Key Points

Network Analysis

  • Road networks are graphs of nodes (intersections) and edges (road segments)
  • ox.graph_from_place downloads OpenStreetMap roads for any named place
  • add_edge_speeds and add_edge_travel_times let you route by time instead of distance
  • nearest_nodes (longitude first), shortest_path and route_to_gdf cover most routing tasks
  • Network distance is more realistic than straight line distance for access studies
  • Match the network type to the people you study, and choose an access measure (nearest facility, cumulative opportunities, service area) that fits the question
  • Check snapping distances and edge effects, and remember that speeds are estimates without traffic

Spatial Autocorrelation with PySAL

  • Spatial weights define who is a neighbor: Rook (edges), Queen (edges and corners), KNN (closest)
  • Global Moran’s I gives one number for clustering across the whole map
  • The permutation p-value tells you whether the pattern could be chance
  • Local Moran’s I (LISA) locates hotspots, coldspots and outliers; always filter by p-value
  • Many local tests produce false positives; use stricter cutoffs or FDR, and treat clusters as leads
  • Results depend on the units (MAUP), the weights, small populations and edges of the study area

Spatial Clustering

  • Reproject to meters before clustering so distances are real
  • DBSCAN and HDBSCAN find dense hotspots of any shape and label scattered points as noise
  • K-Means splits all points into k round zones; choose k with the elbow method
  • Hierarchical clustering shows how groups merge, but is slow on large datasets
  • Pick the method that matches your question: hotspots or zones
  • Choose eps with a k-distance plot and k with the elbow method or silhouette score
  • A dense cluster may reflect the population at risk, duplicate locations or reporting, not high risk

NDVI Analysis

  • NDVI compares NIR and red reflectance; for Landsat 8 and 9 these are bands 5 and 4
  • Convert Level-2 values to reflectance (value * 0.0000275 - 0.2) before computing NDVI
  • rasterio reads and writes rasters; reuse the input profile when saving
  • np.digitize turns NDVI into classes; n breaks give n + 1 classes
  • Vegetation’s spectral signature (low red, high NIR) is what NDVI measures; normalizing reduces brightness effects
  • Interpret NDVI with care: thresholds vary, dense canopies saturate, pixels mix, and clouds and seasons change the values