In this advanced GIS project, I used spatial statistics to analyze wildfire patterns across South Carolina. After filtering a dataset to isolate wildfires, I applied Hot Spot Analysis (Getis-Ord Gi) to identify statistically significant clusters of fire activity. A hot spot in this context refers to an area where incidents are not only frequent, but also significantly concentrated relative to neighboring areas, indicating a meaningful spatial relationship rather than a random distribution.

I explored both global and local spatial statistics including median center, standard deviation ellipse, and spatial autocorrelation to interpret distribution trends and spatial intensity. The resulting layout visualizes areas of heightened wildfire risk through statistical clustering, providing insight that goes beyond raw counts. This project demonstrated how inferential spatial tools can inform strategic decision-making across wildfire management, land use planning, and risk-based resource deployment.