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Digital Climate Justice Simulator (2D)

A flat equity map of climate monitoring: sample regions by data-network resolution and watch high-hazard, low-resource regions stay invisible as "blind spots" until coverage catches up.

Climate, Ecology & Environment2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

This 2D companion runs the same equity-risk model as the 3D version on a flat, scrolling map instead of a rotating globe: each dot is a synthetic monitoring region with its own hazard-exposure and vulnerability values, monitoring resolution decides which regions actually get sampled, and the Equity Lens pulses any unsampled region whose blended risk score is high — making visible the real-world pattern where under-resourced regions are also under-monitored.

⚙ Under the hood

2D climate-justice map: a resolution slider controls which of ~2,880 synthetic regions are sampled, an equity-weighting slider blends raw hazard against hazard×vulnerability risk, and an Equity Lens toggle flags unsampled high-risk "blind spot" regions.

climate justicedata equityclimate monitoringvulnerability indexenvironmental data science

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install

What does the equity risk formula mean?

Risk = (1 - w)·E + w·(E·V), where E is hazard exposure and V is socioeconomic vulnerability. At w=0 only raw hazard is shown; at w=1 the color also factors in how vulnerable the population is, so a high-hazard but low-vulnerability region can rank lower than a moderate-hazard, high-vulnerability one.

Why do some high-risk regions never get sampled?

Each region has a synthetic data-density value that is deliberately anti-correlated with vulnerability, mirroring real-world climate monitoring networks: under-resourced regions tend to have sparser sensor coverage. As you raise monitoring resolution, well-instrumented regions appear first, leaving the least-monitored, often highest-risk regions as blind spots the longest.