From an agent-based laboratory to the real world: Effects of ?neighborhood? size on urban sprawl

Agent-based models (ABMs) have been established as a valuable research tool in the study of complex humanenvironment systems. However, it is still challenging to produce generalizable and practical results with ABMs for theory development. We use the case of the effects of neighborhood size on urban sprawl to illustrate the practice of generating and testing general conclusions from ABMs. In agent-based urban land-change models, homebuyer agents assess land utilities with a bundle of attributes including the quality of focal or zonal neighborhoods. The size of such neighborhoods, or more precisely the spatial assessment units (SAUs), has significant impacts on simulated urban patterns, yet this sensitivity of modeled impacts has not been validated with empirical evidence. Using an ABM that features markets and competitive bidding, we explored how the neighborhood or SAU size affects urban development patterns under various scenarios of homebuyer preferences, which produced a consistent finding that bigger SAUs tended to generate more sprawled patterns. To bring this finding to the real world, we evaluated relationships in data between sprawl indices in US metropolitan areas and the average sizes of their school districts-one type of real-world SAU in neighborhood delineation and a strong input to homebuyers' assessment of housing quality. While the finding has practical policy implications for urban planning and public education, this case exemplifies a path for simple ABMs to support empirically grounded theory development.

Automated summary

This article illustrates the importance of agent-based models in studying complex human-environment systems and demonstrates how ABMs can be used to generate and test general conclusions through a case study on the effects of neighborhood size on urban sprawl.