The effects of sample size and sample prevalence on cellular automata simulation of urban growth

ABSTRACT

This study investigates the effects of sample size and sample prevalence on cellular automata (CA) simulation of urban growth. We take the CA models based on an artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM) as examples, to simulate the urban growth of Wuhan city in China and the Wuhan Metropolitan Area under different sampling schemes. The results of the CA models based on the ANN, LR, and SVM methods are generally consistent. The sampling scheme with a small sample size and a low sample prevalence should be discarded because of the high uncertainty. Sample size determines the robustness of a CA model, whereas sample prevalence affects the performance of a CA model when there are sufficient samples. In particular, the closer the sample prevalence is to the population prevalence, the higher the simulation accuracy and the lower the shape complexity and fragmentation of the simulated urban patterns. We suggest that the optimal sampling scheme has a sample rate of 1% and a sample prevalence that is the same as the population prevalence. The selection of the optimal sampling scheme is independent of the population sizes represented by different study areas.

KEYWORDS:

• Cellular automata
• urban growth simulation
• sample size
• sample rate
• sample prevalence

Acknowledgments

We thank the anonymous reviewers and journal editors for their constructive comments and suggestions that greatly improved the article.

Supplemental data

Supplemental data for this article can be accessed here.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The data and code that support the findings of this study are openly available at [https://doi.org/10.6084/m9.figshare.12824861.v1].

Additional information

Notes on contributors

Bin Zhang

Bin Zhang is a PhD Candidate at School of Resource and Environmental Sciences at Wuhan University, China. His research interests include urban growth simulation, land use/cover change, and environmental modelling. He is especially interested in simulating urban expansion using cellular automata models and optimization methods.

Chang Xia

Chang Xia is a PhD candidate at Department of Urban Planning and Design at The University of Hong Kong, Hong Kong SAR. He received his Master’s and Bachelor’s degree in land use management from Wuhan University. His research interests include urban and land use modeling, urban morphology, environmental hazards, and GIS and big data.