Food Desert Detection System
IBM Case Competition
Participant · Fall 2024 · DFW Area
Overview
Developed an AI-powered solution to identify and address food deserts in the Dallas-Fort Worth metropolitan area. The project combined machine learning, geospatial analysis, and data visualization to provide actionable insights for improving food accessibility in underserved communities.
Challenge
Food deserts are areas with limited access to affordable and nutritious food, particularly affecting low-income communities. Traditional methods of identifying these areas were time-consuming and often inaccurate, making it difficult for local governments and organizations to effectively allocate resources and implement solutions.
Solution
Leveraged IBM Watson AI to analyze geospatial data, demographic information, and food access metrics. Created a predictive model that could identify food deserts with high accuracy and provide recommendations for optimal placement of food resources and community programs.
Key Features
AI-powered food desert identification
Interactive data visualization dashboard
Predictive modeling for resource allocation
Geospatial analysis and mapping
Community impact assessment tools
Government reporting and recommendations
Results & Impact
Increased food desert detection accuracy by 25% compared to traditional methods
Successfully presented data-driven strategies to IBM judges and local government officials
Provided actionable recommendations for improving food access in 15+ DFW communities
Integrated multiple API data streams for comprehensive model training
Ensured high data quality through automated validation processes