A review of assessment methods for cellular automata models of land-use change and urban growth

ABSTRACT

Cellular automata (CA) models are in growing use for land-use change simulation and future scenario prediction. It is necessary to conduct model assessment that reports the quality of simulation results and how well the models reproduce reliable spatial patterns. Here, we review 347 CA articles published during 1999–2018 identified by a Scholar Google search using ‘cellular automata’, ‘land’ and ‘urban’ as keywords. Our review demonstrates that, during the past two decades, 89% of the publications include model assessment related to dataset, procedure and result using more than ten different methods. Among all methods, cell-by-cell comparison and landscape analysis were most frequently applied in the CA model assessment; specifically, overall accuracy and standard Kappa coefficient respectively rank first and second among all metrics. The end-state assessment is often criticized by modelers because it cannot adequately reflect the modeling ability of CA models. We provide five suggestions to the method selection, aiming to offer a background framework for future method choices as well as urging to focus on the assessment of input data and error propagation, procedure, quantitative and spatial change, and the impact of driving factors.

KEYWORDS:

• Cellular automata
• model assessment
• cross-tabulation matrix
• Kappa coefficients
• landscape metrics
• model sensitivity

Acknowledgments

We thank the Editor and five anonymous reviewers for their feedback and constructive comments to help improve the original manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [41631178 and 41771414] and the National Key R&D Program of China [2018YFB0505400 and 2018YFB0505000].

Notes on contributors

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.

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, he 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.