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Coupling Cellular Automata and a Genetic Algorithm to Generate a Vibrant Urban Form—A Case Study of Wuhan, China

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Listed:
  • Renyang Wang

    (Research Institute of New Economic, Ningbo University of Finance & Economics, Ningbo 315175, China)

  • Qingsong He

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Lu Zhang

    (School of Public Administration, Central China Normal University, Wuhan 430079, China)

  • Huiying Wang

    (School of Government, University of International Business and Economics, Beijing 100029, China)

Abstract

Enhancing urban vitality is a key goal for both the government and ordinary urban residents, and creating this vitality is emphasized in China’s urban development strategy. Enhancing urban vitality through the rational design of urban forms is a leading topic of Western urban research. An urban growth pattern (UGP) reflects the dual characteristics of a static pattern and the dynamic evolution of the external urban form. It affects urban vitality by influencing the spatial allocation of internal structural elements and patterns in the adjacent location. The cellular automata (CA) mode can effectively simulate the aggregation process of urban growth (infilling expansion or edge expansion). However, it does not simulate the diffusion of urban growth, specifically the evolution of outlying expansion. In addition, CA focuses on learning, simulating, and building knowledge about geographic processes, but does not spatially optimize collaborative land use against multiple objectives or model multi-scale land use. As such, this paper applies a coupling model called the “promoting urban vitality model,” based on cellular automata (CA) and genetic algorithm (GA) (abbreviated as UV-CAGA). UV-CAGA optimally allocates cells with different UGPs, creating a city form that promotes urban vitality. Wuhan, the largest city in Central China, was selected as a case study to simulate and optimize its urban morphology for 2025. The main findings were as follows. (1) The urban vitality of the optimized urban form scheme was 4.8% higher than the simulated natural expansion scheme. (2) Compared to 2015, after optimization, the simulated sizes of the newly increased outlying, edge, and infilling areas in 2025 were 6.51 km 2 , 102.69 km 2 , and 23.48 km 2 , respectively; these increases accounted for 4.90%, 77.32%, and 17.68%, respectively, of the newly increased construction land area. This indicated that Wuhan is expected to have a very compact urban form. (3) The infilling expansion type resulted in the highest average urban vitality level (0.215); the edge expansion type had the second highest level (0.206); outlying growth achieved the lowest vitality level (0.199). The UV-CAGA model proposed in this paper improves on existing geographical process simulation and spatial optimization models. The study successfully couples the “bottom-up” CA model and “top-down” genetic algorithm to generate dynamic urban form optimization simulations. This significantly improves upon traditional CA models, which do not simulate the “diffusion” process. At the same time, the spatial optimization framework of the genetic algorithm in the model also provides insights related to other effects related to urban form optimization, such as urban environmental security, commuting, and air pollution. The integration of related research is expected to enrich and improve urban planning tools and improve the topic’s scientific foundation.

Suggested Citation

  • Renyang Wang & Qingsong He & Lu Zhang & Huiying Wang, 2021. "Coupling Cellular Automata and a Genetic Algorithm to Generate a Vibrant Urban Form—A Case Study of Wuhan, China," IJERPH, MDPI, vol. 18(21), pages 1-15, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11013-:d:660443
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    References listed on IDEAS

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    1. Resnik, D.B., 2010. "Urban sprawl, smart growth, and deliberative democracy," American Journal of Public Health, American Public Health Association, vol. 100(10), pages 1852-1856.
    2. Lingjun Tang & Yu Lin & Sijia Li & Sheng Li & Jingyi Li & Fu Ren & Chao Wu, 2018. "Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data," Sustainability, MDPI, vol. 10(12), pages 1-21, December.
    3. Xiaoyan Li & Yanchuan Mou & Huiying Wang & Chaohui Yin & Qingsong He, 2018. "How Does Polycentric Urban Form Affect Urban Commuting? Quantitative Measurement Using Geographical Big Data of 100 Cities in China," Sustainability, MDPI, vol. 10(12), pages 1-14, December.
    4. Marcińczak, Szymon & Bartosiewicz, Bartosz, 2018. "Commuting patterns and urban form: Evidence from Poland," Journal of Transport Geography, Elsevier, vol. 70(C), pages 31-39.
    5. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    6. Rao, Yingxue & Dai, Jingyi & Dai, Deyi & He, Qingsong, 2021. "Effect of urban growth pattern on land surface temperature in China: A multi-scale landscape analysis of 338 cities," Land Use Policy, Elsevier, vol. 103(C).
    7. He, Qingsong & He, Weishan & Song, Yan & Wu, Jiayu & Yin, Chaohui & Mou, Yanchuan, 2018. "The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’," Land Use Policy, Elsevier, vol. 78(C), pages 726-738.
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