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Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data

Author

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  • Xintao Liu

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
    Smart Cities Research Institute (SCRI), The Hong Kong Polytechnic University, Kowloon, Hong Kong)

  • Ziwei Lin

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong)

  • Jianwei Huang

    (Institute of Space and Earth Information Science, The Chinese University of Hong Kong (CUHK), Shatin, Hong Kong)

  • He Gao

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong)

  • Wenzhong Shi

    (Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
    Smart Cities Research Institute (SCRI), The Hong Kong Polytechnic University, Kowloon, Hong Kong)

Abstract

The measurement of medical service accessibility is typically based on driving or Euclidean distance. However, in most non-emergency cases, public transport is the travel mode used by the public to access medical services. Yet, there has been little evaluation of the public transport system-based inequality of medical service accessibility. This work uses massive real smart card data (SCD) and an improved potential model to estimate the public transport-based medical service accessibility in Beijing, China. These real SCD data are used to calculate travel costs in terms of time and distance, and medical service accessibility is estimated using an improved potential model. The spatiotemporal variations and patterns of medical service accessibility are explored, and the results show that it is unevenly spatiotemporally distributed across the study area. For example, medical service accessibility in urban areas is higher than that in suburban areas, accessibility during peak periods is higher than that during off-peak periods, and accessibility on weekends is generally higher than that on weekdays. To explore the association of medical service accessibility with socio-economic factors, the relationship between accessibility and house price is investigated via a spatial econometric analysis. The results show that, at a global level, house price is positively correlated with medical service accessibility. In particular, the medical service accessibility of a higher-priced spatial housing unit is lower than that of its neighboring spatial units, owing to the positive spatial spillover effect of house price. This work sheds new light on the inequality of medical service accessibility from the perspective of public transport, which may benefit urban policymakers and planners.

Suggested Citation

  • Xintao Liu & Ziwei Lin & Jianwei Huang & He Gao & Wenzhong Shi, 2021. "Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data," IJERPH, MDPI, vol. 18(5), pages 1-18, March.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:5:p:2711-:d:512626
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    References listed on IDEAS

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    1. Li, Qingquan & Zhang, Tong & Wang, Handong & Zeng, Zhe, 2011. "Dynamic accessibility mapping using floating car data: a network-constrained density estimation approach," Journal of Transport Geography, Elsevier, vol. 19(3), pages 379-393.
    2. Weibull, Jorgen W., 1976. "An axiomatic approach to the measurement of accessibility," Regional Science and Urban Economics, Elsevier, vol. 6(4), pages 357-379, December.
    3. Liu, Yong & Fan, Peilei & Yue, Wenze & Song, Yan, 2018. "Impacts of land finance on urban sprawl in China: The case of Chongqing," Land Use Policy, Elsevier, vol. 72(C), pages 420-432.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Mavoa, Suzanne & Witten, Karen & McCreanor, Tim & O’Sullivan, David, 2012. "GIS based destination accessibility via public transit and walking in Auckland, New Zealand," Journal of Transport Geography, Elsevier, vol. 20(1), pages 15-22.
    6. Yingqi Guo & Shu-Sen Chang & Mengni Chen & Paul S. F. Yip, 2018. "Do Poorer Areas Have Poorer Access to Services in Hong Kong? A Small-Area Analysis Based on Multiple Spatial Accessibility Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(1), pages 1-21, July.
    7. Jing Luo & Guangping Chen & Chang Li & Bingyan Xia & Xuan Sun & Siyun Chen, 2018. "Use of an E2SFCA Method to Measure and Analyse Spatial Accessibility to Medical Services for Elderly People in Wuhan, China," IJERPH, MDPI, vol. 15(7), pages 1-17, July.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    9. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    10. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
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    Cited by:

    1. Ying Liu & Han Gu & Yuyu Shi, 2022. "Spatial Accessibility Analysis of Medical Facilities Based on Public Transportation Networks," IJERPH, MDPI, vol. 19(23), pages 1-15, December.

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