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.
Highlights
Cellular automata (CA) method is a discrete dynamic modeling technique that has been widely applied in fields related to spatiotemporal distributions [1,2,3,4]
The correlation matrix of spatial variables was calculated using the samples (Table 2), showing significant correlations among these spatial variables. Traditional methods, such as multi-criteria evaluation (MCE) technique and logistic regression, are not able to avoid the negative effects of multicollinearity and are relatively weak in providing correct weights for the variables
This paper demonstrates that CA models can accurately simulate urban growth using global and local constraints that reflect various environmental concerns
Summary
Cellular automata (CA) method is a discrete dynamic modeling technique that has been widely applied in fields related to spatiotemporal distributions [1,2,3,4]. A CA model based on artificial neural networks (ANN) was developed to calculate land conversion probabilities and model dynamic land use in a GIS environment [21] This model was used to simulate the multiple land use changes in a rapidly growing area of Guangdong Province, China. A heuristic CA model of urban land use change was proposed based on a simulated annealing (SA) algorithm and was successfully applied to simulate the urban growth in one of Shanghai’s outer suburbs [22]. A landscape expansion index was incorporated into CA (LEI-CA) to simulate both the adjacent and outlying urban growth of Dongguan City in southern China [15] This approach demonstrated an improvement when compared to the logistic-CA model in terms of urban simulation accuracy. Reconsideration of the statistical approaches is necessary for CA-based urban modeling
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