Census GeocodingModule Overview
Geocoding converts census addresses into spatial coordinates
GeoPandas enables spatial operations on tabular data
OpenStreetMap provides rich contextual geographic layers
Combining census + OSM reveals spatial patterns and inequality
Spatial context transforms demographic data into actionable insight
Network AnalysisModule Overview
OSMnx simplifies downloading and converting OSM road networks
Graphs model movement and connectivity in space
NetworkX allows shortest path and routing analysis
Visualization helps interpret accessibility patterns
Spatial AnalysisModule Overview
PySAL provides tools for weights, autocorrelation, clustering, and modeling
Queen and rook weights define spatial neighbors differently
Moran’s I measures global autocorrelation
Local Moran (LISA) identifies hotspots and coldspots
GeoPandas and PySAL together form a powerful spatial analysis workflow
Spatial ClusteringModule Overview
Spatial clustering groups geographic points into meaningful patterns
K-Means is simple but assumes circular clusters
Hierarchical clustering builds clusters step-wise
DBSCAN is best for irregular shapes and detecting noise
Always visualize your clusters to interpret them correctly
NDVI AnalysisModule Overview
NDVI uses Red & NIR reflectance from satellite imagery
Landsat Band 4 = Red, Band 5 = NIR for NDVI
NDVI ranges from -1 (water) to +1 (healthy vegetation)
Python tools: rasterio, numpy, matplotlib
NDVI maps reveal vegetation patterns visually and quantitatively