Hi folks,
I’m writing today about plotting geojson files with Matplotlib’s Basemap. In a previouspost I laid out how to plot shapefiles using Basemap.
geojson is an open file format for representing geographical data based on java script notation. They are composed of points, lines, and polygons or ‘multiple’ (e.g. multipolygons composed of several polygons), with accompanying properties. The basic structure is one of names and vales, where names are always strings and values may be strings, objects, arrays, or logical literal.
The geojson structure we will be considering here is a collection of features, where each feature contains a geometry and properties. Each geojson feature must contain properties and geometry. Properties could be things like country name, country code, state, etc. The geometry must contain a type (point, line, polygons, etc.) and coordinates (likely an array of lat-long). Below is an excerpt of a geojson file specifying Agro-Ecological Zones (AEZs) within the various GCAM regions.
{ "type": "FeatureCollection", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } }, "features": [ { "type": "Feature", "id": 1, "properties": { "ID": 1.000000, "GRIDCODE": 11913.000000, "CTRYCODE": 119.000000, "CTRYNAME": "Russian Fed", "AEZ": 13.000000, "GCAM_ID": "Russian Fed-13" }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 99.5, 78.5 ], [ 98.33203125, 78.735787391662598 ], [ 98.85723876953125, 79.66796875 ], [ 99.901641845703125, 79.308036804199219 ], [ 99.5, 78.5 ] ] ] ] } }, { "type": "Feature", "id": 2, "properties": { "ID": 2.000000, "GRIDCODE": 11913.000000, "CTRYCODE": 119.000000, "CTRYNAME": "Russian Fed", "AEZ": 13.000000, "GCAM_ID": "Russian Fed-13" }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 104.5, 78.0 ], [ 104.0, 78.0 ], [ 99.5, 78.0 ], [ 99.5, 78.5 ], [ 100.2957763671875, 78.704218864440918 ], [ 102.13778686523437, 79.477890968322754 ], [ 104.83050537109375, 78.786871910095215 ], [ 104.5, 78.0 ] ] ] ] } }, { "type": "Feature", "id": 3, "properties": { "ID": 3.000000, "GRIDCODE": 2713.000000, "CTRYCODE": 27.000000, "CTRYNAME": "Canada", "AEZ": 13.000000, "GCAM_ID": "Canada-13" }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ -99.5, 77.5 ], [ -100.50860595703125, 77.896504402160645 ], [ -101.76053619384766, 77.711499214172363 ], [ -104.68202209472656, 78.563323974609375 ], [ -105.71781158447266, 79.692866325378418 ], [ -99.067413330078125, 78.600395202636719 ], [ -99.5, 77.5 ] ] ] ] } } }Now that we have some understanding of the geojson structure, plotting the information therein should be as straightforward as traversing that structure and tying geometries to data. We do the former using the geojson python package and the latter using pretty basic python manipulation. To do the actual plotting, we’ll use PolygonPatches from the descartes library and recycle most of the code from my previous post.
We start by importing the necessary libraries and then open the geojson file.
import geojson from descartes import PolygonPatch import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap import numpy as np with open("aez-w-greenland.geojson") as json_file: json_data = geojson.load(json_file)We then define a MatplotLib Figure, and generate a Basemap object as a ‘canvas’ to draw the geojson geometries on.
plt.clf() ax = plt.figure(figsize=(10,10)).add_subplot(111)#fig.gca() m = Basemap(projection='robin', lon_0=0,resolution='c') m.drawmapboundary(fill_color='white', zorder=-1) m.drawparallels(np.arange(-90.,91.,30.), labels=[1,0,0,1], dashes=[1,1], linewidth=0.25, color='0.5',fontsize=14) m.drawmeridians(np.arange(0., 360., 60.), labels=[1,0,0,1], dashes=[1,1], linewidth=0.25, color='0.5',fontsize=14) m.drawcoastlines(color='0.6', linewidth=1)Next, we iterate over the nested features in this file and pull out the coordinate list defining each feature’s geometry (line 2). In lines 4-5 we also pull out the feature’s name and AEZ, which I can tie to GCAM data.
for i in range(2799): coordlist = json_data.features[i]['geometry']['coordinates'][0] if i < 2796: name = json_data.features[i]['properties']['CTRYNAME'] aez = json_data.features[i]['properties']['AEZ'] for j in range(len(coordlist)): for k in range(len(coordlist[j])): coordlist[j][k][0],coordlist[j][k][1]=m(coordlist[j][k][0],coordlist[j][k][1]) poly = {"type":"Polygon","coordinates":coordlist}#coordlist ax.add_patch(PolygonPatch(poly, fc=[0,0.5,0], ec=[0,0.3,0], zorder=0.2 )) ax.axis('scaled') plt.draw() plt.show()Line 9 is used to convert the coordinate list from lat/long units to meters. Depending on what projection you’re working in and what units your inputs are in, you may or may not need to do this step.
The final lines are used to add the polygon to the figure, and to make the face color of each polygon green and the border dark green. Which generates the figure:
To get a bit more fancy, we could tie the data to a colormap and then link that to the facecolor of the polygons. For instance, the following figure shows the improvement in maize yields over the next century in the shared socio-economic pathway 1 (SSP 1), relative to a reference scenario (SSP 2).
