Machine Learning for Local Government: Using Maximum Entropy to Predict Parcel Development
Skill Level: Intermediate, Advanced
Intended Audience: GIS User, Student
Abstract
How can machine learning help predict where development will happen next? This presentation applies Maximum Entropy (MaxEnt) modeling to evaluate development suitability across vacant parcels in Champaign County, Illinois. Using a binary classification of parcel codes to distinguish developed from undeveloped land, I generated presence and background points, then modeled suitability using four environmental predictors — population density, proximity to roads, distance to existing development, and topographic slope — drawn from local, USGS, and Census sources. Implemented in ArcGIS Pro’s Presence-Only Prediction tool (though open-source alternatives will be discussed) with a hinge basis function, the model achieved strong discriminative accuracy, with distance to existing development emerging as the dominant predictor. I’ll discuss what these findings suggest for local land-use planning, along with key limitations tied to parcel data quality and classification ambiguity, and close with directions for extending this suitability-modeling approach.
Presenter
Brianne Holt
GIS Technician, Champaign County GIS Consortium
Brianne Holt is a Spatial Data Science Master’s student at Pennsylvania State University with a GCert in Geospatial Programming and Web Map Development. She currently works as a GIS Technician at the Champaign County GIS Consortium in Urbana, Illinois, and is a published cartographer in Global Treks & Adventures’ 2022 publication, A Trail Guide to Reykjavik, Iceland. Her interests in GIS span machine learning, development and programming, systems architecture and design, and conveying complex information through data visualization. She is also a vintage tech fanatic and bookworm, with non-GIS hobbies including retro PC restoration, media preservation, Japanese language and literature, and collecting more books than she could possibly have time to read. She brings a combination of curiosity and technical range in GIS to help translate advanced geospatial concepts into practical tools for local government.