Geographical accessibility to healthcare by point-of–interest data from online maps: a comparative study
Published: 20 December 2024
Abstract Views: 771
PDF: 167
Supplementary Materials: 43
HTML: 4
Supplementary Materials: 43
HTML: 4
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Geographical accessibility is important for promoting health equity, and calculating it requires the locations of all existing healthcare facilities in a region. Authoritative location data collected by governments is accurate but mostly not publicly available, while point-of-interest (POI) data from online sources, such as Baidu Maps and AutoNavi Maps are easily accessible. However, the accuracy of the latter has not been thoroughly analyzed. Taking Baotou, a medium-sized city in China, as an example, we assessed the suitability of using POI data for measuring geographic accessibility to healthcare facilities. We computed the difference of geographic accessibility calculated based on POI data and that on authoritative data. Logistic regression and a multiple linear regression model was applied to identify factors related to the consistency between the two data sources. Compared to authoritative data, POI data exhibited discrepancies, with completeness of 54.9% and accuracy of 63.7%. Geographic accessibility calculated based on both data showed similar patterns, with good consistency for hospitals and in urban areas. However, large differences (>30 minutes) were shown in rural areas for primary healthcare facilities. The differences were small regarding to population-weighted average accessibility (with slight underestimation of 3.07 minutes) and population coverage across various levels of accessibility (with differences less than 1% of the population) for the entire area. In conclusion, POI data can be considered for use in both urban areas and at the level of entire city; however, awareness should be raised in rural areas.
Altmetrics
Downloads
Download data is not yet available.
Citations
Ather AA. 2010. How many volunteers does it take to map an area well? The validity of linus' law to volunteered geographic information. Cartogr J 47:315-322. DOI: https://doi.org/10.1179/000870410X12911304958827
Batini C, Cappiello C, Francalanci C, Maurino A. 2009. Methodologies for data quality assessment and improvement. ACM Comput Surv 41:1-52. DOI: https://doi.org/10.1145/1541880.1541883
Borkowska S, Pokonieczny K. 2022. Analysis of openstreetmap data quality for selected counties in poland in terms of sustainable development. Sustainability 14:3728. DOI: https://doi.org/10.3390/su14073728
Cao W-R, Shakya P, Karmacharya B, Xu DR, Hao Y-T, Lai Y-S. 2021. Equity of geographical access to public health facilities in nepal. BMJ Glob Health 6:e006786. DOI: https://doi.org/10.1136/bmjgh-2021-006786
Centers for Medicare & Medicaid Services. 2022. Centers for medicare & medicaid services (cms). Accessed 25 May 2022. Available from: https://www.cms.gov
Deng L, Shao Ja, Guo Y, Xu X. 2015. Spatial accessibility of medical services in mountainous regions based on modified two-step floating catchment area method:A case study of shizhu county, chongqing Prog Geogr 34:716-725.
Dijkstra EW. 1959. A note on two problems in connexion with graphs. Numer Math. DOI: https://doi.org/10.1007/BF01386390
dos Anjos Luis A, Cabral P. 2016. Geographic accessibility to primary healthcare centers in mozambique. Int J Equity Health 15:1-13. DOI: https://doi.org/10.1186/s12939-016-0455-0
Federal Joint Committee. 2022. Federal joint committee. Accessed 22 May 2022. Available from: https://www.g-ba.de/
Geldsetzer P, Reinmuth M, Ouma PO, Lautenbach S, Okiro EA, Bärnighausen T, Zipf A. 2020. Mapping physical access to health care for older adults in sub-saharan africa and implications for the covid-19 response: A cross-sectional analysis. Lancet Healthy Longev 1:e32-e42. DOI: https://doi.org/10.1016/S2666-7568(20)30010-6
General Office of the State Council of the People's Republic of China. 2015. National health service system planning outline (2015-2020). Chin Pract J Rural Dr 22:1-11.
