HotSpots

Inspiration

Finding a good restaurant can be overwhelming, especially in busy areas where long wait times and inconsistent reviews make decision-making difficult. We wanted to create a tool that provides a clear, data-driven way to discover the best dining spots at a glance.

What it does

HotSpots visualises restaurant popularity on an interactive heatmap using real-time ratings and review data. Users can:

Find top-rated restaurants nearby with high customer satisfaction.
View live heatmaps showing trending dining spots.

How we built it

Frontend: React and TypeScript for a responsive UI. Backend: unicorn and Python to handle data aggregation. APIs Used: Google Places API for restaurant data.

Challenges we ran into

Data accuracy: Ensuring the heatmap reflects real-time popularity required filtering unreliable data points. Performance optimisation: Handling and rendering large amounts of location-based data while maintaining smooth user interaction. API limitations: Managing request limits and efficiently structuring API calls for real-time updates. Collaboration: Coordinating work across the team and integrating different components smoothly.

Accomplishments that we're proud of

Successfully integrating real-time restaurant data into an intuitive heatmap. Implementing smooth and responsive UI/UX for seamless user experience. Overcoming API restrictions by optimizing data fetching and caching.

What we learned

How to efficiently process and visualize location-based data. Optimizing API requests and database queries for performance. Effective team collaboration using version control and agile development.

What's next for HotSpots

User-generated data: Allow users to submit real-time wait times and reviews. AI-powered recommendations: Predict trending restaurants based on historical data. More filters & insights: Include additional restaurant attributes like noise levels and dietary options.