Weighting mechanics and the spatial pattern of composite metrics of heat vulnerability in Atlanta, Georgia, USA.

This study constructs two biophysical metrics; one based on Land Surface Temperatures (LST) and an integrated spectral index. The latter is an aggregate of Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI). The goal is to determine how disparate weighting techniques, data transformation approaches, and spatial visualization pathways influence the computation of composite heat metrics. Using composite images made of aggregated images from late May to Early September within Google Earth Engine, we generated four composites by combining biophysical metrics with SoVI using equal and Eigen-based weightings informed by Principal Component Analysis (PCA). We compared equal interval classification, global and local Moran's as pathways for spatial visualization of hotspots. We utilized several data transformation techniques in a Geographic Information System (GIS), including rescaling, reclassification, zonal statistics, and spatial weighting. Mann Kendall and Sen's Slope detected and quantified monotonic trends in each spectral index. The results show that the LST biophysical metric and its composites indicate increased heat susceptibility over time, with disproportionately exposed core metro counties. The integrated spectral index and its proxies showed reduced vulnerability hence not a good proxy for LST. At the same time, the Mann Kendall and Sen's Slope found persistent increases in NDVI and NDWI and decreases in NDBI and NDBaI. However, opposite trends were evident in core city counties. The LST-based composites and spectral indices-based composites varied in the spatial-temporal distribution of hotspots. Disparate weighting mechanics, data transformation techniques, and visualization alternatives influence the magnitude and spatial-temporal distribution of heat hotspots.