GroceryLink DC - 37

Inspiration

Food deserts disproportionately impact underserved communities, limiting access to fresh and healthy food. Inspired by the need for data-driven urban planning, we created GroceryLink DC—a tool to visualize and improve food accessibility in Washington, DC. By simulating how residents travel to grocery stores, we aim to help policymakers and urban planners make informed decisions on store placements.

What it does

GroceryLink DC is an interactive geospatial simulator that:

Maps grocery store accessibility using real-world demographic and geographic data. Simulates resident movement to analyze how people navigate to grocery stores. Identifies food deserts and highlights underserved areas. Allows users to test solutions by adding new grocery stores and measuring their impact in real-time.

How we built it

Frontend: Next.js, TypeScript, Mapbox GL JS for interactive mapping. Geospatial Analysis: Turf.js for spatial calculations and the Mapbox Directions API for route modeling. Data Sources: DC Open Data for census demographics, grocery store locations, and food access metrics. Backend & Database: MongoDB to store grocery store locations and simulation data.

Challenges we ran into

Data Integration: Cleaning and merging multiple datasets to ensure accurate food accessibility modeling. Real-time Simulation: Optimizing performance while animating large-scale resident movement across the city. User Interaction: Designing an intuitive interface for users to manipulate store locations and analyze the impact.

Accomplishments that we're proud of

Successfully built a dynamic, interactive visualization that models real-world food accessibility. Developed a drag-and-drop simulation that allows users to test new grocery store placements.

What we learned

Geospatial data processing and optimization for large-scale simulations. Urban planning insights on how infrastructure impacts food accessibility. Effective team collaboration in integrating multiple technologies within a short timeframe.

What's next for GroceryLink DC

Enhancing Data Accuracy: Incorporate real-time store inventory data and consumer demand patterns. Expanding Beyond DC: Apply the model to other cities facing food insecurity challenges. AI-Powered Insights: Use machine learning to predict optimal store placements for maximum impact.