Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones

ABSTRACT

Modeling urban growth in Economic development zones (EDZs) can help planners determine appropriate land policies for these regions. However, sometimes EDZs are established in remote areas outside of central cities that have no historical urban areas. Existing models are unable to simulate the emergence of urban areas without historical urban land in EDZs. In this study, a cellular automaton (CA) model based on fuzzy clustering is developed to address this issue. This model is implemented by coupling an unsupervised classification method and a modified CA model with an urban emergence mechanism based on local maxima. Through an analysis of the planning policies and existing infrastructure, the proposed model can detect the potential start zones and simulate the trajectory of urban growth independent of the historical urban land use. The method is validated in the urban emergence simulation of the Taiping Bay development zone in Dalian, China from 2013 to 2019. The proposed model is applied to future simulation in 2019–2030. The results demonstrate that the proposed model can be used to predict urban emergence and generate the possible future urban form, which will assist planners in determining the urban layout and controlling urban growth in EDZs.

KEYWORDS:

• Economic development zone
• urban emergence simulation
• fuzzy C-means algorithm
• cellular automata

Acknowledgments

We sincerely thank the editors and the three anonymous reviewers for their useful comments and suggestions that significantly strengthened this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and codes availability statement

The data and codes that support the findings of this study are available at figshare.com with the identifier [DOI: 10.6084/m9.figshare.11676222].

Additional information

Funding

This research was funded by the National Key R & D Program of China (2017YFA0604404); the National Natural Science Foundation of China (Grant No. 41671398 and Grant No. 41901332); and the China Postdoctoral Science Foundation funded project (Grant No. 2019M652729).

Notes on contributors

Xun Liang

Xun Liang is currently a post-doctor researcher in the School of Geography and Information Engineering, China University of Geosciences. His research focuses on land use simulation and Spatio-temporal modeling.

Xiaoping Liu

Xiaoping Liu is a Professor in the School of Geography and Planning, Sun Yat-sen University. His research interests include land-use planning, urban expansion, big data, and land-use simulation.

Guangliang Chen

Guangliang Chen is currently an engineer at Guangzhou lantu Geographic Information Technology Co., Ltd. His research focuses on land use simulation and urban computing.

Jiye Leng

Jiye Leng is a Master student in the Department of Geography and Planning, University of Toronto. His research interests include remote sensing in global carbon cycle modeling.

Youyue Wen

Youyue Wen is currently an engineer in the South China Institute of Environmental Sciences. MEE. His research focuses on terrestrial ecosystem modeling, environmental remote sensing application, environmental pollution improvement.

Guangzhao Chen

Guangzhao Chen is a PhD Candidate in the School of Geography and Planning, Sun Yat-sen University. His research interests include land-use simulation, urban expansion, and environmental analysis.