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首页> 外文期刊>Computers，environment and urban systems >Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR
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Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR

机译：使用空间异质蜂窝自动机模型建模城市生长：空间滞后，空间误差和GWR的比较

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摘要

Many methods can be used to construct geographical cellular automata (CA) models of urban land use, but most do not adequately capture spatial heterogeneity in urban dynamics. Spatial regression is particularly appropriate to address the problem to reproduce urban patterns. To examine the advantages and disadvantages of spatial regression, we compare a spatial lag CA model (SLM-CA), a spatial error CA model (SEM-CA) and a geographically-weighted regression CA model (GWR-CA) by simulating urban growth at Nanjing, China. Each CA model is calibrated from 1995 to 2005 and validated from 2005 to 2015. Among these, SLM and SEM are spatial autoregressive (SAR) models that consider spatial autocorrelation of urban growth and yield highly similar land transition probability maps. Both SAR-CA and GWR-CA accurately reproduce urban growth at Nanjing during the calibration and validation phases, yielding overall accuracies (OAs) exceeding 94% and 85%, respectively. SAR-CA is superior in simulating urban growth when measured by OA and figure-of-merit (FOM) while GWR-CA is superior regarding the ability to address spatial heterogeneity. A concentric ring buffer-based assessment shows OA valleys that correspond to FOM peaks, where the ranges of valleys and peaks indicate the areas with active urban development. By comparison, SAR-CA captures more newly-urbanized patches in highly-dense urban areas and shows better performance in terms of simulation accuracy; whereas, GWR-CA captures more in the suburbs and shows better ability to address spatial heterogeneity. Our results demonstrate that spatial regression can help produce accurate simulations of urban dynamics featured by spatial heterogeneity, either implicitly or explicitly. Our work should help select appropriate CA models of urban growth in different terrain and socioeconomic contexts.
机译：许多方法可用于构建城市土地利用的地理蜂窝自动机（CA）模型，但大多数都不会充分捕获城市动态的空间异质性。空间回归特别适合解决重现城市模式的问题。为了检查空间回归的优点和缺点，我们通过模拟城市增长，比较空间LAG CA模型（SLM-CA），空间误差CA模型（SEM-CA）和地理加权回归CA模型（GWR-CA）在南京，中国。每个CA型号从1995年到2005年校准，并于2005年到2015年验证。其中，SLM和SEM是空间自动增加（SAR）模型，可考虑城市成长的空间自相关，并产生高度相似的土地转换概率图。 SAR-CA和GWR-CA都准确地在校准和验证阶段进行了在南京的城市生长，分别产生超过94％和85％的总体精度（OAS）。当通过OA和典型值（FOM）测量时，SAR-CA在模拟城市生长时，而GWR-CA在解决空间异质性的能力方面是优越的。基于同心的环形缓冲评估显示OA谷，与FOM峰值相对应，其中谷和峰的范围表明了城市发展有源的区域。相比之下，SAR-CA在高密集的城市地区捕获了更多的新城市化贴片，在模拟精度方面表现出更好的性能;虽然，GWR-CA在郊区捕获更多，并显示出解决空间异质性的更好能力。我们的结果表明，空间回归可以通过隐含或明确地，有助于产生由空间异质性所特色的城市动态模拟。我们的工作应该有助于选择不同地形和社会经济背景下的城市增长的适当CA模型。

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  • 来源
    《Computers，environment and urban systems》 |2020年第5期|101459.1-101459.14|共14页
  • 作者

    Gao Chen; Feng Yongjiu; Tong Xiaohua; Lei Zhenkun; Chen Shurui; Zhai Shuting;

  • 作者单位

    Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China|Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China|Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China;

    Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China|Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China|Tongji Univ Coll Architecture & Urban Planning Shanghai 200092 Peoples R China;

    Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China|Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China;

    Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China;

    Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China;

    Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Urban growth modeling; Transition rules; Spatial regression; Spatial heterogeneity; Figure-of-merit; Nanjing;

    机译：城市增长建模;过渡规则;空间回归;空间异质性;图 - 优点;南京;

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