Beyond the Forecast: Building HeatShield Chennai for Street-Level Heat Risk
What if your route could think beyond distance and account for the heat you face? HeatShield Chennai transforms weather data into hyperlocal heat-risk insights, combining AWS-powered analytics, environmental factors, and heat-aware navigation to help people make more informed travel decisions. By connecting street-level risk estimation with personalised guidance and cloud-based alerts, it reimagines how technology can help Chennai navigate a hotter future.
Chennai Is Heating Up. What If Your Route Could Think About Heat?
The shortest way home may not be the route that makes the most sense on a hot Chennai afternoon.
Two streets. Almost the same distance. Very different exposure to heat.
Most navigation apps help us find the quickest way from one place to another. But on a scorching afternoon in Chennai, distance is only part of the decision. The route itself matters.
HeatShield Chennai explores a different question: what if navigation could consider not just how far you walk, but also the heat risk along the way?
Built on AWS, HeatShield combines weather data, local environmental factors, heat-risk estimation, and heat-aware routing to help people explore their surroundings with heat in mind.
1. From City-Wide Forecasts to Local Heat Risk
A city-wide forecast provides useful information, but it cannot explain how conditions may differ from one street to another.
HeatShield Chennai focuses on a dense 5 km × 5 km high-exposure urban corridor across South-Central Chennai—encompassing key commercial, transit, and residential hubs including T. Nagar, Saidapet, Nandanam, Guindy, and Teynampet—divided into 100 micro-cells of approximately 500 m × 500 m each. This creates a truly localized view of heat risk instead of treating the entire city as if microclimate conditions were uniform.
The application retrieves hourly temperature, relative humidity, and apparent temperature data from Open-Meteo . An hourly AWS Lambda process refreshes the weather inputs and recalculates cell-level risk estimates. If live weather data is unavailable, the system can fall back to an offline climate model.
The result is a map designed to help users explore how estimated heat risk varies across the selected area.
2. Making Heat Risk Understandable—and Routes More Useful
A coloured map is only useful if people can understand what it represents.
HeatShield combines weather conditions with factors such as greenery, built-up density, time of day, and sensor anomalies to estimate risk. Its model produces a score from 0 to 100, grouped into green, amber, and red risk bands.
The aim is not to label a location as definitively safe or dangerous. It is to provide an understandable estimate that can support better-informed decisions.
The project also explores a different approach to navigation: the shortest route may not always be the preferred route.
Using a directed walking graph and Dijkstra's algorithm, HeatShield offers two route options:
- Fast route: prioritises walking distance.
- Cool route: adjusts route costs according to estimated heat risk and selected cooling-related points of interest.
This allows users to compare a distance-focused journey with a route that gives greater consideration to heat exposure. The route is a model-based recommendation, not a guarantee of lower real-world body temperature.
3. Different People, Different Needs
Heat does not affect everyone in the same way. Outdoor workers, children, and older adults may have different levels of vulnerability and different needs.
HeatShield includes profile-based guidance to explore how recommendations can vary by user profile. It also provides an interactive map, a time-of-day timeline, English and Tamil language support, and voice-related accessibility features.
For municipal users, a dashboard offers another perspective: instead of viewing one journey, they can examine estimated risk across multiple cells and identify locations that may deserve closer attention.
The broader idea is to connect individual decisions with a view that could support local planning—without presenting model estimates as official public-health warnings.
4. How AWS Powers HeatShield Chennai
AWS provides the cloud infrastructure connecting the application's data, calculations, and user-facing experience.
- Amazon S3 and Amazon CloudFront host and deliver the frontend and spatial assets.
- Amazon API Gateway and AWS Lambda handle API requests and application logic.
- Amazon DynamoDB stores sensor readings and calculated cell scores, with a time-to-live policy for expiring suitable records.
- Amazon EventBridge Scheduler triggers the hourly refresh workflow.
- Amazon Bedrock supports contextual, bilingual heat-safety guidance, with a fixed-template fallback.
- Amazon SNS delivers heat-risk alerts when the configured score threshold is reached.
- AWS SAM supports infrastructure definition and deployment.
During the hourly workflow, the system retrieves available weather data, recalculates the 100 cell-level scores, updates stored results, and checks whether alert conditions have been met.
5. From Simulation to a Working Cloud Workflow
A heat-aware system needs more than a map. It needs a way to refresh conditions, process incoming data, update risk estimates, and respond when configured alert thresholds are reached.
HeatShield's cloud workflow brings these pieces together. The hourly refresh is triggered through Amazon EventBridge Scheduler, weather inputs are processed by AWS Lambda, and updated cell scores are stored in Amazon DynamoDB. Amazon SNS has also been tested to deliver alerts when the configured risk threshold is reached. Amazon Bedrock supports contextual guidance, with a template-based fallback available.
To test how changing local readings could affect the system, we built a Python sensor simulator that sends sample telemetry through Amazon API Gateway. A Lambda handler processes the readings and stores them in DynamoDB for use by the risk-calculation workflow.
This approach lets us exercise the data flow and test changing conditions without depending on physical hardware during development. The current sensor readings are simulated; physical sensors have not yet been deployed.
The result is a deployed prototype that brings together live weather inputs when available, scheduled processing, risk estimation, interactive routing, and cloud-based alerts. Each component contributes to the larger goal: making local heat risk easier to explore and understand.
6. Taking Heat-Aware Navigation Further
HeatShield is a starting point for making urban heat information more actionable. The next stage is to strengthen the system's local accuracy and extend its real-world usefulness.
That means exploring physical sensor integration, comparing estimated risk against observed local conditions, refining the environmental inputs, and evaluating route recommendations through clearly defined test scenarios.
As the evidence improves, HeatShield can evolve from a working prototype into a more thoroughly validated tool for residents and urban planners. The focus remains on keeping the system understandable: users should be able to explore the estimates, compare routes, and recognise what the available data can—and cannot—tell them.
A Different Way to Think About Urban Heat
Heat is not experienced as a single number on a forecast. It is experienced while walking to work, waiting outdoors, travelling through a neighbourhood, or carrying out essential daily tasks.
HeatShield Chennai explores how AWS can help turn weather data and geospatial analysis into a more local, understandable view of heat risk—and how navigation can consider more than distance alone.
The goal is not simply to put heat on a map. It is to make local heat risk easier to see, question, and act on.
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article