Hu S, Zhao R, Cui Y, Zhang D, Ge Y. 2023. Identifying the uneven distribution of health and education services in china using open geospatial data. Geogr Sustain 4:91-9. DOI: https://doi.org/10.1016/j.geosus.2023.01.002
Jia P, Wang Y, Yang M, Wang L, Yang X, Shi X, Yang L, Wen J, Liu Y, Yang M, Xin J, Zhang F, Jiang L, Chi C, Zhang L, Ma X, Ma X, Zhao L, Li W. 2022. Inequalities of spatial primary healthcare accessibility in china. Soc Sci Med 314:115458. DOI: https://doi.org/10.1016/j.socscimed.2022.115458
Khazi-Syed A, Pecherskiy M, Krambeck H. 2023. Analyzing access to health facilities by road using unconventional data sources. Transp Res Rec 2677:169-177. DOI: https://doi.org/10.1177/03611981221098693
Klinkhardt C, Kühnel F, Heilig M, Lautenbach S, Wörle T, Vortisch P, Kuhnimhof T. 2023. Quality assessment of openstreetmap’s points of interest with large-scale real data. Transp Res Rec 2677:661-674. DOI: https://doi.org/10.1177/03611981231169280
Kotavaara O, Nivala A, Lankila T, Huotari T, Delmelle E, Antikainen H. 2021. Geographical accessibility to primary health care in finland–grid-based multimodal assessment. Appl Geogr 136:102583. DOI: https://doi.org/10.1016/j.apgeog.2021.102583
Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ. 2012. Population distribution, settlement patterns and accessibility across africa in 2010. PLoS One 7:e31743. DOI: https://doi.org/10.1371/journal.pone.0031743
Li X, Lu J, Hu S, Cheng KK, De Maeseneer J, Meng Q, Mossialos E, Xu DR, Yip W, Zhang H. 2017. The primary health-care system in china. Lancet 390:2584-2594. DOI: https://doi.org/10.1016/S0140-6736(17)33109-4
Liu S, Qin Y, Xu Y. 2019. Inequality and influencing factors of spatial accessibility of medical facilities in rural areas of china: A case study of henan province. Int J Environ Res Public Health 16:1833. DOI: https://doi.org/10.3390/ijerph16101833
Maina J, Ouma PO, Macharia PM, Alegana VA, Mitto B, Fall IS, Noor AM, Snow RW, Okiro EA. 2019. A spatial database of health facilities managed by the public health sector in sub saharan africa. Sci Data 6:134. DOI: https://doi.org/10.1038/s41597-019-0142-2
Miller EJ. 2018. Accessibility: Measurement and application in transportation planning. Transp Rev 38:551-555. DOI: https://doi.org/10.1080/01441647.2018.1492778
Ministry of Health of the People’s Republic of China. 2012. Notice of the ministry of health on the implementation of five systems including the national health resources and medical services survey system. Accessed 20 November 2022. Available from: http://www.nhc.gov.cn/zwgkzt/ppxxhjs1/201301/d09bc944bb57409f8522f94874694e5c.shtml
Mooney P, Corcoran P, Ciepluch B. 2013. The potential for using volunteered geographic information in pervasive health computing applications. J Ambient Intell Humaniz Comput 4:731-745. DOI: https://doi.org/10.1007/s12652-012-0149-4
Mullen WF, Jackson SP, Croitoru A, Crooks A, Stefanidis A, Agouris P. 2015. Assessing the impact of demographic characteristics on spatial error in volunteered geographic information features. GeoJournal 80:587-605. DOI: https://doi.org/10.1007/s10708-014-9564-8
National Bureau of Statistics. 2021. National health resources and medical service statistical survey system. Accessed 13 October 2023. Available from: http://www.stats.gov.cn/tjfw/bmdcxmsp/bmzd/202201/t20220121_1826888.html
Palk L, Okano JT, Dullie L, Blower S. 2020. Travel time to health-care facilities, mode of transportation, and hiv elimination in malawi: A geospatial modelling analysis. Lancet Glob Health 8:e1555-e1564. DOI: https://doi.org/10.1016/S2214-109X(20)30351-X
Penchansky R, Thomas JW. 1981. The concept of access: Definition and relationship to consumer satisfaction. Med Care 19:127-140. DOI: https://doi.org/10.1097/00005650-198102000-00001
Psyllidis A, Gao S, Hu Y, Kim E-K, McKenzie G, Purves R, Yuan M, Andris C. 2022. Points of interest (poi): A commentary on the state of the art, challenges, and prospects for the future. Comput Urban Sci 2:20. DOI: https://doi.org/10.1007/s43762-022-00047-w
Rader B, Astley CM, Sy KTL, Sewalk K, Hswen Y, Brownstein JS, Kraemer MUG. 2020. Geographic access to united states sars-cov-2 testing sites highlights healthcare disparities and may bias transmission estimates. J Travel Med 27:taaa076. DOI: https://doi.org/10.1093/jtm/taaa076
Sohu News. 2016. Conversation with gu weihao from baidu maps: Data technology shapes the future of maps. Accessed October 23 2023. Available from: https://www.sohu.com/a/109243758_449147
Steiniger S, Poorazizi ME, Scott DR, Fuentes C, Crespo R. Can we use openstreetmap pois for the evaluation of urban accessibility? In: Proceedings of the GIScience 2016, 2016. Montreal, Canada, Vol. 1. DOI: https://doi.org/10.21433/B31167F0678P
