Cooling where it Counts to Target Climate Adaptation within Cities Figures Replication Package

This package contains the code and processed data to reproduce Figures 2 to 8 of the paper, which assesses where reflective coatings on roads, parking lots and roofs would most reduce heat-related mortality in Los Angeles. It also includes the data needed to re-estimate the temperature–mortality response functions. Contents code/: Python scripts that generate each figure (make_figures.py runs all of them) and an R script (fit_mortality.R) that estimates the temperature–mortality response functions. The regression uses zip code and year fixed effects, with 95% confidence intervals from a 500-draw bootstrap. data/regression/: inputs for the regression: annual all-cause mortality and population by California zip code and age group, temperature polynomial sums by zip code and year, Los Angeles County zip code indicators, and annual zip-code median household income, 2003–2020. data/coefficients/: the estimated response-function parameters used in the paper. data/analysis/: grid-cell outputs: avoided deaths and life years for 41,737 cells of 250 m over 18 weather years, cell metadata, grid geometry, and county and neighborhood totals. data/ (top level): albedo rasters for roads, parking lots and rooftops, the albedo cooling-potential grid, and the deadly-day counts used in Figure 3. output/: the figures and regression outputs produced by the code. sources.csv: how each input file was prepared.