Assessing Working Models' Impact on Land Cover Dynamics through Multi-Agent Based Modeling and Artificial Neural Networks:  A Case Study of Roanoke, VA

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Virginia Tech


The transition towards flexible work arrangements, notably work-from-home (WFH) practices, has prompted significant discourse on their potential to reshape urban landscapes. While existing urban growth models (UGM) offer insights into environmental and economic impacts, There is a need to study the urban phenomena from the bottom-up style, considering the essential influence of individuals' behavior and decision-making process at disaggregate and local levels (Brail, 2008, p. 89). Addressing this gap, this study aims to comprehensively understand how evolving work modalities influence the urban form and land use patterns by focusing on socioeconomic and environmental factors. This research employs an Agent-Based Model (ABM) and Artificial Neural Network (ANN), integrated with GIS technologies, to predict the future Land Use and Land Cover (LULC) changes within Roanoke, Virginia. The study uniquely explores the dynamic interplay between macro-level policies and micro-level individual behaviors—categorized by employment types, social activities, and residential choices—shedding light on their collective impact on urban morphology. Contrary to conventional expectations, findings reveal that the current low rate in WFH practices has not significantly redirected urban development trends towards sprawl but rather has emphasized urban densification, largely influenced by on-site work modalities. This observation is corroborated by WFH ratios not exceeding 10% in any analyzed census tract. Regarding model performance, the integration of micro-agents into the model substantially improved its accuracy from 86% to 89.78%, enabling a systematic analysis of residential preferences between WFH and on-site working (WrOS) agents. Furthermore, logistic regression analysis and decision score maps delineate the distinct spatial preferences of these agent groups, highlighting a pronounced suburban and rural preference among WFH agents, in contrast to the urban-centric inclination of WrOS agents. Utilizing ABM and ANN integrated with GIS technologies, this research advances the precision and complexity of urban growth predictions. The findings contribute valuable insights for urban planners and policymakers and underline the intricate relationships between work modalities and urban structure, challenging existing paradigms and setting a precedent for future urban planning methodologies.



urban growth modeling, urban expansion, agent-based model, artificial neural network, multi-subagent, work from home, remote work, metropolitan areas, large-scale planning, human decision, master plan