Title: Accounting for Regulatory Zoning in Land Change Science and Modeling

Advisors: Dr. Georgina Sanchez, Director of Research Engagement of the Center for Geospatial Analytics and Dr. Ross Meentemeyer, Executive Director of the Center for Geospatial Analytics

Abstract: Local land use policies shape when and where economic development, conservation, and hazard mitigation activities occur. Likewise, the field of land change science seeks to predict impacts and tradeoffs of land-use decisions over time. However, data constraints, domain complexity, and fragmented policy structures have limited the representation of granular, local priorities in land change models (LCMs). We examine how regulatory zoning—the most common land use planning tool in the US—shapes land change outcomes by 1) quantifying zoning’s effect on locations of historical urban land change, 2) closing data gaps using machine learning to create spatially-complete zoning maps, and 3) integrating zoning into a landscape-scale LCM. This research bridges traditional land change science, modeling, and planning practice by capturing realistic impacts of local planning decisions on large-scale predictions of future land change.