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Measurement and Spatial Difference Analysis on the Accessibility of Road Networks in Major Cities of China

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  • Xiaorui Zhang

    (Department of Urban Planning, Hefei University of Technology, Hefei 230009, China
    Laboratory of Digital Human Habitat Studies, Hefei University of Technology, Hefei 230009, China)

  • Andong Ren

    (Department of Urban Planning, Hefei University of Technology, Hefei 230009, China)

  • Lihua Chen

    (Department of Architecture, Hefei University of Technology, Hefei 230009, China)

  • Xianyou Zheng

    (Department of Architecture, Hefei University of Technology, Hefei 230009, China)

Abstract

Given the current lack of accessibility research on road networks in 36 major cities in China, the accessibility and its spatial difference were measured by using space syntax and Moran index. The purpose is to provide an important decision-making basis for the Chinese government to grasp the accessibility level of China’s urban roads in general and formulate urban traffic development policies. The results show that the mean value of the global integration average is only 1.0009, indicating that the accessibility level is not optimistic in general. The accessibility of 36 major cities was divided into four levels: very low, low, high, and very high. Only four cities, namely Beijing, Shijiazhuang, Xi’an, and Zhengzhou, were at a very high level. The spatial differences of accessibility presented a spatial pattern of "middle-high, east-middle, and west-low". The global integration average, intelligibility, and synergy had significant global spatial autocorrelation, while the local spatial agglomeration distributions of these three indexes were dominated by high-high types. The five cities of Beijing, Shijiazhuang, Hohhot, Taiyuan, and Zhengzhou constituted the core area of high-value clustering of local spatial autocorrelation and presented a spatial form of inverted "T" shape. The research still has some limitations. The reasons for the low accessibility of developed cities, such as Guangzhou and Nanjing, are worth further analysis. Besides, the main possible influencing factors affecting accessibility, such as urban road density and spatial form, are also worthy of further analysis.

Suggested Citation

  • Xiaorui Zhang & Andong Ren & Lihua Chen & Xianyou Zheng, 2019. "Measurement and Spatial Difference Analysis on the Accessibility of Road Networks in Major Cities of China," Sustainability, MDPI, vol. 11(15), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4209-:d:254642
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    References listed on IDEAS

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    2. Lin Lin & Xueming (Jimmy) Chen & Anne Vernez Moudon, 2021. "Measuring the Urban Forms of Shanghai’s City Center and Its New Districts: A Neighborhood-Level Comparative Analysis," Sustainability, MDPI, vol. 13(15), pages 1-18, July.
    3. Xuesong Sun & Zaisheng Zhang, 2021. "Coupling and Coordination Level of the Population, Land, Economy, Ecology and Society in the Process of Urbanization: Measurement and Spatial Differentiation," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    4. Hui Shi & Zhen You & Zhiming Feng & Yanzhao Yang, 2019. "Numerical Simulation and Spatial Distribution of Transportation Accessibility in the Regions Involved in the Belt and Road Initiative," Sustainability, MDPI, vol. 11(22), pages 1-14, November.
    5. Ali Soltani & Andrew Allan & Masoud Javadpoor & Jaswanth Lella, 2022. "Space Syntax in Analysing Bicycle Commuting Routes in Inner Metropolitan Adelaide," Sustainability, MDPI, vol. 14(6), pages 1-13, March.
    6. Ying Liu & Han Gu & Yuyu Shi, 2022. "Spatial Accessibility Analysis of Medical Facilities Based on Public Transportation Networks," IJERPH, MDPI, vol. 19(23), pages 1-15, December.
    7. Meng-Ting Tsai & Hung-Wen Chang, 2023. "Contribution of Accessibility to Urban Resilience and Evacuation Planning Using Spatial Analysis," IJERPH, MDPI, vol. 20(4), pages 1-17, February.

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