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ISPRS Int. J. Geo-Inf. 2016, 5(12), 243; doi:10.3390/ijgi5120243

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

1
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2
The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources (Ministry of Education), Shanghai Ocean University, Shanghai 201306, China
3
College of Surveying and Geo-informatics, Tongji University, Shanghai 200092, China
4
Key Laboratory of Ecohydrology of Inland River Basin, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
5
School of Geography, Planning and Environmental Management, University of Queensland, Brisbane 4072, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Qiming Zhou, Zhilin Li and Wolfgang Kainz
Received: 25 September 2016 / Revised: 30 October 2016 / Accepted: 30 November 2016 / Published: 16 December 2016
(This article belongs to the Special Issue Advances and Innovations in Land Use/Cover Mapping)
View Full-Text   |   Download PDF [4932 KB, uploaded 16 December 2016]   |  

Abstract

We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The key factors (the spatial variables) extracted are uncorrelated, resulting in effective transition rules for urban growth modeling. The PLS-CA model was applied to simulate the rapid urban growth of Songjiang District, an outer suburb in the Shanghai Municipality of China from 1992 to 2008. Among the three components acquired by PLS, the first two explained more than 95% of the total variance. The results showed that the PLS-CA simulated pattern of urban growth matched the observed pattern with an overall accuracy of 85.8%, as compared with 83.5% of a logistic-regression-based CA model for the same area. The PLS-CA model is readily applicable to simulations of urban growth in other rapidly urbanizing areas to generate realistic land use patterns and project future scenarios. View Full-Text
Keywords: urban growth; dynamic simulation; cellular automata; partial least squares (PLS) regression; geographical information systems (GIS); accuracy analysis urban growth; dynamic simulation; cellular automata; partial least squares (PLS) regression; geographical information systems (GIS); accuracy analysis
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Feng, Y.; Liu, M.; Chen, L.; Liu, Y. Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment. ISPRS Int. J. Geo-Inf. 2016, 5, 243.

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