IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i7p1095-d865235.html
   My bibliography  Save this article

Spatial Heterogeneity of Public Service Facilities in the Living Circle and Its Influence on Housing Prices: A Case Study of Central Urban Dalian, China

Author

Listed:
  • Jinlian Hao

    (School of Yungangology, Shanxi Datong University, Datong 037009, China)

  • Haitao Ma

    (Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

The spatial layout of public service facilities (PSFs) markedly influences residents’ quality of life. Based on Baidu map data, spatial information on 27,552 PSFs across eight categories was collected for urban Dalian, China, and analyzed using the nearest neighbor index and nuclear density. Then, PSF accessibility across eight dimensions of residential quarters was calculated based on the cumulative opportunity method, and its impact on housing prices was analyzed. The results revealed the following: (1) The degree of spatial agglomeration for PSFs varied, with that of business facilities being higher than that of other public welfare facilities. The distribution of business facilities was characterized by a dense center and sparse periphery, whereas public welfare facilities were laid out in a relatively balanced “multi-center” distribution across the study area. (2) Significant spatial differences in the number and types of accessible resident facilities were identified. The number of accessible PSFs in the core area of central urban regions was large and the types were relatively complete, whereas the accessible PSFs in the western and northern marginal areas were limited in number, few in type, and lacking across certain categories, such as educational facilities and life services. (3) The spatial distribution of PSF accessibility was unbalanced. The accessibility of various PSFs in the Shahekou District was the highest, followed by that in the Zhongshan, Xigang, and Ganjingzi Districts. (4) The accessibility of educational, sport, and cultural facilities, and the total accessibility and greening rate of residential areas were the most significantly positively correlated with housing prices; however, the number of households in residential areas and the distances between residential areas and large shopping centers were significantly negatively correlated. Our findings will expand the research perspective of PSFs, provide a basis for meeting residents’ needs and a rational allocation of PSFs, and provide references for people’s decisions to buy houses.

Suggested Citation

  • Jinlian Hao & Haitao Ma, 2022. "Spatial Heterogeneity of Public Service Facilities in the Living Circle and Its Influence on Housing Prices: A Case Study of Central Urban Dalian, China," Land, MDPI, vol. 11(7), pages 1-16, July.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:1095-:d:865235
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/7/1095/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/7/1095/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Keunhyun Park & Reid Ewing & Sadegh Sabouri & Dong-ah Choi & Shima Hamidi & Guang Tian, 2020. "Guidelines for a Polycentric Region to Reduce Vehicle Use and Increase Walking and Transit Use," Journal of the American Planning Association, Taylor & Francis Journals, vol. 86(2), pages 236-249, April.
    2. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu & Yang, Jiawen & Yin, Jie, 2020. "Threshold and moderating effects of land use on metro ridership in Shenzhen: Implications for TOD planning," Journal of Transport Geography, Elsevier, vol. 89(C).
    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. Yong Jiang & Yangyang Liu & Zelei Liu & Chunwei Wang & Zhipeng Shi & Hongbo Zhao & Dongqi Sun & Wei Sun & Xiangquan Wang, 2022. "Spatial Distribution Characteristics of Public Fitness Venues: An Urban Accessibility Perspective," Sustainability, MDPI, vol. 15(1), pages 1-16, December.
    2. Feng Ren & Jinbo Zhang & Xiuyun Yang, 2023. "Study on the Effect of Job Accessibility and Residential Location on Housing Occupancy Rate: A Case Study of Xiamen, China," Land, MDPI, vol. 12(4), pages 1-21, April.

