Parameter sensitivity analysis of urban cellular automata model

Yihan ZHANG; Jigang QIAO; Wanhua LIU; Sirui CAI; Qianxin DING; Xiaowei CHEN

doi:10.11834/jrs.20187145

Remote Sensing Applications | Views : 0 下载量: 896 CSCD: 6

PDF
Export
Share
Collection
Album

Parameter sensitivity analysis of urban cellular automata model
Vol. 22, Issue 6, Pages: 1051-1059(2018)
Received：26 May 2017，

Accepted：27 December 2017，

Published：2018-11
DOI： 10.11834/jrs.20187145
稿件说明：

移动端阅览

张亦汉, 乔纪纲, 刘婉华, 蔡思锐, 丁倩欣, 陈小微. 2018. 城市扩张CA模型的参数敏感性分析. 遥感学报, 22(6): 1051–1059 DOI： 10.11834/jrs.20187145.

Zhang Y H, Qiao J G, Liu W H, Cai S R, Ding Q X and Chen X W. 2018. Parameter sensitivity analysis of urban cellular automata model. Journal of Remote Sensing, 22(6): 1051–1059 DOI： 10.11834/jrs.20187145.

摘要

以广州市番禺区为研究区，构建了相应的城市扩张CA模型，从采样、邻域结构和微观元胞尺度等方面研究了CA模型的敏感性。首先通过改变模型采样比例、样本各个类别的比例等研究样本对模型参数的影响。然后分析不同的邻域结构与模型模拟精度的关系，并从微观尺度分析邻域元胞对中心元胞的影响。最后从空间尺度上分析CA模型在各种不同分辨率下的模拟结果，用景观指数剖析模拟结果的形态，同时在元胞摩尔邻域内分析其3×3邻域的城市发展密度变化情况。实验表明：(1)适当提高采样比例，会得到精度较高的权重，但训练样本中城市用地的比例应该与城市用地的转变量在全区的占比相匹配。(2)不论是采用摩尔邻域还是冯诺依曼邻域，模拟精度均随着空间尺度的增加而降低。在同一空间尺度下，采用摩尔邻域的模拟结果略好。相比冯诺依曼4个邻域元胞，摩尔邻域中的角点对中心元胞具有更大的影响。(3)随着空间分辨的降低，模拟结果的斑块数、斑块密度、聚集度和分形维度值在减少，结构变得简单，而且在微观的摩尔邻域中城市发展密度正在减少，即由高密度向低密度转换。

Abstract

Land-use change is the hotspot in the research on global change and sustainable development. The change of urban land use has attracted the attention of many scholars. Cellular Automata (CA) model

which is characterized by its powerful space–time dynamic simulation capability

is one of the important tools for urban land-use change. However

existing research does not systematically analyze the effect of sampling

neighborhood

structure

and different resolutions in the CA model. Therefore

this paper intends to carry out systematic sensitivity analysis in an urban CA model to obtain the quantitative accuracy effect by different factors

such as sampling and neighborhood structure

and the optimal model simulation results. The critical part of CA models is transition rules

which are usually represented by exogenous impact factors such as roads

highways

and towns. These factors (variables) can be addressed by incorporating multicriteria evaluation (MCE) form into CA

which is transformed from MCE into a logistic form to obtain the parameters with a more objective method. This study applies the Monte Carlo method to create different sample ratios that can be used to obtain the weight of the CA model. We also test the CA model’s sensitivity by using different neighborhood structures. Landscape metrics are adopted to verify the accuracy of simulation results in different spatial resolutions. These methods can help determine the best combination for the CA model. After the model is applied to Panyu

the core area of the Pearl River Delta

simulation results can be obtained by using three combinations and processes. First

different sampling ratios and category proportions are used to study the parameter changes under different sample groups. Second

different neighborhood structures are used to find the relationship between model accuracy and neighborhood structure. Finally

the simulation results and changes in different resolutions

landscape index

and 3×3 micro-neighborhood are analyzed. Different simulation results are determined for different combinations of sample ratio

neighborhood

and spatial resolution. Our findings are as follows: (1) High precision weights can be obtained using high sampling ratios

and the proportion of urban in the sample should be consistent with the change rate in the study area. (2) Regardless of which kind of neighborhood structure is used

the simulation accuracy decreases with low-resolution data. However

the simulation accuracy of the Moore neighborhood will be better than that of the Von Neumann neighborhood. The corner cells have a greater effect than the adjacent cells. (3) The patch number

patch density

concentration

and fractal dimension values fall with low-resolution data. The structure of simulation results becomes simple

and the development density of the Moore neighborhood decreases.

关键词

Keywords

references

Chen Y M, Li X, Liu X P, and Ai B. 2014. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science, 28(2): 234–255

Chen Y M, Li X, Wang S J and Liu X P. 2012. Defining agents’ behaviour based on urban economic theory to simulate complex urban residential dynamics. International Journal of Geographical Information Science, 26(7): 1155–1172

Guang Q F and Clarke K C. 2010. A general-purpose parallel raster processing programming library test application using a geographic cellular automata model. International Journal of Geographical Information Science, 24(5): 695–722 ])

柯新利, 邓祥征, 何书金. 2010. 地理元胞自动机模型的尺度敏感性及原因. 地理研究, 29(5): 863–872

Ke X L, Deng X Z and He S J. 2010. Scale sensitivity and its causality for geo-cellular automata modelling. Geographical Research, 29(5): 863–872 (

Li X and Liu X P. 2006. An extended cellular automaton using case‐based reasoning for simulating urban development in a large complex region. International Journal of Geographical Information Science, 20(10): 1109–1136

