Spatial Data Analysis With Python
summary
Claims that lightweight Python tooling turns spatial data into actionable signals: a reproducible Jupyter workflow loads DHS cluster points, reprojects and creates a 5 km buffer around GPS clusters, masks VIIRS night‑lights with rasterio and aggregates mean radiance, then joins results to asset scores. Uses geopandas, shapely, pyproj and numpy for transforms and plotting. Applied to Nigeria, the pipeline shows a clear positive correlation between night‑light intensity and asset index and the code lives on GitHub.
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chapters (6)
- 0:00Spatial data analysis intro
- 3:19Why geospatial analysis matters
- 10:12Coordinate reference systems
- 17:14Transforming projections
- 22:03Nighttime lights analysis
- 33:31Q&A
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