IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i22p12160-d683167.html
   My bibliography  Save this article

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

Author

Listed:
  • Yisheng Peng

    (School of Urban Design, Wuhan University, Wuhan 430072, China)

  • Jiahui Liu

    (Faculty of Construction and Environment, The Hong Kong Polytechnic University, Hongkong 999077, China)

  • Tianyao Zhang

    (Harbin Institute of Technology Shenzhen, School of Architecture, Shenzhen 518055, China)

  • Xiangyang Li

    (Institute of Central China Development, Wuhan University, Wuhan 430072, China)

Abstract

Urban population density distribution contributes towards a deeper understanding of peoples’ activities patterns and urban vibrancy. The associations between the distribution of urban population density and land use are crucial to improve urban spatial structure. Despite numerous studies on population density distribution and land use, the significance of spatial dependence has attained less attention. Based on the Baidu heat map data and points of interests data in the main urban zone of Guangzhou, China, the current paper first investigated the spatial evolution and temporal distribution characteristics of urban population density and examined the spatial spillover influence of land use on it through spatial correlation analysis methods and the spatial Durbin model. The results show that the urban population density distribution is characterized by aggregation in general and varies on weekends and weekdays. The changes in population density within a day present a trend of “rapid growth-gentle decline-rapid growth-rapid decline”. Furthermore, the spatial spillover effects of land use exist and play the same important roles in population density distribution as the direct effects. Additionally, different types of land use show diverse direct effects and spatial spillover effects at various times. These findings suggest that balancing the population density distribution should consider the indirect effect from neighboring areas, which hopefully provide implications for urban planners and policy makers in utilizing the rational allocation of public resources and regarding optimization of urban spatial structure.

