Geospatial approach to accessibility of referral hospitals using geometric network analysts and spatial distribution models of covid-19 spread cases based on gis in bekasi city, west java

https://doi.org/10.22146/ijg.66099

Ruki Ardiyanto(1*), Supriatna Supriatna(2), Tito L. Indra(3), Masita Dwi Mandini Manesa(4)

(1) Agency for the Assessment and Application of Technology (BPPT) and Department of Geography, University of Indonesia
(2) Department Geography, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok.
(3) Department Geography, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok.
(4) Department Geography, Faculty of Mathematics and Natural Sciences, University of Indonesia, Depok.
(*) Corresponding Author

Abstract


Bekasi City has a high population density, as seen from its growth rate in 2020. Therefore, geospatial analysis is required to support and provide effective and efficient health services, evaluate the need for referral hospital capacity, and minimize the spread of COVID-19 cases in this city. The geospatial methods used in this study are Geometric Network Analyst and Geographic Weighted Regression (GWR), with Service Area (SA) used for analysis. The results based on the distance between the referral hospitals and settlements in Bekasi City showed that more than 2.201 million people, or 90%, have been well covered. Meanwhile, regarding travel time, 1.792 million people or 73% in eight sub-districts are in well-served areas. Conversely, referral hospitals do not cover four sub-districts, namely Bantar Gebang, Jati Sampurna, Medan Satria, and Jati Asih. The spatial modeling analysis results using GWR with spatial-temporal data recapitulation of data reports for eight months showed predictions for the spread of confirmed cases in six sub-districts, namely West Bekasi, North Bekasi, East Bekasi, Medan Satria, Mustika Jaya, and Rawalumbu. This implies that local governments need to suggest more referral hospitals serving people who live far from the existing referral hospitals.

Keywords


COVID-19, Geospatial, Geometric Network Analyst, Service Area, Linear Regression, Destination Hospital

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References

Algharib, S. M. (2011). Distance and coverage: an assessment of location-allocation models for fire stations in Kuwait City, Kuwait. Kent State University.

BPS Kota Bekasi. (2021). Kota bekasi 2021. BPS Kota Bekasi. Retrieved from https://bekasikota.bps.go.id/publication/2021/02/26/d93e792ac92f8b00b513ea2b/kota-bekasi-dalam-angka-2021.html

Charlton, M., Fotheringham, S., & Brunsdon, C. (2009). Geographically weighted regression. White Paper. National Centre for Geocomputation. National University of Ireland Maynooth, 2.

Dinas Kesehatan Kota Bekasi. (2020). Rekapitulasi kecamatan dan kelurahan kasus konfirmasi kota bekasi, (1), 2020.

Forkuo, E. K., & Quaye-Ballard, J. A. (2013). GIS based fire emergency response system. International Journal of Remote Sensing and GIS, 2(1), 32–40.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.

Isa, U., Liman, M., Mohammed, M., Mathew, O., & Yayo, Y. (2016). Spatial Analysis of Fire Service Station in Kano Metropolis, Nigeria. IOSR J Humanit Soc Sci, 21(9), 45–52.

Jamtsho, S., Corner, R., & Dewan, A. (2015). Spatio-temporal analysis of spatial accessibility to primary health care in Bhutan. ISPRS International Journal of Geo-Information, 4(3), 1584–1604.

Jovanović, A., Klimek, P., Renn, O., Schneider, R., Øien, K., Brown, J., … Jelić, M. (2020). Assessing resilience of healthcare infrastructure exposed to COVID-19: emerging risks, resilience indicators, interdependencies and international standards. Environment Systems and Decisions, 40(2), 252–286.

Kompas. (2020). Alarm Untuk Kota Bekasi Faskes Pasien Covid-19 Semakin Menipis. Retrieved December 12, 2020, from https://megapolitan.kompas.com/read/2020/12/22/07530881/alarm-untuk-kota-bekasi-faskes-pasien-covid-19-semakin-menipis?page=all.

Kuupiel, D., Adu, K. M., Bawontuo, V., Adogboba, D. A., Drain, P. K., Moshabela, M., & Mashamba-Thompson, T. P. (2020). Geographical accessibility to glucose-6-phosphate dioxygenase deficiency point-of-care testing for antenatal care in Ghana. Diagnostics, 10(4), 229.

Lakhani, A. (2020). Which Melbourne metropolitan areas are vulnerable to COVID-19 based on age, disability, and access to health services? Using spatial analysis to identify service gaps and inform delivery. Journal of Pain and Symptom Management, 60(1), e41–e44.

