Feelscape

Inspiration

We’ve all seen weather maps, traffic maps and population heatmaps. But what if there were a map that could tell you how people really feel in various neighborhoods in a city? That’s the concept that launched Feelscape. We were trying to construct something that could display the emotional beat of a location are they happy there? Stressed? Calm? Like what does a city feel like, not just look like. We were inspired by the growing emphasis on mental health and how under represented it is visually. The concept of overlaying feelings on top of Google Maps seemed like a neat but significant approach to investigating that.

What it does

Feelscape visualizes the emotional state of different locations on a map. It collects sentiment data, converts it into happiness scores tied to coordinates, and displays them as a heatmap using the Google Maps API. Users can explore how a city “feels” overall or search specific areas. It also supports topic-based queries type in any topic, and the map highlights locations where people are expressing emotions about it, showing how different regions feel about that specific subject in real time.

How we built it

Shlok engineered the script that collects emotional data essentially happiness or sentiment ratings from various sources, each associated with a location. Kaustubh managed to transform that raw data into something useable giving it a happiness score and plotting it in coordinate space. Revanth got to work on the frontend, designing a clean UI and leveraging the Google Maps JavaScript API to visualize those scores on a heatmap. And finally, Piyush built the backend with Node.js and Express that tied everything together, queried data from MongoDB, and served it to the frontend. Once plugged in, the device displays a real-time ’mood map. You can explore a particular region and discover what people there are feeling, or simply browse the emotional terrain of the entire map.

Challenges we ran into

Getting the emotional scores to show up meaningfully on a map was trial and error—some areas were too red, others too faint. Configuring the Google Maps heatmap and calibrating the visuals (radius, gradient, weight) turned out to be more difficult than anticipated. It took some work filtering the data on the backend and syncing it in real time without making the UI lag. And of course… coordinating timing and fixing last minute glitches like “why is Mumbai magically the happiest anywhere on Earth?

Accomplishments that we're proud of

Built a fully working emotional heatmap using real-time sentiment data Integrated the Google Maps API to visualize mood across locations smoothly Created a responsive UI with live query support for both global and topic-based emotion mapping Got the backend, frontend, and database to talk to each other seamlessly

What we learned

This project pushed each of us. We had to learn how to normalize/process emotion data, deal with google maps heatmap tuning, manage frontend/backend sync and make the experience feel responsive without cluttering the UI.We also discovered how little touches , such as the manner in which we fade out the global view when a local query is open , can make the product seem much more natural.

What's next for Feelscape

Add real-time data from social media sources like twitter or threads Let users submit their own mood check-ins anonymously to contribute to the map Introduce time filters to see how emotions in a city change over hours or days Build city-wise emotion reports and leaderboard Expand to a mobile app Experiment with machine learning to predict mood shifts across regions