489,88386,4103,4.4,60865,117
50009,VT,Essex County,3143,2903,240,7.6,34535,66.4
50011,VT,Franklin County,26327,24859,1468,5.6,53412,102.7
50013,VT,Grand Isle County,3911,3640,271,6.9,56241,108.1
50015,VT,Lamoille County,16122,15094,1028,6.4,46549,89.5
50017,VT,Orange County,16275,15358,917,5.6,50192,96.5
50019,VT,Orleans County,15169,13938,1231,8.1,39598,76.1
50021,VT,Rutland County,34968,32639,2329,6.7,46542,89.4
50023,VT,Washington County,34855,32964,1891,5.4,54840,105.4
50025,VT,Windham County,25129,23701,1428,5.7,46566,89.5
50027,VT,Windsor County,32490,30773,1717,5.3,49295,94.7
51000,VA,Virginia,4306174,4037341,268833,6.2,61877,100
51001,VA,Accomack County,19056,17686,1370,7.2,37714,60.9
51003,VA,Albemarle County,55747,53035,2712,4.9,63972,103.4
51005,VA,Alleghany County,7181,6583,598,8.3,45108,72.9
51007,VA,Amelia County,6697,6234,463,6.9,49399,79.8
51009,VA,Amherst County,15567,14450,1117,7.2,44494,71.9
51011,VA,Appomattox County,7354,6811,543,7.4,44097,71.3
51013,VA,Arlington County,136359,131185,5174,3.8,98060,158.5
51015,VA,Augusta County,39106,36773,2333,6,51036,82.5
51017,VA,Bath County,2796,2644,152,5.4,42423,68.6
51019,VA,Bedford County,35899,33691,2208,6.2,58264,94.2
51021,VA,Bland County,3477,3269,208,6,41654,67.3
51023,VA,Botetourt County,17967,16970,997,5.5,62204,100.5
51025,VA,Brunswick County,6903,6167,736,10.7,35207,56.9
51027,VA,Buchanan County,9527,8829,698,7.3,32722,52.9
51029,VA,Buckingham County,7624,6971,653,8.6,37440,60.5
51031,VA,Campbell County,28126,26237,1889,6.7,47450,76.7
51033,VA,Caroline County,14863,13687,1176,7.9,52496,84.8
51035,VA,Carroll County,14387,13048,1339,9.3,34891,56.4
51036,VA,Charles City County,3944,3619,325,8.2,45960,74.3
51037,VA,Charlotte County,5502,5004,498,9.1,35677,57.7
51041,VA,Chesterfield County,177154,166299,10855,6.1,71074,114.9
51043,VA,Clarke County,8040,7641,399,5,74472,120.4
51045,VA,Craig County,2598,2402,196,7.5,45789,74
51047,VA,Culpeper County,21582,20095,1487,6.9,58596,94.7
51049,VA,Cumberland County,4880,4532,348,7.1,40080,64.8
51051,VA,Dickenson County,6689,6150,539,8.1,33494,54.1
51053,VA,Dinwiddie County,14162,13210,952,6.7,50152,81.1
51057,VA,Essex County,5756,5285,471,8.2,42550,68.8
51059,VA,Fairfax County,638882,611477,27405,4.3,105409,170.4
51061,VA,Fauquier County,37311,35491,1820,4.9,85096,137.5
51063,VA,Floyd County,7612,7095,517,6.8,39997,64.6
51065,VA,Fluvanna County,14329,13584,745,5.2,62086,100.3
51067,VA,Franklin County,28987,27069,1918,6.6,49003,79.2
51069,VA,Frederick County,43881,41274,2607,5.9,64501,104.2
51071,VA,Giles County,8518,7886,632,7.4,43139,69.7
51073,VA,Gloucester County,20316,19124,1192,5.9,58282,94.2
51075,VA,Goochland County,11837,11207,630,5.3,76804,124.1
51077,VA,Grayson County,7005,6280,725,10.3,31286,50.6
51079,VA,Greene County,11006,10442,564,5.1,60710,98.1
51081,VA,Greensville County,4446,4053,393,8.8,37861,61.2
51083,VA,Halifax County,16427,14683,1744,10.6,35170,56.8
51085,VA,Hanover County,55837,52612,3225,5.8,71714,115.9
51087,VA,Henrico County,174249,163620,10629,6.1,58110,93.9
51089,VA,Henry County,24180,21451,2729,11.3,32596,52.7
51091,VA,Highland County,1144,1064,80,7,37286,60.3
51093,VA,Isle of Wight County,19318,18059,1259,6.5,60922,98.5
51095,VA,James City County,35170,33319,1851,5.3,73575,118.9
51097,VA,King and Queen County,3418,3166,252,7.4,45087,72.9
51099,VA,King George County,10767,9979,788,7.3,75935,122.7
51101,VA,King William County,8752,8164,588,6.7,57779,93.4
51103,VA,Lancaster County,5579,5068,511,9.2,43470,70.3
51105,VA,Lee County,10267,9488,779,7.6,30341,49
51107,VA,Loudoun County,188286,180425,7861,4.2,119525,193.2
51109,VA,Louisa County,17080,15885,1195,7,53267,86.1
51111,VA,Lunenburg County,5425,4929,496,9.1,34488,55.7
51113,VA,Madison County,7346,6963,383,5.2,52116,84.2
51115,VA,Mathews County,4528,4256,272,6,54113,87.5
51117,VA,Mecklenburg County,13810,12351,1459,10.6,36372,58.8
51119,VA,Middlesex County,5149,4817,332,6.4,46171,74.6
51121,VA,Montgomery County,48996,45876,3120,6.4,44066,71.2
51125,VA,Nelson County,7958,7528,430,5.4,46055,74.4
51127,VA,New Kent County,10312,9651,661,6.4,69885,112.9
51131,VA,Northampton County,6554,6029,525,8,35594,57.5
51133,VA,Northumberland County,5923,5392,531,9,45908,74.2
51135,VA,Nottoway County,6674,6161,513,7.7,36712,59.3
51137,VA,Orange County,15713,14581,1132,7.2,56967,92.1
51139,VA,Page County,12317,10977,1340,10.9,40118,64.8
51141,VA,Patrick County,9017,8160,857,9.5,34486,55.7
51143,VA,Pittsylvania County,33162,30403,2759,8.3,40664,65.7
51145,VA,Powhatan County,14584,13743,841,5.8,71507,115.6
51147,VA,Prince Edward County,10353,9405,948,9.2,36503,59
51149,VA,Prince George County,14823,13808,1015,6.8,56374,91.1
51153,VA,Prince William County,230911,219095,11816,5.1,93101,150.5
51155,VA,Pulaski County,18077,16814,1263,7,39054,63.1
51157,VA,Rappahannock County,4247,4028,219,5.2,59277,95.8
51159,VA,Richmond County,4108,3776,332,8.1,39624,64
51161,VA,Roanoke County,49882,47111,2771,5.6,60382,97.6
51163,VA,Rockbridge County,11333,10600,733,6.5,42567,68.8
51165,VA,Rockingham County,43270,40907,2363,5.5,51826,83.8
51167,VA,Russell County,12080,10956,1124,9.3,30562,49.4
51169,VA,Scott County,10256,9426,830,8.1,35342,57.1
51171,VA,Shenandoah County,20608,19171,1437,7,47669,77
51173,VA,Smyth County,15076,13609,1467,9.7,34916,56.4
51175,VA,Southampton County,8233,7529,704,8.6,43374,70.1
51177,VA,Spotsylvania County,68606,64922,3684,5.4,75627,122.2
51179,VA,Stafford County,71111,67440,3671,5.2,90748,146.7
51181,VA,Surry County,3864,3563,301,7.8,46486,75.1
51183,VA,Sussex County,4515,4106,409,9.1,37329,60.3
51185,VA,Tazewell County,21441,20042,1399,6.5,36521,59
51187,VA,Warren County,20904,19572,1332,6.4,56450,91.2
51191,VA,Washington County,28577,26328,2249,7.9,40513,65.5
51193,VA,Westmoreland County,9324,8624,700,7.5,43175,69.8
51195,VA,Wise County,20374,19019,1355,6.7,36286,58.6
51197,VA,Wythe County,16323,15049,1274,7.8,39879,64.4
51199,VA,York County,34049,32220,1829,5.4,80178,129.6
51510,VA,Alexandria city,92159,87696,4463,4.8,82070,132.6
51515,VA,Bedford city,2592,2367,225,8.7,36552,59.1
51520,VA,Bristol city,8362,7593,769,9.2,29612,47.9
51530,VA,Buena Vista city,3398,3127,271,8,32855,53.1
51540,VA,Charlottesville city,23007,21590,1417,6.2,41826,67.6
51550,VA,Chesapeake city,119508,111771,7737,6.5,65699,106.2
51570,VA,Colonial Heights city,9205,8505,700,7.6,48299,78.1
51580,VA,Covington city,2696,2447,249,9.2,35921,58.1
51590,VA,Danville city,19783,17419,2364,11.9,31545,51
51595,VA,Emporia city,2657,2324,333,12.5,32801,53
51600,VA,Fairfax city,14022,13121,901,6.4,87307,141.1
51610,VA,Falls Church city,7864,7301,563,7.2,117481,189.9
51620,VA,Franklin city,3913,3469,444,11.3,30725,49.7
51630,VA,Fredericksburg city,14263,12911,1352,9.5,50522,81.6
51640,VA,Galax city,3263,2934,329,10.1,31740,51.3
51650,VA,Hampton city,66713,61088,5625,8.4,48656,78.6
51660,VA,Harrisonburg city,25823,23938,1885,7.3,36370,58.8
51670,VA,Hopewell city,10657,9527,1130,10.6,36792,59.5
51678,VA,Lexington city,2391,2118,273,11.4,40105,64.8
51680,VA,Lynchburg city,35900,32973,2927,8.2,36657,59.2
51683,VA,Manassas city,22031,20598,1433,6.5,65590,106
51685,VA,Manassas Park city,8452,8015,437,5.2,71976,116.3
51690,VA,Martinsville city,5686,4645,1041,18.3,30227,48.9
51700,VA,Newport News city,91864,84661,7203,7.8,47969,77.5
51710,VA,Norfolk city,107278,98291,8987,8.4,42201,68.2
51720,VA,Norton city,1943,1820,123,6.3,32624,52.7
51730,VA,Petersburg city,14473,12706,1767,12.2,32615,52.7
51735,VA,Poquoson city,6658,6297,361,5.4,81040,131
51740,VA,Portsmouth city,45125,41221,3904,8.7,41910,67.7
51750,VA,Radford city,8328,7651,677,8.1,33848,54.7
51760,VA,Richmond city,103936,94233,9703,9.3,38368,62
51770,VA,Roanoke city,49136,45110,4026,8.2,38482,62.2
51775,VA,Salem city,13121,12308,813,6.2,44499,71.9
51790,VA,Staunton city,11741,10934,807,6.9,41006,66.3
51800,VA,Suffolk city,42867,39808,3059,7.1,61335,99.1
51810,VA,Virginia Beach city,230961,217179,13782,6,64107,103.6
51820,VA,Waynesboro city,10009,9216,793,7.9,40342,65.2
51830,VA,Williamsburg city,6314,5433,881,14,46358,74.9
51840,VA,Winchester city,14715,13586,1129,7.7,42539,68.7
53000,WA,Washington,3484814,3165348,319466,9.2,56811,100
53001,WA,Adams County,8515,7716,799,9.4,40675,71.6
53003,WA,Asotin County,10234,9318,916,9,41864,73.7
53005,WA,Benton County,97225,89869,7356,7.6,61539,108.3
53007,WA,Chelan County,40518,37100,3418,8.4,48458,85.3
53009,WA,Clallam County,29242,26208,3034,10.4,47903,84.3
53011,WA,Clark County,214130,187500,26630,12.4,56750,99.9
53013,WA,Columbia County,1516,1346,170,11.2,42463,74.7
53015,WA,Cowlitz County,43269,38043,5226,12.1,43611,76.8
53017,WA,Douglas County,21708,19970,1738,8,47958,84.4
53019,WA,Ferry County,2938,2520,418,14.2,34543,60.8
53021,WA,Franklin County,37402,34109,3293,8.8,48904,86.1
53023,WA,Garfield County,1027,951,76,7.4,46302,81.5
53025,WA,Grant County,41906,37703,4203,10,43149,76
53027,WA,Grays Harbor County,30230,26232,3998,13.2,40886,72
53029,WA,Island County,31720,28823,2897,9.1,58179,102.4
53031,WA,Jefferson County,12360,11138,1222,9.9,47045,82.8
53033,WA,King County,1105546,1015967,89579,8.1,68596,120.7
53035,WA,Kitsap County,122726,113007,9719,7.9,60294,106.1
53037,WA,Kittitas County,21045,19186,1859,8.8,41823,73.6
53039,WA,Klickitat County,10823,9679,1144,10.6,43340,76.3
53041,WA,Lewis County,30352,26287,4065,13.4,42072,74.1
53043,WA,Lincoln County,4825,4413,412,8.5,47602,83.8
53045,WA,Mason County,24527,21775,2752,11.2,44331,78
53047,WA,Okanogan County,21123,18975,2148,10.2,38680,68.1
53049,WA,Pacific County,8706,7622,1084,12.5,37632,66.2
53051,WA,Pend Oreille County,5270,4608,662,12.6,37457,65.9
53053,WA,Pierce County,387244,349308,37936,9.8,55215,97.2
53055,WA,San Juan County,7980,7416,564,7.1,52371,92.2
53057,WA,Skagit County,56997,51222,5775,10.1,52013,91.6
53059,WA,Skamania County,5170,4525,645,12.5,51548,90.7
53061,WA,Snohomish County,385373,349640,35733,9.3,63766,112.2
53063,WA,Spokane County,230985,209201,21784,9.4,48693,85.7
53065,WA,Stevens County,18118,15931,2187,12.1,41166,72.5
53067,WA,Thurston County,129026,118365,10661,8.3,59694,105.1
53069,WA,Wahkiakum County,1516,1320,196,12.9,41286,72.7
53071,WA,Walla Walla County,30870,28533,2337,7.6,47570,83.7
53073,WA,Whatcom County,106230,97387,8843,8.3,51315,90.3
53075,WA,Whitman County,21675,20243,1432,6.6,35860,63.1
53077,WA,Yakima County,124749,112193,12556,10.1,41666,73.3
54000,WV,West Virginia,799883,736078,63805,8,38587,100
54001,WV,Barbour County,6866,6245,621,9,33004,85.5
54003,WV,Berkeley County,45409,41808,3601,7.9,51029,132.2
54005,WV,Boone County,8951,8291,660,7.4,38357,99.4
54007,WV,Braxton County,5736,5156,580,10.1,32369,83.9
54009,WV,Brooke County,10410,9227,1183,11.4,41034,106.3
54011,WV,Cabell County,43275,40109,3166,7.3,36864,95.5
54013,WV,Calhoun County,2817,2461,356,12.6,28581,74.1
54015,WV,Clay County,3229,2844,385,11.9,29802,77.2
54017,WV,Doddridge County,3206,2940,266,8.3,35261,91.4
54019,WV,Fayette County,17997,16442,1555,8.6,32316,83.7
54021,WV,Gilmer County,3312,3067,245,7.4,33196,86
54023,WV,Grant County,5079,4520,559,11,37953,98.4
54025,WV,Greenbrier County,15088,13832,1256,8.3,35924,93.1
54027,WV,Hampshire County,9498,8754,744,7.8,36588,94.8
54029,WV,Hancock County,13815,12162,1653,12,39110,101.4
54031,WV,Hardy County,6551,6003,548,8.4,38013,98.5
54033,WV,Harrison County,31034,28882,2152,6.9,39899,103.4
54035,WV,Jackson County,10994,9802,1192,10.8,41066,106.4
54037,WV,Jefferson County,24765,23285,1480,6,59280,153.6
54039,WV,Kanawha County,90347,83994,6353,7,40409,104.7
54041,WV,Lewis County,8095,7575,520,6.4,37270,96.6
54043,WV,Lincoln County,7703,6949,754,9.8,32517,84.3
54045,WV,Logan County,13314,12196,1118,8.4,34001,88.1
54047,WV,McDowell County,7735,6958,777,10,23751,61.6
54049,WV,Marion County,26490,24707,1783,6.7,39774,103.1
54051,WV,Marshall County,14439,13065,1374,9.5,41974,108.8
54053,WV,Mason County,10113,8899,1214,12,35132,91
54055,WV,Mercer County,24168,22260,1908,7.9,33500,86.8
54057,WV,Mineral County,13783,12750,1033,7.5,38073,98.7
54059,WV,Mingo County,8917,8134,783,8.8,31681,82.1
54061,WV,Monongalia County,49759,47099,2660,5.3,43447,112.6
54063,WV,Monroe County,5695,5293,402,7.1,37066,96.1
54065,WV,Morgan County,7167,6573,594,8.3,40762,105.6
54067,WV,Nicholas County,10714,9746,968,9,36080,93.5
54069,WV,Ohio County,20425,18784,1641,8,36836,95.5
54071,WV,Pendleton County,3485,3259,226,6.5,37042,96
54073,WV,Pleasants County,3151,2852,299,9.5,41596,107.8
54075,WV,Pocahontas County,3443,3042,401,11.6,34928,90.5
54077,WV,Preston County,15794,14696,1098,7,40375,104.6
54079,WV,Putnam County,27005,25129,1876,6.9,53964,139.9
54081,WV,Raleigh County,33208,30871,2337,7,38156,98.9
54083,WV,Randolph County,12210,11033,1177,9.6,33529,86.9
54085,WV,Ritchie County,4368,3979,389,8.9,35421,91.8
54087,WV,Roane County,5398,4720,678,12.6,28131,72.9
54089,WV,Summers County,4615,4180,435,9.4,30751,79.7
54091,WV,Taylor County,7272,6715,557,7.7,39055,101.2
54093,WV,Tucker County,2739,2445,294,10.7,35019,90.8
54095,WV,Tyler County,3664,3287,377,10.3,36952,95.8
54097,WV,Upshur County,10654,9791,863,8.1,36719,95.2
54099,WV,Wayne County,17025,15654,1371,8.1,35418,91.8
54101,WV,Webster County,3067,2735,332,10.8,28697,74.4
54103,WV,Wetzel County,6335,5582,753,11.9,38457,99.7
54105,WV,Wirt County,2339,2082,257,11,36084,93.5
54107,WV,Wood County,39075,35788,3287,8.4,38005,98.5
54109,WV,Wyoming County,8143,7427,716,8.8,32851,85.1
55000,WI,Wisconsin,3062259,2833431,228828,7.5,50401,100
55001,WI,Adams County,9959,8915,1044,10.5,40339,80
55003,WI,Ashland County,8657,7835,822,9.5,36683,72.8
55005,WI,Barron County,24270,22326,1944,8,44666,88.6
55007,WI,Bayfield County,7855,7067,788,10,42558,84.4
55009,WI,Brown County,141012,131386,9626,6.8,52744,104.6
55011,WI,Buffalo County,7481,7022,459,6.1,44425,88.1
55013,WI,Burnett County,8175,7430,745,9.1,40295,79.9
55015,WI,Calumet County,27317,25752,1565,5.7,66543,132
55017,WI,Chippewa County,33750,31241,2509,7.4,50120,99.4
55019,WI,Clark County,17099,15702,1397,8.2,40056,79.5
55021,WI,Columbia County,32184,29817,2367,7.4,57457,114
55023,WI,Crawford County,9232,8484,748,8.1,41181,81.7
55025,WI,Dane County,298714,283668,15046,5,60489,120
55027,WI,Dodge County,46471,42838,3633,7.8,52130,103.4
55029,WI,Door County,16757,14978,1779,10.6,46441,92.1
55031,WI,Douglas County,22955,21331,1624,7.1,45935,91.1
55033,WI,Dunn County,25397,23747,1650,6.5,45878,91
55035,WI,Eau Claire County,56138,52583,3555,6.3,47660,94.6
55037,WI,Florence County,2346,2144,202,8.6,46041,91.3
55039,WI,Fond du Lac County,55301,51339,3962,7.2,53305,105.8
55041,WI,Forest County,4662,4199,463,9.9,39282,77.9
55043,WI,Grant County,28316,26510,1806,6.4,45748,90.8
55045,WI,Green County,19862,18465,1397,7,53051,105.3
55047,WI,Green Lake County,10040,9222,818,8.1,47094,93.4
55049,WI,Iowa County,14126,13218,908,6.4,51740,102.7
55051,WI,Iron County,2805,2501,304,10.8,38355,76.1
55053,WI,Jackson County,9744,8934,810,8.3,41337,82
55055,WI,Jefferson County,41909,38492,3417,8.2,53710,106.6
55057,WI,Juneau County,13220,11990,1230,9.3,41226,81.8
55059,WI,Kenosha County,85651,77935,7716,9,51966,103.1
55061,WI,Kewaunee County,11687,10873,814,7,54727,108.6
55063,WI,La Crosse County,65842,62066,3776,5.7,49698,98.6
55065,WI,Lafayette County,9230,8657,573,6.2,48231,95.7
55067,WI,Langlade County,10397,9419,978,9.4,41994,83.3
55069,WI,Lincoln County,14756,13286,1470,10,45927,91.1
55071,WI,Manitowoc County,44334,40731,3603,8.1,48804,96.8
55073,WI,Marathon County,72495,67001,5494,7.6,51908,103
55075,WI,Marinette County,21694,19641,2053,9.5,39705,78.8
55077,WI,Marquette County,7624,6903,721,9.5,41814,83
55078,WI,Menominee County,1537,1279,258,16.8,30156,59.8
55079,WI,Milwaukee County,461017,419601,41416,9,40780,80.9
55081,WI,Monroe County,24635,22993,1642,6.7,45841,91
55083,WI,Oconto County,20396,18624,1772,8.7,53280,105.7
55085,WI,Oneida County,18374,16654,1720,9.4,39418,78.2
55087,WI,Outagamie County,97101,90257,6844,7,56901,112.9
55089,WI,Ozaukee County,47022,44203,2819,6,74631,148.1
55091,WI,Pepin County,4004,3755,249,6.2,45193,89.7
55093,WI,Pierce County,23796,22420,1376,5.8,57866,114.8
55095,WI,Polk County,23744,21750,1994,8.4,46872,93
55097,WI,Portage County,42422,39608,2814,6.6,50712,100.6
55099,WI,Price County,7613,7001,612,8,41576,82.5
55101,WI,Racine County,98241,89457,8784,8.9,52900,105
55103,WI,Richland County,9786,9100,686,7,43222,85.8
55105,WI,Rock County,78687,71223,7464,9.5,47857,95
55107,WI,Rusk County,6856,6120,736,10.7,36810,73
55109,WI,St. Croix County,47248,44453,2795,5.9,64490,128
55111,WI,Sauk County,35114,32487,2627,7.5,48874,97
55113,WI,Sawyer County,8766,7902,864,9.9,40002,79.4
55115,WI,Shawano County,22143,20261,1882,8.5,42795,84.9
55117,WI,Sheboygan County,62208,57484,4724,7.6,51541,102.3
55119,WI,Taylor County,10406,9491,915,8.8,42460,84.2
55121,WI,Trempealeau County,16463,15439,1024,6.2,46203,91.7
55123,WI,Vernon County,14814,13810,1004,6.8,42670,84.7
55125,WI,Vilas County,10470,9405,1065,10.2,40901,81.2
55127,WI,Walworth County,54928,50623,4305,7.8,52300,103.8
55129,WI,Washburn County,7763,7076,687,8.8,39777,78.9
55131,WI,Washington County,74331,69184,5147,6.9,67760,134.4
55133,WI,Waukesha County,215069,201089,13980,6.5,73025,144.9
55135,WI,Waupaca County,27976,25779,2197,7.9,47601,94.4
55137,WI,Waushara County,12734,11611,1123,8.8,39836,79
55139,WI,Winnebago County,96133,89811,6322,6.6,49279,97.8
55141,WI,Wood County,41005,37834,3171,7.7,46558,92.4
56000,WY,Wyoming,304242,286131,18111,6,56044,100
56001,WY,Albany County,20249,19324,925,4.6,45651,81.5
56003,WY,Big Horn County,5247,4888,359,6.8,49929,89.1
56005,WY,Campbell County,27726,26451,1275,4.6,70438,125.7
56007,WY,Carbon County,7827,7320,507,6.5,52855,94.3
56009,WY,Converse County,7792,7416,376,4.8,59507,106.2
56011,WY,Crook County,3575,3385,190,5.3,55294,98.7
56013,WY,Fremont County,19715,18319,1396,7.1,45731,81.6
56015,WY,Goshen County,6586,6209,377,5.7,41014,73.2
56017,WY,Hot Springs County,2627,2493,134,5.1,41845,74.7
56019,WY,Johnson County,4018,3731,287,7.1,53577,95.6
56021,WY,Laramie County,45064,42049,3015,6.7,53839,96.1
56023,WY,Lincoln County,8341,7648,693,8.3,60062,107.2
56025,WY,Natrona County,42907,40370,2537,5.9,52904,94.4
56027,WY,Niobrara County,1307,1246,61,4.7,41712,74.4
56029,WY,Park County,15451,14507,944,6.1,50141,89.5
56031,WY,Platte County,4280,4028,252,5.9,47916,85.5
56033,WY,Sheridan County,16312,15183,1129,6.9,53217,95
56035,WY,Sublette County,7776,7505,271,3.5,75242,134.3
56037,WY,Sweetwater County,25138,23867,1271,5.1,69756,124.5
56039,WY,Teton County,13539,12514,1025,7.6,69095,123.3
56041,WY,Uinta County,11008,10368,640,5.8,61266,109.3
56043,WY,Washakie County,4454,4194,260,5.8,49747,88.8
56045,WY,Weston County,3304,3115,189,5.7,53313,95.1
72000,PR,Puerto Rico,1274971,1074590,200381,15.7,,
72001,PR,Adjuntas Municipio,4942,3857,1085,22,,
72003,PR,Aguada Municipio,15269,12726,2543,16.7
72005,PR,Aguadilla Municipio,20685,17375,3310,16
72007,PR,Aguas Buenas Municipio,8618,7117,1501,17.4
72009,PR,Aibonito Municipio,7630,6057,1573,20.6
72011,PR,Anasco Municipio,10935,9185,1750,16
72013,PR,Arecibo Municipio,30490,25579,4911,16.1
72015,PR,Arroyo Municipio,5782,4416,1366,23.6
72017,PR,Barceloneta Municipio,7603,6228,1375,18.1
72019,PR,Barranquitas Municipio,7729,6258,1471,19
72021,PR,Bayamon Municipio,75930,65628,10302,13.6
72023,PR,Cabo Rojo Municipio,19482,16902,2580,13.2
72025,PR,Caguas Municipio,52649,44669,7980,15.2
72027,PR,Camuy Municipio,11308,9568,1740,15.4
72029,PR,Canovanas Municipio,14510,11806,2704,18.6
72031,PR,Carolina Municipio,70370,61595,8775,12.5
72033,PR,Catano Municipio,8288,6846,1442,17.4
72035,PR,Cayey Municipio,15821,12935,2886,18.2
72037,PR,Ceiba Municipio,4805,3942,863,18
72039,PR,Ciales Municipio,4758,3691,1067,22.4
72041,PR,Cidra Municipio,14286,11614,2672,18.7
72043,PR,Coamo Municipio,11773,9195,2578,21.9
72045,PR,Comerio Municipio,5214,4059,1155,22.2
72047,PR,Corozal Municipio,10214,8239,1975,19.3
72049,PR,Culebra Municipio,761,685,76,10
72051,PR,Dorado Municipio,12869,11291,1578,12.3
72053,PR,Fajardo Municipio,14050,11547,2503,17.8
72054,PR,Florida Municipio,3478,2769,709,20.4
72055,PR,Guanica Municipio,6068,4822,1246,20.5
72057,PR,Guayama Municipio,14325,11344,2981,20.8
72059,PR,Guayanilla Municipio,6765,5500,1265,18.7
72061,PR,Guaynabo Municipio,40729,36716,4013,9.9
72063,PR,Gurabo Municipio,15976,13723,2253,14.1
72065,PR,Hatillo Municipio,12540,10265,2275,18.1
72067,PR,Hormigueros Municipio,6345,5382,963,15.2
72069,PR,Humacao Municipio,19982,15900,4082,20.4
72071,PR,Isabela Municipio,15703,13187,2516,16
72073,PR,Jayuya Municipio,4141,3223,918,22.2
72075,PR,Juana Diaz Municipio,17325,14372,2953,17
72077,PR,Juncos Municipio,12866,10548,2318,18
72079,PR,Lajas Municipio,8211,6754,1457,17.7
72081,PR,Lares Municipio,10210,8455,1755,17.2
72083,PR,Las Marias Municipio,4470,3837,633,14.2
72085,PR,Las Piedras Municipio,12572,10229,2343,18.6
72087,PR,Loiza Municipio,7700,6275,1425,18.5
72089,PR,Luquillo Municipio,7327,5932,1395,19
72091,PR,Manati Municipio,12843,10555,2288,17.8
72093,PR,Maricao Municipio,1695,1305,390,23
72095,PR,Maunabo Municipio,3504,2649,855,24.4
72097,PR,Mayaguez Municipio,28025,22989,5036,18
72099,PR,Moca Municipio,14458,12097,2361,16.3
72101,PR,Morovis Municipio,9086,7303,1783,19.6
72103,PR,Naguabo Municipio,8019,6473,1546,19.3
72105,PR,Naranjito Municipio,8650,7120,1530,17.7
72107,PR,Orocovis Municipio,5224,4033,1191,22.8
72109,PR,Patillas Municipio,5696,4378,1318,23.1
72111,PR,Penuelas Municipio,8063,6564,1499,18.6
72113,PR,Ponce Municipio,61131,51923,9208,15.1
72115,PR,Quebradillas Municipio,7923,6591,1332,16.8
72117,PR,Rincon Municipio,5486,4537,949,17.3
72119,PR,Rio Grande Municipio,17012,14204,2808,16.5
72121,PR,Sabana Grande Municipio,8546,7029,1517,17.8
72123,PR,Salinas Municipio,8717,6523,2194,25.2
72125,PR,San German Municipio,12188,10045,2143,17.6
72127,PR,San Juan Municipio,151664,134187,17477,11.5
72129,PR,San Lorenzo Municipio,13236,10868,2368,17.9
72131,PR,San Sebastian Municipio,14615,12033,2582,17.7
72133,PR,Santa Isabel Municipio,9902,8015,1887,19.1
72135,PR,Toa Alta Municipio,26961,23502,3459,12.8
72137,PR,Toa Baja Municipio,32647,28268,4379,13.4
72139,PR,Trujillo Alto Municipio,29439,26191,3248,11
72141,PR,Utuado Municipio,8426,6757,1669,19.8
72143,PR,Vega Alta Municipio,12206,10323,1883,15.4
72145,PR,Vega Baja Municipio,18402,15507,2895,15.7
72147,PR,Vieques Municipio,3186,2675,511,16
72149,PR,Villalba Municipio,8615,6912,1703,19.8
72151,PR,Yabucoa Municipio,10553,7963,2590,24.5
72153,PR,Yauco Municipio,15355,12830,2525,16.4
\ No newline at end of file
diff --git a/projects/FOLIUM/us_state_map.html b/projects/FOLIUM/us_state_map.html
new file mode 100644
index 0000000..50fc242
--- /dev/null
+++ b/projects/FOLIUM/us_state_map.html
@@ -0,0 +1,163 @@
+<!DOCTYPE html>
+<head>
+   <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
+
+   
+   
+   
+   
+
+   <style>
+
+      html, body {
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+        height: 100%;
+        margin: 0;
+        padding: 0;
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+
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+          font: 10px sans-serif;
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+<body>
+
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+
+   <script>
+
+      queue()
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+          .defer(d3.json, 'us-states.json')
+          .await(makeMap)
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+
+          var map = L.map('map').setView([48, -102], 3);
+
+          L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
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+              minZoom: 1,
+              attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
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+        .data(color.range().map(function(d, i) {
+          return {
+            x0: i ? x(color.domain()[i - 1]) : x.range()[0],
+            x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
+            z: d
+          };
+        }))
+      .enter().append("rect")
+        .attr("height", 10)
+        .attr("x", function(d) { return d.x0; })
+        .attr("width", function(d) { return d.x1 - d.x0; })
+        .style("fill", function(d) { return d.z; });
+
+    g.call(xAxis).append("text")
+        .attr("class", "caption")
+        .attr("y", 21)
+        .text('Unemployment Rate (%)');
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+      };
+
+   </script>
+</body>
\ No newline at end of file
diff --git a/projects/FOLIUM/us_state_map_2.html b/projects/FOLIUM/us_state_map_2.html
new file mode 100644
index 0000000..14a3d3f
--- /dev/null
+++ b/projects/FOLIUM/us_state_map_2.html
@@ -0,0 +1,163 @@
+<!DOCTYPE html>
+<head>
+   <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
+   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css" />
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js" charset="utf-8"></script>
+   <script src="https://cdnjs.cloudflare.com/ajax/libs/queue-async/1.0.7/queue.min.js"></script>
+
+   
+   
+   
+   
+
+   <style>
+
+      html, body {
+        width: 100%;
+        height: 100%;
+        margin: 0;
+        padding: 0;
+      }
+
+      .legend {
+          padding: 0px 0px;
+          font: 10px sans-serif;
+          background: white;
+          background: rgba(255,255,255,0.8);
+          box-shadow: 0 0 15px rgba(0,0,0,0.2);
+          border-radius: 5px;
+      }
+
+      .key path {
+        display: none;
+      }
+
+   </style>
+</head>
+
+<body>
+
+   <div id="map" style="width: 100%; height: 100%"></div>
+
+   <script>
+
+      queue()
+          .defer(d3.json, 'data.json')
+          .defer(d3.json, 'us-states.json')
+          .await(makeMap)
+
+      function onEachFeature(feature, layer) {
+          // does this feature have a property named popupContent?
