362 lines
8.5 KiB
Plaintext
362 lines
8.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualizing spatial data with Python"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"import pandas as pd\n",
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"import geopandas\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"pd.options.display.max_rows = 10"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# o13 filepaths\n",
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"countries = geopandas.read_file(\"/home/user/data/natural_earth2/ne_10m_admin_0_countries.shp\")\n",
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"cities = geopandas.read_file(\"/home/user/data/natural_earth2/ne_10m_populated_places.shp\")\n",
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"rivers = geopandas.read_file(\"/home/user/data/natural_earth2/ne_10m_rivers_lake_centerlines.shp\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## GeoPandas visualization functionality"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Basic plot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"countries.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Adjusting the figure size"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"countries.plot(figsize=(15, 15))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Removing the box / x and y coordinate labels"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ax = countries.plot(figsize=(15, 15))\n",
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"ax.set_axis_off()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Coloring based on column values\n",
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"\n",
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"Let's first create a new column with the GDP per capita:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"countries = countries[(countries['pop_est'] >0 ) & (countries['name'] != \"Antarctica\")]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"countries['gdp_per_cap'] = countries['gdp_md_est'] / countries['pop_est'] * 100"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"ax = countries.plot(figsize=(15, 15), column='gdp_md_est' )\n",
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"ax.set_axis_off()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"and now we can use this column to color the polygons: (TODO)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#ax = countries.plot(figsize=(15, 15), column='gdp_per_cap', scheme='quantiles', legend=True)\n",
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"#ax.set_axis_off()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Combining different dataframes on a single plot\n",
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"\n",
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"The `.plot` method returns a matplotlib Axes object, which can then be re-used to add additional layers to that plot with the `ax=` keyword:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ax = countries.plot(figsize=(15, 15))\n",
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"cities.plot(ax=ax, color='red', markersize=10)\n",
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"ax.set_axis_off()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ax = countries.plot(edgecolor='k', facecolor='none', figsize=(15, 10))\n",
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"rivers.plot(ax=ax)\n",
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"cities.plot(ax=ax, color='C1')\n",
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"ax.set(xlim=(-20, 60), ylim=(-40, 40))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using `cartopy`\n",
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"\n",
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"Cartopy is the base matplotlib cartographic library, and it is used by `geoplot` under the hood to provide projection-awareness.\n",
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"\n",
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"http://scitools.org.uk/cartopy/docs/latest/index.html\n",
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"\n",
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"The following example is taken from the docs: http://geopandas.readthedocs.io/en/latest/gallery/cartopy_convert.html#sphx-glr-gallery-cartopy-convert-py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from cartopy import crs as ccrs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Define the CartoPy CRS object.\n",
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"crs = ccrs.AlbersEqualArea()\n",
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"\n",
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"# This can be converted into a `proj4` string/dict compatible with GeoPandas\n",
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"crs_proj4 = crs.proj4_init\n",
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"countries_ae = countries.to_crs(crs_proj4)\n",
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"\n",
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"# Here's what the plot looks like in GeoPandas\n",
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"countries_ae.plot()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Interactive web-based visualizations\n",
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"\n",
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"There are nowadays many libraries that target interactive web-based visualizations and that can handle geospatial data. Some packages with an example for each:\n",
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"\n",
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"- Bokeh: https://bokeh.pydata.org/en/latest/docs/gallery/texas.html\n",
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"- GeoViews (other interface to Bokeh/matplotlib): http://geo.holoviews.org\n",
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"- Altair: https://altair-viz.github.io/gallery/choropleth.html\n",
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"- Plotly: https://plot.ly/python/#maps\n",
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"- ..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Another popular javascript library for online maps is [Leaflet.js](https://leafletjs.com/), and this has python bindings in the [folium](https://github.com/python-visualization/folium) and [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) packages."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"An example with folium:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"# TODO Folium\n",
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"import folium"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m = folium.Map([48.8566, 2.3429], zoom_start=6, tiles=\"OpenStreetMap\")\n",
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"folium.GeoJson(cities.to_json()).add_to(m)\n",
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"m"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m = folium.Map([48.8566, 2.3429], zoom_start=6, tiles=\"OpenStreetMap\")\n",
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"folium.GeoJson(countries.to_json()).add_to(m)\n",
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"m"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using `geoplot`\n",
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"\n",
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"The `geoplot` packages provides some additional functionality compared to the basic `.plot()` method on GeoDataFrames:\n",
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"\n",
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"- High-level plotting API (with more plot types as geopandas)\n",
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"- Native projection support\n",
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"\n",
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"https://residentmario.github.io/geoplot/index.html"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO\n",
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"import geoplot\n",
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"import geoplot.crs as gcrs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={\n",
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" 'projection': gcrs.Orthographic(central_latitude=40.7128, central_longitude=-74.0059)\n",
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"})\n",
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"geoplot.choropleth(countries, hue='gdp_per_cap', projection=gcrs.Orthographic(), ax=ax,\n",
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" cmap='magma', linewidth=0.5, edgecolor='white', k=None)\n",
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"ax.set_global()\n",
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"ax.outline_patch.set_visible(True)\n",
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"#ax.coastlines()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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