Capturing Multiscalar Feedbacks in Urban Land Change: A Coupled System Dynamics Spatial Logistic Approach

Burak Güneralp, Michael K Reilly and Karen C Seto
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Burak Güneralp: School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511, USA
Michael K Reilly: School of Earth Sciences, Stanford University, 397 Panama Mall, Stanford, CA 94305, USA
Karen C Seto: School of Forestry and Environmental Studies, Yale University, 195 Prospect Street, New Haven, CT 06511, USA

Environment and Planning B, 2012, vol. 39, issue 5, 858-879

Abstract: In this paper we ask two questions: Does a multiscalar urban land-change model that couples a region-scale system dynamics model with a local-scale spatial logit model better predict the amount of urban land change than either model alone? Does a multiscalar urban land-change model that couples regional and local-scale factors better predict the spatial patterns of urban land change than a standalone local-scale spatial logit model? To examine these questions, we develop a coupled system dynamics spatial logit (CSDSL) model for the Pearl River Delta, China, that incorporates region-scale population and economic factors with local-scale biophysical and accessibility factors. In terms of predicting the amounts of urban land change, the CSDSL model is 15% and 18% more accurate than the standalone spatial logit and system dynamics models, respectively. In terms of predicting the spatial pattern of urban land change, the CSDSL model slightly outperforms the spatial logit model as measured by four spatial pattern metrics: number of urban patches, urban edge density, average urban patch size, and spatial irregularity of the urban area. Both the CSDSL and spatial logit models underpredict the number of discrete urban patches (by 64% and 80%, respectively) and the urban edge density (by 42% and 62%, respectively). While both models overpredict the average urban patch size, the spatial logit model overpredicts by over 316%, while the CSDSL overpredicts by 192%. Finally, the models perform equally well in predicting the spatial irregularity of urban areas and the location of urban change. Taken together, these results demonstrate that the CSDSL model outperforms a standalone spatial logit or system dynamics model in predicting the amount and spatial complexity of urban land change. The results also show that predicting urban land-change patterns remains more difficult than predicting total amounts of change.

Keywords: Urban modeling; urban growth forecasting; urbanization; urban expansion; land use change; China; spatially explicit model; multiscale modeling (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:39:y:2012:i:5:p:858-879

DOI: 10.1068/b36151

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