Summary and Setup

This module teaches advanced methods of spatial analysis in Python. It is written for social scientists who already know the everyday operations (buffers, overlays, map algebra and zonal statistics), which are taught in the Data Processing module.

Each episode explains one method on this site, then links to three short Colab notebooks (Beginner, Intermediate and Advanced), each about 20 to 30 minutes.

Episode Question it answers Tools
Network Analysis How far is it by road, and how long does it take? osmnx, networkx
Spatial Autocorrelation with PySAL Are similar values clustered, and where? libpysal, esda, splot
Spatial Clustering Where do points concentrate into hotspots? scikit-learn, scipy
NDVI Analysis How green is the landscape in a satellite image? rasterio, numpy, scikit-learn

The episodes can be taken in any order, though Spatial Clustering builds lightly on ideas from PySAL. Geocoding (turning addresses into points and downloading OpenStreetMap features) is taught in the Data Collection module and is good preparation for Network Analysis. QGIS users will find raster analysis of elevation and satellite imagery in Workshop 2026 Track A (DEM analysis, NDVI and flood mapping), and network analysis in the Case Studies module’s Grocery Access Part 2.

Prerequisite

Prerequisites

  • A Google account, to open the notebooks in Google Colab
  • Basic Python, pandas and GeoPandas (Foundations notebooks 2a to 4c); the Data Processing module’s Geoprocessing in Python episode is recommended
  • A free Census API key of your own (needed for the PySAL episode)

New to Python or Colab? Start with the Foundations module, then read the Setup page.

This module is part of Enabling the Geospatial Turn in the Social Sciences, a set of training modules developed at Purdue University with support from the National Science Foundation.

The Python episodes run in Google Colab, a free online notebook service, so you do not need to install Python.

What you need

  • A Google account
  • A web browser (Chrome, Firefox, Edge or Safari)
  • A free Census API key. Every learner needs their own. Request one at https://api.census.gov/data/key_signup.html; it arrives by email within a few minutes, and you must click the activation link in that email before it works. The PySAL episode cannot run without it.

Opening a notebook

  1. Click a notebook link in the Notebooks table at the end of any episode. It opens in Colab.
  2. Click File > Save a copy in Drive. Your own copy keeps your changes; the original stays read only.
  3. Run cells from top to bottom. Click a cell and press Shift + Enter, or click the play button to its left.
  4. The first time you run a cell, Colab may warn that the notebook was not written by Google. Click Run anyway.
Callout

Colab tips

  • Run cells in order. Most errors come from skipping a cell, for example the one that reads the data.
  • Start over cleanly with Runtime > Restart session and run all.
  • Data lives in memory, so it disappears when the session ends. Rerun the notebook to get it back. To keep an optional output (such as ndvi.tif), open the Files panel on the left, right click the file and choose Download.
  • Cells that start with !pip install add libraries Colab does not include by default. They take a few seconds and only need to run once per session.

Data

You never need to download data. Every notebook reads its data straight from the web into memory: pd.read_csv() and gpd.read_file() accept a web address (including zipped shapefiles), and rasterio.open() reads satellite images the same way.

Episode Data Source
Network Analysis Road network of West Lafayette, Indiana OpenStreetMap, through OSMnx
PySAL Indiana counties and Marion County tracts, ACS 2023 5-year estimates U.S. Census Bureau API and cartographic boundary files
Spatial Clustering One year of Chicago crime reports (CrimesChicago20220225.csv) Chicago Data Portal
NDVI Landsat 8/9 Collection 2 Level-2 bands over Indiana (band2.TIF to band5.TIF) USGS

The Landsat bands are about 90 MB each, so reading them takes a few seconds.

Working offline or on your own computer (optional)

If you prefer to run the notebooks locally, install Anaconda and create an environment with the libraries used in this module:

BASH 

conda create -n spatialturn -c conda-forge python=3.11 geopandas osmnx rasterio libpysal esda splot mapclassify scikit-learn folium geopy jupyterlab
conda activate spatialturn
jupyter lab

Then download a notebook from GitHub (File > Download > Download .ipynb in Colab) and open it in JupyterLab. The data is still read from the web, so you need an internet connection.