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.
A Heat Map Is Not Enough: Turning Chennai’s Heat Data into Decisions
Data can tell us that a city is hot. But can it help us decide where to walk?
A temperature forecast gives us a number. A heat map gives us a picture. But when someone steps outside on a scorching afternoon in Chennai, neither necessarily answers the question that matters most: Which way should I go?
Urban heat is not experienced uniformly. Two nearby streets can offer different levels of shade, greenery, and exposure. Yet everyday navigation often focuses on distance and travel time, leaving heat out of the decision.
We built HeatShield Chennai to explore what happens when environmental data becomes part of the journey itself.
By combining AWS cloud services, weather information, geospatial analysis, and heat-aware routing, the project aims to make local heat risk easier to understand and consider when choosing a route.
1. Looking Beyond the City-Wide Forecast
A single weather reading cannot describe every neighbourhood. HeatShield Chennai explores a more local approach by focusing on a 5 km × 5 km corridor between T. Nagar and Guindy.
The corridor is divided into 100 cells, each approximately 500 m × 500 m. This grid provides a structured way to estimate how heat risk may vary across the selected area.
The application retrieves hourly temperature, relative humidity, and apparent temperature from Open-Meteo . AWS Lambda processes the available weather inputs and updates the estimated risk for each cell. When live weather data is unavailable, the application can use an offline climate-model fallback.
Instead of presenting one number for an entire city, HeatShield lets users explore a more local representation of estimated heat conditions.
2. Making Environmental Data Actionable
Collecting data is only one part of the challenge. The more important question is how to turn that data into something people can understand.
HeatShield estimates cell-level heat risk using factors including weather conditions, greenery, built-up density, time of day, and sensor anomalies. The resulting score ranges from 0 to 100 and is grouped into green, amber, and red bands.
These scores are model estimates, not direct measurements of every street's temperature. Their purpose is to make the system's assessment easier to explore—not to declare a location definitively safe or dangerous.
This leads to the project's central feature: heat-aware navigation.
Using a directed walking graph and Dijkstra's algorithm, HeatShield provides two options:
- Fast Route: prioritises walking distance.
- Cool Route: adjusts route costs based on estimated heat risk and selected cooling-related points of interest.
Users can compare the two options instead of treating the shortest distance as the only consideration. The cool route is a model-based recommendation; actual heat exposure may differ depending on real-world conditions.
3. Designing for People, Not Just Maps
Environmental information becomes more useful when people can interact with it in ways that suit their needs.
HeatShield includes user profiles that influence its guidance, an interactive map, and a time-of-day timeline. English and Tamil language support, along with voice-related accessibility features, helps make the interface more approachable.
The project also includes a municipal dashboard. While an individual user may want to compare two walking routes, a municipal user may want to identify cells with higher estimated risk across the study area.
These different views connect two perspectives: helping people consider heat during everyday journeys and helping planners explore where further attention may be useful.
The objective is not to replace official forecasts or public-health guidance. It is to investigate how local estimates can complement the information already available.
4. Connecting the System Through AWS
HeatShield relies on AWS to connect its frontend, data-processing workflows, storage, and alerting capabilities.
- Amazon S3 and Amazon CloudFront deliver the frontend and spatial assets.
- Amazon API Gateway and AWS Lambda support API requests and application logic.
- Amazon DynamoDB stores sensor readings and calculated cell scores.
- Amazon EventBridge Scheduler triggers the hourly weather-refresh workflow.
- Amazon Bedrock supports contextual, bilingual heat-safety guidance, with a fixed-template fallback.
- Amazon SNS sends alerts when the configured heat-risk threshold is reached.
- AWS SAM supports infrastructure definition and deployment.
During the scheduled refresh, the system retrieves available weather inputs, recalculates the 100 cell-level scores, updates stored results, and checks alert conditions.
For sensor-data testing, a Python simulator sends sample telemetry through an API Gateway endpoint. A Lambda handler processes the incoming readings and stores them in DynamoDB.
[Insert the AWS architecture diagram here.]
5. Testing the Data Flow
A system that combines changing weather conditions and sensor inputs needs a way to exercise its workflows before relying on real-world observations.
HeatShield's current implementation supports testing the scheduled refresh, cell-score updates, simulated sensor ingestion, and configured alert workflow. Amazon SNS alert delivery has been tested, and the application brings the resulting information into an interactive experience for exploring estimated risk and comparing routes.
Using simulated telemetry makes it possible to test how changing readings move through the system without requiring physical sensors during development.
That distinction is important: the current sensor readings are simulated, and physical sensors have not yet been deployed. The prototype demonstrates the software workflow, not a completed network of street-level monitoring devices.
The same principle guides how we interpret the output. A calculated score can help explore a possible risk pattern, but real-world validation is needed before drawing conclusions about actual street conditions or reductions in heat exposure.
6. From a Working Prototype to Greater Local Accuracy
HeatShield Chennai explores how cloud infrastructure can connect environmental data with decisions at street level. The next stage is to strengthen the evidence behind those decisions.
Potential improvements include integrating physical sensors, comparing estimated risk against observed local conditions, refining environmental inputs, and evaluating route recommendations through defined test scenarios.
These steps would help establish where the estimates are useful, where they need improvement, and how the system could better support residents and urban planners.
The broader opportunity is to make environmental information not only available, but easier to interpret and act upon.
When Data Meets Everyday Decisions
Urban heat is more than a number on a screen. It shapes everyday journeys, outdoor work, and the way people experience their neighbourhoods.
HeatShield Chennai explores a simple but meaningful possibility: what if navigation considered environmental conditions alongside distance?
By bringing weather inputs, geospatial analysis, route calculations, and cloud-based workflows together on AWS, the project takes a step towards making local heat risk more visible and understandable.
Because sometimes, the most useful route is not simply the one that gets us there fastest. It is the one that helps us make a more informed decision along the way.
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article