Capturing multiscalar feedbacks in urban land change: a coupled system dynamics spatial logistic approach
Capturing multiscalar feedbacks in urban land change: a coupled system dynamics spatial logistic approach
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DOI：
10.1068/b36151
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发表时间：
2012-01-01
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影响因子：
--
通讯作者：
Seto, Karen C.
中科院分区：
文献类型：
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作者：
Gueneralp, Burak;Reilly, Michael K.;Seto, Karen C.
在本文中，我们问两个问题：一个多尺度的城市土地变化模型，耦合区域尺度的系统动力学模型与局部尺度的空间logit模型更好地预测城市土地变化的数量比单独的模型？耦合区域和局部尺度因子的多尺度城市土地变化模型是否比独立的局部尺度空间logit模型更好地预测城市土地变化的空间格局？为了研究这些问题，我们开发了一个耦合系统动力学空间logit（CSDSL）模型的珠江三角洲，中国，该模型将区域规模的人口和经济因素与当地规模的生物物理和可访问性因素。在预测城市土地变化量方面，CSDSL模型比独立的空间logit和系统动力学模型分别准确15%和18%。在预测城市土地变化的空间格局方面，CSDSL模型略优于空间logit模型测量的四个空间格局指标：城市斑块的数量，城市边缘密度，平均城市斑块大小，城市区域的空间不规则性。CSDSL和空间logit模型都低估了离散城市斑块的数量（分别为64%和80%）和城市边缘密度（分别为42%和62%）。虽然这两种模型都高估了平均城市斑块大小，但空间logit模型高估了316%以上，而CSDSL高估了192%。最后，这些模型在预测城市地区的空间不规则性和城市变化的位置方面同样表现良好。综上所述，这些结果表明，CSDSL模型优于一个独立的空间logit或系统动力学模型在预测城市土地变化的数量和空间复杂性。结果还表明，预测城市土地变化模式仍然比预测变化总量更困难。
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.