+          if (feature.properties && feature.properties.popupContent) {
+              layer.bindPopup(feature.properties.popupContent);
+              }
+          };
+
+      function makeMap(error, data_1,gjson_1) {
+
+          
+
+          
+
+          function matchKey(datapoint, key_variable){
+              if (typeof key_variable[0][datapoint] === 'undefined') {
+                  return null;
+              }
+              else {
+                  return parseFloat(key_variable[0][datapoint]);
+              };
+          };
+
+          
+          var color = d3.scale.threshold()
+              .domain([5, 6, 7, 8, 9, 10])
+              .range(['#EDF8FB', '#BFD3E6', '#9EBCDA', '#8C96C6', '#8C6BB1', '#88419D', '#6E016B']);
+          
+
+          var map = L.map('map').setView([48, -102], 3);
+
+          L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {
+              maxZoom: 18,
+              minZoom: 1,
+              attribution: 'Map data (c) <a href="http://openstreetmap.org">OpenStreetMap</a> contributors'
+          }).addTo(map);
+
+          
+          function style_1(feature) {
+    return {
+        fillColor: color(matchKey(feature.id, data_1)),
+        weight: 1,
+        opacity: 0.5,
+        color: 'black',
+        fillOpacity: 0.7
+    };
+}
+          
+
+          
+          gJson_layer_1 = L.geoJson(gjson_1, {style: style_1,onEachFeature: onEachFeature}).addTo(map)
+          
+
+          
+              var legend = L.control({position: 'topright'});
+
+    legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
+
+    legend.addTo(map);
+
+    var x = d3.scale.linear()
+    .domain([0, 11])
+    .range([0, 400]);
+
+    var xAxis = d3.svg.axis()
+        .scale(x)
+        .orient("top")
+        .tickSize(1)
+        .tickValues([5, 6, 7, 8, 9, 10]);
+
+    var svg = d3.select(".legend.leaflet-control").append("svg")
+        .attr("id", 'legend')
+        .attr("width", 450)
+        .attr("height", 40);
+
+    var g = svg.append("g")
+        .attr("class", "key")
+        .attr("transform", "translate(25,16)");
+
+    g.selectAll("rect")
+        .data(color.range().map(function(d, i) {
+          return {
+            x0: i ? x(color.domain()[i - 1]) : x.range()[0],
+            x1: i < color.domain().length ? x(color.domain()[i]) : x.range()[1],
+            z: d
+          };
+        }))
+      .enter().append("rect")
+        .attr("height", 10)
+        .attr("x", function(d) { return d.x0; })
+        .attr("width", function(d) { return d.x1 - d.x0; })
+        .style("fill", function(d) { return d.z; });
+
+    g.call(xAxis).append("text")
+        .attr("class", "caption")
+        .attr("y", 21)
+        .text('Unemployment Rate (%)');
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+          
+
+      };
+
+   </script>
+</body>
\ No newline at end of file
diff --git a/projects/IRIS/cartopy-earth-at-night.ipynb b/projects/IRIS/cartopy-earth-at-night.ipynb
new file mode 100644
index 0000000..bec8098
--- /dev/null
+++ b/projects/IRIS/cartopy-earth-at-night.ipynb
@@ -0,0 +1,91 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import cartopy\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "## NASA earth-at-night simple\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PRmEwYLqUSvPplXbdY+bZd9dZELDrw0KEPCCccxiSUl6MLmbpuIUcBiIpJ4um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\nBOvGcFKBfWycVGDHjFOri9zZ3KUsK4RMkULhBVhrcF6gpitzlrm5Do9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AvmGIWxeu+EEj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8P2YZ8TZ5dIU4/zIdYG9NMC50NK7XDaIsOQPC8pjCdKGhgjybMUEXiEErTqDaIgYTTIqCUJutB4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\nNtvbA3Z3M4ZDReFDB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kHgEfguukiZjSd0Dno8uL3Do7sH3L+3z/HxGF2IEoUG5HHZ0xPS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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4d38c7d310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## note: requires a live internet connection\n",
+    "##  and availability in your area of NASA wmts service\n",
+    "\n",
+    "url = 'http://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'\n",
+    "layer = 'VIIRS_CityLights_2012'\n",
+    "\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "ax.add_wmts(url, layer)\n",
+    "\n",
+    "## Set the extent (x0, x1, y0, y1) of the map in the given\n",
+    "##   coordinate system.\n",
+    "\n",
+    "ax.set_extent((-123.2, -120, 36.4, 41))\n",
+    "\n",
+    "##--\n",
+    "plt.title('San Francisco Bay Area plus, April/October 2012')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/cartopy-ne2-cache-test.ipynb b/projects/IRIS/cartopy-ne2-cache-test.ipynb
new file mode 100644
index 0000000..d6d58ed
--- /dev/null
+++ b/projects/IRIS/cartopy-ne2-cache-test.ipynb
@@ -0,0 +1,74 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import cartopy\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "## Natural Earth 2 cache test\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ax = plt.axes(projection=ccrs.PlateCarree() )     \n",
+    "\n",
+    "ax.add_feature(cfeature.LAND)\n",
+    "ax.add_feature(cfeature.OCEAN)\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "ax.add_feature(cfeature.BORDERS, linestyle=':')\n",
+    "ax.add_feature(cfeature.LAKES, alpha=0.5)\n",
+    "ax.add_feature(cfeature.RIVERS)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/cartopy-quiver-regrid.ipynb b/projects/IRIS/cartopy-quiver-regrid.ipynb
new file mode 100644
index 0000000..318b082
--- /dev/null
+++ b/projects/IRIS/cartopy-quiver-regrid.ipynb
@@ -0,0 +1,127 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import cartopy.crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regridding vectors with quiver\n",
+    "------------------------------\n",
+    "\n",
+    "This example demonstrates the regridding functionality in quiver (there exists\n",
+    "equivalent functionality in :meth:`cartopy.mpl.geoaxes.GeoAxes.barbs`).\n",
+    "\n",
+    "Regridding can be an effective way of visualising a vector field, particularly\n",
+    "if the data is dense or warped.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sample_data(shape=(20, 30)):\n",
+    "    \"\"\"\n",
+    "    Returns ``(x, y, u, v, crs)`` of some vector data\n",
+    "    computed mathematically. The returned CRS will be a North Polar\n",
+    "    Stereographic projection, meaning that the vectors will be unevenly\n",
+    "    spaced in a PlateCarree projection.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    crs = ccrs.NorthPolarStereo()\n",
+    "    scale = 1e7\n",
+    "    x = np.linspace(-scale, scale, shape[1])\n",
+    "    y = np.linspace(-scale, scale, shape[0])\n",
+    "\n",
+    "    x2d, y2d = np.meshgrid(x, y)\n",
+    "    u = 10 * np.cos(2 * x2d / scale + 3 * y2d / scale)\n",
+    "    v = 20 * np.cos(6 * x2d / scale)\n",
+    "\n",
+    "    return x, y, u, v, crs\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    plt.figure(figsize=(8, 10))\n",
+    "\n",
+    "    x, y, u, v, vector_crs = sample_data(shape=(50, 50))\n",
+    "    ax1 = plt.subplot(2, 1, 1, projection=ccrs.PlateCarree())\n",
+    "    ax1.coastlines()\n",
+    "    ax1.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())\n",
+    "    ax1.quiver(x, y, u, v, transform=vector_crs)\n",
+    "\n",
+    "    ax2 = plt.subplot(2, 1, 2, projection=ccrs.PlateCarree())\n",
+    "    plt.title('The same vector field regridded')\n",
+    "    ax2.coastlines()\n",
+    "    ax2.set_extent([-45, 55, 20, 80], ccrs.PlateCarree())\n",
+    "    ax2.quiver(x, y, u, v, transform=vector_crs, regrid_shape=20)\n",
+    "\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/cartopy-quiver.ipynb b/projects/IRIS/cartopy-quiver.ipynb
new file mode 100644
index 0000000..005557b
--- /dev/null
+++ b/projects/IRIS/cartopy-quiver.ipynb
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import cartopy\n",
+    "import cartopy.crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sample_data(shape=(20, 30)):\n",
+    "    \"\"\"\n",
+    "    Returns ``(x, y, u, v, crs)`` of some vector data\n",
+    "    computed mathematically. The returned crs will be a rotated\n",
+    "    pole CRS, meaning that the vectors will be unevenly spaced in\n",
+    "    regular PlateCarree space.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    crs = ccrs.RotatedPole(pole_longitude=177.5, pole_latitude=37.5)\n",
+    "\n",
+    "    x = np.linspace(311.9, 391.1, shape[1])\n",
+    "    y = np.linspace(-23.6, 24.8, shape[0])\n",
+    "\n",
+    "    x2d, y2d = np.meshgrid(x, y)\n",
+    "    u = 10 * (2 * np.cos(2 * np.deg2rad(x2d) + 3 * np.deg2rad(y2d + 30)) ** 2)\n",
+    "    v = 20 * np.cos(6 * np.deg2rad(x2d))\n",
+    "\n",
+    "    return x, y, u, v, crs\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    ax = plt.axes(projection=ccrs.Orthographic(-10, 45))\n",
+    "\n",
+    "    ax.add_feature(cartopy.feature.OCEAN, zorder=0)\n",
+    "    ax.add_feature(cartopy.feature.LAND, zorder=0, edgecolor='black')\n",
+    "\n",
+    "    ax.set_global()\n",
+    "    ax.gridlines()\n",
+    "\n",
+    "    x, y, u, v, vector_crs = sample_data()\n",
+    "    ax.quiver(x, y, u, v, transform=vector_crs)\n",
+    "\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`cartopy.geoaxes.quiver`\n",
+    "\n",
+    "  **Cartopy matplotlib.quiver PolyCollection.**\n",
+    "\n",
+    "Plot a 2-D field of arrows on a projected plane.\n",
+    "\n",
+    "        Extra Kwargs:\n",
+    "\n",
+    "        * transform: :class:`cartopy.crs.Projection` or matplotlib transform\n",
+    "            The coordinate system in which the vectors are defined.\n",
+    "\n",
+    "        * regrid_shape: int or 2-tuple of ints\n",
+    "            If given, specifies that the points where the arrows are\n",
+    "            located will be interpolated onto a regular grid in\n",
+    "            projection space. If a single integer is given then that\n",
+    "            will be used as the minimum grid length dimension, while the\n",
+    "            other dimension will be scaled up according to the target\n",
+    "            extent's aspect ratio. If a pair of ints are given they\n",
+    "            determine the grid length in the x and y directions\n",
+    "            respectively.\n",
+    "\n",
+    "        * target_extent: 4-tuple\n",
+    "            If given, specifies the extent in the target CRS that the\n",
+    "            regular grid defined by *regrid_shape* will have. Defaults\n",
+    "            to the current extent of the map projection.\n",
+    "\n",
+    "        See :func:`matplotlib.pyplot.quiver` for details on arguments\n",
+    "        and other keyword arguments.\n",
+    "\n",
+    "        .. note::\n",
+    "\n",
+    "           The vector components must be defined as grid eastward and\n",
+    "           grid northward.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "`matplotlib.quiver`\n",
+    "\n",
+    "  **Specialized PolyCollection for arrows.**\n",
+    "\n",
+    "Plot a 2-D field of barbs.\n",
+    "\n",
+    "Call signatures::\n",
+    "\n",
+    "        barb(U, V, **kw)\n",
+    "        barb(U, V, C, **kw)\n",
+    "        barb(X, Y, U, V, **kw)\n",
+    "        barb(X, Y, U, V, C, **kw)\n",
+    "\n",
+    "*Arguments:*\n",
+    "\n",
+    "    *X*, *Y*:\n",
+    "        The x and y coordinates of the barb locations\n",
+    "        (default is head of barb; see *pivot* kwarg)\n",
+    "\n",
+    "    *U*, *V*:\n",
+    "        Give the x and y components of the barb shaft\n",
+    "\n",
+    "    *C*:\n",
+    "        An optional array used to map colors to the barbs\n",
+    "\n",
+    "All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y*\n",
+    "are absent, they will be generated as a uniform grid.  If *U* and *V*\n",
+    "are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)``\n",
+    "match the column and row dimensions of *U*, then *X* and *Y* will be\n",
+    "expanded with :func:`numpy.meshgrid`.\n",
+    "\n",
+    "    *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not\n",
+    "    supported at present.\n",
+    "\n",
+    "*Keyword arguments:*\n",
+    "\n",
+    "    *length*:\n",
+    "        Length of the barb in points; the other parts of the barb\n",
+    "        are scaled against this.\n",
+    "        Default is 9\n",
+    "\n",
+    "    *pivot*: [ 'tip' | 'middle' ]\n",
+    "        The part of the arrow that is at the grid point; the arrow rotates\n",
+    "        about this point, hence the name *pivot*.  Default is 'tip'\n",
+    "\n",
+    "    *barbcolor*: [ color | color sequence ]\n",
+    "        Specifies the color all parts of the barb except any flags.  This\n",
+    "        parameter is analagous to the *edgecolor* parameter for polygons,\n",
+    "        which can be used instead. However this parameter will override\n",
+    "        facecolor.\n",
+    "\n",
+    "    *flagcolor*: [ color | color sequence ]\n",
+    "        Specifies the color of any flags on the barb.  This parameter is\n",
+    "        analagous to the *facecolor* parameter for polygons, which can be\n",
+    "        used instead. However this parameter will override facecolor.  If\n",
+    "        this is not set (and *C* has not either) then *flagcolor* will be\n",
+    "        set to match *barbcolor* so that the barb has a uniform color. If\n",
+    "        *C* has been set, *flagcolor* has no effect.\n",
+    "\n",
+    "    *sizes*:\n",
+    "        A dictionary of coefficients specifying the ratio of a given\n",
+    "        feature to the length of the barb. Only those values one wishes to\n",
+    "        override need to be included.  These features include:\n",
+    "\n",
+    "        - 'spacing' - space between features (flags, full/half barbs)\n",
+    "\n",
+    "        - 'height' - height (distance from shaft to top) of a flag or\n",
+    "          full barb\n",
+    "\n",
+    "        - 'width' - width of a flag, twice the width of a full barb\n",
+    "\n",
+    "        - 'emptybarb' - radius of the circle used for low magnitudes\n",
+    "\n",
+    "    *fill_empty*:\n",
+    "        A flag on whether the empty barbs (circles) that are drawn should\n",
+    "        be filled with the flag color.  If they are not filled, they will\n",
+    "        be drawn such that no color is applied to the center.  Default is\n",
+    "        False\n",
+    "\n",
+    "    *rounding*:\n",
+    "        A flag to indicate whether the vector magnitude should be rounded\n",
+    "        when allocating barb components.  If True, the magnitude is\n",
+    "        rounded to the nearest multiple of the half-barb increment.  If\n",
+    "        False, the magnitude is simply truncated to the next lowest\n",
+    "        multiple.  Default is True\n",
+    "\n",
+    "    *barb_increments*:\n",
+    "        A dictionary of increments specifying values to associate with\n",
+    "        different parts of the barb. Only those values one wishes to\n",
+    "        override need to be included.\n",
+    "\n",
+    "        - 'half' - half barbs (Default is 5)\n",
+    "\n",
+    "        - 'full' - full barbs (Default is 10)\n",
+    "\n",
+    "        - 'flag' - flags (default is 50)\n",
+    "\n",
+    "    *flip_barb*:\n",
+    "        Either a single boolean flag or an array of booleans.  Single\n",
+    "        boolean indicates whether the lines and flags should point\n",
+    "        opposite to normal for all barbs.  An array (which should be the\n",
+    "        same size as the other data arrays) indicates whether to flip for\n",
+    "        each individual barb.  Normal behavior is for the barbs and lines\n",
+    "        to point right (comes from wind barbs having these features point\n",
+    "        towards low pressure in the Northern Hemisphere.)  Default is\n",
+    "        False\n",
+    "\n",
+    "Barbs are traditionally used in meteorology as a way to plot the speed\n",
+    "and direction of wind observations, but can technically be used to\n",
+    "plot any two dimensional vector quantity.  As opposed to arrows, which\n",
+    "give vector magnitude by the length of the arrow, the barbs give more\n",
+    "quantitative information about the vector magnitude by putting slanted\n",
+    "lines or a triangle for various increments in magnitude, as show\n",
+    "schematically below::\n",
+    "\n",
+    "     :     /\\    \\\\\n",
+    "     :    /  \\    \\\\\n",
+    "     :   /    \\    \\    \\\\\n",
+    "     :  /      \\    \\    \\\\\n",
+    "     : ------------------------------\n",
+    "\n",
+    ".. note the double \\\\ at the end of each line to make the figure\n",
+    ".. render correctly\n",
+    "\n",
+    "The largest increment is given by a triangle (or \"flag\"). After those\n",
+    "come full lines (barbs). The smallest increment is a half line.  There\n",
+    "is only, of course, ever at most 1 half line.  If the magnitude is\n",
+    "small and only needs a single half-line and no full lines or\n",
+    "triangles, the half-line is offset from the end of the barb so that it\n",
+    "can be easily distinguished from barbs with a single full line.  The\n",
+    "magnitude for the barb shown above would nominally be 65, using the\n",
+    "standard increments of 50, 10, and 5.\n",
+    "\n",
+    "linewidths and edgecolors can be used to customize the barb.\n",
+    "Additional :class:`~matplotlib.collections.PolyCollection` keyword\n",
+    "arguments:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print 'matplotlib.__version__ => ' + matplotlib.__version__\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/cartopy-rasterio-plot.ipynb b/projects/IRIS/cartopy-rasterio-plot.ipynb
new file mode 100644
index 0000000..f6de359
--- /dev/null
+++ b/projects/IRIS/cartopy-rasterio-plot.ipynb
@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from osgeo import gdal\n",
+    "from osgeo import gdal_array\n",
+    "\n",
+    "import rasterio\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "## DEM plot via cartopy, rasterio, pyplot imshow\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "=="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Rasterio\n",
+    "##   Clean and fast\n",
+    "##     geospatial raster I/O for Python programmers who use Numpy\n",
+    "\n",
+    "with rasterio.open('sample_data/SanMateo_CA.tif') as src:\n",
+    "    data = src.read()\n",
+    "    data = np.transpose( data, [1,2,0])\n",
+    "\n",
+    "\n",
+    "## show selected data attributes\n",
+    "## -------------------------------------\n",
+    "# type(data)  numpy.ma.core.MaskedArray\n",
+    "# data.ndim   3\n",
+    "# data.shape  (1080, 864, 1)\n",
+    "# data.dtype  dtype('float32')\n",
+    "\n",
+    "## NOTE: when using rasterio and matplotlib only,\n",
+    "##  the column order for the trivial case of lat/lon/measure\n",
+    "##  is NOT handled automatically..  \n",
+    "##  column reordering is REQUIRED   np.transpose()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Rasterio supplies a simple dictionary of important GeoTIFF metadata\n",
+    "src.meta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## numpy ndarray indexing example\n",
+    "##  show the measure values for a 2x2 area\n",
+    "##  no-data options are available using a masked array\n",
+    "##    http://docs.scipy.org/doc/numpy/reference/maskedarray.generic.html#what-is-a-masked-array\n",
+    "\n",
+    "data[0:2,0:2,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## pyplot Image-Show \n",
+    "##  http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow\n",
+    "##\n",
+    "##  Example of using matplotlib to directly display a GeoTIFF\n",
+    "##\n",
+    "##  Cartopy supplies a library of mapping transformations\n",
+    "##   use an idiom to calculate the correct display bounds\n",
+    "##\n",
+    "##  data[:,:,0] refers to a numpy ndarray: all-x, all-y, 0th measure\n",
+    "##\n",
+    "\n",
+    "xmin = src.transform[0]\n",
+    "xmax = src.transform[0] + src.transform[1]*src.width\n",
+    "ymin = src.transform[3] + src.transform[5]*src.height\n",
+    "ymax = src.transform[3]\n",
+    "\n",
+    "##   Mercator etc..\n",
+    "crs = ccrs.PlateCarree()\n",
+    "\n",
+    "ax = plt.axes(projection=crs)\n",
+    "plt.imshow(  data[:,:,0], origin='upper', \n",
+    "             extent=[xmin, xmax, ymin, ymax], \n",
+    "             cmap=plt.get_cmap('gist_earth'),\n",
+    "             transform=crs, interpolation='nearest')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "=="
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Show the same content, but with a colorbar legend for the data\n",
+    "##\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "ax = plt.subplot(111, projection=ccrs.PlateCarree())\n",
+    "\n",
+    "#elev, crs, extent = srtm_composite(12, 47, 1, 1)\n",
+    "plt.imshow( data[:,:,0], transform=ccrs.PlateCarree(),\n",
+    "           cmap='gist_earth')\n",
+    "cb = plt.colorbar(orientation='vertical')\n",
+    "cb.set_label('Altitude')\n",
+    "plt.title(\"DEM\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%whos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-basics.ipynb b/projects/IRIS/iris-basics.ipynb
new file mode 100644
index 0000000..ec6c732
--- /dev/null
+++ b/projects/IRIS/iris-basics.ipynb
@@ -0,0 +1,183 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import iris\n",
+    "import iris.coord_systems\n",
+    "import iris.cube\n",
+    "\n",
+    "import iris.plot as iplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "cube_path = 'sample_data/rotated_pole.nc'\n",
+    "cube = iris.load_cube(cube_path)\n",
+    "\n",
+    "cube.coord('grid_latitude').guess_bounds()\n",
+    "cube.coord('grid_longitude').guess_bounds()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print cube.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Basic Plots\n",
+    "iplt.contourf(cube)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "iplt.pcolor(cube)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## subplots\n",
+    "\n",
+    "plt.subplot(221)\n",
+    "plt.title('Default')\n",
+    "iplt.contourf(cube)\n",
+    "plt.gca().coastlines()\n",
+    "\n",
+    "# Second sub-plot\n",
+    "plt.subplot(222, projection=ccrs.Mollweide(central_longitude=120))\n",
+    "plt.title('Molleweide')\n",
+    "iplt.contourf(cube)\n",
+    "plt.gca().coastlines()\n",
+    "\n",
+    "# Third sub-plot (the projection part is redundant, but a useful\n",
+    "# test none-the-less)\n",
+    "ax = plt.subplot(223, projection=iplt.default_projection(cube))\n",
+    "plt.title('Native')\n",
+    "iplt.contour(cube)\n",
+    "ax.coastlines()\n",
+    "\n",
+    "# Fourth sub-plot\n",
+    "ax = plt.subplot(2, 2, 4, projection=ccrs.PlateCarree())\n",
+    "plt.title('PlateCarree')\n",
+    "iplt.contourf(cube)\n",
+    "ax.coastlines()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.axes(projection=ccrs.PlateCarree(central_longitude=40))\n",
+    "plt.gca().coastlines()\n",
+    "iplt.pcolormesh(cube)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "iplt.outline(cube)\n",
+    "plt.gca().coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "iplt.points(cube)\n",
+    "plt.gca().coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-contourf-exner.ipynb b/projects/IRIS/iris-contourf-exner.ipynb
new file mode 100644
index 0000000..3d6539a
--- /dev/null
+++ b/projects/IRIS/iris-contourf-exner.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Deriving Exner Pressure and Air Temperature\n",
+    "===========================================\n",
+    "\n",
+    "This example shows some processing of cubes in order to derive further related\n",
+    "cubes; in this case the derived cubes are Exner pressure and air temperature\n",
+    "which are calculated by combining air pressure, air potential temperature and\n",
+    "specific humidity. Finally, the two new cubes are presented side-by-side in a\n",
+    "plot.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.ticker\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import iris\n",
+    "import iris.coords as coords\n",
+    "import iris.iterate\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def limit_colorbar_ticks(contour_object):\n",
+    "    \"\"\"\n",
+    "    Takes a contour object which has an associated colorbar and limits the\n",
+    "    number of ticks on the colorbar to 4.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # Under Matplotlib v1.2.x the colorbar attribute of a contour object is\n",
+    "    # a tuple containing the colorbar and an axes object, whereas under\n",
+    "    # Matplotlib v1.3.x it is simply the colorbar.\n",
+    "    try:\n",
+    "        colorbar = contour_object.colorbar[0]\n",
+    "    except (AttributeError, TypeError):\n",
+    "        colorbar = contour_object.colorbar\n",
+    "\n",
+    "    colorbar.locator = matplotlib.ticker.MaxNLocator(4)\n",
+    "    colorbar.update_ticks()\n",
+    "\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    #fname = iris.sample_data_path('colpex.pp')\n",
+    "    fname = 'sample_data/colpex.pp'\n",
+    "\n",
+    "    # The list of phenomena of interest\n",
+    "    phenomena = ['air_potential_temperature', 'air_pressure']\n",
+    "\n",
+    "    # Define the constraint on standard name and model level\n",
+    "    constraints = [iris.Constraint(phenom, model_level_number=1) for\n",
+    "                   phenom in phenomena]\n",
+    "\n",
+    "    air_potential_temperature, air_pressure = iris.load_cubes(fname,\n",
