IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i21p14106-d956912.html
   My bibliography  Save this article

A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods

Author

Listed:
  • Ali Khosravi Kazazi

    (Department of Surveying Engineering, Shahid Rajaee Teacher Training University, Tehran 16788-15811, Iran)

  • Fariba Amiri

    (Department of Computer Engineering, Shariati Technical and Vocational College, Tehran 16851-18918, Iran)

  • Yaser Rahmani

    (Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 84112, USA)

  • Raheleh Samouei

    (Social Determinants of Health Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran)

  • Hamidreza Rabiei-Dastjerdi

    (Social Determinants of Health Research Center, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
    School of Architecture, Planning and Environmental Policy & CeADAR (Ireland’s National Centre for Applied Data Analytics & AI), University College Dublin (UCD), Belfield, D14 E099 Dublin, Ireland)

Abstract

The unequal distribution of healthcare services is the main obstacle to achieving health equity and sustainable development goals. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility to four different types of healthcare services, including hospitals, pharmacies, clinics, and medical laboratories at Isfahan’s census blocks level, in a multivariate study. Regarding the nature of spatial accessibility, machine learning unsupervised clustering methods are utilized to analyze the spatial accessibility in the city. Initially, the study area was grouped into five clusters using three unsupervised clustering methods: K-Means, agglomerative, and bisecting K-Means. Then, the intersection of the results of the methods is considered to be conclusive evidence. Finally, using the conclusive evidence, a supervised clustering method, KNN, was applied to generate the map of the spatial accessibility situation in the study area. The findings of this study show that 47%, 22%, and 31% of city blocks in the study area have rich, medium, and poor spatial accessibility, respectively. Additionally, according to the study results, the healthcare services development is structured in a linear pattern along a historical avenue, Chaharbagh. Although the scope of this study was limited in terms of the supply and demand rates, this work gives more information and spatial insights for researchers, planners, and policymakers aiming to improve accessibility to healthcare and sustainable urban development. As a recommendation for further research work, it is suggested that other influencing factors, such as the demand and supply rates, should be integrated into the method.

Suggested Citation

  • Ali Khosravi Kazazi & Fariba Amiri & Yaser Rahmani & Raheleh Samouei & Hamidreza Rabiei-Dastjerdi, 2022. "A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods," Sustainability, MDPI, vol. 14(21), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14106-:d:956912
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/21/14106/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/21/14106/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xinrui Pei & Peng Guo & Qiyi Chen & Junrong Li & Zifei Liu & Yanling Sun & Xiakun Zhang, 2022. "An Improved Multi-Mode Two-Step Floating Catchment Area Method for Measuring Accessibility of Urban Park in Tianjin, China," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    2. James Saxon & Daniel Snow, 2020. "A Rational Agent Model for the Spatial Accessibility of Primary Health Care," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 110(1), pages 205-222, January.
    3. Nala Alahmari & Sarah Alswedani & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Rashid Mehmood, 2022. "Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia," Sustainability, MDPI, vol. 14(6), pages 1-41, March.
    4. Hamidreza Rabiei‐Dastjerdi & Stephen A. Matthews, 2021. "Who gets what, where, and how much? Composite index of spatial inequality for small areas in Tehran," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 191-205, February.
    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. Iris Mone & Suela Vasil & Albano Alia & Sonela Xinxo & Kliton Muça & Genc Burazeri, 2023. "Socio-Demographic Correlates of Barriers to Access Healthcare Services among Children in Post-Communist Albania," Sustainability, MDPI, vol. 15(11), pages 1-12, May.
    2. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, 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. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.
    2. Hamidreza Rabiei-Dastjerdi & Gavin McArdle, 2021. "Novel Exploratory Spatiotemporal Analysis to Identify Sociospatial Patterns at Small Areas Using Property Transaction Data in Dublin," Land, MDPI, vol. 10(6), pages 1-16, May.
    3. Shao, Yaxiong & Luo, Wei, 2022. "Supply-demand adjusted two-steps floating catchment area (SDA-2SFCA) model for measuring spatial access to health care," Social Science & Medicine, Elsevier, vol. 296(C).
    4. Eman Alqahtani & Nourah Janbi & Sanaa Sharaf & Rashid Mehmood, 2022. "Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling," Sustainability, MDPI, vol. 14(20), pages 1-65, October.
    5. James Saxon & Julia Koschinsky & Karina Acosta & Vidal Anguiano & Luc Anselin & Sergio Rey, 2022. "An open software environment to make spatial access metrics more accessible," Journal of Computational Social Science, Springer, vol. 5(1), pages 265-284, May.
    6. Hu, Yujie & Wang, Changzhen & Li, Ruiyang & Wang, Fahui, 2020. "Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach," Journal of Transport Geography, Elsevier, vol. 86(C).
    7. Ghadah Alkhayat & Syed Hamid Hasan & Rashid Mehmood, 2022. "SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting," Energies, MDPI, vol. 15(18), pages 1-55, September.
    8. G. Arbia & V. Nardelli & N. Salvini & I. Valentini, 2024. "New accessibility measures based on unconventional big data sources," Papers 2401.13370, arXiv.org.
    9. Matheus Pereira Libório & Alexandre Magno Alves Diniz & Hamidreza Rabiei-Dastjerd & Oseias da Silva Martinuci & Carlos Augusto Paiva da Silva Martins & Petr Iakovlevitch Ekel, 2023. "A Decision Framework for Identifying Methods to Construct Stable Composite Indicators That Capture the Concept of Multidimensional Social Phenomena: The Case of Social Exclusion," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    10. Gordon Cromley & Jie Lin, 2023. "Examining the interplay between racial segregation patterns and access to hospital care," Environment and Planning B, , vol. 50(1), pages 117-129, January.

    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:jsusta:v:14:y:2022:i:21:p:14106-:d:956912. 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.