Our Mission
Utilizing data analytics, geospatial algorithms, API integrations, and linear optimization techniques, Meal Mvrs aims to be a game-changer in the food donation ecosystem. Our web application connects individuals to nearby food banks and optimizes the redistribution of surplus food from restaurants to these shelters.
Our Process
Data Gathering and Geolocation Services
We initiated our project by deploying web scraping methodologies and API calls to assemble an exhaustive dataset of food shelters within a 5-mile radius of Boston. To facilitate real-time navigation, we utilized API-based geocoding and reverse geocoding to convert each food shelter's address into latitude and longitude coordinates. We applied the same technique to capture the live location of users. Our algorithm, built on Euclidean distance calculations, then routes individuals to the nearest food shelter, all visualized through the integration of Maps API for seamless directions.
Load Balancing and Resource Allocation
In the second phase, our team developed a robust, data-driven framework for restaurant-to-shelter food allocation. Using poverty statistics as an indicator, we implemented a variety of linear optimization techniques to balance the load among shelters. We employed Monte Carlo simulations to estimate the food demand across various Boston neighborhoods like the North End, South Boston, and Mission Hill. This ensured an equitable distribution of resources and prevented any single shelter from being swamped.
By merging machine learning, data analytics, geospatial intelligence, and API integrations, our web application not only directs users to the nearest food banks in real-time but also smartly reallocates restaurant surplus to where it's needed most.
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