HeatShield Bay

San Francisco Bay Area

Policy + Climate + Civic Tech

Heat and air-risk decisions you can act on this quarter.

A neighborhood dashboard that combines heat risk, canopy, and vulnerability signals into prioritized interventions and a policy checklist for cities.

Local fit: Multi-jurisdiction complexity (cities, counties, utilities), high cost of living, climate/air quality events, and stark digital divide within a high-tech region.

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Core user stories

As a resident, I search my neighborhood and see heat risk plus nearby cooling resources.
As a community org, I download an outreach list and share a low-bandwidth resource link.
As staff, I compare interventions (trees, cool roofs, hydration sites) by cost and expected impact.

Clickable demo scope (what you build)

Neighborhood search → risk card + resource map.
Intervention recommender: choose budget → top 5 actions (mock logic).
Printable/shareable resource page (mobile-first).

Team of four roles

Product & Policy Lead: Define the intervention menu and equity guardrails; pick 2–3 neighborhoods to seed with plausible resource lists.
UX / Frontend: Build neighborhood search + risk card + resource map; optimize for SMS-link/low-bandwidth use.
Backend / Data: Seed risk layers (mock tiers), resources, and intervention logic; generate printable outreach page.
Partnerships / Story / Ops: Validate with a mutual-aid or public health contact; craft ‘prevent harm’ story; deploy.

54-hour build plan

Hours 0–6: Pick one user segment; do 5–8 quick interviews; lock the “one workflow” MVP.
Hours 6–24: Build the clickable flow end-to-end with stubbed data; draft the policy narrative.
Hours 24–40: Add one policy-ready output (brief/dashboard/export); tighten UX; seed data for 2–3 neighborhoods.
Hours 40–54: Polish demo script; add analytics mock; finalize pitch + one-page handout.

What to show in the final demo

The “happy path” (one user completes the core task in under 2 minutes).
One policy-ready output (a brief, dashboard view, or export).
One local proof point (seeded neighborhoods / agencies / partner types relevant to San Francisco Bay Area).

How it can earn revenue without becoming extractive

Revenue fit (values-aligned): sell a low-cost subscription to local governments/nonprofits, or charge implementation/support for pilots—avoid extractive fees on community participation.

Guardrails: publish scoring criteria, minimize data collection, and default to community ownership where possible (co-op/commons patterns).