Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules

ABSTRACT

A novel generalized pattern search (GPS)-based cellular automata (GPS-CA) model was developed to simulate urban land-use change in a GIS environment. The model is built on a fitness function that computes the difference between the observed results produced from remote-sensing images and the simulated results produced by a general CA model. GPS optimization incorporating genetic algorithms (GAs) searches for the minimum difference, i.e. the smallest accumulated residuals, in fitting the CA transition rules. The CA coefficients captured by the GPS method have clear physical meanings that are closely associated with the dynamic mechanisms of land-use change. The GPS-CA model was applied to simulate urban land-use change in Kunshan City in the Yangtze River Delta from 2000 to 2015. The results show that the GPS method had a smaller root mean squared error (0.2821) than a logistic regression (LR) method (0.5256) in fitting the CA transition rules. The GPS-CA model thus outperformed the LR-CA model, with an overall accuracy improvement of 4.7%. As a result, the GPS-CA model should be a superior tool for modeling land-use change as well as predicting future scenarios in response to different conditions to support the sustainable urban development.

KEYWORDS:

• Urban land-use change
• cellular automata
• logistic regression (LR)
• generalized pattern search (GPS)
• global optimization
• simulation accuracy

Acknowledgments

I would like to thank Editor Shawn Laffan and three anonymous reviewers for their constructive comments and efforts to improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [Grant 41406146] and Natural Science Foundation of Shanghai Municipality [Grant 13ZR1419300].