City Heatwave Risk Dashboard from Open Weather and Census Data

The problem cities face with heatwaves

Heatwaves kill quietly. The people most at risk are older adults living alone, outdoor workers, people without reliable electricity for fans, and children in crowded housing. Cities that have written heat action plans, such as Ahmedabad in India, which is often cited as an early example, rely on two things: an early warning based on forecasts, and knowing where vulnerable people live so help reaches them first.

Many cities have neither in one place. Weather forecasts are national and city-wide, census data sits in tables nobody joins with the weather, and NGOs decide where to set up water points by instinct.

What already exists and the gap

• NOAA HeatRisk in the US gives a colour-coded heat risk forecast for US locations only.
• Open-Meteo provides free forecast and historical weather APIs, including ERA5 reanalysis back to 1940, under a CC BY 4.0 licence.
• National meteorological departments issue heatwave advisories, usually at district or city level and as text or images.
• Heat vulnerability indices appear in academic papers and some city reports, usually as a one-time static map.

The gap is a small, running tool that joins daily forecasts with neighbourhood-level vulnerability and gives city teams and NGOs a ranked list of where to act this week. It is a beginner-friendly data project because every input is open and the processing is clear, but it is still useful to real people.

What you build

Roles: public viewer (sees the map), NGO or city user (sees the ranked list and exports), admin (sets thresholds and areas).

Flows:

1. Admin loads neighbourhood boundaries and census indicators for one city.
2. A daily job fetches forecasts for each area and stores them.
3. Heat index and warning levels are calculated per area and day.
4. The vulnerability score is combined with the hazard level to rank areas.
5. The dashboard shows the map, the ranked list and the history view; users export a PDF or CSV for field teams.

The technically interesting part

The hard part for a beginner project is joining data at different scales honestly. Weather comes as grid points, census data comes by administrative areas that change between censuses, and boundaries may be poorly documented. Decide how you assign weather to areas (centroid, or area-weighted average of grid cells), document it, and show how much the choice changes the ranking.

The second part is thresholds that mean something. Do not invent warning levels; take them from a published heat action plan or health guidance, and show them on a methodology page. Your supervisor will ask why an area is red; you need a clear answer.

How to evaluate it

• Forecast check: for one past summer, compare days your system would have flagged with observed station data; report hits, misses and false alarms.
• Sensitivity: how much the top ten ranking changes under different vulnerability weightings.
• Reliability: the daily job runs for four weeks with logged successes and failures.
• Users: at least five people from NGOs, local government or health workers use the ranked list; record whether they understood it and what they would change.

Suggested plan

1. Weeks 1 to 2: pick a city, collect boundaries and census indicators, find a published heat action plan.
2. Weeks 3 to 5: weather ingestion job and storage.
3. Weeks 6 to 8: heat index, warning levels, vulnerability score.
4. Weeks 9 to 11: map dashboard, ranked list, history view.
5. Weeks 12 to 15: validation against past data, user feedback, write-up.

Path to a product

First users are NGOs and relief groups that run summer water and cooling camps and need to show donors why they chose locations. Offer a free public map and paid hosted instances for NGOs, city administrations or hospitals that want alerts, custom layers and exports. Insurance and employers with outdoor workers are later customers.

Scope warning

Do not build your own weather model or forecast. Do not claim health outcome predictions such as deaths. Keep one city in the core scope; a second city is a stretch goal only if the first works well.

Learn what you need on Sefism

• Data Analytics roadmap : Python, SQL and visualisation basics in order.
• Data Analytics cheat sheet : pandas and chart reminders.
• Our World in Data : clear examples of climate and health charts.
• From Data to Viz : choosing colour scales and map types.
• Storytelling with Data : making a warning map people act on.

Get help with this idea

The key decisions are the city, the warning thresholds and how you assign weather to neighbourhoods. If you want a second opinion on scope, stack or how to evaluate it before you pitch it to your supervisor, a mentor can help you shape it. You still build it; the guidance is about making the plan realistic and the results defensible. New here? create a free account first.

• Discuss this idea with a mentor
• Send a query to Tauseef
• Book a 1:1 session