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Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis

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  • Jianhua Ni

    (Department of Geographic Information Science, Nanjing University, Nanjing 210093, China
    Department of Resources Environment and Tourism Management, West Anhui University, Luan 237012, China)

  • Tianlu Qian

    (Department of Geographic Information Science, Nanjing University, Nanjing 210093, China)

  • Changbai Xi

    (Department of Geographic Information Science, Nanjing University, Nanjing 210093, China)

  • Yikang Rui

    (Department of Geographic Information Science, Nanjing University, Nanjing 210093, China)

  • Jiechen Wang

    (Department of Geographic Information Science, Nanjing University, Nanjing 210093, China
    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210093, China
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China)

Abstract

The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

Suggested Citation

  • Jianhua Ni & Tianlu Qian & Changbai Xi & Yikang Rui & Jiechen Wang, 2016. "Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis," IJERPH, MDPI, vol. 13(8), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:8:p:833-:d:76240
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    References listed on IDEAS

    as
    1. Jianhua Ni & Jinyin Wang & Yikang Rui & Tianlu Qian & Jiechen Wang, 2015. "An Enhanced Variable Two-Step Floating Catchment Area Method for Measuring Spatial Accessibility to Residential Care Facilities in Nanjing," IJERPH, MDPI, vol. 12(11), pages 1-15, November.
    2. Xie, Zhixiao & Yan, Jun, 2013. "Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach," Journal of Transport Geography, Elsevier, vol. 31(C), pages 64-71.
    3. Wang, Fahui & Antipova, Anzhelika & Porta, Sergio, 2011. "Street centrality and land use intensity in Baton Rouge, Louisiana," Journal of Transport Geography, Elsevier, vol. 19(2), pages 285-293.
    4. Porta, Sergio & Crucitti, Paolo & Latora, Vito, 2006. "The network analysis of urban streets: A dual approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 853-866.
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    Cited by:

    1. Zhensheng Wang & Ke Nie, 2019. "Measuring Spatial Patterns of Health Care Facilities and Their Relationships with Hypertension Inpatients in a Network-Constrained Urban System," IJERPH, MDPI, vol. 16(17), pages 1-22, September.
    2. Wei Wang & Zihao Zhou & Jun Chen & Wen Cheng & Jian Chen, 2021. "Analysis of Location Selection of Public Service Facilities Based on Urban Land Accessibility," IJERPH, MDPI, vol. 18(2), pages 1-20, January.
    3. Chien-Chih Wang & Jin-Jiang Jhu, 2019. "On the Application of Clustering and Classification Techniques to Analyze Metabolic Syndrome Severity Distribution Area and Critical Factors," IJERPH, MDPI, vol. 16(9), pages 1-10, May.
    4. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.
    5. Ying Liu & Huan Wang & Cheng Sun & Huifang Wu, 2022. "Equity Measurement of Public Sports Space in Central Urban Areas Based on Residential Scale Data," IJERPH, MDPI, vol. 19(5), pages 1-14, March.

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