+    "                                                              constraints)\n",
+    "\n",
+    "    # Define a coordinate which represents 1000 hPa\n",
+    "    p0 = coords.AuxCoord(1000, long_name='P0', units='hPa')\n",
+    "    # Convert reference pressure 'p0' into the same units as 'air_pressure'\n",
+    "    p0.convert_units(air_pressure.units)\n",
+    "\n",
+    "    # Calculate Exner pressure\n",
+    "    exner_pressure = (air_pressure / p0) ** (287.05 / 1005.0)\n",
+    "    # Set the name (the unit is scalar)\n",
+    "    exner_pressure.rename('exner_pressure')\n",
+    "\n",
+    "    # Calculate air_temp\n",
+    "    air_temperature = exner_pressure * air_potential_temperature\n",
+    "    # Set the name (the unit is K)\n",
+    "    air_temperature.rename('air_temperature')\n",
+    "\n",
+    "    # Now create an iterator which will give us lat lon slices of\n",
+    "    # exner pressure and air temperature in the form\n",
+    "    # (exner_slice, air_temp_slice).\n",
+    "    lat_lon_slice_pairs = iris.iterate.izip(exner_pressure,\n",
+    "                                            air_temperature,\n",
+    "                                            coords=['grid_latitude',\n",
+    "                                                    'grid_longitude'])\n",
+    "\n",
+    "    # For the purposes of this example, we only want to demonstrate the first\n",
+    "    # plot.\n",
+    "    lat_lon_slice_pairs = [next(lat_lon_slice_pairs)]\n",
+    "\n",
+    "    plt.figure(figsize=(8, 4))\n",
+    "    for exner_slice, air_temp_slice in lat_lon_slice_pairs:\n",
+    "        plt.subplot(121)\n",
+    "        cont = qplt.contourf(exner_slice)\n",
+    "\n",
+    "        # The default colorbar has a few too many ticks on it, causing text to\n",
+    "        # overlap. Therefore, limit the number of ticks.\n",
+    "        limit_colorbar_ticks(cont)\n",
+    "\n",
+    "        plt.subplot(122)\n",
+    "        cont = qplt.contourf(air_temp_slice)\n",
+    "        limit_colorbar_ticks(cont)\n",
+    "        iplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-contourf-gtmps.ipynb b/projects/IRIS/iris-contourf-gtmps.ipynb
new file mode 100644
index 0000000..fa311d1
--- /dev/null
+++ b/projects/IRIS/iris-contourf-gtmps.ipynb
@@ -0,0 +1,216 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import iris\n",
+    "import iris.coords as coords\n",
+    "import iris.plot as iplt\n",
+    "\n",
+    "from six.moves import zip\n",
+    "import os.path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Global average annual temperature maps\n",
+    "======================================\n",
+    "\n",
+    "Produces maps of global temperature forecasts from the A1B and E1 scenarios.\n",
+    "\n",
+    "The data used comes from the HadGEM2-AO model simulations for the A1B and E1 scenarios, both of which were derived using the IMAGE\n",
+    "Integrated Assessment Model (Johns et al. 2010; Lowe et al. 2009).\n",
+    "\n",
+    "References\n",
+    "----------\n",
+    "\n",
+    "* Johns T.C., et al. (2010) Climate change under aggressive mitigation: The ENSEMBLES multi-model experiment. Climate\n",
+    "    Dynamics (submitted)\n",
+    "\n",
+    "* Lowe J.A., C.D. Hewitt, D.P. Van Vuuren, T.C. Johns, E. Stehfest, J-F. Royer, and P. van der Linden, 2009. New\n",
+    "    Study For Climate Modeling, Analyses, and Scenarios. Eos Trans. AGU, Vol 90, No. 21.\n",
+    "\n",
+    "\n",
+    "\n",
+    "###  http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def cop_metadata_callback(cube, field, filename):\n",
+    "    \"\"\" A function which adds an \"Experiment\" coordinate which comes from the filename. \"\"\"\n",
+    "\n",
+    "    # Extract the experiment name (such as a1b or e1) from the filename (in this case it is just the parent folder's name)\n",
+    "    containing_folder = os.path.dirname(filename)\n",
+    "    experiment_label = os.path.basename(containing_folder)\n",
+    "\n",
+    "    # Create a coordinate with the experiment label in it\n",
+    "    exp_coord = coords.AuxCoord(experiment_label, long_name='Experiment', units='no_unit')\n",
+    "\n",
+    "    # and add it to the cube\n",
+    "    cube.add_aux_coord(exp_coord)\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    # Load e1 and a1 using the callback to update the metadata\n",
+    "    #dpath = iris.sample_data_path('E1.2098.pp')\n",
+    "    dpath = 'sample_data/E1.2098.pp'\n",
+    "    e1 = iris.load_cube(  dpath,\n",
+    "                        callback=cop_metadata_callback)\n",
+    "\n",
+    "    #dpath = iris.sample_data_path('A1B.2098.pp')\n",
+    "    dpath = 'sample_data/A1B.2098.pp'\n",
+    "    a1b = iris.load_cube(  dpath,\n",
+    "                         callback=cop_metadata_callback)\n",
+    "\n",
+    "    # Load the global average data and add an 'Experiment' coord it\n",
+    "    #dpath = iris.sample_data_path('pre-industrial.pp')\n",
+    "    dpath = 'sample_data/pre-industrial.pp'\n",
+    "    global_avg = iris.load_cube( dpath )\n",
+    "\n",
+    "    # Define evenly spaced contour levels: -2.5, -1.5, ... 15.5, 16.5 with the specific colours\n",
+    "    levels = np.arange(20) - 2.5\n",
+    "    red = np.array([0, 0, 221, 239, 229, 217, 239, 234, 228, 222, 205, 196, 161, 137, 116, 89, 77, 60, 51]) / 256.\n",
+    "    green = np.array([16, 217, 242, 243, 235, 225, 190, 160, 128, 87, 72, 59, 33, 21, 29, 30, 30, 29, 26]) / 256.\n",
+    "    blue = np.array([255, 255, 243, 169, 99, 51, 63, 37, 39, 21, 27, 23, 22, 26, 29, 28, 27, 25, 22]) / 256.\n",
+    "\n",
+    "    # Put those colours into an array which can be passed to conourf as the specific colours for each level\n",
+    "    colors = np.array([red, green, blue]).T\n",
+    "\n",
+    "    # Subtract the global\n",
+    "\n",
+    "\n",
+    "    # Iterate over each latitude longitude slice for both e1 and a1b scenarios simultaneously\n",
+    "    for e1_slice, a1b_slice in zip(e1.slices(['latitude', 'longitude']),\n",
+    "                                   a1b.slices(['latitude', 'longitude'])):\n",
+    "\n",
+    "        time_coord = a1b_slice.coord('time')\n",
+    "\n",
+    "        # Calculate the difference from the mean\n",
+    "        delta_e1 = e1_slice - global_avg\n",
+    "        delta_a1b = a1b_slice - global_avg\n",
+    "\n",
+    "        # Make a wider than normal figure to house two maps side-by-side\n",
+    "        fig = plt.figure(figsize=(12, 5))\n",
+    "\n",
+    "        # Get the time datetime from the coordinate\n",
+    "        time = time_coord.units.num2date(time_coord.points[0])\n",
+    "        # Set a title for the entire figure, giving the time in a nice format of \"MonthName Year\". Also, set the y value for the\n",
+    "        # title so that it is not tight to the top of the plot.\n",
+    "        fig.suptitle('Annual Temperature Predictions for ' + time.strftime(\"%Y\"), y=0.9, fontsize=18)\n",
+    "\n",
+    "        # Add the first subplot showing the E1 scenario\n",
+    "        plt.subplot(121)\n",
+    "        plt.title('HadGEM2 E1 Scenario',  fontsize=10)\n",
+    "        iplt.contourf(delta_e1, levels, colors=colors, linewidth=0, extend='both')\n",
+    "        plt.gca().coastlines()\n",
+    "        # get the current axes' subplot for use later on\n",
+    "        plt1_ax = plt.gca()\n",
+    "\n",
+    "        # Add the second subplot showing the A1B scenario\n",
+    "        plt.subplot(122)\n",
+    "        plt.title('HadGEM2 A1B-Image Scenario',  fontsize=10)\n",
+    "        contour_result = iplt.contourf(delta_a1b, levels, colors=colors, linewidth=0, extend='both')\n",
+    "        plt.gca().coastlines()\n",
+    "        # get the current axes' subplot for use later on\n",
+    "        plt2_ax = plt.gca()\n",
+    "\n",
+    "\n",
+    "        # Now add a colourbar who's leftmost point is the same as the leftmost point of the left hand plot\n",
+    "        # and rightmost point is the rightmost point of the right hand plot\n",
+    "\n",
+    "        # Get the positions of the 2nd plot and the left position of the 1st plot\n",
+    "        left, bottom, width, height = plt2_ax.get_position().bounds\n",
+    "        first_plot_left = plt1_ax.get_position().bounds[0]\n",
+    "\n",
+    "        # the width of the colorbar should now be simple\n",
+    "        width = left - first_plot_left + width\n",
+    "\n",
+    "        # Add axes to the figure, to place the colour bar\n",
+    "        colorbar_axes = fig.add_axes([first_plot_left, bottom + 0.07, width, 0.03])\n",
+    "\n",
+    "        # Add the colour bar\n",
+    "        cbar = plt.colorbar(contour_result, colorbar_axes, orientation='horizontal')\n",
+    "\n",
+    "        # Label the colour bar and add ticks\n",
+    "        cbar.set_label(e1_slice.units)\n",
+    "        cbar.ax.tick_params(length=0)\n",
+    "\n",
+    "        iplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8O9evtpWyrS/znjwHLkcQWO9OKIUyBqWNX1XKC0ND8LytOCf/OCcqVleVuKoa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\n2cydU8rkyQu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rLVgXaOmRTHPwgoJgnUHfHiEee7OEXoVBti4KIQIhVCwB2m+zjagQPjLs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Z4g88WJyjbbmOlFjokjQJ8IcOkKYNWFPrvQ6eJycF1N1nHozGFljCPA5AWHco1IRrfSPXZo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\nDs4lVb52vcWfvtnk/FTK9amE8Zaf5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2LL/f5ZB80koRknpM7RiEIalYhrzdbjzrlT+ED2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\nEREREZHV8f8B9HYTRD29PzMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fa266bcdd50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-gtmps-ensemble.ipynb b/projects/IRIS/iris-gtmps-ensemble.ipynb
new file mode 100644
index 0000000..ef7602e
--- /dev/null
+++ b/projects/IRIS/iris-gtmps-ensemble.ipynb
@@ -0,0 +1,210 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Seasonal ensemble model plots\n",
+    "=============================\n",
+    "\n",
+    "This example demonstrates the loading of a lagged ensemble dataset from the GloSea4 model, which is then used to\n",
+    "produce two types of plot:\n",
+    "\n",
+    " * The first shows the \"postage stamp\" style image with an array of 14 images, one for each ensemble member with\n",
+    "   a shared colorbar. (The missing image in this example represents ensemble member number 6 which was a failed run)\n",
+    "\n",
+    " * The second plot shows the data limited to a region of interest, in this case a region defined for forecasting\n",
+    "   ENSO (El Nino-Southern Oscillation), which, for the purposes of this example, has had the ensemble mean subtracted\n",
+    "   from each ensemble member to give an anomaly surface temperature. In practice a better approach would be to take the\n",
+    "   climatological mean, calibrated to the model, from each ensemble member.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def realization_metadata(cube, field, fname):\n",
+    "    \"\"\"\n",
+    "    A function which modifies the cube's metadata to add a \"realization\" (ensemble member) coordinate from the filename if one\n",
+    "    doesn't already exist in the cube.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    # add an ensemble member coordinate if one doesn't already exist\n",
+    "    if not cube.coords('realization'):\n",
+    "        # the ensemble member is encoded in the filename as *_???.pp where ??? is the ensemble member\n",
+    "        realization_number = fname[-6:-3]\n",
+    "\n",
+    "        import iris.coords\n",
+    "        realization_coord = iris.coords.AuxCoord(np.int32(realization_number), 'realization')\n",
+    "        cube.add_aux_coord(realization_coord)\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    # extract surface temperature cubes which have an ensemble member coordinate, adding appropriate lagged ensemble metadata\n",
+    "    #dpath = iris.sample_data_path('GloSea4', 'ensemble_???.pp')\n",
+    "    dpath = 'sample_data/GloSea4/ensemble_???.pp'\n",
+    "    surface_temp = iris.load_cube(  dpath,\n",
+    "                  iris.Constraint('surface_temperature', realization=lambda value: True),\n",
+    "                  callback=realization_metadata,\n",
+    "                  )\n",
+    "\n",
+    "    # ----------------------------------------------------------------------------------------------------------------\n",
+    "    # Plot #1: Ensemble postage stamps\n",
+    "    # ----------------------------------------------------------------------------------------------------------------\n",
+    "\n",
+    "    # for the purposes of this example, take the last time element of the cube\n",
+    "    last_timestep = surface_temp[:, -1, :, :]\n",
+    "\n",
+    "    # Make 50 evenly spaced levels which span the dataset\n",
+    "    contour_levels = np.linspace(np.min(last_timestep.data), np.max(last_timestep.data), 50)\n",
+    "\n",
+    "    # Create a wider than normal figure to support our many plots\n",
+    "    plt.figure(figsize=(12, 6), dpi=100)\n",
+    "\n",
+    "    # Also manually adjust the spacings which are used when creating subplots\n",
+    "    plt.gcf().subplots_adjust(hspace=0.05, wspace=0.05, top=0.95, bottom=0.05, left=0.075, right=0.925)\n",
+    "\n",
+    "    # iterate over all possible latitude longitude slices\n",
+    "    for cube in last_timestep.slices(['latitude', 'longitude']):\n",
+    "\n",
+    "        # get the ensemble member number from the ensemble coordinate\n",
+    "        ens_member = cube.coord('realization').points[0]\n",
+    "\n",
+    "        # plot the data in a 4x4 grid, with each plot's position in the grid being determined by ensemble member number\n",
+    "        # the special case for the 13th ensemble member is to have the plot at the bottom right\n",
+    "        if ens_member == 13:\n",
+    "            plt.subplot(4, 4, 16)\n",
+    "        else:\n",
+    "            plt.subplot(4, 4, ens_member+1)\n",
+    "\n",
+    "        cf = iplt.contourf(cube, contour_levels)\n",
+    "\n",
+    "        # add coastlines\n",
+    "        plt.gca().coastlines()\n",
+    "\n",
+    "    # make an axes to put the shared colorbar in\n",
+    "    colorbar_axes = plt.gcf().add_axes([0.35, 0.1, 0.3, 0.05])\n",
+    "    colorbar = plt.colorbar(cf, colorbar_axes, orientation='horizontal')\n",
+    "    colorbar.set_label('%s' % last_timestep.units)\n",
+    "\n",
+    "    # limit the colorbar to 8 tick marks\n",
+    "    import matplotlib.ticker\n",
+    "    colorbar.locator = matplotlib.ticker.MaxNLocator(8)\n",
+    "    colorbar.update_ticks()\n",
+    "\n",
+    "    # get the time for the entire plot\n",
+    "    time_coord = last_timestep.coord('time')\n",
+    "    time = time_coord.units.num2date(time_coord.bounds[0, 0])\n",
+    "\n",
+    "    # set a global title for the postage stamps with the date formated by \"monthname year\"\n",
+    "    plt.suptitle('Surface temperature ensemble forecasts for %s' % time.strftime('%B %Y'))\n",
+    "\n",
+    "    iplt.show()\n",
+    "\n",
+    "    # ----------------------------------------------------------------------------------------------------------------\n",
+    "    # Plot #2: ENSO plumes\n",
+    "    # ----------------------------------------------------------------------------------------------------------------\n",
+    "\n",
+    "    # Nino 3.4 lies between: 170W and 120W, 5N and 5S, so define a constraint which matches this\n",
+    "    nino_3_4_constraint = iris.Constraint(longitude=lambda v: -170+360 <= v <= -120+360, latitude=lambda v: -5 <= v <= 5)\n",
+    "\n",
+    "    nino_cube = surface_temp.extract(nino_3_4_constraint)\n",
+    "\n",
+    "    # Subsetting a circular longitude coordinate always results in a circular coordinate, so set the coordinate to be non-circular\n",
+    "    nino_cube.coord('longitude').circular = False\n",
+    "\n",
+    "    # Calculate the horizontal mean for the nino region\n",
+    "    mean = nino_cube.collapsed(['latitude', 'longitude'], iris.analysis.MEAN)\n",
+    "\n",
+    "    # Calculate the ensemble mean of the horizontal mean. To do this, remove the \"forecast_period\" and\n",
+    "    # \"forecast_reference_time\" coordinates which span both \"relalization\" and \"time\".\n",
+    "    mean.remove_coord(\"forecast_reference_time\")\n",
+    "    mean.remove_coord(\"forecast_period\")\n",
+    "    ensemble_mean = mean.collapsed('realization', iris.analysis.MEAN)\n",
+    "\n",
+    "    # take the ensemble mean from each ensemble member\n",
+    "    mean -= ensemble_mean.data\n",
+    "\n",
+    "    plt.figure()\n",
+    "\n",
+    "    for ensemble_member in mean.slices(['time']):\n",
+    "        # draw each ensemble member as a dashed line in black\n",
+    "        iplt.plot(ensemble_member, '--k')\n",
+    "\n",
+    "    plt.title('Mean temperature anomaly for ENSO 3.4 region')\n",
+    "    plt.xlabel('Time')\n",
+    "    plt.ylabel('Temperature anomaly / K')\n",
+    "\n",
+    "    iplt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-hovmoller.ipynb b/projects/IRIS/iris-hovmoller.ipynb
new file mode 100644
index 0000000..c2418b7
--- /dev/null
+++ b/projects/IRIS/iris-hovmoller.ipynb
@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.dates as mdates\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt\n",
+    "import iris.unit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hovmoller diagram of monthly surface temperature\n",
+    "================================================\n",
+    "\n",
+    "This example demonstrates the creation of a Hovmoller diagram with fine control over plot ticks and labels.\n",
+    "The data comes from the Met Office OSTIA project and has been pre-processed to calculate the monthly mean sea\n",
+    "surface temperature.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def main():\n",
+    "    #fname = iris.sample_data_path('ostia_monthly.nc')\n",
+    "    fname = 'sample_data/ostia_monthly.nc'\n",
+    "\n",
+    "    # load a single cube of surface temperature between +/- 5 latitude\n",
+    "    cube = iris.load_cube(fname, iris.Constraint('surface_temperature', latitude=lambda v: -5 < v < 5))\n",
+    "\n",
+    "    # Take the mean over latitude\n",
+    "    cube = cube.collapsed('latitude', iris.analysis.MEAN)\n",
+    "\n",
+    "    # Now that we have our data in a nice way, lets create the plot\n",
+    "    # contour with 20 levels\n",
+    "    qplt.contourf(cube, 20)\n",
+    "\n",
+    "    # Put a custom label on the y axis\n",
+    "    plt.ylabel('Time / years')\n",
+    "\n",
+    "    # Stop matplotlib providing clever axes range padding\n",
+    "    plt.axis('tight')\n",
+    "\n",
+    "    # As we are plotting annual variability, put years as the y ticks\n",
+    "    plt.gca().yaxis.set_major_locator(mdates.YearLocator())\n",
+    "\n",
+    "    # And format the ticks to just show the year\n",
+    "    plt.gca().yaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
+    "\n",
+    "    iplt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-iplt-tripolar.ipynb b/projects/IRIS/iris-iplt-tripolar.ipynb
new file mode 100644
index 0000000..c25f6f4
--- /dev/null
+++ b/projects/IRIS/iris-iplt-tripolar.ipynb
@@ -0,0 +1,138 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import iris\n",
+    "import iris.analysis.cartography\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Tri-Polar Grid Projected Plotting\n",
+    "=================================\n",
+    "\n",
+    "This example demonstrates cell plots of data on the semi-structured ORCA2 model\n",
+    "grid.\n",
+    "\n",
+    "First, the data is projected into the PlateCarree coordinate reference system.\n",
+    "\n",
+    "Second four pcolormesh plots are created from this projected dataset,\n",
+    "using different projections for the output image.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    # Load data\n",
+    "    #filepath = iris.sample_data_path('orca2_votemper.nc')\n",
+    "    filepath = 'sample_data/orca2_votemper.nc'\n",
+    "    cube = iris.load_cube(filepath)\n",
+    "\n",
+    "    # Choose plot projections\n",
+    "    projections = {}\n",
+    "    projections['Mollweide'] = ccrs.Mollweide()\n",
+    "    projections['PlateCarree'] = ccrs.PlateCarree()\n",
+    "    projections['NorthPolarStereo'] = ccrs.NorthPolarStereo()\n",
+    "    projections['Orthographic'] = ccrs.Orthographic(central_longitude=-90,\n",
+    "                                                    central_latitude=45)\n",
+    "\n",
+    "    pcarree = projections['PlateCarree']\n",
+    "    # Transform cube to target projection\n",
+    "    new_cube, extent = iris.analysis.cartography.project(cube, pcarree,\n",
+    "                                                         nx=400, ny=200)\n",
+    "\n",
+    "    # Plot data in each projection\n",
+    "    for name in sorted(projections):\n",
+    "        fig = plt.figure()\n",
+    "        fig.suptitle('ORCA2 Data Projected to {}'.format(name))\n",
+    "        # Set up axes and title\n",
+    "        ax = plt.subplot(projection=projections[name])\n",
+    "        # Set limits\n",
+    "        ax.set_global()\n",
+    "        # plot with Iris quickplot pcolormesh\n",
+    "        qplt.pcolormesh(new_cube)\n",
+    "        # Draw coastlines\n",
+    "        ax.coastlines()\n",
+    "\n",
+    "        iplt.show()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-np-agg.ipynb b/projects/IRIS/iris-np-agg.ipynb
new file mode 100644
index 0000000..940c525
--- /dev/null
+++ b/projects/IRIS/iris-np-agg.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt\n",
+    "from iris.util import rolling_window\n",
+    "from iris.analysis import Aggregator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Calculating a custom statistic\n",
+    "==============================\n",
+    "\n",
+    "This example shows how to define and use a custom\n",
+    ":class:`iris.analysis.Aggregator`, that provides a new statistical operator for\n",
+    "use with cube aggregation functions such as :meth:`~iris.cube.Cube.collapsed`,\n",
+    ":meth:`~iris.cube.Cube.aggregated_by` or\n",
+    ":meth:`~iris.cube.Cube.rolling_window`.\n",
+    "\n",
+    "In this case, we have a 240-year sequence of yearly average surface temperature\n",
+    "over North America, and we want to calculate in how many years these exceed a\n",
+    "certain temperature over a spell of 5 years or more.\n",
+    "\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Define a function to perform the custom statistical operation.\n",
+    "# Note: in order to meet the requirements of iris.analysis.Aggregator, it must\n",
+    "# do the calculation over an arbitrary (given) data axis.\n",
+    "def count_spells(data, threshold, axis, spell_length):\n",
+    "    \"\"\"\n",
+    "    Function to calculate the number of points in a sequence where the value\n",
+    "    has exceeded a threshold value for at least a certain number of timepoints.\n",
+    "\n",
+    "    Generalised to operate on multiple time sequences arranged on a specific\n",
+    "    axis of a multidimensional array.\n",
+    "\n",
+    "    Args:\n",
+    "\n",
+    "    * data (array):\n",
+    "        raw data to be compared with value threshold.\n",
+    "\n",
+    "    * threshold (float):\n",
+    "        threshold point for 'significant' datapoints.\n",
+    "\n",
+    "    * axis (int):\n",
+    "        number of the array dimension mapping the time sequences.\n",
+    "        (Can also be negative, e.g. '-1' means last dimension)\n",
+    "\n",
+    "    * spell_length (int):\n",
+    "        number of consecutive times at which value > threshold to \"count\".\n",
+    "\n",
+    "    \"\"\"\n",
+    "    if axis < 0:\n",
+    "        # just cope with negative axis numbers\n",
+    "        axis += data.ndim\n",
+    "    # Threshold the data to find the 'significant' points.\n",
+    "    data_hits = data > threshold\n",
+    "    # Make an array with data values \"windowed\" along the time axis.\n",
+    "    hit_windows = rolling_window(data_hits, window=spell_length, axis=axis)\n",
+    "    # Find the windows \"full of True-s\" (along the added 'window axis').\n",
+    "    full_windows = np.all(hit_windows, axis=axis+1)\n",
+    "    # Count points fulfilling the condition (along the time axis).\n",
+    "    spell_point_counts = np.sum(full_windows, axis=axis, dtype=int)\n",
+    "    return spell_point_counts\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def main():\n",
+    "    # Load the whole time-sequence as a single cube.\n",
+    "    #file_path = iris.sample_data_path('E1_north_america.nc')\n",
+    "    file_path = 'sample_data/E1_north_america.nc'\n",
+    "    cube = iris.load_cube(file_path)\n",
+    "\n",
+    "    # Make an aggregator from the user function.\n",
+    "    SPELL_COUNT = Aggregator('spell_count',\n",
+    "                             count_spells,\n",
+    "                             units_func=lambda units: 1)\n",
+    "\n",
+    "    # Define the parameters of the test.\n",
+    "    threshold_temperature = 280.0\n",
+    "    spell_years = 5\n",
+    "\n",
+    "    # Calculate the statistic.\n",
+    "    warm_periods = cube.collapsed('time', SPELL_COUNT,\n",
+    "                                  threshold=threshold_temperature,\n",
+    "                                  spell_length=spell_years)\n",
+    "    warm_periods.rename('Number of 5-year warm spells in 240 years')\n",
+    "\n",
+    "    # Plot the results.\n",
+    "    qplt.contourf(warm_periods, cmap='RdYlBu_r')\n",
+    "    plt.gca().coastlines()\n",
