Urban expansion simulation and scenario prediction using cellular automata: comparison between individual and multiple influencing factors.

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ID: 21292
2019
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Abstract
Quantifying the contribution of driving factors is crucial to urban expansion modeling based on cellular automata (CA). The objective of this study is to compare individual-factor-based (IFB) models and multi-factor-based (MFB) models as well as examine the impacts of each factor on future urban scenarios. We quantified the contribution of driving factors using a generalized additive model (GAM), and calibrated six IFB-DE-CA models and fifteen MFB-DE-CA models using a differential evolution (DE) algorithm. The six IFB-DE-CA models and five MFB-DE-CA models were selected to simulate the 2005-2015 urban expansion of Hangzhou, China, and all IFB-DE-CA models were applied to project future urban scenarios out to the year 2030. Our results show that terrain (DEM) and population density (POP) are the two most influential factors affecting urban expansion of Hangzhou, indicating the dominance of biophysical and demographic drivers. All DE-CA models produced defensible simulations for 2015, with overall accuracy exceeding 89%. The IFB-DE-CA models based on DEM and POP outperformed some MFB-DE-CA models, suggesting that multiple factors are not necessarily more effective than a single factor in simulating present urban patterns. The future scenarios produced by the IFB-DE-CA models are substantially shaped by the corresponding factors. These scenarios can inform urban modelers and policy-makers as to how Hangzhou city will evolve if the corresponding factors are individually focused. This study improves our understanding of the effects of driving factors on urban expansion and future scenarios when incorporating the factors separately.
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feng2019urbanenvironmental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Feng, Yongjiu;Wang, Jiafeng;Tong, Xiaohua;Shafizadeh-Moghadam, Hossein;Cai, Zongbo;Chen, Shurui;Lei, Zhenkun;Gao, Chen;
Journal Environmental monitoring and assessment
Year 2019
DOI
10.1007/s10661-019-7451-y
URL
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