Li X and Yeh A G O. 1998. Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta. International Journal of Remote Sensing, 19(8): 1501–1518

Li X and Yeh A G O. 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4): 323–343

Li X and Yeh A G O. 2004. Data mining of cellular automata’s transition rules. International Journal of Geographical Information Science, 18(8): 723–744

黎夏, 叶嘉安. 2005. 基于神经网络的元胞自动机及模拟复杂土地利用系统. 地理研究, 24(1): 19–27

Li X and Ye J A. 2005. Cellular automata for simulating complex land use systems using neural networks. Geographical Research, 24(1): 19–27 (

Li X, Lao C H, Liu X P and Chen Y M. 2011. Coupling urban cellular automata with ant colony optimization for zoning protected natural areas under a changing landscape. International Journal of Geographical Information Science, 25(4): 575–593

黎夏, 李丹, 刘小平, 何晋强. 2009. 地理模拟优化系统GeoSOS及前沿研究. 地球科学进展, 24(8): 899–907

Li X, Li D, Liu X P and He J Q. 2009. Geographical simulation and optimization system (GeoSOS) and its cutting-edge researches. Advances in Earth Science, 24(8): 899–907 (

Li X C, Liu X P and Yu L. 2014. A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules. International Journal of Geographical Information Science, 28(7): 1317–1335

Lin J Y and Li X. 2015. Simulating urban growth in a metropolitan area based on weighted urban flows by using web search engine. International Journal of Geographical Information Science, 29(10): 1721–1736

刘纪远, 刘明亮, 庄大方, 张增祥, 邓祥征. 2002. 中国近期土地利用变化的空间格局分析. 中国科学(D辑), 32(12): 1031–1040

Liu J Y, Liu M L, Zhuang D F, Zhang Z X and Deng X Z. 2002. Study on spatial pattern of land-use change in China during 1995—2000. Science in China Series D: Earth Sciences, 32(12): 1031–1040 (

Liu X P, Li X, Liu L, He J Q and Ai B. 2008. A bottom-up approach to discover transition rules of cellular automata using ant intelligence. International Journal of Geographical Information Science, 22(11/12): 1247–1269

Liu X P, Li X, Shi X, Zhang X H and Chen Y M. 2010. Simulating land-use dynamics under planning policies by integrating artificial immune systems with cellular automata. International Journal of Geographical Information Science, 24(5): 783–802

Liu X P, Li X, Yeh A G O, He J Q and Tao J. 2007. Discovery of transition rules for geographical cellular automata by using ant colony optimization. Science in China Series D: Earth Sciences, 50(10): 1578–1588

Liu X P, Ma L, Li X, Ai B, Li S Y and He Z J. 2014. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. International Journal of Geographical Information Science, 28(1): 148–163

彭建, 王仰麟, 张源, 叶敏婷, 吴健生. 2006. 土地利用分类对景观格局指数的影响. 地理学报, 61(2): 157–168

Peng J, Wang Y L, Zhang Y, Ye M T and Wu J S. 2006. Research on the influence of land use classification on landscape metrics. Acta Geographica Sinica, 61(2): 157–168 (

王雷, 李丛丛, 应清, 程晓, 王晓昳, 李雪艳, 胡娈运, 梁璐, 俞乐, 黄华兵, 宫鹏. 2012. 中国1990~2010年城市扩张卫星遥感制图. 科学通报, 57(16): 1388–1399

Wang L, Li C C, Ying Q, Cheng X, Wang X Y, Li X Y, Hu L Y, Liang L, Yu L, Huang H B and Gong P. 2012. China’s urban expansion from 1990 to 2010 determined with satellite remote sensing. Chinese Science Bulletin, 57(16): 1388–1399 (

王羊, 高阳, 赵琳, 赵志强, 李双成. 2011. 元胞自动机模型的尺度敏感性分析. 北京大学学报(自然科学版), 47(4): 750–758

Wang Y, Gao Y, Zhao L, Zhao Z Q and Li S C. 2011. Scale sensitive analysis of cellular automata model. Acta Scientiarum Naturalium Universitatis Pekinensis, 47(4): 750–758 (

White R and Engelen G. 1993. Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns. Environment and Planning A: Economy and Space, 25(8): 1175–1199

Wu F L. 2002. Calibration of stochastic cellular automata: the application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8): 795–818

吴浩, 周璐, 史文中, 陈晓玲, 杨海生, 程志萍, 叶露萍. 2013. 基于正交试验设计的土地利用变化元胞自动机模拟过程的尺度敏感性分析. 地理科学, 33(10): 1252–1258

Wu H, Zhou L, Shi W Z, Chen X L, Yang H S, Cheng Z P and Ye L P. 2013. Scale sensitive of cellular automata model for the simulation of land use change based on orthogonal experiment. Scientia Geographica Sinica, 33(10): 1252–1258 (

张亦汉, 黎夏, 刘小平, 乔纪纲. 2011. 基于数据同化的元胞自动机. 遥感学报, 15(3): 475–491

Zhang Y H, Li X, Liu X P and Qiao J G. 2011. The CA model based on data assimilation. Journal of Remote Sensing, 15(3): 475–491 (

Zhang Y H, Li X, Liu X P and Qiao J G. 2015. Self-modifying CA model using dual ensemble Kalman filter for simulating urban land-use changes. International Journal of Geographical Information Science, 29(9): 1612–1631 ])

Alert me when the article has been cited

提交

Urban expansion simulation by coupling remote sensing observations and cellular automata

Spatial-temporal water area monitoring of the Miyun Reservoir using remote sensing imagery from 1984 to 2020

Channel selection for AIUS temperature inversion based on GF-5

Spatial information technology for facilitating "three-plan integration" using geographical simulation and optimization