+    "    iplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-pcolormesh.ipynb b/projects/IRIS/iris-pcolormesh.ipynb
new file mode 100644
index 0000000..8fa8060
--- /dev/null
+++ b/projects/IRIS/iris-pcolormesh.ipynb
@@ -0,0 +1,175 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcols\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import iris\n",
+    "import iris.coord_categorisation\n",
+    "import iris.plot as iplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Colouring anomaly data with logarithmic scaling\n",
+    "===============================================\n",
+    "\n",
+    "In this example, we need to plot anomaly data where the values have a\n",
+    "\"logarithmic\" significance  -- i.e. we want to give approximately equal ranges\n",
+    "of colour between data values of, say, 1 and 10 as between 10 and 100.\n",
+    "\n",
+    "As the data range also contains zero, that obviously does not suit a simple\n",
+    "logarithmic interpretation.  However, values of less than a certain absolute\n",
+    "magnitude may be considered \"not significant\", so we put these into a separate\n",
+    "\"zero band\" which is plotted in white.\n",
+    "\n",
+    "To do this, we create a custom value mapping function (normalization) using\n",
+    "the matplotlib Norm class `matplotlib.colours.SymLogNorm\n",
+    "<http://matplotlib.org/api/colors_api.html#matplotlib.colors.SymLogNorm>`_.\n",
+    "We use this to make a cell-filled pseudocolour plot with a colorbar.\n",
+    "\n",
+    "NOTE: By \"pseudocolour\", we mean that each data point is drawn as a \"cell\"\n",
+    "region on the plot, coloured according to its data value.\n",
+    "This is provided in Iris by the functions :meth:`iris.plot.pcolor` and\n",
+    ":meth:`iris.plot.pcolormesh`, which call the underlying matplotlib\n",
+    "functions of the same names (i.e. `matplotlib.pyplot.pcolor\n",
+    "<http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pcolor>`_\n",
+    "and  `matplotlib.pyplot.pcolormesh\n",
+    "<http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pcolormesh>`_).\n",
+    "See also: http://en.wikipedia.org/wiki/False_color#Pseudocolor.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def main():\n",
+    "    # Load a sample air temperatures sequence.\n",
+    "    #file_path = iris.sample_data_path('E1_north_america.nc')\n",
+    "    file_path = 'sample_data/E1_north_america.nc'\n",
+    "    temperatures = iris.load_cube(file_path)\n",
+    "\n",
+    "    # Create a year-number coordinate from the time information.\n",
+    "    iris.coord_categorisation.add_year(temperatures, 'time')\n",
+    "\n",
+    "    # Create a sample anomaly field for one chosen year, by extracting that\n",
+    "    # year and subtracting the time mean.\n",
+    "    sample_year = 1989\n",
+    "    year_temperature = temperatures.extract(iris.Constraint(year=sample_year))\n",
+    "    time_mean = temperatures.collapsed('time', iris.analysis.MEAN)\n",
+    "    anomaly = year_temperature - time_mean\n",
+    "\n",
+    "    # Construct a plot title string explaining which years are involved.\n",
+    "    years = temperatures.coord('year').points\n",
+    "    plot_title = 'Temperature anomaly'\n",
+    "    plot_title += '\\n{} differences from {}-{} average.'.format(\n",
+    "        sample_year, years[0], years[-1])\n",
+    "\n",
+    "    # Define scaling levels for the logarithmic colouring.\n",
+    "    minimum_log_level = 0.1\n",
+    "    maximum_scale_level = 3.0\n",
+    "\n",
+    "    # Use a standard colour map which varies blue-white-red.\n",
+    "    # For suitable options, see the 'Diverging colormaps' section in:\n",
+    "    # http://matplotlib.org/examples/color/colormaps_reference.html\n",
+    "    anom_cmap = 'bwr'\n",
+    "\n",
+    "    # Create a 'logarithmic' data normalization.\n",
+    "    anom_norm = mcols.SymLogNorm(linthresh=minimum_log_level,\n",
+    "                                 linscale=0,\n",
+    "                                 vmin=-maximum_scale_level,\n",
+    "                                 vmax=maximum_scale_level)\n",
+    "    # Setting \"linthresh=minimum_log_level\" makes its non-logarithmic\n",
+    "    # data range equal to our 'zero band'.\n",
+    "    # Setting \"linscale=0\" maps the whole zero band to the middle colour value\n",
+    "    # (i.e. 0.5), which is the neutral point of a \"diverging\" style colormap.\n",
+    "\n",
+    "    # Create an Axes, specifying the map projection.\n",
+    "    plt.axes(projection=ccrs.LambertConformal())\n",
+    "\n",
+    "    # Make a pseudocolour plot using this colour scheme.\n",
+    "    mesh = iplt.pcolormesh(anomaly, cmap=anom_cmap, norm=anom_norm)\n",
+    "\n",
+    "    # Add a colourbar, with extensions to show handling of out-of-range values.\n",
+    "    bar = plt.colorbar(mesh, orientation='horizontal', extend='both')\n",
+    "\n",
+    "    # Set some suitable fixed \"logarithmic\" colourbar tick positions.\n",
+    "    tick_levels = [-3, -1, -0.3, 0.0, 0.3, 1, 3]\n",
+    "    bar.set_ticks(tick_levels)\n",
+    "\n",
+    "    # Modify the tick labels so that the centre one shows \"+/-<minumum-level>\".\n",
+    "    tick_levels[3] = r'$\\pm${:g}'.format(minimum_log_level)\n",
+    "    bar.set_ticklabels(tick_levels)\n",
+    "\n",
+    "    # Label the colourbar to show the units.\n",
+    "    bar.set_label('[{}, log scale]'.format(anomaly.units))\n",
+    "\n",
+    "    # Add coastlines and a title.\n",
+    "    plt.gca().coastlines()\n",
+    "    plt.title(plot_title)\n",
+    "\n",
+    "    # Display the result.\n",
+    "    iplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-polyfit.ipynb b/projects/IRIS/iris-polyfit.ipynb
new file mode 100644
index 0000000..cfae03e
--- /dev/null
+++ b/projects/IRIS/iris-polyfit.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import iris\n",
+    "import iris.quickplot as qplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Fitting a polynomial\n",
+    "====================\n",
+    "\n",
+    "This example demonstrates computing a polynomial fit to 1D data from an Iris\n",
+    "cube, adding the fit to the cube's metadata, and plotting both the 1D data and\n",
+    "the fit.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def main():\n",
+    "    #fname = iris.sample_data_path('A1B_north_america.nc')\n",
+    "    fname = 'sample_data/A1B_north_america.nc'\n",
+    "    cube = iris.load_cube(fname)\n",
+    "\n",
+    "    # Extract a single time series at a latitude and longitude point.\n",
+    "    location = next(cube.slices(['time']))\n",
+    "\n",
+    "    # Calculate a polynomial fit to the data at this time series.\n",
+    "    x_points = location.coord('time').points\n",
+    "    y_points = location.data\n",
+    "    degree = 2\n",
+    "\n",
+    "    p = np.polyfit(x_points, y_points, degree)\n",
+    "    y_fitted = np.polyval(p, x_points)\n",
+    "\n",
+    "    # Add the polynomial fit values to the time series to take\n",
+    "    # full advantage of Iris plotting functionality.\n",
+    "    long_name = 'degree_{}_polynomial_fit_of_{}'.format(degree, cube.name())\n",
+    "    fit = iris.coords.AuxCoord(y_fitted, long_name=long_name,\n",
+    "                               units=location.units)\n",
+    "    location.add_aux_coord(fit, 0)\n",
+    "\n",
+    "    qplt.plot(location.coord('time'), location, label='data')\n",
+    "    qplt.plot(location.coord('time'),\n",
+    "              location.coord(long_name),\n",
+    "              'g-', label='polynomial fit')\n",
+    "    plt.legend(loc='best')\n",
+    "    plt.title('Trend of US air temperature over time')\n",
+    "\n",
+    "    qplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-projs.ipynb b/projects/IRIS/iris-projs.ipynb
new file mode 100644
index 0000000..5fb5dd1
--- /dev/null
+++ b/projects/IRIS/iris-projs.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy as np\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Plotting in different projections\n",
+    "=================================\n",
+    "\n",
+    "This example shows how to overlay data and graphics in different projections,\n",
+    "demonstrating various features of Iris, Cartopy and matplotlib.\n",
+    "\n",
+    "We wish to overlay two datasets, defined on different rotated-pole grids.\n",
+    "To display both together, we make a pseudocoloured plot of the first, overlaid\n",
+    "with contour lines from the second.\n",
+    "We also add some lines and text annotations drawn in various projections.\n",
+    "\n",
+    "We plot these over a specified region, in two different map projections.\n",
+    "\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Define a Cartopy 'ordinary' lat-lon coordinate reference system.\n",
+    "crs_latlon = ccrs.PlateCarree()\n",
+    "\n",
+    "\n",
+    "def make_plot(projection_name, projection_crs):\n",
+    "\n",
+    "    # Create a matplotlib Figure.\n",
+    "    fig = plt.figure()\n",
+    "\n",
+    "    # Add a matplotlib Axes, specifying the required display projection.\n",
+    "    # NOTE: specifying 'projection' (a \"cartopy.crs.Projection\") makes the\n",
+    "    # resulting Axes a \"cartopy.mpl.geoaxes.GeoAxes\", which supports plotting\n",
+    "    # in different coordinate systems.\n",
+    "    ax = plt.axes(projection=projection_crs)\n",
+    "\n",
+    "    # Set display limits to include a set region of latitude * longitude.\n",
+    "    # (Note: Cartopy-specific).\n",
+    "    ax.set_extent((-80.0, 20.0, 10.0, 80.0), crs=crs_latlon)\n",
+    "\n",
+    "    # Add coastlines and meridians/parallels (Cartopy-specific).\n",
+    "    ax.coastlines(linewidth=0.75, color='navy')\n",
+    "    ax.gridlines(crs=crs_latlon, linestyle='-')\n",
+    "\n",
+    "    # Plot the first dataset as a pseudocolour filled plot.\n",
+    "    #maindata_filepath = iris.sample_data_path('rotated_pole.nc')\n",
+    "    maindata_filepath = 'sample_data/rotated_pole.nc'\n",
+    "    main_data = iris.load_cube(maindata_filepath)\n",
+    "    # NOTE: iplt.pcolormesh calls \"pyplot.pcolormesh\", passing in a coordinate\n",
+    "    # system with the 'transform' keyword:  This enables the Axes (a cartopy\n",
+    "    # GeoAxes) to reproject the plot into the display projection.\n",
+    "    iplt.pcolormesh(main_data, cmap='RdBu_r')\n",
+    "\n",
+    "    # Overplot the other dataset (which has a different grid), as contours.\n",
+    "    #overlay_filepath = iris.sample_data_path('space_weather.nc')\n",
+    "    overlay_filepath = 'sample_data/space_weather.nc'\n",
+    "    overlay_data = iris.load_cube(overlay_filepath, 'total electron content')\n",
+    "    # NOTE: as above, \"iris.plot.contour\" calls \"pyplot.contour\" with a\n",
+    "    # 'transform' keyword, enabling Cartopy reprojection.\n",
+    "    iplt.contour(overlay_data, 20,\n",
+    "                 linewidths=2.0, colors='darkgreen', linestyles='-')\n",
+    "\n",
+    "    # Draw a margin line, some way in from the border of the 'main' data...\n",
+    "    # First calculate rectangle corners, 7% in from each corner of the data.\n",
+    "    x_coord, y_coord = main_data.coord(axis='x'), main_data.coord(axis='y')\n",
+    "    x_start, x_end = np.min(x_coord.points), np.max(x_coord.points)\n",
+    "    y_start, y_end = np.min(y_coord.points), np.max(y_coord.points)\n",
+    "    margin = 0.07\n",
+    "    margin_fractions = np.array([margin, 1.0 - margin])\n",
+    "    x_lower, x_upper = x_start + (x_end - x_start) * margin_fractions\n",
+    "    y_lower, y_upper = y_start + (y_end - y_start) * margin_fractions\n",
+    "    box_x_points = x_lower + (x_upper - x_lower) * np.array([0, 1, 1, 0, 0])\n",
+    "    box_y_points = y_lower + (y_upper - y_lower) * np.array([0, 0, 1, 1, 0])\n",
+    "    # Get the Iris coordinate sytem of the X coordinate (Y should be the same).\n",
+    "    cs_data1 = x_coord.coord_system\n",
+    "    # Construct an equivalent Cartopy coordinate reference system (\"crs\").\n",
+    "    crs_data1 = cs_data1.as_cartopy_crs()\n",
+    "    # Draw the rectangle in this crs, with matplotlib \"pyplot.plot\".\n",
+    "    # NOTE: the 'transform' keyword specifies a non-display coordinate system\n",
+    "    # for the plot points (as used by the \"iris.plot\" functions).\n",
+    "    plt.plot(box_x_points, box_y_points, transform=crs_data1,\n",
+    "             linewidth=2.0, color='white', linestyle='--')\n",
+    "\n",
+    "    # Mark some particular places with a small circle and a name label...\n",
+    "    # Define some test points with latitude and longitude coordinates.\n",
+    "    city_data = [('London', 51.5072, 0.1275),\n",
+    "                 ('Halifax, NS', 44.67, -63.61),\n",
+    "                 ('Reykjavik', 64.1333, -21.9333)]\n",
+    "    # Place a single marker point and a text annotation at each place.\n",
+    "    for name, lat, lon in city_data:\n",
+    "        plt.plot(lon, lat, marker='o', markersize=7.0, markeredgewidth=2.5,\n",
+    "                 markerfacecolor='black', markeredgecolor='white',\n",
+    "                 transform=crs_latlon)\n",
+    "        # NOTE: the \"plt.annotate call\" does not have a \"transform=\" keyword,\n",
+    "        # so for this one we transform the coordinates with a Cartopy call.\n",
+    "        at_x, at_y = ax.projection.transform_point(lon, lat,\n",
+    "                                                   src_crs=crs_latlon)\n",
+    "        plt.annotate(\n",
+    "            name, xy=(at_x, at_y), xytext=(30, 20), textcoords='offset points',\n",
+    "            color='black', backgroundcolor='white', size='large',\n",
+    "            arrowprops=dict(arrowstyle='->', color='white', linewidth=2.5))\n",
+    "\n",
+    "    # Add a title, and display.\n",
+    "    plt.title('A pseudocolour plot on the {} projection,\\n'\n",
+    "              'with overlaid contours.'.format(projection_name))\n",
+    "    iplt.show()\n",
+    "\n",
+    "\n",
+    "def main():\n",
+    "    # Demonstrate with two different display projections.\n",
+    "    make_plot('Equidistant Cylindrical', ccrs.PlateCarree())\n",
+    "    make_plot('North Polar Stereographic', ccrs.NorthPolarStereo())\n",
+    "    make_plot('LambertConformal', ccrs.LambertConformal())\n",
+    "    make_plot('Robinson', ccrs.Robinson())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ccrs.Projection?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "* AlbersEqualArea\n",
+    "* AzimuthalEquidistant\n",
+    "* EuroPP\n",
+    "* Gnomonic\n",
+    "* GOOGLE_MERCATOR\n",
+    "* LambertConformal\n",
+    "* LambertCylindrical\n",
+    "* Mercator\n",
+    "* Miller\n",
+    "* Mollweide\n",
+    "* Orthographic\n",
+    "* OSGB\n",
+    "* Robinson\n",
+    "* RotatedGeodetic\n",
+    "* RotatedPole\n",
+    "* Stereographic\n",
+    "* TransverseMercator\n",
+    "* UTM\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-qplt-contour.ipynb b/projects/IRIS/iris-qplt-contour.ipynb
new file mode 100644
index 0000000..ac6ca4a
--- /dev/null
+++ b/projects/IRIS/iris-qplt-contour.ipynb
@@ -0,0 +1,146 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import iris\n",
+    "import iris.quickplot as qplt\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "## IRIS Quickplot example -- Contour, fill, pcolormesh\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## demo data of Sea Surface Temperatures as netCDF\n",
+    "file_path = '/usr/lib/python2.7/dist-packages/cartopy/data/netcdf/'\n",
+    "filename = 'HadISST1_SST_update.nc'\n",
+    "cubes = iris.load(file_path+filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## IRIS format - show a text description of the 0th cube\n",
+    "\n",
+    "print 'Cubes defined: ' + str(len(cubes))\n",
+    "print cubes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## store the 0th time slice of all lat,lon data points\n",
+    "sst0 = cubes[0][0,...]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "##  IRIS Quickplot contour\n",
+    "##     http://scitools.org.uk/iris/docs/v1.6/iris/iris/quickplot.html#iris.quickplot.contour\n",
+    "contour = qplt.contour(sst0)\n",
+    "\n",
+    "# Add coastlines to the map created by contour.\n",
+    "plt.gca().coastlines()\n",
+    "\n",
+    "# Add contour labels based on the contour we have just created.\n",
+    "plt.clabel(contour)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Filled polygons, values legend, continental interior legend\n",
+    "##  http://scitools.org.uk/iris/docs/v1.6/iris/iris/quickplot.html#iris.quickplot.contour\n",
+    "\n",
+    "qplt.contourf(sst0)\n",
+    "plt.gca().stock_img()\n",
+    "plt.gca().coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Pseudocolor Mesh\n",
+    "##  http://scitools.org.uk/iris/docs/v1.6/iris/iris/quickplot.html#iris.quickplot.pcolormesh\n",
+    "\n",
+    "qplt.pcolormesh(sst0)\n",
+    "plt.gca().stock_img()\n",
+    "plt.gca().coastlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-qplt-netcdf-dem.ipynb b/projects/IRIS/iris-qplt-netcdf-dem.ipynb
new file mode 100644
index 0000000..92ff00b
--- /dev/null
+++ b/projects/IRIS/iris-qplt-netcdf-dem.ipynb
@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt\n",
+    "\n",
+    "## netCDF via IRIS example\n",
+    "##  Live 8.5  *  darkblue-b\n",
+    "##"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Convert a DEM GeoTIFF to netCDF with gdal\n",
+    "!rm 'sample_data/SanMateo_CA.cf'\n",
+    "!gdal_translate -of netcdf sample_data/SanMateo_CA.tif sample_data/SanMateo_CA.cf\n",
+    "\n",
+    "## previously extracted from a large USGS DEM via\n",
+    "##   gdal_translate -projwin -122.44 37.7 -122.2 37.4 -of GTiff imgn38w123_1.img  SanMateo_CA.tif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## Open the netCDF with IRIS\n",
+    "res = iris.load('sample_data/SanMateo_CA.cf')\n",
+    "\n",
+    "print ' TYPE: ', type(res)\n",
+    "print 'CUBES: ', res\n",
+    "print ' REPR: ', res[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## color visualization of elevation data using IRIS Quickplot\n",
+    "iplt.pcolormesh(res[0], cmap=plt.get_cmap('gist_earth'))\n",
+    "iplt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "contour = qplt.contour(res[0])\n",
+    "\n",
+    "## use the center point for a quick demo\n",
+    "lon = -122.3200000\n",
+    "lat =   37.5500000\n",
+    "\n",
+    "plt.scatter(lon, lat, marker=(5, 1), color='red', s=200)\n",
+    "plt.title(\"Welcome to San Mateo\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-qplt-netcdf.ipynb b/projects/IRIS/iris-qplt-netcdf.ipynb
new file mode 100644
index 0000000..a965200
--- /dev/null
+++ b/projects/IRIS/iris-qplt-netcdf.ipynb
@@ -0,0 +1,111 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import numpy.ma as ma\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Ionosphere space weather\n",
+    "========================\n",
+    "\n",
+    "This space weather example plots a filled contour of rotated pole point\n",
+    "data with a shaded relief image underlay. The plot shows aggregated\n",
+    "vertical electron content in the ionosphere.\n",
+    "\n",
+    "The plot exhibits an interesting outline effect due to excluding data\n",
+    "values below a certain threshold.\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def main():\n",
+    "    # Load the \"total electron content\" cube.\n",
+    "    #filename = iris.sample_data_path('space_weather.nc')\n",
+    "    filename = 'sample_data/space_weather.nc'\n",
+    "    cube = iris.load_cube(filename, 'total electron content')\n",
+    "\n",
+    "    # Explicitly mask negative electron content.\n",
+    "    cube.data = ma.masked_less(cube.data, 0)\n",
+    "\n",
+    "    # Plot the cube using one hundred colour levels.\n",
+    "    qplt.contourf(cube, 100)\n",
+    "    plt.title('Total Electron Content')\n",
+    "    plt.xlabel('longitude / degrees')\n",
+    "    plt.ylabel('latitude / degrees')\n",
+    "    plt.gca().stock_img()\n",
+    "    plt.gca().coastlines()\n",
+    "    iplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-qplt-pp.ipynb b/projects/IRIS/iris-qplt-pp.ipynb
new file mode 100644
index 0000000..aeb7eca
--- /dev/null
+++ b/projects/IRIS/iris-qplt-pp.ipynb
@@ -0,0 +1,144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Quickplot of a 2d cube on a map\n",
+    "===============================\n",
+    "\n",
+    "This example demonstrates a contour plot of global air temperature.\n",
+    "The plot title and the labels for the axes are automatically derived from the metadata.\n",
+    "(added example of to_xml -ed)\n",
+    "\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def main():\n",
+    "    #fname = iris.sample_data_path('air_temp.pp')\n",
+    "    fname = 'sample_data/air_temp.pp'\n",
+    "    temperature = iris.load_cube(fname)\n",
+    "\n",
+    "    # Plot #1: contourf with axes longitude from -180 to 180\n",
+    "    fig = plt.figure(figsize=(12, 5))\n",
+    "    plt.subplot(121)\n",
+    "    qplt.contourf(temperature, 15)\n",
+    "    plt.gca().coastlines()\n",
+    "\n",
+    "    # Plot #2: contourf with axes longitude from 0 to 360\n",
+    "    proj = ccrs.PlateCarree(central_longitude=-180.0)\n",
+    "    ax = plt.subplot(122, projection=proj)\n",
+    "    qplt.contourf(temperature, 15)\n",
+    "    plt.gca().coastlines()\n",
+    "    iplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#fname = iris.sample_data_path('air_temp.pp')\n",
+    "fname = 'sample_data/air_temp.pp'\n",
+    "t0 = iris.load_cube(fname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "t0.xml()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "whos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/IRIS/iris-rpole.ipynb b/projects/IRIS/iris-rpole.ipynb
new file mode 100644
index 0000000..7f3a9d7
--- /dev/null
+++ b/projects/IRIS/iris-rpole.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import iris\n",
+    "import iris.plot as iplt\n",
+    "import iris.quickplot as qplt\n",
+    "import iris.analysis.cartography"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "Rotated pole mapping\n",
+    "=====================\n",
+    "\n",
+    "This example uses several visualisation methods to achieve an array of\n",
+    "differing images, including:\n",
+    "\n",
+    " * Visualisation of point based data\n",
+    " * Contouring of point based data\n",
+    " * Block plot of contiguous bounded data\n",
+    " * Non native projection and a Natural Earth shaded relief image underlay\n",
+    "\n",
+    "\n",
+    "### http://scitools.org.uk/iris/docs/v1.9.0/html/gallery.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def main():\n",
+    "    #fname = iris.sample_data_path('rotated_pole.nc')\n",
+    "    fname = 'sample_data/rotated_pole.nc'\n",
+    "    air_pressure = iris.load_cube(fname)\n",
+    "\n",
+    "    # Plot #1: Point plot showing data values & a colorbar\n",
+    "    plt.figure()\n",
+    "    points = qplt.points(air_pressure, c=air_pressure.data)\n",
+    "    cb = plt.colorbar(points, orientation='horizontal')\n",
+    "    cb.set_label(air_pressure.units)\n",
+    "    plt.gca().coastlines()\n",
+    "    iplt.show()\n",
+    "\n",
+    "    # Plot #2: Contourf of the point based data\n",
+    "    plt.figure()\n",
+    "    qplt.contourf(air_pressure, 15)\n",
+    "    plt.gca().coastlines()\n",
+    "    iplt.show()\n",
+    "\n",
+    "    # Plot #3: Contourf overlayed by coloured point data\n",
+    "    plt.figure()\n",
+    "    qplt.contourf(air_pressure)\n",
+    "    iplt.points(air_pressure, c=air_pressure.data)\n",
+    "    plt.gca().coastlines()\n",
+    "    iplt.show()\n",
+    "\n",
+    "    # For the purposes of this example, add some bounds to the latitude\n",
+    "    # and longitude\n",
+    "    air_pressure.coord('grid_latitude').guess_bounds()\n",
+    "    air_pressure.coord('grid_longitude').guess_bounds()\n",
+    "\n",
+    "    # Plot #4: Block plot\n",
+    "    plt.figure()\n",
+    "    plt.axes(projection=ccrs.PlateCarree())\n",
+    "    iplt.pcolormesh(air_pressure)\n",
+    "    plt.gca().stock_img()\n",
+    "    plt.gca().coastlines()\n",
+    "    iplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "main()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/README.md b/projects/PYSAL/README.md
new file mode 100644
index 0000000..8448ee0
--- /dev/null
+++ b/projects/PYSAL/README.md
@@ -0,0 +1,132 @@
+
+# Spatial Data Analysis with PySAL and GeoDaSpace
+
+**[Sergio Rey] and [Luc Anselin]**
+
+**November 11, 2015**
+
+**NARSC  2015, Portland, Oregon**
+
+## Tutorial Description
+
+A unique feature of this tutorial is the use of Python based software tools for spatial data analysis. Python is an object oriented scripting language that is gaining rapid adoption in the scientific computing and data science communities. To facilitate its adoption within the GIScience community, Rey and Anselin have collaborated on the creation of [PySAL]: Python Library for Spatial Analysis. Since its initial release in July 2010, PySAL has been downloaded over 75,000 times and is now included in well- known open source scientific data analysis distributions, such as Anaconda. This two-part tutorial will introduce participants to the latest version of PySAL as well as to GeoDaSpace, a GUI application based on PySAL designed for spatial econometric analysis. The first component introduces the basic organization and philosophy of PySAL, with a special focus on exploratory spatial data analysis. In the second part of the tutorial the focus moves to practical spatial regression analysis using GeoDaSpace and the PySAL spreg module.