Wang K, Bai J, Dang X. 2020. Spatial difference and equity analysis for accessibility to three-level medical services based on actual medical behavior in shaanxi, china. Int J Environ Res Public Health 18:112. DOI: https://doi.org/10.3390/ijerph18010112
Weiss DJ, Nelson A, Gibson HS, Temperley W, Peedell S, Lieber A, Hancher M, Poyart E, Belchior S, Fullman N. 2018. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553:333-336. DOI: https://doi.org/10.1038/nature25181
Weiss DJ, Nelson A, Vargas-Ruiz CA, Gligorić K, Bavadekar S, Gabrilovich E, Bertozzi-Villa A, Rozier J, Gibson HS, Shekel T, Kamath C, Lieber A, Schulman K, Shao Y, Qarkaxhija V, Nandi AK, Keddie SH, Rumisha S, Amratia P, Arambepola R, Chestnutt EG, Millar JJ, Symons TL, Cameron E, Battle KE, Bhatt S, Gething PW. 2020. Global maps of travel time to healthcare facilities. Nat Med 26:1835-1838. DOI: https://doi.org/10.1038/s41591-020-1059-1
World Health Organization. 2023. Universal health coverage (uhc). Accessed 20 October 2023. Available from: https://www.who.int/news-room/fact-sheets/detail/universal-health-coverage-(uhc)
WorldPop. 2020. Population counts / unconstrained individual countries 2000-2020 un adjusted (1km resolution). Accessed 25 May 2022. Available from: https://www.worldpop.org/
Wuhan University. 2018. 【geoscience café】sharing of cutting-edge technologies in the production of autonavi map data. Accessed 5 October 2023. Available from: https://liesmars.whu.edu.cn/info/1063/3912.htm
Yang S, Shen J, Konečný M, Wang Y, Štampach R. Study on the spatial heterogeneity of the poi quality in openstreetmap. In: Proceedings of the the 7th International Conference on Cartography and GIS, 2018. Sozopol, Bulgaria, 18-23.
Zhang Y, Li W, Deng H, Li Y. 2020. Evaluation of public transport-based accessibility to health facilities considering spatial heterogeneity. J Adv Transp 2020:1-10. DOI: https://doi.org/10.1155/2020/7645153
How to Cite
Geographical accessibility to healthcare by point-of–interest data from online maps: a comparative study. (2024). Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1322
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Mathuros Tipayamongkholgul, Sunisa Lisakulruk, Socio-geographical factors in vulnerability to dengue in Thai villages: a spatial regression analysis , Geospatial Health: Vol. 5 No. 2 (2011)
- Hassan M. Khormi, Lalit Kumar, Ramze A. Elzahrany, Modeling spatio-temporal risk changes in the incidence of dengue fever in Saudi Arabia: a geographical information system case study , Geospatial Health: Vol. 6 No. 1 (2011)
- Jennifer Weisent, Barton Rohrbach, John R. Dunn, Agricola Odoi, Detection of high risk campylobacteriosis clusters at three geographic levels , Geospatial Health: Vol. 6 No. 1 (2011)
- Alba Valéria Machado da Silva, Monica de Avelar Figueiredo Mafra Magalhães, Reginaldo Peçanha Brazil, João Carlos Araujo Carreira, Ecological study and risk mapping of leishmaniasis in an endemic area of Brazil based on a geographical information systems approach , Geospatial Health: Vol. 6 No. 1 (2011)
- Teresa M. Salgado, Rebekah Moles, Shalom I. Benrimoj, Fernando Fernandez-Llimos, Designing a model to minimize inequities in hemodialysis facilities distribution , Geospatial Health: Vol. 6 No. 1 (2011)
- Mathuros Tipayamongkholgul, Sunisa Lisakulruk, Response to Comments on Socio-geographical factors in vulnerability to dengue in Thai villages: a spatial regression analysis by Tipayamongkholgul and Lisakulruk , Geospatial Health: Vol. 6 No. 1 (2011)
- Subhash C. Arya, Nirmala Agarwal, Comments on Socio-geographical factors in vulnerability to dengue in Thai villages: a spatial regression analysis by Tipayamongkholgul and Lisakulruk , Geospatial Health: Vol. 6 No. 1 (2011)
- Kun Yang, Le-Ping Sun, Yi-Xin Huang, Guo-Jing Yang, Feng Wu, De-Rong Hang, Wei Li, Jian-Feng Zhang, Yong-Sheng Liang, Xiao-Nong Zhou, A real-time platform for monitoring schistosomiasis transmission supported by Google Earth and a web-based geographical information system , Geospatial Health: Vol. 6 No. 2 (2012)
- Hassan M. Khormi, Lalit Kumar, Assessing the risk for dengue fever based on socioeconomic and environmental variables in a geographical information system environment , Geospatial Health: Vol. 6 No. 2 (2012)
- Isabella Vilhena Freire Martins, Barbara Rauta de Avelar, Maria Julia Salim Pereira, Adevair Henrique da Fonseca, Application of a geographical information system approach for risk analysis of fascioliasis in southern EspÃrito Santo state, Brazil , Geospatial Health: Vol. 6 No. 3 (2012)
You may also start an advanced similarity search for this article.
