Each dot is a monitored region on a flat equirectangular map. Every region has a climate hazard exposure value E and a socioeconomic vulnerability value V (both from smooth synthetic fields). Equity-adjusted risk blends the two:
Risk = (1 - w)·E + w·(E · V)
Coverage(region) = sampled if dataDensity(region) < resolution
In the real world, low-income and historically under-resourced regions often have sparser climate sensor networks and less granular data — so as "monitoring resolution" rises, well-resourced regions get sampled first, leaving high-risk/low-data "blind spots" exposed longer.
- Monitoring resolution — raises the global sampling density; regions with poor existing data infrastructure only appear at high resolution.
- Equity weighting (w) — blends raw hazard color (w=0) with equity-adjusted risk that also factors in vulnerability (w=1).
- Hazard intensity — scales the underlying climate hazard field, simulating worsening climate change.
- Pan speed — scrolls the map sideways, for inspecting all regions.
- Equity Lens toggle — highlights unsampled high-risk "blind spot" regions in pulsing red.
Real application: climate justice researchers combine satellite hazard data with census-derived vulnerability indices to find where investment in monitoring and adaptation is most urgently needed.