Test students' ability to apply GIS and spatial statistics to identify and prioritize neighborhoods for urban heat mitigation in a mid-sized city. Assess skills in selecting and processing spatial datasets (land surface temperature from remote sensing, land cover, imperviousness, tree canopy, demographic and health data), constructing heat vulnerability indices, performing spatial analyses (hotspot analysis, spatial autocorrelation, GWR or spatial regression), and ranking intervention priorities with consideration of equity, cost, and co-benefits. Require interpretation of maps, discussion of assumptions and limitations, and formulation of clear, stakeholder-ready recommendations for mitigation actions (e.g., tree planting, cool roofs, green infrastructure).