+
+## Prerequisites
+
+- Previous experience with Python programming is recommended
+- Participants should bring their own laptops to the workshop
+- Software should be installed prior to traveling to the workshop (instructions below)
+
+### Software Requirements
+**Please note that PySAL is pre-installed here -- feb2016
+
+
+For the workshop we will require the following packages be installed
+
+- PySAL 1.10.0
+- SciPy
+- Numpy
+- iPython 
+- ipywidgets
+- jupyter
+- matplotlib
+- pandas
+- folium
+- [GeoDaSpace][GeoDaSpace]
+
+There are a number of ways to install PySAL and these dependencies. For the workshop, if you do not yet have the dependencies installed we suggest using one of two scientific Python distributions (below). These have the advantages of including most of the dependencies for PySAL as well as PySAL itself. Moreover, both allow for updating PySAL to the most recent release  (1.10 released July 31, 2015) which is more current that what is listed in either distribution. Both of these distributions also allow for installation of our final dependency, folium.
+
+#### PySAL via Anaconda Python Distribution
+
+1. Install [Anaconda Python Distribution][Anaconda]
+2. Open a terminal (Mac or Linux) or Powershell (Windows)
+2. `pip install -U pysal`
+3. `pip install -U folium`
+
+#### Creating a Custom Conda Environment
+
+If you already have Anaconda installed and you did not want to modify your default environemnt, you can create a *custom conda environment* for this session using the following commands:
+
+1. `conda create -n pysal110 scipy matplotlib jupyter ipython pandas ipywidgets`
+2. `source activate pysal110`
+4. `pip install -U pysal`
+5. `pip install -U folium`
+
+When you are done working in this environment, you can get back to your default environment with:
+
+ `source deactivate`
+
+
+#### PySAL via Enthought Canopy
+
+Note that the Academic version of Canopy comes with PySAL version 1.7. For this workshop we will be using PySAL 1.10. Upgrading in Canopy can be done as follows:
+
+1. Install [Canopy][Canopy]
+2. Run Canopy
+3. From the menu select `Tools Canopy Terminal`
+4. `pip install -U pysal`
+5. `pip install -U folium`
+
+
+
+#### Testing Your Installation
+
+Once you have installed all the dependencies, you can check to confirm everything is ready to go.
+
+For Anaconda:
+
+1. Open a terminal (Mac or Linux) or Powershell (Windows)
+2. `ipython notebook`
+3. In the browser click `New Notebook`
+3. In the first cell in the notebook enter  
+   `import pysal`
+   `pysal.version`
+   Then `<Shift-Enter>` (i.e., hit the Shift then the Enter Key)
+4. In the second cell in the notebook enter  
+   `import folium`
+   Then `<Shift-Enter>`
+5. In the third cell enter
+   `!which python`
+   Then `<Shift-Enter>`
+ 
+Your screen should look something like:
+![Anaconda setup](esda/figures/anaconda.png)
+
+
+For Enthought Canopy:
+
+2. Run Canopy
+3. From the menu select `Tools Canopy Terminal`
+2. `ipython notebook`
+3. In the browser click `New Notebook`
+3. In the first cell in the notebook enter  
+   `import pysal`
+   `pysal.version`
+   Then `<Shift-Enter>` (i.e., hit the Shift then the Enter Key)
+4. In the second cell in the notebook enter  
+   `import folium`
+   Then `<Shift-Enter>`
+5. In the third cell enter
+   `!which python`
+   Then `<Shift-Enter>`
+ 
+
+Your screen should look something like:
+![Enthought setup](esda/figures/enthought.png)
+
+
+#### Issues
+
+If you run into any problems, double check that you have installed both the upgraded version of PySAL and folium (see above). If problems persist, please contact me <sjsrey@gmail.com>.
+
+
+[PySAL]: http://pysal.org
+[GeoDaSpace]: https://geodacenter.asu.edu/software/downloads/geodaspace
+[Anaconda]: http://continuum.io/downloads.html
+[Canopy]: https://www.enthought.com/store
+[VirtualBox]: https://www.virtualbox.org/wiki/Downloads
+[VirtualBox 4.3.12]: http://download.virtualbox.org/virtualbox/4.3.12/VirtualBox-4.3.12-93733-Win.exe
+[Vagrant]: http://www.vagrantup.com/downloads.html
+[Vagrantfile]: Vagrantfile
+[Sergio Rey]: https://geoplan.asu.edu/people/sergio-j-rey
+[Luc Anselin]: https://geoplan.asu.edu/people/luc-anselin
diff --git a/projects/PYSAL/esda/00_introduction.ipynb b/projects/PYSAL/esda/00_introduction.ipynb
new file mode 100644
index 0000000..ba6487b
--- /dev/null
+++ b/projects/PYSAL/esda/00_introduction.ipynb
@@ -0,0 +1,240 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spatial Data Analysis with PySAL and GeoDaSpace\n",
+    "\n",
+    "\n",
+    "Authors: Sergio Rey and Luc Anselin\n",
+    "\n",
+    "Date: 2015-11-11 \n",
+    "\n",
+    "Location: NARSC 2015, Portland, Oregon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "A unique feature of this tutorial is the use of Python based software tools for spatial data analysis. Python is an object oriented scripting language that is gaining rapid adoption in the scientific computing and data science communities. To facilitate its adoption within the GIScience community, Rey and Anselin have collaborated on the creation of PySAL: Python Library for Spatial Analysis. Since its initial release in July 2010, PySAL has been downloaded over 75,000 times and is now included in well- known open source scientific data analysis distributions, such as Anaconda. This two-part tutorial will introduce participants to the latest version of PySAL as well as to GeoDaSpace, a GUI application based on PySAL designed for spatial econometric analysis. The first component introduces the basic organization and philosophy of PySAL, with a special focus on exploratory spatial data analysis. In the second part of the tutorial the focus moves to practical spatial regression analysis using GeoDaSpace and the PySAL spreg module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outline\n",
+    "\n",
+    "This is a full-day tutorial broken into two components.\n",
+    "\n",
+    "### Part I: Exploratory Spatial Data Analysis with PySAL\n",
+    "\n",
+    "- PySAL introduction\n",
+    "- Notebooks and installation\n",
+    "- Visualization\n",
+    "- Spatial data processing\n",
+    "- Spatial weights\n",
+    "- GeoVisualization\n",
+    "- Global spatial autocorrelation\n",
+    "- Local spatial autocorrelation\n",
+    "\n",
+    "### Part II: Spatial Regression Analysis with GeoDaSpace\n",
+    "\n",
+    "- GeoDaSpace introduction\n",
+    "- Basic regression\n",
+    "- Spatial diagnostics\n",
+    "- Nonspatial endogeneity\n",
+    "- Spatial lag model\n",
+    "- Spatial error model\n",
+    "- Spatial heterogeneity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PySAL: Python Spatial Analysis Library\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Origins and Objectives\n",
+    "\n",
+    "- Leverage Existing Tools Development\n",
+    "\n",
+    "    - GeoDa/PySpace\n",
+    "    - STARS\n",
+    "\n",
+    "- Develop Core Library\n",
+    "\n",
+    "    - spatial data *analytical* functions\n",
+    "    - enhanced specialization and modularity\n",
+    "    - fill *void* in geospatial Python libraries\n",
+    "    \n",
+    "- Flexible Delivery Mechanisms\n",
+    "\n",
+    "    - interactive shell\n",
+    "    - GUI\n",
+    "    - Toolkits\n",
+    "    - webservices\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Components\n",
+    "![PySAL](figures/pysalgraph.png)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Delivery Mechanisms\n",
+    "\n",
+    "### Interactive Shell: iPython Notebook\n",
+    "![pysalipnb](figures/pysalnb.png)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "### Toolkits: ArcGIS + spreg\n",
+    "![arctool](figures/10_arctool.png)\n",
+    "### Toolkits: QGIS + pysal\n",
+    "![arctool](figures/qgis.png)\n",
+    "### Webservices: cgPySAL\n",
+    "\n",
+    "\n",
+    "![cgpysal](figures/cgpysal.png)\n",
+    "\n",
+    "\n",
+    "### GUI: Crime Analytics for Space-Time (CAST)\n",
+    "\n",
+    "![cast](figures/cast.png)\n",
+    "\n",
+    "### GUI: GeoDaSpace (PySAL-spreg)\n",
+    "![gsgui](figures/gsgui.png)\n",
+    "\n",
+    "![gsout](figures/gsoutput.png)\n",
+    "\n",
+    "![weightsviewer](figures/weightsviewer.png)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Team\n",
+    "\n",
+    "\n",
+    "\n",
+    "| Serge Rey       | Luc Anselin      |\n",
+    "|-----------------|------------------|\n",
+    "| Charles Schnidt | David Folch      |\n",
+    "| Myunghwa Hwang  | Dani Arribas     |\n",
+    "| Phil Stephens   | Julia Koschinsky |\n",
+    "| Pedro Amaral    | Nick Malizia     |\n",
+    "| Xing Kang       | Xun Li           |\n",
+    "| Mark McCann     | Ran Wei          |\n",
+    "| Nancy Lozano    | Jing Yao         |\n",
+    "| Jay Laura       | Levi Wolf        |\n",
+    "| Sizhe Wang      | Wei Kang         |\n",
+    "\n",
+    "\n",
+    "and, contributions from *many* others!\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Releases\n",
+    "\n",
+    "### Six-Month Release Cycle\n",
+    "  - 1.0 July 2010\n",
+    "  - 1.1 January 2011\n",
+    "  - 1.2 July 2011\n",
+    "  - 1.3 January 2012\n",
+    "  - 1.4 July 2012\n",
+    "  - 1.5 January 2013\n",
+    "  - 1.6 July 2013\n",
+    "  - 1.7 January 2014\n",
+    "  - **1.8 July 2014**\n",
+    "  \n",
+    "### Scientific Python Distributions\n",
+    "\n",
+    "#### Anaconda Python Distribution\n",
+    "![anaconda](figures/anaconda_pysal.png)\n",
+    "\n",
+    "#### Enthought Canopy \n",
+    "\n",
+    "![anaconda](figures/enthought_pysal.png)\n",
+    "\n",
+    "\n",
+    "\n",
+    "## Acknowledgements\n",
+    "\n",
+    "![funding](figures/ack.png)\n",
+    "\n",
+    "\n",
+    "  \n",
+    "## Development\n",
+    "\n",
+    "[![PySAL.org](figures/pysal_org.png)](http://pysal.org)\n",
+    "\n",
+    "## References\n",
+    "\n",
+    "1. S. J. Rey. Python Spatial Analysis Library (PySAL): An update and illustration. In C. Brunsdon and A. SIngleton, editors, Geocomputation. Sage, In Press 2014.\n",
+    "2. S. J. Rey and L. Anselin. PySAL: A Python library of spatial analytical methods. In M. M. Fischer and A. Getis, editors, Handbook of Applied Spatial Analysis, pages 175–193. Springer, Berlin, 2010.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/00_notebook_intro.ipynb b/projects/PYSAL/esda/00_notebook_intro.ipynb
new file mode 100644
index 0000000..9dc9694
--- /dev/null
+++ b/projects/PYSAL/esda/00_notebook_intro.ipynb
@@ -0,0 +1,400 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# PySAL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Documentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "HTML('<iframe src=http://pysal.org/?useformat=mobile width=800 height=550></iframe>')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Source Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Source is at http://github.com/pysal"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to IPython Notebook\n",
+    "\n",
+    "We will cover the basic operations/interface of the notebook which we will, in turn, use for the remainder of the workshop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What is IPython Notebook (now called Jupyter)\n",
+    "\n",
+    "- web-based interface for working with Python (and other languages)\n",
+    "- Similar in spirit to a scientific notebook\n",
+    "    - record experiments\n",
+    "    - document\n",
+    "    - share\n",
+    "- literate programming\n",
+    "- simple JSON format\n",
+    "    - web citizen\n",
+    "    - git friendly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Starting the notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From a shell or terminal we can start the notebook with:\n",
+    "```\n",
+    "ipython notebook\n",
+    "```\n",
+    "\n",
+    "This brings up the **dashboard** which will list any notebooks encountered in the current working directory.\n",
+    "\n",
+    "You can either open an existing notebook or create a new one from the dashboard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image\n",
+    "def show_image(img):\n",
+    "    i = Image(filename = img)\n",
+    "    return i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "show_image('images/dashboard.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook Inteface"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Menu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "show_image('images/menu.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Help System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "show_image('images/help.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Keyboard Shortcuts\n",
+    "\n",
+    "A list of keyboard shortcuts can be revealed by entering `h`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "show_image('images/shortcuts.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note there are two *modes* for the notebook, *Edit Mode* and *Command mode*. The latter is used to *manipulate* notebook cells, while the former is used to *edit* the content of a cell.\n",
+    "\n",
+    "- To enter *Command Mode* press `ESC`.\n",
+    "- To enter *Edit Mode* press `Enter` on a cell.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Navigation\n",
+    "\n",
+    "When in command mode, you can use keyboard shortcuts to move from cell to cell\n",
+    "\n",
+    "- `k` moves the cursor up one cell\n",
+    "- `j` moves the cursor down one cell\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cell Types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Code Cells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = range(10) # Shift-Enter to execute and create a new cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = range(10)\n",
+    "y = [ xi*3 for xi in x] # Ctrl-Return to execute but stay in the current cell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Comments\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Text Cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In command mode, `t` will set the cell to text. After that, pressing `Enter` will let you edit the text."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Markdown Cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In command mode, ```m``` gives us a [Markdown] cell.\n",
+    "\n",
+    "\n",
+    "[Markdown]: http://daringfireball.net/projects/markdown/\n",
+    "\n",
+    "We can then use Markdown syntax to do things like lists:\n",
+    "\n",
+    "- first\n",
+    "- second\n",
+    "- third\n",
+    "  - nested one\n",
+    "  - nested two\n",
+    "- fourth\n",
+    "\n",
+    "\n",
+    "As well as **LaTeX**.\n",
+    "\n",
+    "This is an in-line equation $\\hat{\\beta} = (X'X)^{-1}X'y$ for the ordinary least squares estimator.\n",
+    "\n",
+    "A display equation is done with\n",
+    "\n",
+    "$$ y = \\rho W y + X\\beta + \\epsilon$$\n",
+    "\n",
+    "**NOTE**: LaTeX will only render if mathjax is available via a network connection or if it has been installed locally."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stoping and Restarting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`00` (enter the zero key twice)  will restart the kernel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exporting and Saving"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can save in several formats via the **File** menu entry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "show_image('images/save.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving\n",
+    "\n",
+    "In command mode, `s` will save the notebook, and mark a checkpoint. You can use checkpoints to rollback to a previous version if needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/01_spatial_data_processing.ipynb b/projects/PYSAL/esda/01_spatial_data_processing.ipynb
new file mode 100644
index 0000000..ac4cff7
--- /dev/null
+++ b/projects/PYSAL/esda/01_spatial_data_processing.ipynb
@@ -0,0 +1,588 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Spatial Data Processing with PySAL\n",
+    "\n",
+    "This notebook will cover basic spatial data processing and using PySAL with the Notebook\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Common Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# our convention is to alias PySAL, NumPy and Pandas\n",
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# check the versions\n",
+    "ps.version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.version.short_version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pd.__version__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading a CSV File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!head data/mexico.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = ps.open(\"data/mexico.csv\")\n",
+    "vnames = [\"pcgdp%d\"%decade for decade in range(1940, 2010, 10)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "vnames"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y = np.transpose(np.array([f.by_col[v] for v in vnames]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "state = f.by_col['State']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f.close() # done with the file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "state"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading a Shapefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# us counties example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "### First the attributes from the dbf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dbf = ps.open('data/NAT.dbf')\n",
+    "header = dbf.header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Read all the numeric variables into a big array\n",
+    "# find the first offset we need\n",
+    "start_col = header.index(\"SOUTH\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "start_col"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "vars = header[8:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "vars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "nat_array = np.array([np.array(dbf.by_col(var)) for var in vars])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "nat_array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "nat_array = nat_array.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "nat_array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dbf.close() # done with the dbf file\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now the geometries "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shp_file = ps.open(\"data/NAT.shp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shp_file.header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "len(shp_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shapes = [ shp_file.next() for i in xrange(len(shp_file)) ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "type(shapes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "len(shapes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "type(shapes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "s0 = shapes[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "s0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dir(s0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shp_file.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading a GeoJSON File"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = open('data/nat.json')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fj = json.load(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fj.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "features = []\n",
+    "for feature in fj['features']:\n",
+    "    features.append(feature)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "features[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/02_plotting.ipynb b/projects/PYSAL/esda/02_plotting.ipynb
new file mode 100644
index 0000000..2a762e1
--- /dev/null
+++ b/projects/PYSAL/esda/02_plotting.ipynb
@@ -0,0 +1,473 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## X,Y Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "I = np.exp(-d)\n",
+    "plt.plot(d,I)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "I = np.exp(-d)\n",
+    "plt.ylabel('I')\n",
+    "plt.xlabel('d')\n",
+    "plt.plot(d,I)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "I = np.exp(-d)\n",
+    "plt.ylabel(r'$I= \\exp(-d)$')\n",
+    "plt.xlabel('d')\n",
+    "plt.plot(d,I)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "type(d)\n",
+    "d[0:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "I = d**(-2)\n",
+    "plt.ylabel(r'$I= 1 / d^2$')\n",
+    "plt.xlabel('d')\n",
+    "plt.plot(d,I)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "Iid2 = d**(-2)\n",
+    "plt.plot(d,Iid2)\n",
+    "plt.ylabel(r'$I$')\n",
+    "Inex = np.exp(-d)\n",
+    "plt.plot(d,Inex)\n",
+    "plt.xlabel('d')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "Iid2 = d**(-2)\n",
+    "plt.plot(d,Iid2, label = r'$I = d^{-2}$')\n",
+    "plt.ylabel(r'$I$')\n",
+    "Inex = np.exp(-d)\n",
+    "plt.plot(d,Inex, label = r'$I = \\exp(-d)$')\n",
+    "plt.xlabel('d')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.legend?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "Iid2 = d**(-2)\n",
+    "plt.plot(d,Iid2, 'r-', label = r'$I = d^{-2}$')\n",
+    "plt.ylabel(r'$I$')\n",
+    "Inex = np.exp(-d)\n",
+    "plt.plot(d,Inex, 'g--',label = r'$I = \\exp(-d)$')\n",
+    "plt.xlabel('d')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = np.linspace(1,10,200)\n",
+    "Iid = d**(-1)\n",
+    "plt.plot(d,Iid, 'b-.', label = r'$I = d^{-1}$')\n",
+    "Iid2 = d**(-2)\n",
+    "plt.plot(d,Iid2, 'r-', label = r'$I = d^{-2}$')\n",
+    "plt.ylabel(r'$I$')\n",
+    "Inex = np.exp(-d)\n",
+    "plt.plot(d,Inex, 'g--',label = r'$I = \\exp(-d)$')\n",
+    "plt.xlabel('d')\n",
+    "plt.legend()\n",
+    "plt.title('Distance Decay')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scatter Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.arange(100)\n",
+    "y = 0.5 + np.random.normal(size=(100,))\n",
+    "plt.scatter(x,y)\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(x,y)\n",
+    "plt.vlines(x.mean(),y.min(),y.max())\n",
+    "plt.hlines(x.min(),x.max(),y.mean())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distributions and Densities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.mlab as mlab\n",
+    "\n",
+    "x = np.random.multivariate_normal([100,70],[[2.,1.7],[1.7,2.]], (50))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x[:,0], x[:,1], \"o\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x0h = plt.hist(x[:,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x1h = plt.hist(x[:,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "n,bins,bars = plt.hist(x[:,1])\n",
+    "\n",
+    "y = mlab.normpdf(bins, np.mean(x[:,1]), np.std(x[:,1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(bins,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(bins,y)\n",
+    "plt.hist(x[:,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(bins,y)\n",
+    "plt.hist(x[:,1],normed=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving to a file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(bins,y)\n",
+    "plt.hist(x[:,1],normed=1)\n",
+    "plt.savefig(\"hist.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!ls *.png"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import gaussian_kde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = x[:,1]\n",
+    "density = gaussian_kde(y)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "xs = np.linspace(y.min(),y.max(),30)\n",
+    "plt.plot(xs, density(xs))\n",
+    "plt.xlabel('y')\n",
+    "plt.ylabel('f(y)')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "xs = np.linspace(y.min()*.8,y.max()*1.2,30)\n",
+    "plt.plot(xs, density(xs))\n",
+    "plt.xlabel('y')\n",
+    "plt.ylabel('f(y)')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a lot more to matplotlib. One can visit the [gallery](http://matplotlib.org/gallery.html) and pull examples in to get a sense of what is possible, and how to adapt examples for your own purposes.\n",
+    "\n",
+    "\n",
+    "For now we move on to generating choropleth maps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/03_choropleth_mapping.ipynb b/projects/PYSAL/esda/03_choropleth_mapping.ipynb
new file mode 100644
index 0000000..afbd427
--- /dev/null
+++ b/projects/PYSAL/esda/03_choropleth_mapping.ipynb
@@ -0,0 +1,495 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Choropleth Mapping With Folium and PySAL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "____\n",
+    "## Author\n",
+    "\n",
+    "Serge Rey [sjsrey@gmail.com](sjsrey@gmail)\n",
+    "\n",
+    "## Requirements\n",
+    "\n",
+    "Since we will be pulling in tiles for basemaps, we need internet connectivity for what follows to work.\n",
+    "____\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "#import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import folium\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Help functions\n",
+    "In order to have folium maps appear in-line we will need two utility functions.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def inline_map(map):\n",
+    "    \"\"\"\n",
+    "    Embeds the HTML source of the map directly into the IPython notebook.\n",
+    "    \n",
+    "    This method will not work if the map depends on any files (json data). Also this uses\n",
+    "    the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
+    "    \"\"\"\n",
+    "    map._build_map()\n",
+    "    return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
+    "\n",
+    "def embed_map(map, path=\"map.html\"):\n",
+    "    \"\"\"\n",
+    "    Embeds a linked iframe to the map into the IPython notebook.\n",
+    "    \n",
+    "    Note: this method will not capture the source of the map into the notebook.\n",
+    "    This method should work for all maps (as long as they use relative urls).\n",
+    "    \"\"\"\n",
+    "    map.create_map(path=path)\n",
+    "    return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The source for these functions is an iPython notebook by [Blake Burkhart](http://nbviewer.ipython.org/gist/bburky/7763555/folium-ipython.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## OpenStreet Map Tile with Folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map = folium.Map(location=[45.52, -122.68], zoom_start=11)\n",
+    "map.simple_marker([45.52, -122.68], popup='Welcome to PySAL @<b>NARSC 2015</b>!')\n",
+    "inline_map(map)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Base layer for south"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=4)\n",
+    "map_osm.geo_json(geo_path = 'data/south.json')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Change the zoom level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5)\n",
+    "map_osm.geo_json(geo_path = 'data/south.json')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binding Attribute Data to the Map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "f = open(r'data/south.json')\n",
+    "q = json.load(f)\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "features = q['features']\n",
+    "len(features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "feature_0 = features[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "feature_0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "indices = []\n",
+    "values = []\n",
+    "for feature in features:\n",
+    "    indices.append(str(feature['properties']['FIPS']))\n",
+    "    values.append(feature['properties']['HR80'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'HR80': values,\n",
+    "                   'FIPS': indices} )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 legend_name='Homicide Rate HR80')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using PySAL Map Classification Schemes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.array(df.HR80.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ps.version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Maximum Breaks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "max_breaks = ps.Maximum_Breaks(y, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bins = max_breaks.bins.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=bins[:-1],\n",
