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Measuring Spatial Patterns of Health Care Facilities and Their Relationships with Hypertension Inpatients in a Network-Constrained Urban System

Author

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  • Zhensheng Wang

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources of China, Shenzhen 518034, China
    Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
    College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
    Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China)

  • Ke Nie

    (Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources of China, Shenzhen 518034, China)

Abstract

There is evidence of a strong correlation between inequality in health care access and disparities in chronic health conditions. Equal access to health care is an important indicator for overall population health, and the urban road network has a significant influence on the spatial distribution of urban service facilities. In this study, the network kernel density estimation was applied to detect the hot spots of health care service along the road network of Shenzhen, and we further explored the influences of population and road density on the aggregate intensity distributions at the community level, using spatial stratified heterogeneity analyses. Then, we measured the spatial clustering patterns of health care facilities in each of the ten districts of Shenzhen using the network K-function, and the interrelationships between health care facilities and hypertension patients. The results can be used to examine the reasonability of the existing health care system, which would be valuable for developing more effective prevention, control, and treatment of chronic health conditions. Further research should consider the influence of nonspatial factors on health care service access.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:17:p:3204-:d:263359
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    References listed on IDEAS

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    1. Yu, Wenhao & Ai, Tinghua & Shao, Shiwei, 2015. "The analysis and delimitation of Central Business District using network kernel density estimation," Journal of Transport Geography, Elsevier, vol. 45(C), pages 32-47.
    2. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    3. Zhensheng Wang & Yang Yue & Qingquan Li & Ke Nie & Wei Tu & Shi Liang, 2017. "Analyzing Risk Factors for Fatality in Urban Traffic Crashes: A Case Study of Wuhan, China," Sustainability, MDPI, vol. 9(6), pages 1-13, May.
    4. 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.
    5. 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.
    6. Jeremiah J. Nieves, 2015. "Combining Transportation Network Models with Kernel Density Methods to Measure the Relative Spatial Accessibility of Pediatric Primary Care Services in Jefferson County, Kentucky," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 6(3), pages 39-57, July.
    7. Chen, Albert Y. & Yu, Ting-Yi, 2016. "Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 408-423.
    8. Shishebori, Davood & Yousefi Babadi, Abolghasem, 2015. "Robust and reliable medical services network design under uncertain environment and system disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 268-288.
    9. Zhensheng Wang & Qingyun Du & Shi Liang & Ke Nie & De-nan Lin & Yan Chen & Jia-jia Li, 2014. "Analysis of the Spatial Variation of Hospitalization Admissions for Hypertension Disease in Shenzhen, China," IJERPH, MDPI, vol. 11(1), pages 1-21, January.
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    1. Mirosław Krzyśko & Waldemar Wołyńki & Marcin Szymkowiak & Andrzej Wojtyła, 2021. "A Spatio-Temporal Analysis of the Health Situation in Poland Based on Functional Discriminant Coordinates," IJERPH, MDPI, vol. 18(3), pages 1-17, January.

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