Category Archives: Gis

Zooming in on maps with sf and ggplot2


  • cropping the geometries in the spatial dataset using sf_crop()
  • restricting the display window via coord_sf()


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Lab report: Development of school sites in eastern Germany

I wanted to share a small lab report on a project about the development of school sites in eastern Germany since 1992. Rita Nikolai (HU Berlin), Marcel Helbig (WZB) and I published our results a few months ago (see this WZB Discussion Paper or this WZBrief), but I’d like to provide some additional information on the (technical) background in this post as this was not the aim of the mentioned papers.

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Tools and packages for geospatial processing with Python

In the social sciences, geospatial data appears quite often. You may have social indicators for different places on earth at different administrative levels, e.g. countries, states or municipalities. Or you may study spatial distribution of hospitals or schools in a given area, or visualize GPS referenced data from an experiment. For such scenarios, there’s fortunately a rich supply of open-source tools and packages. As I’ve worked recently quite a lot with geospatial data, I want to introduce some of this software, especially those available for the Python programming language.

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Creating and plotting Voronoi regions for geographic data with geovoronoi

Recently, I’ve worked a lot with geospatial data in Python. One thing that we needed for our analysis was generating Voronoi regions (or “cells”) from a given set of coordinates inside certain administrative boundaries (a country, a state, etc.). Such regions are interesting for spatial analysis, because each random point inside a Voronoi region is closest to the cell’s “origin point” (the point the cell was generated from) than to any other cell’s origin. As a practical example: In Melbourne parents can see which is the closest school for their home, by looking at an online map of Voronoi regions of schools.

These regions allow to calculate an estimate of a “coverage”: For each point’s Voronoi region, the area can be calculated, which represents the area theoretically covered by this point. Referring to the Melbourne example: The schools at the edge of the city cover a larger area than those in the city center. This approach of course does not take geographic properties into account. So if there’s a large lake inside a cell, it is also part of the covered area. Still, Voronoi tessellation is useful when looking at how the shape of the Voronoi regions changes over time, for example when new schools open or others close. We could then see for example, if the coverage of schools in the city center becomes better over the years, whereas in the rural areas it gets more sparse.

So all in all, Voronoi regions can be a very useful tool in spatial data analysis. QGIS provides a tool for Voronoi tessellation but we needed a more flexible approach that also fit into our workflow and could be used in our Python scripts. I decided to write a small Python package named geovoronoi that takes a set of points, a boundary object (the geographic shape enclosing the points – e.g. a country boundary) and then calculates the Voronoi regions using SciPy. These regions are then “cut” to the enclosing shape (using the excellent shapely package). The resulting Voronoi cells can then be used for further calculations (areas, distances, unions, etc.) and can also be visualized on a map.

The package geovoronoi is now available on PyPI (install it with pip install geovoronoi[plotting]) and the source is uploaded on the WZB’s GitHub page.

Geocoding an address and performing point-polygon tests with GDAL/OGR in Python

Suppose you have a list of addresses and want to connect them with some kind of location-based information. For example, your addresses might scatter across several neighborhoods and you want to find out to which neighborhood each address belongs, because you have further information (like mean income, percentage of migrants, etc.) about each neighborhood and want to combine it with your data. In many countries, administrative authorities gather such geographical information and provide the data on their websites.

In the given scenario, three steps are necessary in order to combine the addresses with geographical information:

  1. Geocoding the address, i.e. finding out the geographical coordinates (latitude, longitude) for this address
  2. Given a file with geographical information (GIS data) that form several distinct areas as polygons, finding out which of these polygons contains the geocoded address
  3. Obtain necessary information such as a neighborhood identifier from the polygon

This short post shows how to do that with the Python packages googlemaps and GDAL.

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