    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. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    2. Singleton, Patrick A. & Park, Keunhyun & Lee, Doo Hong, 2021. "Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data," Journal of Transport Geography, Elsevier, vol. 93(C).
    3. Bo Wan & Xudan Zhao & Yuhan Sun & Tao Yang, 2023. "Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)," Sustainability, MDPI, vol. 15(10), pages 1-27, May.
    4. Tong, Zhaomin & Zhang, Ziyi & An, Rui & Liu, Yaolin & Chen, Huiting & Xu, Jiwei & Fu, Shihang, 2024. "Detecting anomalous commuting patterns: Mismatch between urban land attractiveness and commuting activities," Journal of Transport Geography, Elsevier, vol. 116(C).
    5. Li, Mengya & Kwan, Mei-Po & Hu, Wenyan & Li, Rui & Wang, Jun, 2023. "Examining the effects of station-level factors on metro ridership using multiscale geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 113(C).
    6. Mi Ye & Ben Derudder & Lei Jiang & Freke Caset & Yingcheng Li, 2023. "The Effects of Urban Polycentricity on Particulate Matter Emissions From Vehicles: Evidence From 102 Chinese Cities," Urban Planning, Cogitatio Press, vol. 8(3), pages 135-147.
    7. Zhang, Zhaolin & Zhai, Guocong & Xie, Kun & Xiao, Feng, 2022. "Exploring the nonlinear effects of ridesharing on public transit usage: A case study of San Diego," Journal of Transport Geography, Elsevier, vol. 104(C).
    8. Shao, Qifan & Zhang, Wenjia & Cao, Xinyu (Jason) & Yang, Jiawen, 2023. "Built environment interventions for emission mitigation: A machine learning analysis of travel-related CO2 in a developing city," Journal of Transport Geography, Elsevier, vol. 110(C).
    9. Houshmand Masoumi & Grzegorz Sierpiński, 2022. "The Land Use and Individual Correlates of Pedestrian Commuting: Who Walks to Their Work or Place of Study in the Large Cities of the MENA Region?," Sustainability, MDPI, vol. 14(10), pages 1-17, May.
    10. Hu, Songhua & Xiong, Chenfeng & Chen, Peng & Schonfeld, Paul, 2023. "Examining nonlinearity in population inflow estimation using big data: An empirical comparison of explainable machine learning models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    11. Su, Shiliang & Wang, Zhuolun & Li, Bozhao & Kang, Mengjun, 2022. "Deciphering the influence of TOD on metro ridership: An integrated approach of extended node-place model and interpretable machine learning with planning implications," Journal of Transport Geography, Elsevier, vol. 104(C).
    12. Zhitao Li & Xiaolu Wang & Fan Gao & Jinjun Tang & Hanmeng Xu, 2024. "Analysis of mobility patterns for urban taxi ridership: the role of the built environment," Transportation, Springer, vol. 51(4), pages 1409-1431, August.
    13. Lixuan Zhao & Dewei Fang & Yang Cao & Shan Sun & Liu Han & Yang Xue & Qian Zheng, 2023. "Impact-Asymmetric Analysis of Bike-Sharing Residents’ Satisfaction: A Case Study of Harbin, China," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    14. Yang, Hongtai & Zheng, Rong & Li, Xuan & Huo, Jinghai & Yang, Linchuan & Zhu, Tong, 2022. "Nonlinear and threshold effects of the built environment on e-scooter sharing ridership," Journal of Transport Geography, Elsevier, vol. 104(C).
    15. Junrong Ma & Li Tian & Yudi Zhang & Xin Yang & Yongfu Li & Ziang Liu & Lin Zhou & Zixuan Wang & Wei Ouyang, 2024. "Global property rights and land use efficiency," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    16. Jixiang Liu & Longzhu Xiao, 2024. "Socioeconomic differences in effect size: predicting commuting mode choice of migrants and locals using a light gradient boosting approach," Transportation, Springer, vol. 51(1), pages 1-24, February.
    17. Yousefzadeh Barri, Elnaz & Farber, Steven & Jahanshahi, Hadi & Beyazit, Eda, 2022. "Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms," Journal of Transport Geography, Elsevier, vol. 105(C).
    18. Liu, Xiang & Chen, Xiaohong & Tian, Mingshu & De Vos, Jonas, 2023. "Effects of buffer size on associations between the built environment and metro ridership: A machine learning-based sensitive analysis," Journal of Transport Geography, Elsevier, vol. 113(C).
    19. Lee, Yongsung & Lee, Bumsoo, 2022. "What’s eating public transit in the United States? Reasons for declining transit ridership in the 2010s," Transportation Research Part A: Policy and Practice, Elsevier, vol. 157(C), pages 126-143.
    20. Xiao Ling & Yue Gao & Guoyong Wu, 2023. "How Does Intensive Land Use Affect Low-Carbon Transition in China? New Evidence from the Spatial Econometric Analysis," Land, MDPI, vol. 12(8), pages 1-26, August.

    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:jlands:v:11:y:2022:i:7:p:1095-:d:865235. 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.