San Diego Heat Risk Explorer
Three pillars of heat risk
Exposure
How much heat does this area face? Air temperature, humidity, number of extreme-heat days, and satellite-measured land surface temperature.
Sensitivity
How vulnerable are the people living here? Age, chronic disease rates, social vulnerability, and environmental health.
Adaptive Capacity
What resources help residents cope with heat? Air conditioning access, tree canopy, income, healthcare access, and housing quality.
Data sources
Exposure
4 sources · 10 variables
The map shows one heat mode at a time: daytime heat, nighttime heat, humid heat (WBGT), or surface temperature.
- MesoWest / Synoptic Data (+ CIMIS), IDW to tract centroids · 7 variables
- NOAA HRRR 3 km analysis, WBGT via Liljegren/Dimiceli · 2 variables
- USGS Landsat (Collection 2 surface temperature) · 1 variable
- LOCA2 downscaled CMIP6 · climate projections for 2050 and 2080 (SSP2-4.5, SSP3-7.0, SSP5-8.5)
Sensitivity
7 sources · 50 variables
- CDC/ATSDR Social Vulnerability Index 2022 · 22 variables
- CDC PLACES (model-based small-area estimates) · 17 variables
- Healthy Places Index (HPI) 3.0 · 5 variables
- ACS 5-year, table C24030 (industry by sex) · 2 variables
- CalHeatScore (CalEPA / OEHHA), per-ZCTA, crosswalked to tract · 2 variables
- ACS 5-year, table B11007 (households by age) · 1 variable
- DOE LEAD Tool (2022) · 1 variable
Adaptive Capacity
7 sources · 23 variables
- Healthy Places Index (HPI) 3.0 · 16 variables
- ACS 5-year, table B25034 (year structure built) · 2 variables
- U.S. Census Bureau LACE 2023 · 1 variable
- Romitti et al. 2022, PNAS Nexus (modeled AC prevalence) · 1 variable
- ACS 5-year, table B25003 (tenure) · 1 variable
- SANDAG / SanGIS regional land use · 1 variable
- NLCD / MRLC land cover · 1 variable
Feedback
To submit a correction, comment, or feature request, open an issue on the project GitHub, or email cjmack@ucsd.edu.