A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods

ABSTRACT

We develop a new geographical cellular automata (CA) modeling framework, named UrbanCA, through reconstructing the essential CA structure and incorporating nonspatial, spatial, and heuristic approaches. The new UrbanCA is featured by 1) the improvement of the CA modeling framework by reformulating relationships among CA components, 2) the development of two scaling parameters to adjust the effects of transition probability and neighborhood, 3) the incorporation of a variety of statistical and heuristic methods to construct transition rules, and 4) the inclusion of urban planning regulations and spatial heterogeneities to project future urban scenarios. To illustrate the effectiveness of UrbanCA, we calibrate a CA model using artificial bee colony (ABC) to simulate the past urban patterns and predict future scenarios in Shanghai of China. The results show that UrbanCA under different scaling parameters is comparable to CA-Markov (as a reference model) concerning the accuracy of the end-state and change simulations, and is better than CA-Markov regarding the driving factor’s ability to explain the modeling outcomes. UrbanCA provides more choices compared to existing CA software packages, and the models are readily calibrated elsewhere to simulate the dynamic urban growth and assess the resulting natural and socioeconomic impacts.

KEYWORDS:

• UrbanCA
• urban growth modeling
• statistical method
• heuristic algorithm
• scaling parameter

Acknowledgments

We would like to thank the Editor and four anonymous reviewers for their time and valuable remarks. This study was supported by the National Natural Science Foundation of China (41771414, 41631178 and 41601414), and the National Key R&D Program of China (2018YFB0505400 and 2018YFB0505402).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41631178,41771414]; National Key R&D Program of China [2018YFB0505400,2018YFB0505402].

Notes on contributors

Yongjiu Feng

Yongjiu Feng is Professor at the College of Surveying and Geo-Informatics, Tongji University, Shanghai, China and Honorary Associate Professor at the University of Queensland, Brisbane, Australia. He received Ph.D. degree from Tongji University in 2009. From 2015 to 2016, and was a Visiting Academic at the University of Queensland. His research interests include land use change modeling, cellular automata, spatial analysis and remote sensing image processing.

Xiaohua Tong

Xiaohua Tong is Professor at the College of Surveying and Geo-Informatics, Tongji University, Shanghai, China. He received Ph.D. degree from Tongji University in 1999. From 2001 to 2003, he was a Post-Doctoral Researcher with the State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, China. He was a Research Fellow with Hong Kong Polytechnic University, Hong Kong, in 2006, and a Visiting Scholar with the University of California, Santa Barbara, CA, USA, from 2008 to 2009. His research interests include photogrammetry and remote sensing, trust in spatial data, and image processing for high-resolution satellite images.