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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
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    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.

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