+    "                 legend_name='Homicide Rate HR80 (Maximum Breaks)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quantiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q5 = ps.Quantiles(y, 5).bins.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=q5[:-1],\n",
+    "                 legend_name='Homicide Rate HR80 (Quintiles)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q6 = ps.Quantiles(y, 6).bins.tolist()\n",
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=q6[:-1],\n",
+    "                 legend_name='Homicide Rate HR80 (Quantiles k=6)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fisher-Jenks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fj= ps.Fisher_Jenks(y, 5).bins.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=fj[:-1],\n",
+    "                 legend_name='Homicide Rate HR80 (Fisher Jenks)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ps.esda.mapclassify.Fisher_Jenks_Sampled?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/04_pysal_mapping.ipynb b/projects/PYSAL/esda/04_pysal_mapping.ipynb
new file mode 100644
index 0000000..8e84997
--- /dev/null
+++ b/projects/PYSAL/esda/04_pysal_mapping.ipynb
@@ -0,0 +1,296 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import folium\n",
+    "import random as RD\n",
+    "\n",
+    "\n",
+    "from pysal.contrib.viz import mapping as maps\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Guide for the `mapping` module in `PySAL`\n",
+    "\n",
+    "Contributors:\n",
+    "\n",
+    "* Dani Arribas-Bel `<daniel.arribas.bel@gmail.com>`\n",
+    "* Serge Rey `<sjsrey@gmail.com>`\n",
+    "\n",
+    "\n",
+    "This document describes the main structure, components and usage of the mapping module in `PySAL`. The is organized around three main layers:\n",
+    "\n",
+    "* A lower-level layer that reads polygon, line and point shapefiles and returns a Matplotlib collection.\n",
+    "* A medium-level layer that performs some usual transformations on a Matplotlib object (e.g. color code polygons according to a vector of values).\n",
+    "* A higher-level layer intended for end-users for particularly useful cases and style preferences pre-defined (e.g. Create a choropleth)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lower-level component\n",
+    "\n",
+    "This includes basic functionality to read spatial data from a file (currently only shapefiles supported) and produce rudimentary Matplotlib objects. The main methods are:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `map_poly_shape`: to read in polygon shapefiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `map_line_shape`: to read in line shapefiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `map_point_shape`: to read in point shapefiles"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These methods all support an option to subset the observations to be plotted (very useful when missing values are present). They can also be overlaid and combined by using the `setup_ax` function. the resulting object is very basic but also very flexible so, for minds used to matplotlib this should be good news as it allows to modify pretty much any property and attribute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shp_link = ps.examples.get_path('columbus.shp')\n",
+    "shp = ps.open(shp_link)\n",
+    "some = [bool(RD.getrandbits(1)) for i in ps.open(shp_link)]\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "base = maps.map_poly_shp(shp)\n",
+    "base.set_facecolor('none')\n",
+    "base.set_linewidth(0.75)\n",
+    "base.set_edgecolor('0.8')\n",
+    "some = maps.map_poly_shp(shp, which=some)\n",
+    "some.set_alpha(0.5)\n",
+    "some.set_linewidth(0.)\n",
+    "cents = np.array([poly.centroid for poly in ps.open(shp_link)])\n",
+    "pts = plt.scatter(cents[:, 0], cents[:, 1])\n",
+    "pts.set_color('red')\n",
+    "\n",
+    "ax = maps.setup_ax([base, some, pts])\n",
+    "fig.add_axes(ax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "some"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Medium-level component\n",
+    "\n",
+    "This layer comprises functions that perform usual transformations on matplotlib objects, such as color coding objects (points, polygons, etc.) according to a series of values. This includes the following methods:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `base_choropleth_classless`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `base_choropleth_unique`\n",
+    "\n",
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "net_link = ps.examples.get_path('eberly_net.shp')\n",
+    "net = ps.open(net_link)\n",
+    "values = np.array(ps.open(net_link.replace('.shp', '.dbf')).by_col('TNODE'))\n",
+    "\n",
+    "pts_link = ps.examples.get_path('eberly_net_pts_onnetwork.shp')\n",
+    "pts = ps.open(pts_link)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "\n",
+    "netm = maps.map_line_shp(net)\n",
+    "netc = maps.base_choropleth_unique(netm, values)\n",
+    "\n",
+    "ptsm = maps.map_point_shp(pts)\n",
+    "ptsm = maps.base_choropleth_classif(ptsm, values)\n",
+    "ptsm.set_alpha(0.5)\n",
+    "ptsm.set_linewidth(0.)\n",
+    "\n",
+    "ax = maps.setup_ax([netc, ptsm])\n",
+    "fig.add_axes(ax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "* `base_choropleth_classif`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Higher-level component\n",
+    "\n",
+    "This currently includes the following end-user functions:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `plot_poly_lines`: very quick shapefile plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "maps.plot_poly_lines(ps.examples.get_path('columbus.shp'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* `plot_choropleth`: for quick plotting of several types of chocopleths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shp_link = ps.examples.get_path('columbus.shp')\n",
+    "values = np.array(ps.open(ps.examples.get_path('columbus.dbf')).by_col('HOVAL'))\n",
+    "\n",
+    "types = ['classless', 'unique_values', 'quantiles',  'equal_interval']\n",
+    "for typ in types:\n",
+    "    maps.plot_choropleth(shp_link, values, typ, title=typ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# To-Do list\n",
+    "\n",
+    "General concepts and specific ideas to implement over time, with enough description so they can be brought to life.\n",
+    "\n",
+    "* Support for points in medium and higher layer\n",
+    "* LISA cluster maps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Caution note on plotting points\n",
+    "\n",
+    "Support for points (dots) is still not quite polished. Ideally, one would like to create a `PathCollection` from scratch so it is analogue to the creation of a `PolyCollection` or `LineCollection`. However, for the time being, we are relying on the wrapper `plt.scatter`, which makes it harder to extract the collection and plug it in a different figure. For that reason, it is recommended that, for the time being, one creates the line and/or polygon map as shown in this notebook and then grabs the output axis and uses `ax.scatter` to overlay the points.\n",
+    "\n",
+    "**NOTE**: the `PathCollection` created by `plt.scatter` is detailed on line 3142 of [`_axes.py`](https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/axes/_axes.py). Maybe we can take some inspiration from there to create our own `PathCollection` for points so they live at the same level as polygons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/05_spatial_weights.ipynb b/projects/PYSAL/esda/05_spatial_weights.ipynb
new file mode 100644
index 0000000..4454e75
--- /dev/null
+++ b/projects/PYSAL/esda/05_spatial_weights.ipynb
@@ -0,0 +1,951 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "#import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regular Lattice Weights "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = ps.lat2W()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$s_0 = \\sum_i \\sum_j w_{i,j}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.s0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.sum( [ c[0]*c[1] for c in w.histogram ] )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$pctnonzero = s_0 / n^2$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.s0 / w.n**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Queen Contiguity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wq = ps.lat2W(rook=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wq.s0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wq.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.sum( [ c[0] * c[1] for c in wq.histogram ] )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wq.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "144. / wq.n**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bishop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb = ps.w_difference(wq,w, constrained = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb.s0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wq[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ps.w_difference?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb.histogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spatial Lag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$l_i = \\sum_{j} w_{i,j} y_j$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.arange(w.n)\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wy = ps.lag_spatial(w,y)\n",
+    "wy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y[[1,5]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y *= 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wy = ps.lag_spatial(w,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y[[1,5]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y[[1,5]].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.transform = 'R'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "${w}_{i,j}^* = w_{i,j} / \\sum_j w_{i,j}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wry = ps.lag_spatial(w,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w[24]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y[[19,23]].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Weights From External Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## shapefiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ls data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wsouth = ps.rook_from_shapefile(\"data/south.shp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wsouth.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wsouth.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wsq = ps.queen_from_shapefile(\"data/south.shp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wsq.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wsouth.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wsq.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wh = np.array(wsouth.histogram)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.bar(wh[:,0],wh[:,1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "whq = np.array(wsq.histogram)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.bar(whq[:,0],whq[:,1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distance Based Weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x,y = np.indices((5,5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x.shape = (25,1)\n",
+    "y.shape = (25,1)\n",
+    "data = np.hstack([x,y])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### knn weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wknn3 = ps.knnW_from_array(data, k = 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wknn3[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wknn3.histogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Distance Bands"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wdb = ps.threshold_binaryW_from_array(data,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wdb[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wdb.histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wdb.cardinalities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Writing to external files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = ps.rook_from_shapefile('data/NAT.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "gal = ps.open('data/nat_copy.gal', 'w')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "gal.write(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "gal.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "gal = ps.open('data/nat_copy.gal', 'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w1 = gal.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w1.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.pct_nonzero == w1.pct_nonzero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/06_taz_example.ipynb b/projects/PYSAL/esda/06_taz_example.ipynb
new file mode 100644
index 0000000..37fc89e
--- /dev/null
+++ b/projects/PYSAL/esda/06_taz_example.ipynb
@@ -0,0 +1,381 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import folium\n",
+    "import random as RD\n",
+    "\n",
+    "from pysal.contrib.viz import mapping as maps\n",
+    "from matplotlib.collections import LineCollection\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "shp = ps.open(ps.examples.get_path(\"taz.shp\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dbf = ps.open(ps.examples.get_path(\"taz.dbf\"))\n",
+    "              "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dbf.header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(9,9))\n",
+    "base = maps.map_poly_shp(shp)\n",
+    "base.set_linewidth(0.75)\n",
+    "base.set_facecolor('none')\n",
+    "base.set_edgecolor('0.8')\n",
+    "ax = maps.setup_ax([base])\n",
+    "fig.add_axes(ax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## County as unique values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cnty = np.array(dbf.by_col(\"CNTY\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "fig = plt.figure(figsize=(9,9))\n",
+    "base = maps.map_poly_shp(shp)\n",
+    "base.set_linewidth(0.75)\n",
+    "base.set_facecolor('none')\n",
+    "base.set_edgecolor('0.8')\n",
+    "counties = maps.base_choropleth_unique(maps.map_poly_shp(shp), cnty)\n",
+    "counties.set_linewidth(0)\n",
+    "counties.set_alpha(.5)\n",
+    "ax = maps.setup_ax([base, counties])\n",
+    "fig.add_axes(ax)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cents = np.array([poly.centroid for poly in shp])\n",
+    "cents[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wrook = ps.rook_from_shapefile(ps.examples.get_path(\"taz.shp\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = wrook\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cents.min(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def w2line_graph(w, centroids):\n",
+    "        \n",
+    "    segments = []\n",
+    "    for i in w.id2i:\n",
+    "        origin = cents[i]\n",
+    "        for j in w.neighbors[i]:\n",
+    "            dest = cents[j]\n",
+    "            ij = [i,j]\n",
+    "            ij.sort()\n",
+    "            segments.append([origin, dest])\n",
+    "\n",
+    "    return segments    \n",
+    "\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "segs = w2line_graph(wrook, cents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## FIXME\n",
+    "if False:\n",
+    "    fig = plt.figure(figsize=(9,9))\n",
+    "    base = maps.map_poly_shp(shp)\n",
+    "    base.set_linewidth(0.75)\n",
+    "    base.set_facecolor('none')\n",
+    "    base.set_edgecolor('0.8')\n",
+    "    segs2 = LineCollection(segs)\n",
+    "    maps._add_axes2col(segs2, shp.bbox)\n",
+    "    segs2.set_linewidth(0.20)\n",
+    "    ax = maps.setup_ax([base, segs])\n",
+    "    fig.add_axes(ax)\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intersection weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb = ps.regime_weights(np.array(dbf.by_col(\"CNTY\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wb.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wint = ps.weights.Wsets.w_intersection(wb, wrook)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "segs = w2line_graph(wint, cents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## FIXME\n",
+    "if False:\n",
+    "    fig = plt.figure(figsize=(9,9))\n",
+    "    base = maps.map_poly_shp(shp)\n",
+    "    base.set_linewidth(0.75)\n",
+    "    base.set_facecolor('none')\n",
+    "    base.set_edgecolor('0.8')\n",
+    "    segs = LineCollection(segs)\n",
+    "    maps._add_axes2col(segs, shp.bbox)\n",
+    "    segs.set_linewidth(0.20)\n",
+    "    ax = maps.setup_ax([base, segs])\n",
+    "    fig.add_axes(ax)\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "segments = w2line_graph(wint, cents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(9,9))\n",
+    "base = maps.map_poly_shp(shp)\n",
+    "base.set_linewidth(0.75)\n",
+    "base.set_facecolor('none')\n",
+    "base.set_edgecolor('0.8')\n",
+    "counties = maps.base_choropleth_unique(maps.map_poly_shp(shp), cnty)\n",
+    "counties.set_linewidth(0)\n",
+    "counties.set_alpha(.5)\n",
+    "segs = LineCollection(segments)\n",
+    "maps._add_axes2col(segs, shp.bbox)\n",
+    "segs.set_linewidth(0.20)\n",
+    "segs.set_color('0.1')\n",
+    "ax = maps.setup_ax([base, counties, segs])\n",
+    "fig.add_axes(ax)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/07_global_spatial_autocorrelation.ipynb b/projects/PYSAL/esda/07_global_spatial_autocorrelation.ipynb
new file mode 100644
index 0000000..1707b55
--- /dev/null
+++ b/projects/PYSAL/esda/07_global_spatial_autocorrelation.ipynb
@@ -0,0 +1,769 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.arange(25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y.shape = (5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.matshow(y,cmap=plt.cm.gray_r)\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.zeros((5,5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Perfect negative autocorrelation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.zeros((25,))\n",
+    "ids = range(0,25,2)\n",
+    "y[ids] = 1\n",
+    "y.shape = (5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.matshow(y, cmap=plt.cm.gray)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y.shape = (25,)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Join Counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "w = ps.lat2W(5,5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$bb_i = \\sum_j y_i w_{i,j} y_j = y_i \\sum_j w_{i,j} y_j$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bbi = y * ps.lag_spatial(w,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bbi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$ww_i = \\sum_j (1-y_i) w_{i,j} (1-y_j) = (1-y_i) \\sum_j w_{i,j} (1-y_j)$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "yc = 1 - y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wwi = yc * ps.lag_spatial(w,yc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wwi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$bw_i = \\sum_{j} w_{i,j} - bb_i - ww_i$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$2 bw = \\sum_i bw_i = \\sum_i ( \\sum_j w_{i,j} - bb_i - ww_i ) = S_0 - bb - ww$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.s0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bw = (w.s0 - bbi.sum() - wwi.sum())/2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "bw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc = ps.Join_Counts(y, w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc.bw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc.p_sim_bb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc.p_sim_bw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import gaussian_kde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "density = gaussian_kde(jc.sim_bw)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "xs = np.linspace(0,50,200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(xs, density(xs))\n",
+    "plt.axvline(x=jc.bw, color='r')\n",
+    "plt.xlabel('bw')\n",
+    "plt.ylabel('f(bw)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "density = gaussian_kde(jc.sim_bb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(xs, density(xs))\n",
+    "plt.axvline(x=jc.bb, color='r')\n",
+    "plt.xlabel('bb')\n",
+    "plt.ylabel('f(bb)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc.bb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.s0/2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import bernoulli"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = bernoulli.rvs(0.5, size=25)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc_random = ps.Join_Counts(y,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y.shape = (5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.matshow(y,cmap=plt.cm.gray_r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc_random.p_sim_bb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "density = gaussian_kde(jc_random.sim_bb)\n",
+    "xs = np.linspace(0,30,200)\n",
+    "plt.plot(xs, density(xs))\n",
+    "plt.axvline(x=jc_random.bb, color='r')\n",
+    "plt.xlabel('bb')\n",
+    "plt.ylabel('f(bb)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc_random.p_sim_bw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc_random.ww"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc_random.bw"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "jc_random.bb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Continuous Variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.arange(w.n)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "yc = y.copy()\n",
+    "yc.shape = (5,5)\n",
+    "plt.matshow(yc,cmap=plt.cm.gray_r)\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Moran's I\n",
+    "\n",
+    "$$I = \\frac{n}{S_0} \\frac{\\sum_i \\sum_j z_i w_{i,j} z_j}{\\sum_i z_iz_i}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mi = ps.Moran(y,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mi.I"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mi.EI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mi.p_norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mi.p_sim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mi.sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "density = gaussian_kde(mi.sim)\n",
+    "xs = np.linspace(mi.sim.min(),mi.sim.max(),200)\n",
+    "plt.plot(xs, density(xs))\n",
+    "plt.axvline(x=mi.I, color='r')\n",
+    "plt.xlabel('I')\n",
+    "plt.ylabel('f(I)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Moran Scatter Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.transform = \"R\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wy = ps.lag_spatial(w, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(y,wy)\n",
+    "plt.vlines(y.mean(),wy.min(),wy.max())\n",
+    "plt.hlines(wy.mean(),y.min(),y.max())\n",
+    "plt.xlabel('y')\n",
+    "plt.ylabel('wy')\n",
+    "plt.title('Moran Scatter Plot')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getis Ord $G$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "g = ps.G(y,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "g.p_sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ps.G?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/08_sp_corr.ipynb b/projects/PYSAL/esda/08_sp_corr.ipynb
new file mode 100644
index 0000000..caba11f
--- /dev/null
+++ b/projects/PYSAL/esda/08_sp_corr.ipynb
@@ -0,0 +1,182 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from scipy.linalg import inv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def draw_map(lamb):\n",
+    "    s = 20\n",
+    "    n = s**2\n",
+    "    w = ps.lat2W(s, s, rook=False)\n",
+    "    w.transform = 'R'\n",
+    "    e = np.random.random((n, 1))\n",
+    "    u = inv(np.eye(n) - lamb * w.full()[0])\n",
+    "    u = np.dot(u, e)\n",
+    "    ul = ps.lag_spatial(w, u)\n",
+    "    u = (u - u.mean()) / np.std(u)\n",
+    "    ul = (ul - ul.mean()) / np.std(ul)\n",
+    "    gu = u.reshape((s, s))\n",
+    "    # Figure\n",
+    "    f = plt.figure(figsize=(9, 4))\n",
+    "    ax1 = f.add_subplot(121)\n",
+    "    ax1.matshow(gu, cmap=plt.cm.YlGn)\n",
+    "    ax1.set_frame_on(False)\n",
+    "    ax1.axes.get_xaxis().set_visible(False)\n",
+    "    ax1.axes.get_yaxis().set_visible(False)\n",
+    "    #---\n",
+    "    ax2 = f.add_subplot(122)\n",
+    "    sc = ax2.scatter(u, ul, linewidth=0)\n",
+    "    ols = ps.spreg.OLS(ul, u)\n",
+    "    tag = \"b = %.3f\"%ols.betas[1][0]\n",
+    "    ax2.plot(u, ols.predy, c='red', label=tag)\n",
+    "    ax2.axvline(0, c='0.5')\n",
+    "    ax2.axhline(0, c='0.5')\n",
+    "    ax2.legend()\n",
+    "    plt.xlabel('u')\n",
+    "    plt.ylabel('Wu')\n",
+    "    plt.suptitle(\"$\\lambda$ = %.2f\"%lamb)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interactive spatial autocorrelation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook illustrates the concept of spatial autocorrelation using the new interactivity in IPython. The data generating process (DGP) considered here is the following:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. $u = \\lambda Wu + \\epsilon$\n",
+    "\n",
+    "1. $u - \\lambda Wu = \\epsilon$\n",
+    "\n",
+    "1. $u (I - \\lambda W) = \\epsilon$\n",
+    "\n",
+    "1. $u = (I - \\lambda W)^{-1} \\epsilon$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Where `u` is a vector spatially autocorrelated, `W` is a spatial weights matrix as you could created with `PySAL`, and $\\epsilon$ is an i.i.d. random vector."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To implement the previous DGP, the simple method `draw_map` (actual code pasted at the bottom of the notebook, so make sure to run that cell beforehand) creates a random vector with degree of spatial autocorrelation $\\lambda$ and allocates it to a lattice geography, where every pixel is assumed to be an area with a value. Right next to it, the function also displays the Moran's scatter plot. Both map and plot depend on the $\\lambda$ parameter that controls the degree of spatial autocorrelation.\n",
+    "\n",
+    "Here's a static version of the function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "draw_map(0.95)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can make the $\\lambda$ value change in an interactive way:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact\n",
+    "interact(draw_map, lamb=(-0.9, 0.9))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Actual plotting function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/09_global_south.ipynb b/projects/PYSAL/esda/09_global_south.ipynb
new file mode 100644
index 0000000..b093d4e
--- /dev/null
+++ b/projects/PYSAL/esda/09_global_south.ipynb
@@ -0,0 +1,897 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import folium\n",
+    "import random as RD\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def inline_map(map):\n",
+    "    \"\"\"\n",
+    "    Embeds the HTML source of the map directly into the IPython notebook.\n",
+    "    \n",
+    "    This method will not work if the map depends on any files (json data). Also this uses\n",
+    "    the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
+    "    \"\"\"\n",
+    "    map._build_map()\n",
+    "    return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
+    "\n",
+    "def embed_map(map, path=\"map.html\"):\n",
+    "    \"\"\"\n",
+    "    Embeds a linked iframe to the map into the IPython notebook.\n",
+    "    \n",
+    "    Note: this method will not capture the source of the map into the notebook.\n",
+    "    This method should work for all maps (as long as they use relative urls).\n",
+    "    \"\"\"\n",
+    "    map.create_map(path=path)\n",
+    "    return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binding Attribute Data to the Map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "f = open(r'data/south.json')\n",
+    "q = json.load(f)\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'type', u'features']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "q.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1412"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "features = q['features']\n",
