A platform for research: civil engineering, architecture and urbanism
From an agent-based laboratory to the real world: Effects of “neighborhood” size on urban sprawl
Abstract Agent-based models (ABMs) have been established as a valuable research tool in the study of complex human-environment 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.
Highlights Agent-based models (ABMs) are challenged to produce generalizable and practical results. We model and explain urban sprawl using a simple theoretical ABM and robust statistical regressions with empirical data. We combine the ABM and empirical testing to illustrate bigger “neighborhood” size positively contributes to sprawl. Our work suggests it is mutually beneficial to draw general conclusions from ABMs and to test them in the real world. We call for agent-based modelers to consider this practice.
From an agent-based laboratory to the real world: Effects of “neighborhood” size on urban sprawl
Abstract Agent-based models (ABMs) have been established as a valuable research tool in the study of complex human-environment 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.
Highlights Agent-based models (ABMs) are challenged to produce generalizable and practical results. We model and explain urban sprawl using a simple theoretical ABM and robust statistical regressions with empirical data. We combine the ABM and empirical testing to illustrate bigger “neighborhood” size positively contributes to sprawl. Our work suggests it is mutually beneficial to draw general conclusions from ABMs and to test them in the real world. We call for agent-based modelers to consider this practice.
From an agent-based laboratory to the real world: Effects of “neighborhood” size on urban sprawl
Sun, Shipeng (author) / Parker, Dawn C. (author) / Brown, Daniel G. (author)
2022-09-26
Article (Journal)
Electronic Resource
English
URBAN GROWTH EXTERNALITIES AND NEIGHBORHOOD INCENTIVES: ANOTHER CAUSE OF URBAN SPRAWL?*
Online Contents | 2013
|TIBKAT | 2014
|Online Contents | 1999
British Library Online Contents | 2001
|The Effects of Sprawl on Neighborhood Social Ties: An Explanatory Analysis
Taylor & Francis Verlag | 2001
|