🤔 What is a geoJson file?
A GeoJSON file is a format used to encode various geographic data structures such as points, lines, and polygons, using a JSON structure. It allows for easy storage and exchange of geographic information systems (GIS) data on the web and between applications.
Let's consider French state boundaries. The original file comes from here but I stored it on the github repo of the python graph gallery for convenience: we will load directly with the code.
How to load a geoJson file into python
You're best friend here is geopandas. If this package hasn't been installed on your machine yet, you can do so with pip install geopandas. Then, load the package:
import geopandas as gpd
data = gpd.read_file(
"https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/france.geojson"
)
print(type(data))Here we are 🎉! data is a geo dataframe. Each row represents an item in the geojson file, i.e. a region of france here.
Columns describe the feature of each region. The geometry column is probably the most important. It provides the coordinates of the region boundary.
Plot the result
Many different options exist to plot a map from a geopandas dataframe. The most common solution is to use a package called geoplot. You can install it with pip install geoplot. Then import it with:
import geoplot
import geoplot.crs as gcrsOnce the library is loaded, the polyplot() function can be used to draw a map of the geospatial data frame. The polyplot() function is used to plot polygons, i.e any type of geographic area.
geoplot.polyplot(
data,
projection=gcrs.AlbersEqualArea(),
edgecolor='darkgrey',
facecolor='lightgrey',
linewidth=.3,
figsize=(12, 8)
)<GeoAxes: >
Here we are, we've loaded a geoJson file, transformed it into a geopandas dataframe and drawn a map with geoplot from it!
Going further
This post explains how to create a map from a geoJSON file.
You might be interested in:
- building a choropleth map, where each region has a color proportionnal to a value
- creating a bubble map, where individual data points are displayed on top of a map, with different sizes
