This collaboration between a land trust, health, and academic institutions focuses on re-greening vacant lots in Cleveland to improve neighborhood health. We've created a data-driven tool that generates neighborhood "personas," combining demographic, health, and behavioral data. This supports WRLC’s mission by identifying priority areas for greening using multi-dimensional insights about both the environment and its residents.
How much of Cleveland's vacant land is needed for new housing? This session demos an interactive Monte Carlo simulation that models 10,000 futures for any neighborhood. Participants will use an AI-created web app to input population growth scenarios and see the acreage required. The results challenge common narratives by showing a likely land surplus even with optimistic growth, sparking new conversations about how we manage this community asset. This session will also demonstrate how to use free AI tools to create simple web apps for more powerful storytelling experiences with publicly available data.