Suggested Citation

  • Yisheng Peng & Jiahui Liu & Tianyao Zhang & Xiangyang Li, 2021. "The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective," IJERPH, MDPI, vol. 18(22), pages 1-19, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12160-:d:683167
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/22/12160/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/22/12160/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qian, Jiang-Hai & Yang, Cheng-Hao & Han, Ding-Ding & Ma, Yu-Gang, 2012. "Multi-scaling mix and non-universality between population and facility density," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(21), pages 5146-5152.
    2. Alice Barreca & Rocco Curto & Diana Rolando, 2020. "Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market," Sustainability, MDPI, vol. 12(1), pages 1-23, January.
    3. Deyi Feng & Lingli Tu & Zhongwei Sun, 2019. "Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps," Sustainability, MDPI, vol. 11(22), pages 1-19, November.
    4. Goodman, A. & Sahlqvist, S. & Ogilvie, D., 2014. "New walking and cycling routes and increased physical activity: One- and 2-year findings from the UK iConnect study," American Journal of Public Health, American Public Health Association, vol. 104(9), pages 38-46.
    5. Xucai Zhang & Yeran Sun & Ting On Chan & Ying Huang & Anyao Zheng & Zhang Liu, 2021. "Exploring Impact of Surrounding Service Facilities on Urban Vibrancy Using Tencent Location-Aware Data: A Case of Guangzhou," Sustainability, MDPI, vol. 13(2), pages 1-23, January.
    6. Diego Firmino Costa da Silva & J. Paul Elhorst & Raul da Mota Silveira Neto, 2017. "Urban and rural population growth in a spatial panel of municipalities," Regional Studies, Taylor & Francis Journals, vol. 51(6), pages 894-908, June.
    7. Tong, Qingmeng & Qiu, Feng, 2020. "Population growth and land development: Investigating the bi-directional interactions," Ecological Economics, Elsevier, vol. 169(C).
    8. Zhao, Pengjun & Lü, Bin & Roo, Gert de, 2011. "Impact of the jobs-housing balance on urban commuting in Beijing in the transformation era," Journal of Transport Geography, Elsevier, vol. 19(1), pages 59-69.
    9. Chaogui Kang & Yu Liu & Xiujun Ma & Lun Wu, 2012. "Towards Estimating Urban Population Distributions from Mobile Call Data," Journal of Urban Technology, Taylor & Francis Journals, vol. 19(4), pages 3-21, October.
    10. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinyao Lin & Yaye Zhuang & Yang Zhao & Hua Li & Xiaoyu He & Siyan Lu, 2022. "Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model," IJERPH, MDPI, vol. 20(1), pages 1-18, December.
    2. Yanbi Chen & Zilong Zhang & Lixia Lang & Zhi Long & Ningfei Wang & Xingpeng Chen & Bo Wang & Ya Li, 2023. "Measuring the Spatial Match between Service Facilities and Population Distribution: Case of Lanzhou," Land, MDPI, vol. 12(8), pages 1-17, August.
    3. Xiaojia Liu & Xi Chen & Yan Huang & Weihong Wang & Mingkan Zhang & Yang Jin, 2023. "Landscape Aesthetic Value of Waterfront Green Space Based on Space–Psychology–Behavior Dimension: A Case Study along Qiantang River (Hangzhou Section)," IJERPH, MDPI, vol. 20(4), pages 1-22, February.
    4. Shuohua Liu & Xiao Zhang & Yifan Zhou & Shunbo Yao, 2021. "Spatiotemporal Evolution and Influencing Factors of Carbon Sink Dynamics at County Scale: A Case Study of Shaanxi Province, China," IJERPH, MDPI, vol. 18(24), pages 1-18, December.
    5. Zonghao Hou & Juan Zhang & Mingyuan Zhang & Gang Li, 2023. "Hospital-system functionality quantification based on supply–demand relationship under earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 213-234, March.
    6. Qilong Ren & Jia Ni & Hui Li & Guangxiong Mao & Wei-Ling Hsu & Jing Yang, 2022. "Analysis on Spatial Characteristics of Supply–Demand Relationship of Amenities in Expanding Central Urban Areas—A Case Study of Huai’an, China," Land, MDPI, vol. 11(8), pages 1-15, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ziwei Hu & Hotaka Kobori & Brent Swallow & Feng Qiu, 2022. "Willingness to pay for multiple dimensions of green open space: Applying a spatial hedonic approach," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 70(3), pages 179-201, September.
    2. Shengxiao Li & Luoye Chen & Pengjun Zhao, 2019. "The impact of metro services on housing prices: a case study from Beijing," Transportation, Springer, vol. 46(4), pages 1291-1317, August.
    3. 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.
    4. Raffaella Santolini, 2020. "Spatial effects on local government efficiency," Papers in Regional Science, Wiley Blackwell, vol. 99(1), pages 183-200, February.
    5. J. Paul Elhorst, 2022. "The dynamic general nesting spatial econometric model for spatial panels with common factors: Further raising the bar," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(3), pages 249-267, December.
    6. Zhao, Mingxuan & Lv, Lianhong & Wu, Jing & Wang, Shen & Zhang, Nan & Bai, Zihan & Luo, Hong, 2022. "Total factor productivity of high coal-consuming industries and provincial coal consumption: Based on the dynamic spatial Durbin model," Energy, Elsevier, vol. 251(C).
    7. Anna M. Ferragina & Giulia Nunziante, 2018. "Are Italian firms performances influenced by innovation of domestic and foreign firms nearby in space and sectors?," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 335-360, September.
    8. Yingcheng Li & Kai Zhu, 2017. "Spatial dependence and heterogeneity in the location processes of new high-tech firms in Nanjing, China," Papers in Regional Science, Wiley Blackwell, vol. 96(3), pages 519-535, August.
    9. Deslatte, Aaron & Szmigiel-Rawska, Katarzyna & Tavares, António F. & Ślawska, Justyna & Karsznia, Izabela & Łukomska, Julita, 2022. "Land use institutions and social-ecological systems: A spatial analysis of local landscape changes in Poland," Land Use Policy, Elsevier, vol. 114(C).
    10. Yanting Tang & Jinlong Gao & Wen Chen, 2022. "The Spatial-Temporal Evolution of Population in the Yangtze River Delta, China: An Urban Hierarchy Perspective," Land, MDPI, vol. 11(10), pages 1-15, October.
    11. Vicente Rios Ibañez, 2014. "What drives regional unemployment convergence?," ERSA conference papers ersa14p924, European Regional Science Association.
    12. Tomasz Kijek & Anna Matras-Bolibok, 2020. "Knowledge-intensive Specialisation and Total Factor Productivity (TFP) in the EU Regional Scope," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 68(1), pages 181-188.
    13. Burhan Can Karahasan & Firat Bilgel, 2018. "Economic Geography, Growth Dynamics and Human Capital Accumulation in Turkey: Evidence from Regional and Micro Data," Working Papers 1233, Economic Research Forum, revised 10 Oct 2018.
    14. Parent, Olivier & LeSage, James P., 2011. "A space-time filter for panel data models containing random effects," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 475-490, January.
    15. Liv Osland & Inge Thorsen, 2013. "Spatial Impacts, Local Labour Market Characteristics and Housing Prices," Urban Studies, Urban Studies Journal Limited, vol. 50(10), pages 2063-2083, August.
    16. Quentin Frère & Matthieu Leprince & Sonia Paty, 2014. "The Impact of Intermunicipal Cooperation on Local Public Spending," Urban Studies, Urban Studies Journal Limited, vol. 51(8), pages 1741-1760, June.
    17. Bottasso, Anna & Conti, Maurizio & Ferrari, Claudio & Tei, Alessio, 2014. "Ports and regional development: A spatial analysis on a panel of European regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 65(C), pages 44-55.
    18. Francesc Amat & Pablo Beramendi & Miriam Hortas-Rico & Vicente Rios, 2020. "How inequality shapes political participation: The role of spatial patterns of political competition," Working Papers. Collection B: Regional and sectoral economics 2002, Universidade de Vigo, GEN - Governance and Economics research Network.
    19. Ling Bai & Tianran Guo & Wei Xu & Kang Luo, 2022. "The Spatial Differentiation and Driving Forces of Ecological Welfare Performance in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(22), pages 1-21, November.
    20. Behr, Andreas & Schiwy, Christoph & Hong, Lucy, 2022. "Impact of Agglomeration Economies on Regional Performance in Germany," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 52(1), May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12160-:d:683167. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.