Agent-Based Model

Agent-Based Model (ABM) for investigating scale effects in urban growth mechanism inference. This model generates synthetic urban development data to study how spatial resolution affects statistical and machine learning models' ability to recover land-use change mechanisms. The ABM simulates residential location choices based on forest quality and service proximity, with configurable spatial aggregation levels. It outputs data for testing the Modifiable Areal Unit Problem (MAUP) effects on mechanism recovery using logistic regression, decision trees, Cox proportional-hazards models, and random forests.