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ISPRS Int. J. Geo-Inf. 2017, 6(5), 149; doi:10.3390/ijgi6050149

Investigation on the Expansion of Urban Construction Land Use Based on the CART-CA Model

1,2,3
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1,2,3,* , 4
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4
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1,2,3
1
School of Tourism and Geographical Science, Yunnan Normal University, Kunming 650500, China
2
Key Laboratory of Remote Sensing of Resources and Environment of Yunnan Province, Kunming 650500, China
3
Geospatial Information Technology Engineering Research Center of Yunnan Province, Kunming 650500, China
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School of Geographic Sciences, Xinyang Normal University, 237 Nanhu Road, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 21 January 2017 / Revised: 18 April 2017 / Accepted: 3 May 2017 / Published: 11 May 2017
View Full-Text   |   Download PDF [4149 KB, uploaded 11 May 2017]   |  

Abstract

Change in urban construction land use is an important factor when studying urban expansion. Many scholars have combined cellular automata (CA) with data mining algorithms to perform relevant simulation studies. However, the parameters for rule extraction are difficult to determine and the rules are simplex, and together, these factors tend to introduce excessive fitting problems and low modeling accuracy. In this paper, we propose a method to extract the transformation rules for a CA model based on the Classification and Regression Tree (CART). In this method, CART is used to extract the transformation rules for the CA. This method first adopts the CART decision tree using the bootstrap algorithm to mine the rules from the urban land use while considering the factors that impact the geographic spatial variables in the CART regression procedure. The weights of individual impact factors are calculated to generate a logistic regression function that reflects the change in urban construction land use. Finally, a CA model is constructed to simulate and predict urban construction land expansion. The urban area of Xinyang City in China is used as an example for this experimental research. After removing the spatial invariant region, the overall simulation accuracy is 81.38% and the kappa coefficient is 0.73. The results indicate that by using the CART decision tree to train the impact factor weights and extract the rules, it can effectively increase the simulation accuracy of the CA model. From convenience and accuracy perspectives for rule extraction, the structure of the CART decision tree is clear, and it is very suitable for obtaining the cellular rules. The CART-CA model has a relatively high simulation accuracy in modeling urban construction land use expansion, it provides reliable results, and is suitable for use as a scientific reference for urban construction land use expansion. View Full-Text
Keywords: cellular automata; CART decision tree; logistic regression; change in urban construction land use cellular automata; CART decision tree; logistic regression; change in urban construction land use
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Yao, Y.; Li, J.; Zhang, X.; Duan, P.; Li, S.; Xu, Q. Investigation on the Expansion of Urban Construction Land Use Based on the CART-CA Model. ISPRS Int. J. Geo-Inf. 2017, 6, 149.

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