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地理科学进展  2014, Vol. 33 Issue (12): 1624-1633    DOI: 10.11820/dlkxjz.2014.12.005
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城市元胞自动机扩展邻域效应的测量与校准研究
廖江福1,2(),唐立娜1(),王翠平3,许通1
1. 中国科学院城市环境研究所城市环境与健康重点实验室,福建 厦门 361021
2. 集美大学计算机工程学院,福建 厦门 361021
3. 集美大学理学院,福建 厦门 361021
Measuring and calibrating extended neighborhood effect of urban cellular automata model based on particle swarm optimization
Jiangfu LIAO1,2(),Lina TANG1(),Cuiping WANG3,Tong XU1
1. Key Lab of Urban Environment and Health, Institute of Urban Environment, CAS, Xiamen 361021, Fujian, China
2. Computer Engineering College, Jimei University, Xiamen 361021, Fujian, China
3. School of Science, Jimei University, Xiamen 361021, Fujian, China
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摘要 

城市元胞模型由于在定量分析与预测城市动态的潜力而受到众多研究者的持续关注。邻域规则是主导城市元胞模型模拟过程的关键组件。研究表明,不同土地利用组合间存在显著的邻域效应,且邻域效应具有惯性、排斥和吸引等影响。然而,传统城市元胞模型主要考虑的是特定分辨率下较小窗口的邻域范围。本文尝试刻画更大窗口的邻域效应及其对元胞模型的影响。基于测量的扩展邻域因子,应用粒子群优化算法校准大窗口邻域规则,并创建了考虑扩展邻域效应的城市元胞模型。为验证模型有效性,将其应用于模拟厦门市1995-2010年期间的城市扩张动态。与3×3摩尔邻域的逻辑回归模型相比较,1995-2010年期间的建设用地模拟精度从80.7%提高到83.9%,总体精度从87.8%提高到89.6%,Kappa系数从70.0%提高到74.5%,表明考虑扩展邻域效应的城市模型取得了更好的模拟效果。

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廖江福
唐立娜
王翠平
许通
关键词 城市扩张元胞自动机扩展邻域效应粒子群智能厦门市    
Abstract

Simulation and quantitative analysis of urban land-use change dynamics are an effective way to understand the evolution of spatial structure in urban systems. Cellular automata (CA) has drawn continuous and increasing interest of researchers in the field of land use and land cover change simulation. Neighborhood rules are a core component of the urban CA model, with varied neighborhood effects among different land use combinations. Most urban CA models constructed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. However, the extended enrichment factor indicates that there are still obvious neighborhood effects in large neighborhoods with a particularly long distance to the central cell. Based on a measured extended enrichment factor for a large neighborhood, we applied particle swarm optimization (PSO) to obtain the parameter settings of neighborhood rules, including various sub-neighborhoods at different distances within the large neighborhood. The extracted neighborhood rules were integrated into a widely used logistic regression urban CA model, Logistic-CA (LNCA), and a large neighborhood urban land use model, PSO-LNCA, was developed. Using Xiamen City as a study case, the PSO-LNCA model was implemented to simulate urban growth during the period between 1995 and 2010. The accuracy of simulated results by the model was evaluated with confusion matrix and Kappa coefficient. Accuracies for built-up land and non-built land and overall accuracy for 2010 are 83.9%, 91.7%, and 89.6%, respectively, and the Kappa coefficient for 2010 is 74.5%. The results show that the PSO-LNCA model achieved significantly higher simulation accuracy for built-up land and Kappa coefficient than the traditional urban CA model with a 3×3 kernel neighborhood (3.2% higher accuracy for built-up land and 4.5% higher for Kappa coefficient, respectively), and also generated relatively higher overall accuracy (1.8% higher). By integrating the extended neighborhood module, the simulation result generated by the PSO-LNCA model is closer to the actual space morphology and structure, compared with the traditional 3×3 kernel Logistic-CA model.

Key wordsurban expansion    cellular automata    extended neighborhood effect    particle swarm optimization    Xiamen
     出版日期: 2014-12-30
基金资助:国家自然科学基金项目(41101143);集美大学陈秋明学科建设基金项目(ZC2014011)
作者简介: 廖江福(1978-),男,福建安溪人,博士生,主要从事地理模拟、空间智能和城市生态研究,E-mail:jfliao@iue.ac.cn
引用本文:   
廖江福,唐立娜,王翠平,许通. 城市元胞自动机扩展邻域效应的测量与校准研究[J]. 地理科学进展, 2014, 33(12): 1624-1633.
Jiangfu LIAO,Lina TANG,Cuiping WANG,Tong XU. Measuring and calibrating extended neighborhood effect of urban cellular automata model based on particle swarm optimization. PROGRESS IN GEOGRAPHY, 2014, 33(12): 1624-1633.
链接本文:  
http://www.progressingeography.com/CN/10.11820/dlkxjz.2014.12.005      或      http://www.progressingeography.com/CN/Y2014/V33/I12/1624
Fig. 1  PSO-LNCA模型邻域结构图
Fig. 2  PSO-LNCA模型框架图
Fig. 3  1995、2010年研究区土地利用分类图(数据来源:中国科学院遥感与数字地球研究所)
Fig. 4  新增城镇用地、工矿用地与其他地类邻域关系随邻域距离的变化
Fig. 5  利用粒子群优化算法校准大窗口邻域规则
Fig. 6  利用PSO-LNCA模拟厦门市1995-2010年期间城市生长
PSO-LNCA 1995-2010模拟(pixels)
建设用地 非建设用地 精度/%
实际分类 建设用地 382035 72806 83.9
非建设用地 98677 1099898 91.7
总精度 89.6
Kappa 74.5
Logistic-CA 1995-2010模拟(pixels)
建设用地 非建设用地 精度/%
实际分类 建设用地 367116 87725 80.7
非建设用地 113596 1084979 90.5
总精度 87.8
Kappa 70.0
Tab. 1  Logistic-CA和PSO-LNCA城市扩展模拟的混淆矩阵
1995-2010/%
建设用地 非建设用地 总体精度 Kappa
LWS = 5, RI = 1 81.5 90.8 88.3 71.1
LWS = 9, RI = 2 82.1 91.0 88.5 71.8
LWS = 21, RI = 4 82.8 91.3 89.0 72.9
LWS = 46, RI = 5 83.9 91.8 89.6 74.5
LWS = 91, RI = 10 83.6 91.6 89.4 73.9
LWS = 151, RI = 15 82.8 91.3 89.0 72.9
Tab. 2  大窗口邻域半径(LWS)和子邻域间隔(RI)对PSO-LNCA模型模拟结果的影响
迭代次数 运行时间/s
Logistic-CA(3×3 kernel) PSO-LNCA(LWS=9, RI=2) PSO-LNCA(LWS=21, RI=4) PSO-LNCA(LWS=46, RI=5)
10 53.8 91.1 96.8 131.2
50 109.7 180.3 209.4 366.7
100 179.6 288.9 351.2 670.7
200 324.0 511.3 630.1 1272.2
400 602.9 942.8 1183.7 2383.6
Tab. 3  Logistic-CA和PSO-LNCA模型的性能分析
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