Choropleth in Geoplot
A choropleth takes data that has been aggregated on some meaningful polygonal level (e.g. census tract, state, country, or continent) and uses color to display it to the reader. It’s a well-known plot type, and it’s perhaps the most general-purpose and well-known of the spatial plot types. A basic choropleth requires polygonal geometries and a hue variable. Change the colormap using matplotlib’s cmap. The legend parameter toggles the legend.
Syntax:
geoplot.choropleth(var)
Example:
Python3
import geoplot as gplt import geopandas as gpd import geoplot.crs as gcrs # Reading the world shapefile boroughs = gpd.read_file(gplt.datasets.get_path('nyc_boroughs')) gplt.choropleth(boroughs, hue='Shape_Area', projection=gcrs.AlbersEqualArea(), cmap='RdPu', legend=True) |
Output:
To pass the keyword argument to the legend, use the legend_kwargs argument. To specify a categorical colormap, use a scheme. Use legend_labels and legend_values to customize the labels and values that appear in the legend. Here we are going to use mapclassify which is an open-source python library for Choropleth map classification. To install mapclassify use:
- mapclassify is available in on conda via the conda-forge channel:
Syntax:
conda install -c conda-forge mapclassify
- mapclassify is also available on the Python Package Index:
Syntax:
pip install -U mapclassify
Example:
Python3
import geoplot as gplt import geopandas as gpd import geoplot.crs as gcrs import mapclassify as mc # Reading the world shapefile contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa')) scheme = mc.FisherJenks(contiguous_usa['population'], k=5) gplt.choropleth( contiguous_usa, hue='population', projection=gcrs.AlbersEqualArea(), edgecolor='white', linewidth=1, cmap='Reds', legend=True, legend_kwargs={'loc': 'lower left'}, scheme=scheme, legend_labels=[ '<3 million', '3-6.7 million', '6.7-12.8 million', '12.8-25 million', '25-37 million' ] ) |
Output:
Working with Geospatial Data in Python
Spatial data, also known as geospatial data, GIS data, or geodata, is a type of numeric data that defines the geographic location of a physical object, such as a building, a street, a town, a city, a country, or other physical objects, using a geographic coordinate system. You may determine not just the position of an object, but also its length, size, area, and shape using spatial data.
To work with geospatial data in python we need the GeoPandas & GeoPlot library
GeoPandas is an open-source project to make working with geospatial data in python easier. GeoPandas extends the data types used by pandas to allow spatial operations on geometric types. Geometric operations are performed shapely. Geopandas further depends on fiona for file access and matplotlib for plotting. GeoPandas depends on its spatial functionality on a large geospatial, open-source stack of libraries (GEOS, GDAL, and PROJ). See the Dependencies section below for more details.
Required dependencies:
- numpy
- pandas (version 0.24 or later)
- shapely (interface to GEOS)
- fiona (interface to GDAL)
- pyproj (interface to PROJ; version 2.2.0 or later)
Further, optional dependencies are:
- rtree (optional; spatial index to improve performance and required for overlay operations; interface to libspatialindex)
- psycopg2 (optional; for PostGIS connection)
- GeoAlchemy2 (optional; for writing to PostGIS)
- geopy (optional; For plotting, these additional for geocoding)
packages may be used:
- matplotlib (>= 2.2.0)
- mapclassify (>= 2.2.0)
Geoplot is a geospatial data visualization library for data scientists and geospatial analysts that want to get things done quickly. Below we’ll cover the basics of Geoplot and explore how it’s applied. Geoplot is for Python 3.6+ versions only.
Note: Please install all the dependencies and modules for the proper functioning of the given codes.
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