The impact of work from home on urban expansion: a GIS simulation via an intelligent self-adapting multiscale agent-based model

Abstract

Urban land use is increasingly shaped by evolving work modalities, particularly the rise of Work from Home (WFH) arrangements. This study introduces a hybrid Artificial Neural Network – Agent-Based Modeling (ANN – ABM) framework to simulate urban expansion under differentiated work behaviors in the Roanoke Area, Virginia, USA. By integrating temporally stratified household behavior data with spatial predictors, the model captures how heterogeneity between WFH agents and Work on Site (WOS) agents influences land-transition probabilities. The simulation framework comprises four ANN classifiers trained on 2011–2019 data, generating land-use suitability surfaces, behavioral preference maps, and local adaptability scores. These were fused into a dynamic land-use change model calibrated to reflect zoning constraints and infrastructure access. Land Use Changeability Scores (LCS) were computed to prioritize high-probability transition cells, guiding annual allocation from 2019 to 2032. Results show that incorporating behavioral agents increased classification accuracy from 86.7% to 89.8%, particularly improving predictions of medium- and high-intensity development. Contrary to expectations, WFH prevalence had a limited effect on urban sprawl, with densification remaining dominant – reflecting persistently low WFH ratios observed in the 2011–2019 baseline data. Projected land transitions emphasize fringe intensification and infill development, with forest and open-space classes in decline. This research contributes to GIScience by enhancing land-use simulation accuracy through the integration of behavioral agents into an intelligent, GIS-based ANN – ABM framework. By incorporating spatial predictors, zoning constraints, and agent heterogeneity, it offers an interpretable and transferable method to simulate urban expansion patterns under emerging work modalities.