Geospatial Analysis of Violation Hotspots

Load and manipulate geographic data (e.g., district shapefiles) using GeoPandas.

Merge model prediction data (violation rates) with geographic dataframes.

Create choropleth maps to visualize violation rates and patterns across different regions.

Describe the concept of spatial autocorrelation and its importance for identifying non-random patterns.

Compute Moran's I statistic to test for global spatial autocorrelation in violation rates.