Beyond the Forecast: Building HeatShield Chennai for Street-Level Heat Risk
HeatShield Chennai offers hyperlocal heat-risk insights by transforming weather data with AWS-powered analytics, environmental factors, and heat-aware navigation. This helps people make informed travel decisions by connecting street-level risk estimation with personalised guidance and cloud-based alerts, reimagining technology’s role in helping Chennai navigate a hotter future.
Chennai’s Heatwave: Optimising Routes for Comfort The shortest route home may not be the most practical on a hot Chennai afternoon. Two streets, nearly the same distance, yet vastly different exposure to heat. Most navigation apps provide the quickest route from one location to another. However, on a scorching afternoon in Chennai, distance is only one factor in the decision. The route itself is crucial. HeatShield Chennai explores a different question: what if navigation could consider not only the distance walked 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.
- 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 localised 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.
- 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, categorised 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.
- 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.
- 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.
- From Simulation to a Working Cloud Workflow
A heat-aware system requires more than a map. It needs a mechanism to refresh conditions, process incoming data, update risk estimates and respond when configured alert thresholds are reached.
HeatShield’s cloud workflow integrates these components. 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 the impact of changing local readings on the system, a Python sensor simulator was developed to send 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 allows us to exercise the data flow and test changing conditions without relying 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 integrates live weather inputs when available, scheduled processing, risk estimation, interactive routing and cloud-based alerts. Each component contributes to the larger objective of making local heat risk more accessible and comprehensible.
- Enhancing Heat-Aware Navigation HeatShield serves as a foundation for making urban heat information more actionable. The next phase involves enhancing the system’s local accuracy and extending its practical utility. This entails exploring physical sensor integration, comparing estimated risk against observed local conditions, refining environmental inputs and evaluating route recommendations through clearly defined test scenarios. As evidence improves, HeatShield can evolve from a working prototype into a more thoroughly validated tool for residents and urban planners. The focus remains on ensuring user comprehension: users should be able to explore estimates, compare routes and discern the limitations of the available data.
A Different Perspective on 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 facilitate the transformation of weather data and geospatial analysis into a more localised and comprehensible view of heat risk—and how navigation can consider more than distance alone.
The objective is not merely to map heat. It is to make local heat risk more visible, interrogable and actionable.
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