Manessa, M. D. M. (2020). Bahan Kuliah Pemodelan Spasial Lanjutan. Depok, Jawa Barat.

Mansour, S., Al Kindi, A., Al-Said, A., Al-Said, A., & Atkinson, P. (2021). Sociodemographic determinants of COVID-19 incidence rates in Oman: Geospatial modelling using multiscale geographically weighted regression (MGWR). Sustainable Cities and Society, 65, 102627.

Marhamah, E., & Jaya, I. (2020). Modeling positive COVID-19 cases in Bandung City by means geographically weighted regression. Commun. Math. Biol. Neurosci., 2020, Article-ID.

Mulrooney, T., Beratan, K., McGinn, C., & Branch, B. (2017). A comparison of raster-based travel time surfaces against vector-based network calculations as applied in the study of rural food deserts. Applied Geography, 78, 12–21.

Nicholl, J., West, J., Goodacre, S., & Turner, J. (2007). The relationship between distance to hospital and patient mortality in emergencies: an observational study. Emergency Medicine Journal, 24(9), 665–668.

Niedzielski, M. A., & Eric Boschmann, E. (2014). Travel time and distance as relative accessibility in the journey to work. Annals of the Association of American Geographers, 104(6), 1156–1182.

PM Perhubungan No.111. (2015). PM_111_Tahun_2015.pdf. Kementerian Perhubungan RI.

Pusat Informasi & Koordinasi Provinsi Jawa Barat. (2022). Data COVID-19 Jawa Barat(3). Jawa Barat: https://pikobar.jabarprov.go.id/. Retrieved from https://pikobar.jabarprov.go.id/table-case

Rakibul, A., Shaharier, A. M., Torit, C., & Mahbub, H. M. (2022). Applications of GIS and geospatial analyses in COVID-19 research: A systematic review. F1000Research, 9.

Silalahi, F. E. S., Hidayat, F., Dewi, R. S., Purwono, N., & Oktaviani, N. (2020). GIS-based approaches on the accessibility of referral hospital using network analysis and the spatial distribution model of the spreading case of COVID-19 in Jakarta, Indonesia. BMC Health Services Research, 20(1), 1–20.

Turnbull, J., Martin, D., Lattimer, V., Pope, C., & Culliford, D. (2008). Does distance matter? Geographical variation in GP out-of-hours service use: an observational study. British Journal of General Practice, 58(552), 471–477.

UU RI Nomor 22. (2009). Undang-undang Republik Indonesia nomor 22 tahun 2009 tentang lalu lintas dan angkutan jalan. Eko Jaya.

Van Wee, B. (2016). Accessible accessibility research challenges. Journal of Transport Geography, 51, 9–16.

Walker, P. G. T., Whittaker, C., Watson, O. J., Baguelin, M., Winskill, P., Hamlet, A., … Green, W. (2020). The impact of COVID-19 and strategies for mitigation and suppression in low-and middle-income countries. Science, 369(6502), 413–422.

Wu, X., & Zhang, J. (2021). Exploration of spatial-temporal varying impacts on COVID-19 cumulative case in Texas using geographically weighted regression (GWR). Environmental Science and Pollution Research, 28(32), 43732–43746.

Yellow Horse, A. J., Yang, T.-C., & Huyser, K. R. (2022). Structural inequalities established the architecture for COVID-19 pandemic among native Americans in Arizona: a geographically weighted regression perspective. Journal of Racial and Ethnic Health Disparities, 9(1), 165–175.

Zannat, K. E., Adnan, M. S. G., & Dewan, A. (2020). A GIS-based approach to evaluating environmental influences on active and public transport accessibility of university students. Journal of Urban Management, 9(3), 331–346.

Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., … Zhu, Y. (2020). COVID-19: challenges to GIS with big data. Geography and Sustainability, 1(1), 77–87.

Zinszer, K., Charland, K., Kigozi, R., Dorsey, G., Kamya, M. R., & Buckeridge, D. L. (2014). Determining health-care facility catchment areas in Uganda using data on malaria-related visits. Bulletin of the World Health Organization, 92, 178–186.




DOI: https://doi.org/10.22146/ijg.66099

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Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 30/E/KPT/2018, Vol 50 No 1 the Year 2018 - Vol 54 No 2 the Year 2022

ISSN 2354-9114 (online), ISSN 0024-9521 (print)

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