+    "len(features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "feature_0 = features[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{u'geometry': {u'coordinates': [[[-80.6280517578125, 40.39815902709961],\n",
+       "    [-80.60203552246094, 40.480472564697266],\n",
+       "    [-80.62545776367188, 40.504398345947266],\n",
+       "    [-80.6336441040039, 40.53913879394531],\n",
+       "    [-80.6688232421875, 40.568214416503906],\n",
+       "    [-80.66793060302734, 40.58207321166992],\n",
+       "    [-80.63754272460938, 40.61391830444336],\n",
+       "    [-80.61175537109375, 40.619998931884766],\n",
+       "    [-80.57462310791016, 40.615909576416016],\n",
+       "    [-80.52220916748047, 40.63713836669922],\n",
+       "    [-80.52456665039062, 40.47871780395508],\n",
+       "    [-80.52377319335938, 40.4029655456543],\n",
+       "    [-80.6280517578125, 40.39815902709961]]],\n",
+       "  u'type': u'Polygon'},\n",
+       " u'properties': {u'BLK60': 3.839454752,\n",
+       "  u'BLK70': 3.2554278095,\n",
+       "  u'BLK80': 2.5607402642,\n",
+       "  u'BLK90': 2.5572616581,\n",
+       "  u'CNTY_FIPS': u'029',\n",
+       "  u'COFIPS': 29.0,\n",
+       "  u'DNL60': 6.1681225056,\n",
+       "  u'DNL70': 6.1714993547,\n",
+       "  u'DNL80': 6.1714631077,\n",
+       "  u'DNL90': 6.0508978146,\n",
+       "  u'DV60': 2.2779893943,\n",
+       "  u'DV70': 2.5591397849,\n",
+       "  u'DV80': 5.0619350519,\n",
+       "  u'DV90': 7.2636377003,\n",
+       "  u'FH60': 9.9812973718,\n",
+       "  u'FH70': 7.8,\n",
+       "  u'FH80': 9.7857968181,\n",
+       "  u'FH90': 12.604551644,\n",
+       "  u'FIPS': u'54029',\n",
+       "  u'FIPSNO': 54029.0,\n",
+       "  u'FP59': 9.6,\n",
+       "  u'FP69': 5.9,\n",
+       "  u'FP79': 6.5327526442,\n",
+       "  u'FP89': 10.17311807,\n",
+       "  u'GI59': 0.2236450331,\n",
+       "  u'GI69': 0.2953773833,\n",
+       "  u'GI79': 0.3322512119,\n",
+       "  u'GI89': 0.3639335641,\n",
+       "  u'HC60': 0.6666666667,\n",
+       "  u'HC70': 1.6666666667,\n",
+       "  u'HC80': 2.6666666667,\n",
+       "  u'HC90': 0.3333333333,\n",
+       "  u'HR60': 1.6828642349,\n",
+       "  u'HR70': 4.1929776011,\n",
+       "  u'HR80': 6.5977204876,\n",
+       "  u'HR90': 0.9460827444,\n",
+       "  u'MA60': 28.9,\n",
+       "  u'MA70': 30.0,\n",
+       "  u'MA80': 31.4,\n",
+       "  u'MA90': 37.7,\n",
+       "  u'MFIL59': 8.8410143105,\n",
+       "  u'MFIL69': 9.2471543451,\n",
+       "  u'MFIL79': 10.073356901,\n",
+       "  u'MFIL89': 10.327970666,\n",
+       "  u'NAME': u'Hancock',\n",
+       "  u'PO60': 39615,\n",
+       "  u'PO70': 39749,\n",
+       "  u'PO80': 40418,\n",
+       "  u'PO90': 35233,\n",
+       "  u'POL60': 10.586963113,\n",
+       "  u'POL70': 10.590339963,\n",
+       "  u'POL80': 10.607030509,\n",
+       "  u'POL90': 10.469738422,\n",
+       "  u'PS60': 1.218684208,\n",
+       "  u'PS70': 1.1368342185,\n",
+       "  u'PS80': 1.0385705291,\n",
+       "  u'PS90': 0.8964534429,\n",
+       "  u'RD60': -1.394676863,\n",
+       "  u'RD70': -1.307438562,\n",
+       "  u'RD80': -1.159302086,\n",
+       "  u'RD90': -0.399028376,\n",
+       "  u'SOUTH': 1,\n",
+       "  u'STATE_FIPS': u'54',\n",
+       "  u'STATE_NAME': u'West Virginia',\n",
+       "  u'STFIPS': 54.0,\n",
+       "  u'UE60': 3.1,\n",
+       "  u'UE70': 2.7,\n",
+       "  u'UE80': 7.0763827919,\n",
+       "  u'UE90': 6.8578070515},\n",
+       " u'type': u'Feature'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "indices = []\n",
+    "values = []\n",
+    "for feature in features:\n",
+    "    indices.append(str(feature['properties']['FIPS']))\n",
+    "    values.append(feature['properties']['HR80'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'HR80': values,\n",
+    "                   'FIPS': indices} )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe src=\"files/map.html\" style=\"width: 100%; height: 510px; border: none\"></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 legend_name='Homicide Rate HR80')\n",
+    "embed_map(map_osm)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.array(df.HR80.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe src=\"files/map.html\" style=\"width: 100%; height: 510px; border: none\"></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins= ps.Quantiles(y, 5).bins.tolist()\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=bins[:-1],\n",
+    "                 legend_name='Homicide Rate HR80 (Quantiles k=5)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Global Spatial Autocorrelation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = ps.queen_from_shapefile('data/south.shp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1412"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.transform = 'r'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(12345)\n",
+    "mi_hr80 = ps.Moran(y, w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.001"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi_hr80.p_sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2521152652175852"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi_hr80.I"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.0007087172218284905"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi_hr80.EI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi_hr80.p_norm"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Create a random map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "yr = np.random.permutation(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df['yrandom'] = yr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe src=\"files/map.html\" style=\"width: 100%; height: 510px; border: none\"></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins= ps.Quantiles(y, 5).bins.tolist()\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=bins[:-1],\n",
+    "                 legend_name='Homicide Rate HR80 (Quantiles k=5)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe src=\"files/map.html\" style=\"width: 100%; height: 510px; border: none\"></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins= ps.Quantiles(y, 5).bins.tolist()\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','yrandom'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=bins[:-1],\n",
+    "                 legend_name='Spatially Random Homicide Rate HR80 (Quantiles k=5)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mi_hr80_random = ps.Moran(df['yrandom'],w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.0075100395407744691"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi_hr80_random.I"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.34799999999999998"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mi_hr80_random.p_sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ps.Moran??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats import gaussian_kde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f3ceb0d5090>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rr4QT1+bPj51kcJaC1PoshYQcPgyzZoWP4kxR9bBUSa3LUkhIylNHELIdOgSnT8dOIikr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\nSarH/wc4mhKxFdmaPgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3cec1f1450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "density = gaussian_kde(mi_hr80.sim)\n",
+    "xs = np.linspace(mi_hr80.sim.min(),mi_hr80.sim.max(),200)\n",
+    "plt.plot(xs, density(xs))\n",
+    "plt.axvline(x=mi_hr80.I, color='r')\n",
+    "plt.xlabel('I')\n",
+    "plt.ylabel('f(I)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f3cec1f14d0>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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UFUshIamXwpgx4ZpMe/bETiIpK5ZCQlIvBXBdQWp1lkJCUj4ctcpSkFqbpZCQlE9cq7IU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\ngRNC1AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f3ceb16ee90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "density = gaussian_kde(mi_hr80.sim)\n",
+    "xs = np.linspace(mi_hr80.sim.min(),mi_hr80.sim.max(),200)\n",
+    "plt.plot(xs, density(xs))\n",
+    "plt.axvline(x=mi_hr80.I, color='r')\n",
+    "plt.axvline(x=mi_hr80_random.I, color='g')\n",
+    "plt.xlabel('I')\n",
+    "plt.ylabel('f(I)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getis Ord Global G"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wd = ps.G(y,w, permutations=999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wd.p_norm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.001"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wd.p_sim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/10_local_south.ipynb b/projects/PYSAL/esda/10_local_south.ipynb
new file mode 100644
index 0000000..bb83fb9
--- /dev/null
+++ b/projects/PYSAL/esda/10_local_south.ipynb
@@ -0,0 +1,977 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import folium\n",
+    "import random as RD\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import folium\n",
+    "def inline_map(map):\n",
+    "    \"\"\"\n",
+    "    Embeds the HTML source of the map directly into the IPython notebook.\n",
+    "    \n",
+    "    This method will not work if the map depends on any files (json data). Also this uses\n",
+    "    the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
+    "    \"\"\"\n",
+    "    map._build_map()\n",
+    "    return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n",
+    "\n",
+    "def embed_map(map, path=\"map.html\"):\n",
+    "    \"\"\"\n",
+    "    Embeds a linked iframe to the map into the IPython notebook.\n",
+    "    \n",
+    "    Note: this method will not capture the source of the map into the notebook.\n",
+    "    This method should work for all maps (as long as they use relative urls).\n",
+    "    \"\"\"\n",
+    "    map.create_map(path=path)\n",
+    "    return HTML('<iframe src=\"files/{path}\" style=\"width: 100%; height: 510px; border: none\"></iframe>'.format(path=path))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Base layer for south"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import folium\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5)\n",
+    "map_osm.geo_json(geo_path = 'data/south.json')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Binding Attribute Data to the Map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "f = open(r'data/south.json')\n",
+    "q = json.load(f)\n",
+    "f.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "features = q['features']\n",
+    "len(features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "indices = []\n",
+    "values = []\n",
+    "for feature in features:\n",
+    "    indices.append(str(feature['properties']['FIPS']))\n",
+    "    values.append(feature['properties']['HR80'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame({'HR80': values,\n",
+    "                   'FIPS': indices} )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.array(df.HR80.tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pysal as ps\n",
+    "w = ps.queen_from_shapefile('data/south.shp')\n",
+    "w.transform = 'r'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = np.array(df['HR80'].tolist())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Local Moran\n",
+    "$li_i = \\frac{n-1}{\\sum_j z_j^2}z_i \\sum_j w_{i,j} z_j$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "li_hr80 = ps.Moran_Local(y,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "li_hr80.p_sim.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sig01 = 1.* (li_hr80.p_sim<=0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sig01.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sig05 = 1.* (li_hr80.p_sim<=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sig05.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df['sig01']  = sig01\n",
+    "df['sig05'] = sig05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','HR80'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 legend_name='Homicide Rate HR80')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','sig01'],\n",
+    "                 fill_color='YlOrRd', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=[0,1],\n",
+    "                 legend_name='Homicide Rate HR80 - Significant LISA (0.01)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Note that folium only is bound to threshold scales - no discrete or ordinal scale yet, so we have to fake it with two class threshold scales."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','sig01'],\n",
+    "                 fill_color='YlOrRd', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 legend_name='Homicide Rate HR80 - Significant LISA (0.01)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','sig05'],\n",
+    "                 fill_color='YlOrRd', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 legend_name='Homicide Rate HR80 - Significant LISA (0.05)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "li_hr80.q"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cold_spots = 1. * (li_hr80.q==3) * (li_hr80.p_sim < 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cold_spots.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Cold spots in pandas'y way\n",
+    "lisa_df = pd.DataFrame({'q': li_hr80.q, 'p_sim': li_hr80.p_sim})\n",
+    "cold = lisa_df[(lisa_df['q']==3) & (lisa_df['p_sim']<0.01)]\n",
+    "print cold.info()\n",
+    "print '\\n~~~~~~~~~~\\n'\n",
+    "# Hot spots in pandas'y way\n",
+    "hot = lisa_df[(lisa_df['q']==1) & (lisa_df['p_sim']<0.01)]\n",
+    "print hot.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot_spots = 1. * (li_hr80.q==1) * (li_hr80.p_sim < 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot_spots.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df['hot_01'] = hot_spots\n",
+    "df['cold_01'] = cold_spots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df.hot_01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df.cold_01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','hot_01'],\n",
+    "                 fill_color='YlOrRd', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=[0,1],\n",
+    "                 legend_name='Homicide Rate HR80 - Hot Spots (0.01)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "map_osm = folium.Map(location=[33.7550, -87.3900], zoom_start=5,)\n",
+    "map_osm.geo_json(geo_path='data/south.json',\n",
+    "                 key_on='feature.properties.FIPS',\n",
+    "                 data_out='data.json', data=df,\n",
+    "                 columns=['FIPS','cold_01'],\n",
+    "                 fill_color='YlGnBu', fill_opacity=0.7,\n",
+    "                 line_opacity=0.2,\n",
+    "                 threshold_scale=[0,1],\n",
+    "                 legend_name='Homicide Rate HR80 - Cold Spots (0.01)')\n",
+    "embed_map(map_osm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dbf = ps.open('data/south.dbf')\n",
+    "dbf.header"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    " df['STATE_NAME'] = dbf.by_col('STATE_NAME')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df['County'] = dbf.by_col('NAME')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot_01 = df['hot_01']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot  = df[hot_01>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df[['STATE_NAME', 'County']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df[[0,2,4]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df[df.hot_01 > 0 ].all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df[0:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot = df[df['hot_01']>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df[df['hot_01']>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df[df['cold_01']>0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print df[df['cold_01']>0].to_string() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "hot_01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = ps.queen_from_shapefile('data/south.shp')\n",
+    "w.transform = 'r'\n",
+    "wy = ps.lag_spatial(w,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.scatter(y,wy, alpha=0.5, lw=0)\n",
+    "plt.vlines(y.mean(),wy.min(),wy.max())\n",
+    "plt.hlines(wy.mean(),y.min(),y.max())\n",
+    "plt.xlabel('y')\n",
+    "plt.ylabel('wy')\n",
+    "lm = ps.spreg.OLS(wy[:, None], y[:, None])\n",
+    "plt.plot(y, lm.predy, color='red')\n",
+    "plt.title('Moran Scatter Plot')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c = 2*hot_01 + df.cold_01\n",
+    "plt.scatter(y,wy, c=c, s=(sig01+1)*50)\n",
+    "plt.vlines(y.mean(),wy.min(),wy.max())\n",
+    "plt.hlines(wy.mean(),y.min(),y.max())\n",
+    "plt.xlabel('y')\n",
+    "plt.ylabel('wy')\n",
+    "plt.title('Moran Scatter Plot')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sig01.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df['wHR80'] = wy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print df[df['cold_01']>0].to_string() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print df[df['hot_01']>0].to_string() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lg = ps.G_Local(y, w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lg.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sig = lg.p_sim < 0.01"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lg.p_sim[sig]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lgs = ps.G_Local(y,w, star=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lgs.p_sim[lgs.p_sim < 0.01]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sum(sig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sum(lgs.p_sim < 0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/11_gol.ipynb b/projects/PYSAL/esda/11_gol.ipynb
new file mode 100644
index 0000000..4612efd
--- /dev/null
+++ b/projects/PYSAL/esda/11_gol.ipynb
@@ -0,0 +1,642 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conway's Game of Life with PySAL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rules ##\n",
+    "\n",
+    "   1. A cell that is currently alive and that has two **or** three live neighbors stays alive\n",
+    "   2. A cell that is currently dead with **exactly** three live neighbors comes alive \n",
+    "   3. All other cells remain dead, or die due to loneliness (less than 2 neighbors) or overcrowding (more than 3 neighbors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from scipy.stats import bernoulli\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "k = 8 # dimension of lattice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = ps.lat2W(k,k,rook=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.neighbors[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.neighbors[45]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.weights[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = bernoulli.rvs(0.45,size=w.n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wy = ps.lag_spatial(w,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "wy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rules ##\n",
+    "\n",
+    "   1. A cell that is currently alive and that has two **or** three live neighbors stays alive\n",
+    "   2. A cell that is currently dead with **exactly** three live neighbors comes alive \n",
+    "   3. All other cells remain dead, or die due to loneliness (less than 2 neighbors) or overcrowding (more than 3 neighbors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rule 1: find live cells and count their neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ywy = y*wy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lw23 = np.nonzero( (ywy==2) + (ywy==3) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lw23"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rule 2: find dead cells with exactly 3 neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dw3 = (1-y) * wy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.nonzero(dw3==3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rules 1 and 2 give us the surviving cells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "live_next = np.nonzero( (ywy==2) + (ywy==3) + (dw3==3) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "live_next"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y[live_next]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we see that in the future, some dead cells will becoming alive. But what about live cells now that die in the next period?\n",
+    "\n",
+    "We know that they will be dead next period. Allocate an array with zeros for the next period and assign the live cells. Everyone else is dead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y1 = np.zeros_like(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y1[live_next] = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y1"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "We would then iterate these steps for future generations. \n",
+    "\n",
+    "Let's place the process inside a function for reuse later"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def generation(y,w):\n",
+    "    y1 = np.zeros_like(y)\n",
+    "    wy = ps.lag_spatial(w,y)\n",
+    "    ywy = y * wy\n",
+    "    live_next = np.nonzero( ( ywy == 2 ) + ( ywy == 3 ) + ( ( 1-y ) * wy == 3 ) )\n",
+    "    y1[live_next] = 1\n",
+    "    return y1\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Try this out on some fresh data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y = bernoulli.rvs(0.45,size=w.n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y1 = generation(y,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y2 = generation(y1,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One interesting initial pattern is the so called R-pentomino.\n",
+    "\n",
+    "We will create one and then run a simulation to see how the solutions evolve."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ngen=350\n",
+    "k = 50\n",
+    "w = ps.lat2W(k, k, rook=False)\n",
+    "#y = bernoulli.rvs(0.45,size=w.n)\n",
+    "y = np.zeros((w.n,))\n",
+    "#R-pentomino pattern\n",
+    "kd2 = k/2\n",
+    "top = kd2 +  k * ( kd2 - 1 )\n",
+    "topr = top + 1\n",
+    "midl = top + k -1\n",
+    "mid = midl + 1\n",
+    "bot = mid + k\n",
+    "y[[top, topr, midl, mid, bot]] = 1\n",
+    "results = {}\n",
+    "for i in xrange(ngen):\n",
+    "    y1 = generation(y,w)\n",
+    "    results[i] = y1\n",
+    "    if np.all(y == y1):\n",
+    "        break\n",
+    "    print i, y.sum(), y1.sum()\n",
+    "    y = y1\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Create some graphs to look at both the the individual maps  from every 10th generation and then plot the aggregate population size by generation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "generations = np.zeros((ngen,))\n",
+    "living = np.zeros_like(generations)\n",
+    "keys = results.keys()\n",
+    "keys.sort()\n",
+    "for i in keys:\n",
+    "    generations[i] = i\n",
+    "    living[i] = results[i].sum()\n",
+    "    if not i%10:\n",
+    "        ymat = results[i]\n",
+    "        ymat.shape = (50,50)\n",
+    "        plt.imshow(ymat,cmap='Greys', interpolation='nearest')\n",
+    "        plt.title(\"Generation %d\"%i)\n",
+    "        plt.show()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "generations.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(generations,living)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ymat = results[ngen-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ymat.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ymat.shape=(50,50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
+    "plt.title(\"Last Generation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ymat = results[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ymat.shape = (50,50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
+    "plt.title('First Generation: R-pentomino')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ymat = results[ngen-2]\n",
+    "ymat.shape=(50,50)\n",
+    "plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
+    "plt.title(\"Penultimate Generation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ymat = results[ngen-1]\n",
+    "ymat.shape=(50,50)\n",
+    "plt.imshow(ymat, cmap='Greys', interpolation='nearest')\n",
+    "plt.title(\"Final Generation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/PYSAL/esda/12_spatial_dynamics.ipynb b/projects/PYSAL/esda/12_spatial_dynamics.ipynb
new file mode 100644
index 0000000..3a9e7ba
--- /dev/null
+++ b/projects/PYSAL/esda/12_spatial_dynamics.ipynb
@@ -0,0 +1,1065 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pysal as ps\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = ps.open(\"data/usjoin.csv\",'r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To determine what is in the file, check the `header` attribute on the file object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f.header"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ok, lets pull in the name variable to see what we have"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "name = f.by_col('Name')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now obtain per capital incomes in 1929 which is in the column associated with `1929`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y1929 = f.by_col('1929')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y1929"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now 2009"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y2009 = f.by_col(\"2009\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y2009"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are read into regular Python lists which are not particularly well suited to efficient data analysis. So let's convert them to numpy arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y2009 = np.array(y2009)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "y2009"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Much better. But pulling these in and converting them a column at a time is tedious and error prone. So we will do all of this in a list comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y = np.array( [ f.by_col(str(year)) for year in range(1929,2010) ] ) * 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y = Y.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "years = np.arange(1929,2010)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(years,Y[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "RY = Y / Y.mean(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(years,RY[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "name = np.array(name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "np.nonzero(name=='Ohio')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(years, RY[32], label='Ohio')\n",
+    "plt.plot(years, RY[0], label='Alabama')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kernel Density (univariate, aspatial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.stats.kde import gaussian_kde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "density = gaussian_kde(Y[:,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Y[:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "density = gaussian_kde(Y[:,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "minY0 = Y[:,0].min()*.90\n",
+    "maxY0 = Y[:,0].max()*1.10\n",
+    "x = np.linspace(minY0, maxY0, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x,density(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d2009 = gaussian_kde(Y[:,-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "minY0 = Y[:,-1].min()*.90\n",
+    "maxY0 = Y[:,-1].max()*1.10\n",
+    "x = np.linspace(minY0, maxY0, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x,d2009(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "minR0 = RY.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "maxR0 = RY.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = np.linspace(minR0, maxR0, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d1929 = gaussian_kde(RY[:,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d2009 = gaussian_kde(RY[:,-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x, d1929(x))\n",
