Sankey in Geoplot
A Sankey diagram depicts the flow of information through a network. It’s useful for displaying the magnitudes of data flowing through a system. This figure places the Sankey diagram in a geospatial context, making it helpful for monitoring traffic loads on a road network or travel volumes between airports, for example. A basic Sankey requires a GeoDataFrame of LineString or MultiPoint geometries. hue adds color gradation to the map. Use matplotlib’s cmap to control the colormap. For a categorical colormap, specify the scheme. legend toggles a legend. Here we are using Mollweide projection
Syntax;
geoplot.sankey(var)
Example:
Python3
import geoplot as gplt import geopandas as gpd import geoplot.crs as gcrs import mapclassify as mc # Reading the world shapefile la_flights = gpd.read_file(gplt.datasets.get_path( 'la_flights' )) world = gpd.read_file(gplt.datasets.get_path( 'world' )) scheme = mc.Quantiles(la_flights[ 'Passengers' ], k = 5 ) ax = gplt.sankey(la_flights, projection = gcrs.Mollweide(), scale = 'Passengers' , hue = 'Passengers' , scheme = scheme, cmap = 'Oranges' , legend = True ) gplt.polyplot(world, ax = ax, facecolor = 'lightgray' , edgecolor = 'white' ) ax.set_global(); ax.outline_patch.set_visible( True ) |
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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