Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China

Feng, Yongjiu, Liu, Yan and Batty, Michael (2015) Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China. Stochastic Environmental Research and Risk Assessment, 30 5: 1387-1400. doi:10.1007/s00477-015-1128-z


Author Feng, Yongjiu
Liu, Yan
Batty, Michael
Title Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China
Journal name Stochastic Environmental Research and Risk Assessment   Check publisher's open access policy
ISSN 1436-3240
1436-3259
Publication date 2015
Year available 2015
Sub-type Article (original research)
DOI 10.1007/s00477-015-1128-z
Volume 30
Issue 5
Start page 1387
End page 1400
Total pages 14
Place of publication Heidelberg, Germany
Publisher Springer
Collection year 2016
Language eng
Formatted abstract
A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an urgent need for good methods to facilitate such delineations. This paper presents a machine learning CA model (termed MachCA) with nonlinear transition rules based on least squares support vector machines (LS-SVM) to simulate such urban growth. By projecting the input dataset into a high dimensional space using the LS-SVM method, an optimal hyper-plane is constructed to separate the complex boundaries between urban and nonurban land, thus enabling the retrieval of nonlinear CA transition rules. In the MachCA model, the transition rules are yes–no decisions on whether a cell changes its state or not, the rules being dynamically updated for each iteration of the model implementation. The application of the MachCA for simulating urban growth in the Shanghai Qingpu–Songjiang area in China reveals that the spatial configurations of rural–urban patterns can be modeled. A comparison of the MachCA model with a conventional CA model fitted by logarithmic regression (termed LogCA) shows that the MachCA model produces more hits and less misses and false alarms due to its capability for capturing the spatial complexity of urban dynamics. This results in improved simulation accuracies, although with only less than 1 % deviation between the overall errors produced by the MachCA and LogCA models. Nevertheless, the way MachCA model use in retrieving the transition rules provides a new method for simulating the dynamic process of urban growth.
Keyword Cellular automata (CA)
Least squares support vector machines (LS-SVM)
Nonlinear transition rules
The Shanghai Qingpu – Songjiang area
Urban growth
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2016 Collection
 
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