+    "plt.plot(x, d2009(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "plt.plot(x, d1929(x), label='1929')\n",
+    "plt.plot(x, d2009(x), label='2009')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Markov Chains"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c = np.array([['b','a','c'],\n",
+    "['c','c','a'],\n",
+    "['c','b','c'],\n",
+    "['a','a','b'],\n",
+    "['a','b','c']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m = ps.Markov(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m.classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m.transitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m.p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### State Per Capita Incomes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "f = ps.open(\"data/usjoin.csv\")\n",
+    "pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])\n",
+    "pci.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Put series into cross-sectional quintiles (i.e., quintiles for each year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q5 = np.array([ps.Quantiles(y).yb for y in pci]).transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q5.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q5[:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pci.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pci[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "we are looping over the rows of y which is ordered Txn (rows are cross sections, row 0 is the cross-section for period 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m5 = ps.Markov(q5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m5.classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m5.transitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m5.p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "m5.steady_state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fmpt = ps.ergodic.fmpt(m5.p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "fmpt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For a state with income in the first quintile, it takes on average 11.5 years for it to first enter the second quintile, 29.6 to get to the third quintile, 53.4 years to enter the fourth, and 103.6 years to reach the richest quintile."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Spatial Markov"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pci.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pci = pci.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pci.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rpci = pci / pci.mean(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rpci[:,0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rpci[:,0].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read in a GAL file to construct our W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = ps.open(ps.examples.get_path(\"states48.gal\")).read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.transform = 'R'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sm = ps.Spatial_Markov(rpci, w, fixed=True, k=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sm.p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for p in sm.P:\n",
+    "    print p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sm.S"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for f in sm.F:\n",
+    "    print f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LISA Markov"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm = ps.LISA_Markov(pci,w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm.classes "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm.transitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm.p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm.steady_state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ps.ergodic.fmpt(lm.p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Test of independence of own chains and lag chains"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm.transitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm.expected_t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "lm.chi_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
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diff --git a/projects/PYSAL/esda/data/mexico.csv b/projects/PYSAL/esda/data/mexico.csv
new file mode 100755
index 0000000..14f87ac
--- /dev/null
+++ b/projects/PYSAL/esda/data/mexico.csv
@@ -0,0 +1,33 @@
+State,pcgdp1940,pcgdp1950,pcgdp1960,pcgdp1970,pcgdp1980,pcgdp1990,pcgdp2000,hanson03,hanson98,esquivel99,inegi,inegi2
+Aguascalientes,10384.000,6234.000,8714.000,16078.000,21022.000,20787.000,27782.000,2.000,2.000,3.000,4.000,4.000
+Baja California,22361.000,20977.000,17865.000,25321.000,29283.000,26839.000,29855.000,1.000,1.000,5.000,1.000,1.000
+Baja California Sur,9573.000,16013.000,16707.000,24384.000,29038.000,25842.000,26103.000,2.000,2.000,6.000,1.000,1.000
+Campeche,3758.000,4929.000,5925.000,10274.000,12166.000,51123.000,36163.000,6.000,5.000,4.000,5.000,5.000
+Chiapas,2934.000,4138.000,5280.000,7015.000,16200.000,8637.000,8684.000,5.000,5.000,7.000,5.000,5.000
+Chihuahua,8578.000,13997.000,16265.000,19178.000,23399.000,25332.000,30735.000,1.000,1.000,5.000,1.000,2.000
+Coahuila,8537.000,9673.000,12318.000,20562.000,25688.000,26084.000,28460.000,1.000,1.000,5.000,2.000,2.000
+Colima,6909.000,6049.000,6036.000,12551.000,17427.000,18313.000,21358.000,3.000,3.000,6.000,4.000,4.000
+Distrito Federal,17816.000,17119.000,23174.000,32386.000,42028.000,43810.000,54349.000,4.000,4.000,1.000,3.000,3.000
+Durango,12132.000,8859.000,9323.000,12700.000,16726.000,17353.000,17379.000,2.000,2.000,3.000,1.000,2.000
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+Jalisco,5309.000,8232.000,9953.000,16288.000,20659.000,20133.000,21610.000,3.000,3.000,6.000,4.000,4.000
+Mexico,3408.000,4972.000,9053.000,17164.000,20165.000,18547.000,16322.000,4.000,4.000,1.000,3.000,3.000
+Michoacan,3327.000,5272.000,5244.000,8109.000,11206.000,10980.000,11838.000,3.000,3.000,7.000,4.000,4.000
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+Oaxaca,1892.000,4538.000,4140.000,5230.000,7730.000,8465.000,9010.000,5.000,5.000,7.000,5.000,5.000
+Puebla,3569.000,6415.000,6542.000,9775.000,13374.000,11895.000,15685.000,3.000,3.000,2.000,3.000,5.000
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+Quintana Roo,21965.000,28747.000,9677.000,17046.000,26695.000,25049.000,33442.000,6.000,5.000,4.000,5.000,5.000
+San Luis Potosi,4372.000,7533.000,6440.000,9721.000,12691.000,15436.000,15866.000,2.000,2.000,3.000,4.000,4.000
+Sinaloa,4840.000,6663.000,9613.000,14477.000,15312.000,15823.000,15242.000,2.000,2.000,6.000,1.000,1.000
+Sonora,6399.000,10345.000,12134.000,22662.000,23181.000,24784.000,24068.000,1.000,1.000,5.000,1.000,1.000
+Tabasco,2459.000,3857.000,6494.000,9367.000,42361.000,16055.000,13360.000,6.000,5.000,4.000,5.000,5.000
+Tamaulipas,7508.000,8536.000,8383.000,17128.000,21937.000,19983.000,23546.000,1.000,1.000,5.000,2.000,2.000
+Tlaxcala,3605.000,4178.000,4357.000,6245.000,9882.000,10339.000,11701.000,3.000,3.000,2.000,3.000,3.000
+Veracruz,5203.000,10143.000,11404.000,12240.000,14252.000,13796.000,12191.000,3.000,3.000,4.000,5.000,5.000
+Yucatan,7990.000,8428.000,10067.000,11665.000,15239.000,13979.000,17509.000,6.000,5.000,4.000,5.000,5.000
+Zacatecas,3734.000,6435.000,5821.000,7426.000,8876.000,11656.000,11130.000,2.000,2.000,3.000,4.000,4.000
diff --git a/projects/PYSAL/esda/data/nat.json b/projects/PYSAL/esda/data/nat.json
new file mode 100644
index 0000000..7f51070
--- /dev/null
+++ b/projects/PYSAL/esda/data/nat.json
@@ -0,0 +1,3090 @@
+{
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diff --git a/projects/PYSAL/esda/data/south.json b/projects/PYSAL/esda/data/south.json
new file mode 100644
index 0000000..e405f00
--- /dev/null
+++ b/projects/PYSAL/esda/data/south.json
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diff --git a/projects/PYSAL/esda/data/usjoin.csv b/projects/PYSAL/esda/data/usjoin.csv
new file mode 100644
index 0000000..24ab198
--- /dev/null
+++ b/projects/PYSAL/esda/data/usjoin.csv
@@ -0,0 +1,49 @@
+"Name","STATE_FIPS",1929,1930,1931,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,1943,1944,1945,1946,1947,1948,1949,1950,1951,1952,1953,1954,1955,1956,1957,1958,1959,1960,1961,1962,1963,1964,1965,1966,1967,1968,1969,1970,1971,1972,1973,1974,1975,1976,1977,1978,1979,1980,1981,1982,1983,1984,1985,1986,1987,1988,1989,1990,1991,1992,1993,1994,1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009
+"Alabama",1,323,267,224,162,166,211,217,251,267,244,252,281,375,518,658,738,784,754,805,881,833,909,1045,1106,1161,1139,1273,1356,1421,1468,1526,1558,1587,1667,1758,1890,2030,2169,2294,2516,2748,2979,3225,3544,3960,4351,4765,5323,5817,6500,7199,7892,8712,9185,9783,10800,11583,12202,12912,13842,14899,15832,16536,17462,17991,18860,19683,20329,21129,22123,22987,23471,24467,25161,26065,27665,29097,30634,31988,32819,32274
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+"California",6,991,887,749,580,546,603,660,771,795,771,781,844,1013,1286,1549,1583,1583,1671,1693,1763,1744,1877,2080,2207,2249,2227,2379,2495,2579,2596,2740,2823,2880,3004,3102,3274,3417,3663,3878,4207,4540,4815,5034,5451,5947,6553,7091,7815,8570,9618,10846,12029,13205,13774,14491,15927,16909,17628,18625,19713,20765,21889,22024,22722,22927,23473,24496,25563,26759,28280,29910,32275,32750,32900,33801,35663,37463,40169,41943,42377,40902
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+"Connecticut",9,1024,921,801,620,583,653,706,806,860,769,836,918,1145,1418,1593,1599,1565,1580,1702,1720,1663,1891,2158,2298,2393,2350,2477,2684,2810,2731,2832,2926,3042,3175,3250,3401,3583,3874,4195,4443,4847,5090,5300,5697,6241,6813,7239,7885,8712,9720,10971,12439,13865,14903,15799,17580,18763,20038,21895,24007,25797,26736,26863,28635,29602,30532,31947,33472,35596,37452,39300,40640,42279,42021,42398,45009,47022,51133,53930,54528,52736
+"Delaware",10,1032,857,775,590,564,645,701,868,949,795,899,1027,1164,1291,1464,1504,1526,1565,1664,1685,1805,2075,2155,2244,2341,2306,2507,2749,2645,2651,2724,2805,2815,2933,3049,3210,3468,3610,3785,4079,4421,4608,4892,5303,5871,6347,6729,7349,7913,8658,9549,10803,11873,12727,13529,14816,16056,16781,17933,19312,20930,21636,22342,23094,23823,24530,25391,26640,27405,29571,30778,31255,32664,33463,34123,35998,37297,39358,40251,40698,40135
+"Florida",12,518,470,398,319,288,348,376,450,487,460,495,522,609,787,1009,1114,1174,1169,1167,1201,1208,1304,1388,1474,1567,1562,1673,1791,1836,1887,2010,2023,2039,2113,2200,2348,2498,2685,2909,3249,3659,4006,4286,4703,5235,5616,5895,6376,7010,7921,8879,10049,11195,11789,12637,13764,14705,15423,16415,17593,19045,19855,20189,20661,21652,22340,23512,24616,25722,26930,27780,28145,28852,29499,30277,32462,34460,36934,37781,37808,36565
+"Georgia",13,347,307,256,200,204,244,268,302,313,290,309,337,419,567,725,831,879,845,887,988,969,1065,1204,1280,1326,1302,1423,1499,1523,1583,1660,1698,1744,1844,1961,2082,2258,2462,2655,2891,3162,3394,3666,4038,4497,4867,5152,5694,6221,6989,7722,8474,9435,10054,10849,12185,13143,13990,14820,15876,16803,17738,18289,19333,20129,21170,22230,23586,24547,26134,27340,27940,28596,28660,29060,29995,31498,32739,33895,34127,33086
+"Idaho",16,507,503,374,274,227,403,399,475,423,426,437,463,596,914,1025,1095,1135,1203,1278,1345,1277,1329,1497,1644,1550,1559,1596,1728,1783,1817,1891,1898,1975,2092,2168,2250,2554,2591,2755,2910,3269,3558,3761,4150,4709,5382,5571,6116,6510,7319,7894,8735,9405,9621,10315,11069,11647,11968,12611,13548,14803,15866,16195,17236,18258,18846,19630,20353,20830,21923,22835,24180,25124,25485,25912,27846,29003,30954,32168,32322,30987
+"Illinois",17,948,807,671,486,437,505,573,650,731,648,704,751,892,1036,1258,1386,1465,1534,1639,1816,1685,1831,2026,2091,2209,2177,2270,2452,2522,2511,2642,2700,2789,2902,2981,3131,3360,3599,3785,4045,4354,4580,4874,5266,5903,6464,6986,7623,8385,9277,10243,11077,12250,12771,13289,14682,15508,16284,17289,18461,19634,20756,21320,22764,23386,24440,25643,27005,28347,29974,31145,32259,32808,33325,34205,35599,36825,39220,41238,42049,40933
+"Indiana",18,607,514,438,310,294,359,421,481,547,472,518,551,724,913,1138,1198,1251,1204,1313,1456,1361,1524,1711,1778,1943,1805,1907,2008,2042,2015,2131,2209,2246,2399,2486,2617,2856,3041,3153,3401,3714,3810,4105,4455,5100,5440,5830,6516,7199,8006,8814,9449,10355,10698,11203,12445,13143,13821,14664,15616,16770,17625,18055,19269,20112,21153,21845,22775,23748,25182,26143,27011,27590,28059,29089,30126,30768,32305,33151,33978,33174
+"Iowa",19,581,510,400,297,253,269,425,393,523,458,475,501,609,835,1024,1003,1092,1245,1217,1642,1356,1532,1638,1725,1657,1788,1670,1758,1939,1990,2026,2061,2180,2284,2432,2549,2833,3067,3093,3312,3652,3862,4005,4473,5398,5596,6192,6580,7283,8438,9114,9671,10968,11227,11485,12798,13395,14020,14899,15315,16562,17380,17859,18939,18929,20498,21181,22713,23798,24844,25615,26723,27315,28232,28835,31027,31656,33177,35008,36726,35983
+"Kansas",20,532,467,401,266,250,287,362,387,428,383,382,425,552,851,1045,1169,1165,1136,1310,1352,1299,1463,1602,1827,1739,1791,1753,1821,1911,2098,2106,2165,2227,2305,2384,2516,2689,2894,3032,3268,3548,3816,4145,4613,5274,5717,6186,6721,7307,8082,9240,10038,11248,11989,12373,13602,14330,14904,15583,16331,17093,18182,18832,19955,20510,21352,21889,23121,24355,25687,26824,27816,28979,29067,30109,31181,32367,34934,36546,37983,37036
+"Kentucky",21,393,325,291,211,205,233,265,294,341,297,305,320,395,538,701,767,803,826,865,995,938,990,1153,1237,1305,1289,1346,1437,1491,1543,1604,1633,1731,1822,1906,1972,2134,2329,2501,2718,2971,3184,3391,3715,4145,4607,4933,5462,6095,6784,7640,8231,9110,9589,9859,11062,11558,11995,12782,13570,14602,15484,16241,17320,17815,18514,19215,20155,21215,22353,23237,24294,24816,25297,25777,26891,27881,29392,30443,31302,31250
+"Louisiana",22,414,355,318,241,227,265,290,330,353,348,360,364,449,592,786,876,889,833,885,1019,1074,1117,1210,1278,1343,1339,1397,1502,1616,1635,1685,1690,1741,1806,1909,2003,2139,2331,2527,2749,2901,3106,3328,3593,3994,4510,4956,5557,6135,6951,7813,8833,10037,10558,10865,11628,12121,12028,12266,13113,13997,15223,16076,16968,17717,18779,19541,20254,21209,22352,22847,23334,25116,25683,26434,27776,29785,33438,34986,35730,35151
+"Maine",23,601,576,491,377,371,416,430,506,510,471,495,526,631,857,1102,1102,1079,1134,1169,1240,1183,1195,1319,1438,1449,1448,1581,1669,1720,1797,1850,1919,1903,1983,2049,2212,2383,2539,2658,2872,3140,3423,3594,3864,4319,4764,5019,5708,6142,6751,7497,8408,9231,9873,10551,11665,12533,13463,14595,15813,16886,17479,17662,18350,18810,19531,20240,21293,22305,23529,24603,25623,27068,27731,28727,30201,30721,32340,33620,34906,35268
+"Maryland",24,768,712,638,512,466,523,548,618,665,633,663,710,870,1117,1287,1324,1312,1315,1355,1500,1496,1642,1815,1944,2017,1938,2047,2184,2267,2258,2324,2407,2507,2638,2722,2881,3055,3284,3516,3831,4209,4573,4894,5291,5822,6382,6878,7536,8191,9062,10035,11230,12403,13246,14228,15693,16961,17966,19216,20626,22001,23023,23571,24358,25104,26046,26896,27844,29222,30850,32465,33872,35430,36293,37309,39651,41555,43990,45827,47040,47159
+"Massachusetts",25,906,836,759,613,559,609,643,714,732,672,724,779,901,1073,1262,1299,1348,1396,1435,1512,1480,1656,1817,1895,1947,1930,2071,2194,2296,2321,2430,2511,2605,2734,2799,2932,3101,3331,3583,3903,4207,4486,4748,5106,5551,6024,6439,6994,7636,8480,9472,10673,11830,12803,13859,15549,16720,17954,19504,21334,22458,23223,23749,24876,25664,26841,28051,29618,31332,33394,35551,37992,39247,39238,39869,41792,43520,46893,49361,50607,49590
+"Michigan",26,790,657,540,394,347,453,530,619,685,572,625,680,829,1050,1354,1391,1325,1329,1466,1563,1527,1718,1896,1985,2202,2076,2238,2275,2309,2246,2358,2438,2427,2592,2728,2949,3215,3450,3554,3906,4145,4194,4501,4966,5552,5926,6279,7084,7957,8834,9701,10369,11125,11462,12243,13576,14734,15573,16130,17198,18276,19022,19318,20278,21390,22862,23975,24447,25570,26807,28113,29612,30196,30410,31446,31890,32516,33452,34441,35215,34280
+"Minnesota",27,599,552,457,363,308,358,451,472,540,494,517,524,615,797,947,1006,1110,1190,1270,1451,1328,1437,1582,1633,1711,1724,1790,1838,1930,2016,2065,2155,2236,2331,2460,2540,2781,2997,3182,3463,3779,4053,4275,4628,5431,5838,6216,6729,7559,8471,9409,10320,11320,11992,12594,14255,15093,15881,16899,17592,18966,20011,20489,21698,22068,23467,24583,26267,27548,29503,30793,32101,32835,33553,34744,36505,37400,39367,41059,42299,40920
+"Mississippi",28,286,202,175,127,131,174,177,229,224,201,205,215,307,439,533,629,629,611,670,802,705,770,851,906,940,928,1045,1051,1063,1156,1245,1237,1322,1362,1499,1557,1688,1839,2001,2197,2408,2641,2867,3208,3613,3936,4205,4757,5259,5806,6549,7076,7901,8301,8615,9463,9922,10293,10913,11695,12540,13164,13806,14711,15468,16549,17185,18044,18885,20013,20688,20993,22222,22540,23365,24501,26120,27276,28772,29591,29318
+"Missouri",29,621,561,491,365,334,367,420,466,508,475,504,519,640,806,963,1068,1130,1191,1224,1376,1327,1427,1554,1659,1737,1726,1818,1905,1949,2044,2126,2156,2215,2330,2427,2533,2738,2895,3067,3370,3561,3843,4107,4443,4937,5282,5733,6306,6991,7787,8713,9390,10457,11035,11716,12960,13868,14505,15250,16086,17083,17751,18560,19542,20295,21267,22094,23099,24252,25403,26376,27445,28156,28771,29702,30847,31644,33354,34558,35775,35106
+"Montana",30,592,501,382,339,298,364,476,475,512,517,533,569,713,901,1150,1183,1206,1308,1484,1642,1412,1654,1806,1823,1810,1771,1894,1929,1983,2068,2023,2075,2025,2363,2330,2366,2548,2730,2805,2955,3284,3625,3789,4355,5012,5380,5794,6200,6636,7721,8299,9143,10244,10672,11045,11705,11900,12465,12996,13362,14623,15524,16509,17114,18072,18129,18764,19383,20167,21324,22019,22569,24342,24699,25963,27517,28987,30942,32625,33293,32699
+"Nebraska",31,596,521,413,307,275,259,409,396,415,405,400,442,551,822,1019,1091,1186,1186,1274,1558,1346,1560,1641,1761,1676,1753,1650,1675,1936,2025,2022,2125,2134,2292,2341,2392,2656,2890,2990,3167,3572,3796,4121,4527,5269,5465,6168,6453,6993,8120,8784,9272,10685,11228,11601,12968,13743,14215,15035,15984,16878,18088,18766,19688,20167,21168,22196,24045,24590,25861,27049,27829,29098,29499,31262,32371,33395,34753,36880,38128,37057
+"Nevada",32,868,833,652,550,495,546,658,843,762,780,861,895,982,1558,1511,1483,1611,1757,1770,1758,1795,1991,2211,2397,2447,2415,2527,2488,2562,2594,2749,2890,2957,3184,3174,3209,3299,3471,3660,4114,4520,4946,5227,5557,6114,6490,7009,7719,8550,9780,10765,11780,12780,12986,13465,14435,15332,16027,16886,18180,19568,20674,21283,22694,23465,24635,25808,27142,28201,29806,31022,30529,30718,30849,32182,34757,37555,38652,40326,40332,38009
+"New Hampshire",33,686,647,558,427,416,476,498,537,565,533,561,579,708,851,976,1054,1111,1150,1217,1291,1274,1348,1508,1577,1653,1703,1829,1900,2007,2004,2124,2197,2281,2392,2427,2552,2708,2963,3162,3441,3744,3896,4102,4423,4880,5279,5592,6258,6892,7786,8781,9915,11079,11906,13041,14534,15819,16974,18371,19759,20635,20713,21326,22154,22521,23820,25008,26042,27607,29679,31114,33332,33940,34335,34892,36758,37536,39997,41720,42461,41882
+"New Jersey",34,918,847,736,587,523,573,625,709,747,697,749,820,957,1167,1429,1554,1582,1526,1571,1648,1624,1802,2000,2114,2232,2219,2300,2448,2551,2525,2660,2764,2830,2990,3064,3224,3414,3652,3892,4237,4525,4835,5127,5521,6043,6576,7037,7703,8462,9408,10469,11778,13057,13999,15036,16549,17652,18711,20230,22142,23595,24766,25153,26597,27101,27885,29277,30795,32372,34310,35551,36983,37959,38240,38768,40603,42142,45668,48172,49233,48123
+"New Mexico",35,410,334,289,208,211,247,292,343,362,338,357,378,474,636,773,881,942,929,1013,1124,1145,1204,1346,1423,1444,1462,1543,1626,1733,1825,1895,1884,1952,2006,2054,2134,2250,2386,2490,2709,2921,3197,3431,3761,4137,4568,5045,5527,6087,6847,7619,8402,9334,9894,10367,11215,11999,12226,12686,13322,14085,14960,15744,16425,17226,17946,18852,19478,20233,21178,21853,22203,24193,24446,25128,26606,28180,29778,31320,32585,32197
+"New York",36,1152,1035,881,676,626,680,722,808,838,789,825,869,996,1169,1384,1538,1644,1697,1725,1771,1729,1858,2002,2057,2144,2175,2295,2416,2522,2553,2695,2788,2866,2980,3070,3254,3422,3657,3923,4295,4603,4887,5179,5538,5980,6492,6955,7477,8153,8979,9927,11095,12364,13344,14188,15739,16734,17827,19031,20604,21966,23315,23942,25199,25589,26359,27721,29266,30480,32236,33890,34547,35371,35332,36077,38312,40592,43892,47514,48692,46844
+"North Carolina",37,332,292,248,187,208,253,271,297,324,295,315,324,422,572,693,766,823,866,900,1003,969,1077,1192,1230,1274,1293,1368,1436,1424,1505,1581,1629,1689,1797,1877,2009,2143,2363,2525,2754,3051,3285,3510,3899,4365,4743,5039,5584,6058,6780,7461,8247,9184,9690,10480,11788,12649,13444,14325,15461,16539,17367,17879,19120,20042,20931,21938,22940,24188,25454,26003,27194,27650,27726,28208,29769,31209,32692,33966,34340,33564
+"North Dakota",38,382,311,187,176,146,180,272,234,326,282,319,355,529,665,966,1031,1046,1088,1494,1483,1211,1360,1444,1318,1336,1364,1490,1556,1598,1847,1679,1821,1653,2320,2142,2112,2463,2507,2592,2719,3052,3214,3669,4377,6172,6120,6334,6184,6427,8136,8398,8095,10342,10990,11386,12307,12811,13126,13565,12745,14357,15880,16270,17692,17830,19033,19084,21166,20798,22767,23313,25068,26118,26770,29109,29676,31644,32856,35882,39009,38672
+"Ohio",39,771,661,563,400,385,455,516,593,648,561,615,658,821,1021,1253,1312,1340,1310,1401,1539,1456,1608,1835,1916,2024,1965,2087,2182,2243,2177,2314,2391,2405,2515,2604,2754,2948,3181,3309,3616,3934,4101,4328,4691,5218,5733,6087,6753,7511,8326,9251,10103,10982,11485,12167,13449,14295,14933,15675,16739,17825,18792,19217,20242,20999,22063,22887,23613,24913,26164,27152,28400,28966,29522,30345,31240,32097,33643,34814,35521,35018
+"Oklahoma",40,455,368,301,216,222,252,298,321,376,346,349,374,432,626,782,947,970,952,1028,1143,1166,1144,1290,1401,1475,1458,1516,1593,1657,1794,1857,1916,1947,1994,2055,2196,2361,2517,2702,2948,3198,3477,3711,4020,4524,4986,5475,5974,6586,7387,8485,9580,11003,11817,11725,12687,13265,13288,13464,14257,15265,16214,16721,17526,18085,18730,19394,20151,21106,22199,22953,23517,25059,25059,25719,27516,29122,31753,32781,34378,33708
+"Oregon",41,668,607,505,379,358,439,458,548,556,531,571,609,818,1118,1381,1389,1357,1379,1504,1646,1604,1657,1830,1912,1916,1867,1978,2070,2055,2106,2244,2283,2349,2457,2541,2685,2852,3033,3201,3448,3677,3940,4212,4625,5135,5726,6181,6913,7556,8476,9415,10196,10862,11128,11832,12866,13547,14162,14911,16062,17222,18253,18806,19558,20404,21421,22668,23649,24845,25958,27023,28350,28866,29387,30172,31217,32108,34212,35279,35899,35210
+"Pennsylvania",42,772,712,600,449,417,482,517,601,636,563,601,650,776,951,1146,1250,1280,1289,1364,1436,1405,1552,1713,1789,1894,1827,1915,2063,2174,2164,2240,2301,2334,2439,2511,2662,2830,3040,3246,3507,3815,4077,4294,4683,5168,5704,6170,6800,7493,8305,9225,10151,11184,11887,12455,13512,14445,15186,16142,17323,18725,19823,20505,21550,22211,22864,23738,24838,26092,27358,28605,29539,30085,30840,31709,33069,34131,36375,38003,39008,38827
+"Rhode Island",44,874,788,711,575,559,600,645,711,731,672,720,750,934,1149,1201,1274,1278,1369,1459,1433,1378,1553,1717,1765,1855,1847,1958,1998,2024,2067,2183,2234,2329,2457,2552,2691,2870,3113,3336,3595,3865,4114,4295,4625,4972,5405,5844,6411,7004,7693,8595,9742,10815,11605,12439,13717,14685,15587,16651,18271,19657,20194,20363,21257,22137,22762,24046,25123,26631,28012,29377,29685,31378,32374,33690,35318,36461,38610,40421,41542,41283
+"South Carolina",45,271,243,205,159,175,211,229,258,273,250,276,310,394,545,648,732,753,780,793,911,873,925,1115,1196,1233,1166,1229,1260,1286,1316,1392,1437,1498,1602,1669,1787,1958,2176,2340,2570,2827,3064,3274,3603,4029,4459,4720,5257,5684,6334,7044,7794,8651,9071,9775,10910,11666,12258,13056,14045,14834,16050,16409,17165,17805,18686,19473,20403,21385,22544,23545,24321,24871,25279,25875,27057,28337,29990,30958,31510,30835
+"South Dakota",46,426,366,241,189,129,184,309,244,323,320,345,361,475,757,846,979,1086,1132,1270,1526,1116,1283,1497,1327,1436,1457,1342,1419,1669,1740,1564,1870,1863,2092,2020,2000,2278,2500,2577,2773,2995,3256,3538,4065,5163,5178,5667,5591,6351,7347,8158,8142,9451,9915,10195,11619,11942,12486,13217,13807,14767,16238,16961,17966,18565,19607,19848,21736,22275,23797,25045,26115,27531,27727,30072,31765,32726,33320,35998,38188,36499
+"Tennessee",47,378,325,277,198,204,245,264,304,334,300,311,340,436,561,728,864,912,872,892,965,951,1028,1122,1178,1276,1274,1327,1422,1476,1512,1598,1618,1690,1771,1855,1965,2128,2332,2473,2741,2967,3189,3451,3808,4298,4696,5017,5574,6108,6895,7618,8319,9196,9695,10276,11453,12247,12995,13909,14910,15883,16821,17503,18840,19741,20696,21800,22450,23324,24576,25574,26239,27059,27647,28501,29734,30764,32314,33578,34243,33512
+"Texas",48,479,412,348,266,257,294,326,372,418,404,417,438,528,719,944,1045,1058,1047,1147,1214,1300,1363,1488,1564,1598,1630,1697,1784,1856,1871,1948,1955,2021,2079,2155,2284,2433,2630,2840,3105,3373,3646,3861,4192,4683,5194,5738,6362,6979,7912,8929,9957,11391,11961,12303,13396,14196,14165,14486,15324,16323,17458,18150,19146,19825,20590,21526,22557,24242,25803,26858,27871,28519,28295,28929,30392,32448,34489,36020,36969,35674
+"Utah",49,551,498,369,305,298,310,389,463,444,444,458,480,594,880,1126,1049,1121,1094,1180,1257,1262,1348,1544,1596,1604,1573,1666,1758,1862,1888,1970,2035,2091,2230,2281,2386,2494,2605,2721,2900,3103,3391,3658,3979,4326,4743,5150,5739,6328,7041,7786,8464,9290,9807,10333,11233,11846,12248,12638,13156,13977,14996,15661,16354,17031,17912,18858,19955,21156,22294,23288,23907,24899,25010,25192,26169,27905,29582,31009,31253,30107
+"Vermont",50,634,576,474,365,338,383,414,471,485,457,491,516,643,775,932,953,1035,1090,1127,1192,1125,1169,1334,1380,1432,1456,1524,1655,1720,1732,1832,1923,1994,2072,2128,2266,2456,2729,2885,3128,3388,3634,3856,4176,4548,4869,5192,5753,6179,7036,7853,8702,9717,10287,10968,12048,12994,13842,14992,16197,17517,18055,18218,19293,19785,20553,21359,22295,23362,24803,25889,26901,28140,28651,29609,31240,31920,34394,36018,36940,36752
+"Virginia",51,434,384,370,284,285,320,350,390,423,390,426,467,582,785,843,900,950,1002,1015,1148,1132,1257,1420,1510,1533,1554,1637,1712,1738,1784,1885,1936,2005,2121,2218,2403,2563,2746,2967,3252,3558,3795,4092,4486,4972,5484,5934,6534,7192,8040,8995,10176,11291,12075,12936,14298,15286,16237,17332,18556,19780,20538,21092,21965,22773,23709,24456,25495,26768,28343,29789,31162,32747,33235,34451,36285,38304,40644,42506,43409,43211
+"Washington",53,741,658,534,402,376,443,490,569,599,582,614,658,864,1196,1469,1527,1419,1401,1504,1624,1595,1721,1874,1973,2066,2077,2116,2172,2262,2281,2380,2436,2535,2680,2735,2858,3078,3385,3566,3850,4097,4205,4381,4731,5312,5919,6533,7181,7832,8887,9965,10913,11903,12431,13124,14021,14738,15522,16300,17270,18670,20026,20901,21917,22414,23119,23878,25287,26817,28632,30392,31528,32053,32206,32934,34984,35738,38477,40782,41588,40619
+"West Virginia",54,460,408,356,257,259,313,337,390,418,370,388,407,498,613,739,820,888,925,1032,1110,1023,1056,1182,1247,1276,1225,1318,1477,1594,1551,1597,1625,1671,1762,1853,1974,2126,2271,2417,2573,2798,3117,3378,3682,4026,4457,4974,5479,6053,6703,7432,8172,8866,9439,9626,10417,10936,11464,11950,12708,13529,14579,15219,16118,16724,17413,17913,18566,19388,20246,20966,21915,23333,24103,24626,25484,26374,28379,29769,31265,31843
+"Wisconsin",55,673,588,469,362,333,380,461,518,551,507,513,547,672,866,1053,1109,1182,1211,1296,1434,1387,1506,1736,1799,1839,1774,1875,1990,2060,2075,2225,2258,2304,2412,2458,2616,2789,3025,3180,3441,3751,3983,4238,4595,5130,5622,6061,6670,7417,8292,9281,10161,11006,11592,12046,13182,13845,14530,15358,16201,17299,18160,18711,19872,20639,21699,22573,23554,24790,26245,27390,28232,29161,29838,30657,31703,32625,34535,35839,36594,35676
+"Wyoming",56,675,585,476,374,371,411,496,551,607,561,587,602,779,944,1150,1224,1258,1362,1506,1610,1647,1719,1955,1912,1932,1858,1913,2011,2132,2172,2278,2312,2387,2502,2535,2588,2743,2906,3121,3315,3584,3919,4269,4709,5410,6172,6701,7212,8152,9384,10572,11753,12879,13251,12723,13493,14242,14004,14194,14968,16383,17996,18867,19550,20287,20957,21514,22098,23820,24927,26396,27230,29122,29828,31544,33721,36683,41548,43453,45177,42504
diff --git a/projects/PYSAL/esda/figures/10_arctool.png b/projects/PYSAL/esda/figures/10_arctool.png
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