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ISPRS Int. J. Geo-Inf. 2015, 4(2), 447-470;

Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

Asia Air Survey (AAS) Co., Ltd., Kanagawa 215-0004, Japan
Department of Computer and Information Science, Faculty of Science and Technology, Seikei University, Tokyo 180-8633, Japan
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 3 October 2014 / Revised: 15 December 2014 / Accepted: 24 March 2015 / Published: 1 April 2015
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Sustainable urban planning and management require reliable land change models, which can be used to improve decision making. The objective of this study was to test a random forest-cellular automata (RF-CA) model, which combines random forest (RF) and cellular automata (CA) models. The Kappa simulation (KSimulation), figure of merit, and components of agreement and disagreement statistics were used to validate the RF-CA model. Furthermore, the RF-CA model was compared with support vector machine cellular automata (SVM-CA) and logistic regression cellular automata (LR-CA) models. Results show that the RF-CA model outperformed the SVM-CA and LR-CA models. The RF-CA model had a Kappa simulation (KSimulation) accuracy of 0.51 (with a figure of merit statistic of 47%), while SVM-CA and LR-CA models had a KSimulation accuracy of 0.39 and −0.22 (with figure of merit statistics of 39% and 6%), respectively. Generally, the RF-CA model was relatively accurate at allocating “non-built-up to built-up” changes as reflected by the correct “non-built-up to built-up” components of agreement of 15%. The performance of the RF-CA model was attributed to the relatively accurate RF transition potential maps. Therefore, this study highlights the potential of the RF-CA model for simulating urban growth. View Full-Text
Keywords: urban growth; land change models; random forest; cellular automata; kappa simulation urban growth; land change models; random forest; cellular automata; kappa simulation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Kamusoko, C.; Gamba, J. Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model. ISPRS Int. J. Geo-Inf. 2015, 4, 447-470.

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