IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i17p3204-d263359.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/17/3204/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/17/3204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    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. 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.

    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. Yaxin Fan & Xinyan Zhu & Bing She & Wei Guo & Tao Guo, 2018. "Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-23, April.
    2. Shenjun Yao & Jinzi Wang & Lei Fang & Jianping Wu, 2018. "Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China," Sustainability, MDPI, vol. 10(12), pages 1-11, December.
    3. Zhang, Sizhe & Cardin, Michel-Alexandre, 2017. "Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 120-140.
    4. Xiaosi Zhang & Jizhong Shao, 2024. "Evaluation of the Suitability of Street Vending Planning in Urban Public Space in the Post-COVID-19 Era," Land, MDPI, vol. 13(4), pages 1-26, April.
    5. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    6. Thomas M. Koutsos & Georgios C. Menexes & Andreas P. Mamolos, 2021. "The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    7. Yang, Yun & Ma, Changxi & Ling, Gang, 2022. "Pre-location for temporary distribution station of urban emergency materials considering priority under COVID-19: A case study of Wuhan City, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    8. Shi, Yishao & Tao, Tianhui & Cao, Xiangyang & Pei, Xiaowen, 2021. "The association between spatial attributes and neighborhood characteristics based on Meituan take-out data: Evidence from shanghai business circles," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    9. Hongwen Xia & Rengkui Liu & Wei Zhou & Wenhui Luo, 2024. "Modeling the Causes of Urban Traffic Crashes: Accounting for Spatiotemporal Instability in Cities," Sustainability, MDPI, vol. 16(20), pages 1-16, October.
    10. Wang, Cheng & Wang, Gang & Guo, Ziru & Dai, Lingjun & Liu, Hongyu & Li, Yufeng & Chen, Hao & Zhao, Yongxiang & Zhang, Yanan & Cheng, Hai, 2020. "Effects of land-use change on the distribution of the wintering red-crowned crane (Grus japonensis) in the coastal area of northern Jiangsu Province, China," Land Use Policy, Elsevier, vol. 90(C).
    11. Zhizhu Lai & Qun Yue & Zheng Wang & Dongmei Ge & Yulong Chen & Zhihong Zhou, 2022. "The min-p robust optimization approach for facility location problem under uncertainty," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1134-1160, September.
    12. Mert Ersen & Ali Hakan Büyüklü & Semra Taşabat Erpolat, 2021. "Analysis of Fatal and Injury Traffic Accidents in Istanbul Sarıyer District with Spatial Statistics Methods," Sustainability, MDPI, vol. 13(19), pages 1-39, October.
    13. Donya Rahmani, 2019. "Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions," Annals of Operations Research, Springer, vol. 283(1), pages 613-641, December.
    14. Junhu Ruan & Felix T. S. Chan & Xiaofeng Zhao, 2018. "Re-Planning the Intermodal Transportation of Emergency Medical Supplies with Updated Transfer Centers," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    15. Qing Ye & Yi Li & Wenzhe Shen & Zhaoze Xuan, 2023. "Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    16. Younes, Hannah & Nasri, Arefeh & Baiocchi, Giovanni & Zhang, Lei, 2019. "How transit service closures influence bikesharing demand; lessons learned from SafeTrack project in Washington, D.C. metropolitan area," Journal of Transport Geography, Elsevier, vol. 76(C), pages 83-92.
    17. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.
    18. Schuster, Hannah & Polleres, Axel & Wachs, Johannes, 2024. "Stress-testing road networks and access to medical care," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    19. Mateusz Ciski & Krzysztof Rząsa & Marek Ogryzek, 2019. "Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling," Sustainability, MDPI, vol. 11(20), pages 1-21, October.
    20. Tianzheng Xiao & Huapu Lu & Jianyu Wang & Katrina Wang, 2021. "Predicting and Interpreting Spatial Accidents through MDLSTM," IJERPH, MDPI, vol. 18(4), pages 1-18, February.

    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:16:y:2019:i:17:p:3204-:d:263359. 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.