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Parameter sensitivity analysis of urban cellular automata model

  • role： First author第一作者
  • Affiliation：

    School of Geography and Tourism, Guangdong University of Finance and Economics, Guangzhou 510320, China

  • Email：zyh4184@163.com
  • Introduction：张亦汉，1985年生，男，副教授，研究方向为地理信息模型与数据同化。E-mail：zyh4184@163.com
Yihan ZHANG,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Tourism, Guangdong University of Finance and Economics, Guangzhou 510320, China

  • Email：qjg821@263.com
  • Introduction：乔纪纲，1973年生，男，副教授，研究方向为遥感与地理信息模型。E-mail：qjg821@263.com
Jigang QIAO*,  
  • Affiliation：

    School of Geography and Tourism, Guangdong University of Finance and Economics, Guangzhou 510320, China

Wanhua LIU,  
  • Affiliation：

    School of Geography and Tourism, Guangdong University of Finance and Economics, Guangzhou 510320, China

Sirui CAI,  
  • Affiliation：

    School of Geography and Tourism, Guangdong University of Finance and Economics, Guangzhou 510320, China

Qianxin DING,  
  • Affiliation：

    School of Geography and Tourism, Guangdong University of Finance and Economics, Guangzhou 510320, China

Xiaowei CHEN

реферат

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.

ключеви́че слова́

cellular automata;urban expansion;sensitivity;landscape index;neighborhood structure

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