Project Iterating

HeatShield

How can extreme heat risk be quantified at neighborhood scale by integrating climate, environmental, and demographic data to identify communities facing compounded vulnerability?

HeatShield develops a data-driven framework for mapping extreme heat risk at fine spatial resolution.

Inputs: Integrates temperature, air quality, wildfire smoke exposure, land-use characteristics, and census-derived demographics
Method: Combines climate observations with population data to model compounded environmental and social vulnerability
Key output: Identifies “double-burden” communities where exposure and sensitivity overlap
Application: Risk maps support targeted mitigation, policy analysis, and equitable decision-making under worsening heat extremes

Explainer

Research at a glance

Code github.com/blueleaflabs/heatshield

Primary methods

Spatial data analysis
Multivariate modeling
Data integration across environmental and demographic domains
Geospatial visualization

Data sources

Gridded temperature datasets
Air quality and wildfire smoke observations
Land-use and urban surface data
U.S. Census demographic data

Outputs

Neighborhood-scale heat risk maps
Composite vulnerability indices
Spatial exposure analyses
Reproducible data pipelines

Field notes

2026-07-05

HeatShield: Making Equity Visible

Mapping where extreme heat, wildfire smoke, and social vulnerability overlap across California, and turning it into something policymakers can act on.