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Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance

Author

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  • Dian Shao

    (School of Architecture, Southeast University, Nanjing 210096, China)

  • Weiting Xiong

    (School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China)

Abstract

Numerous studies have suggested a positive correlation between spatial and population densities. However, few have systematically conducted quantitative analysis and deciphered the detailed correlation in block scale. Here, we construct a population–space correlation algorithm to quantify and compare the correlation between mobile phone signalling data and vector spatial data and identify blocks with uneven population density. We analyse the influences of various urban spatial characteristics on population density and the distribution characteristics of the identified city blocks. Changzhou City, China, was selected as the study case. The results indicate that (1) population density distribution is unbalanced only when spatial density exceeds a critical value, reflecting the level and sphere of influence of blocks with varying spatial densities; (2) low population density distribution is concentrated in the zonal space, along the boundary between primary and secondary urban centres; (3) spatial characteristics affecting population density distribution vary with the type of block, and the green landscape’s attractiveness is reduced. Our study provides a novel perspective on quantifying the link between urban form and population distribution. It can help decision-makers and planners in accurately recommending urban intervention in population density distribution by adjusting the spatial morphology and promoting rational use of urban public resources.

Suggested Citation

  • Dian Shao & Weiting Xiong, 2022. "Does High Spatial Density Imply High Population Density? Spatial Mechanism of Population Density Distribution Based on Population–Space Imbalance," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:5776-:d:812425
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    1. Bauer, Thomas K. & Epstein, Gil S. & Gang, Ira N., 2002. "Herd Effects or Migration Networks? The Location Choice of Mexican Immigrants in the U.S," IZA Discussion Papers 551, Institute of Labor Economics (IZA).
    2. Lessmann, Christian, 2014. "Spatial inequality and development — Is there an inverted-U relationship?," Journal of Development Economics, Elsevier, vol. 106(C), pages 35-51.
    3. Ruolz Ariste, 2019. "Availability of health workforce in urban and rural areas in relation to Canadian seniors," International Journal of Health Planning and Management, Wiley Blackwell, vol. 34(2), pages 510-520, April.
    4. Tian, Guangjin & Jiang, Jing & Yang, Zhifeng & Zhang, Yaoqi, 2011. "The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region, China," Ecological Modelling, Elsevier, vol. 222(3), pages 865-878.
    5. Evert J Meijers & Martijn J Burger, 2010. "Spatial Structure and Productivity in US Metropolitan Areas," Environment and Planning A, , vol. 42(6), pages 1383-1402, June.
    6. Guangqing Chi & Stephen J. Ventura, 2011. "An Integrated Framework of Population Change: Influential Factors, Spatial Dynamics, and Temporal Variation," Growth and Change, Wiley Blackwell, vol. 42(4), pages 549-570, December.
    7. Gabriel, Stuart A. & Nothaft, Frank E., 2001. "Rental Housing Markets, the Incidence and Duration of Vacancy, and the Natural Vacancy Rate," Journal of Urban Economics, Elsevier, vol. 49(1), pages 121-149, January.
    8. Seung-Chul Noh & Jung-Ho Park, 2021. "Café and Restaurant under My Home: Predicting Urban Commercialization through Machine Learning," Sustainability, MDPI, vol. 13(10), pages 1-22, May.
    9. Minagawa, Kentaro & Sumiyoshi, Kazushi, 1999. "Studies on the optimal location of commerce - basic laws considering the distribution of population," International Journal of Production Economics, Elsevier, vol. 60(1), pages 295-300, April.
    10. Yanpeng Gao & Xiaofei Xu & Ye Wei, 2021. "Analysis on the imbalance of population flow network during the Spring Festival travel rush in China in 2015," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    11. Small Kenneth A. & Song Shunfeng, 1994. "Population and Employment Densities: Structure and Change," Journal of Urban Economics, Elsevier, vol. 36(3), pages 292-313, November.
    12. Yun-Myong Yi & Tae-Hyoung Tommy Gim, 2018. "What Makes an Old Market Sustainable? An Empirical Analysis on the Economic and Leisure Performances of Traditional Retail Markets in Seoul," Sustainability, MDPI, vol. 10(6), pages 1-22, May.
    13. Zhenchao Zhang & Weixin Luan & Chuang Tian & Min Su & Zeyang Li, 2021. "Spatial Distribution Equilibrium and Relationship between Construction Land Expansion and Basic Education Schools in Shanghai Based on POI Data," Land, MDPI, vol. 10(10), pages 1-17, October.
    14. Lingbo Liu & Zhenghong Peng & Hao Wu & Hongzan Jiao & Yang Yu, 2018. "Exploring Urban Spatial Feature with Dasymetric Mapping Based on Mobile Phone Data and LUR-2SFCAe Method," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    15. Lawrence Frank & Mark Bradley & Sarah Kavage & James Chapman & T. Lawton, 2008. "Urban form, travel time, and cost relationships with tour complexity and mode choice," Transportation, Springer, vol. 35(1), pages 37-54, January.
    16. Shaojun Liu & Ling Zhang & Yi Long, 2019. "Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion," Sustainability, MDPI, vol. 11(15), pages 1-27, July.
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