The Spatial Pattern of the Spread of the COVID-19 Pandemic (Case Study: DKI Jakarta Province)
(1) Institut Teknologi Padang
(2) Badan Informasi Geospasial
(3) Institut Teknologi Padang
(4) Institut Teknologi Padang
Abstract
The COVID-19 pandemic has been running in Indonesia for more than two years. The first case was found in March 2020. DKI Jakarta as the capital city of the country with a high population density and an economic center that was threatened because the area has a high vulnerability to the spread of COVID-19. The number of confirmed cases that continue to soar and the spread that is difficult to be controlled have resulted in the DKI Jakarta government taking policies such as implementing large-scale social restrictions (PSBB), which aims to stop the spread of COVID-19 and to look for patterns of spread of COVID-19. This study uses a geographic information system in looking for patterns of the spread of COVID-19. The analytical method used is spatial autocorrelation, which is carried out using the Moran Index. In addition, the autocorrelation test was also carried out using a Local Indicator of Spatial Autocorrelation (LISA) with the results in the form of a cluster map and a map of significance. The Ordinary Least Squares analysis method is a regression technique that provides a global model for understanding and predicting variables in research. The correlation variables used in this research are Markets, Supermarkets, Buses, and Stations. The result of this study is the spatial autocorrelation of the pattern of spread of COVID-19 between villages and spatially the distribution pattern is clustered. In the OLS regression distribution pattern, the supermarket variable with an R-Squared value of 0.128555 or 12% affects the spread of COVID-19. Based on the calculation of R-Square, Koenker (BP) in addition to the OLS model, the assumption of homoscedasticity is not met, so the model is Ordinary Least Squares not good compared to other models in analyzing the pattern of the spread of COVID-19 in DKI Jakarta.
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Algert, S.J., Agrawal, A., & Lewis, D.S., 2006. Disparities in Access to Fresh Produce in Low-Income Neighborhoods in Los Angeles. American Journal of Preventive Medicine, 30(5), pp.365–370.
Anselin, L., 1995. Local Indicators of Spatial Association—LISA. Geogr Anal, 27(2), pp.93–115.
Anselin, L., Syabri, I., & Kho, Y., 2006. GeoDa: An Introduction to Spatial Data Analysis. Geogr Anal, 38, pp.5–22.
Cliff, A.D., & Ord, J.K., 1981. Spatial Processes: Models & Applications. Taylor & Francis.
Djalante, R., Lassa, J., Setiamarga, D., Sudjatma, A., Indrawan, M., Haryanto, B., Mahfud, C., Sinapoy, M.S., Djalante, S., Rafliana, I., Gunawan, L.A., Surtiari, G.A.T., & Warsilah, H., 2020. Review and Analysis of Current Responses to COVID-19 in Indonesia: Period of January to March 2020. Prog Disaster Sci, 6.
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., Xiao, Y., Gao, H., Guo, L., Xie, J., Wang, G., Jiang, R., Gao, Z., Jin, Q., Wang, J., & Cao, B., 2020. Clinical Features of Patients Infected with 2019 Novel Coronavirus in Wuhan, China. Lancet, 395(10223), pp.497–506.
Ijumulana, J., Ligate, F., Bhattacharya, P., Mtalo, F., & Zhang, C., 2020. Spatial Analysis and GIS Mapping of Regional Hotspots and Potential Health Risk of Fluoride Concentrations in Groundwater of Northern Tanzania. Science of The Total Environment, 735, pp.139584.
Jaber, A.S., Hussein, A.K., Kadhim, N.A., & Abid, A., 2022. A Moran’ s I Autocorrelation and Spatial Cluster Analysis for Identifying Coronavirus Disease COVID-19 in Iraq using GIS Approach. Caspian Journal of Environmental Sciences, 20(1), pp.55–60.
Jesri, N., Saghafipour, A., Koohpaei, A., Farzinnia, B., Jooshin, M.K., Abolkheirian, S., & Sarvi, M., 2021. Mappingcl and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA). BMC Public Health, 21(1), pp.1–11.
Kan, Z., Kwan, M.P., Wong, M.S., Huang, J., & Liu, D., 2021. Identifying the Space-Time Patterns of COVID-19 Risk and Their Associations with Different Built Environment Features in Hong Kong. Science of the Total Environment, 772(December 2019), pp.145379.
Koch, T., 2005. Cartographies of Disease: Maps, Mapping, and Medicine. ESRI Press.
Liu, M., Liu, M., Li, Z., Zhu, Y., Liu, Y., Wang, X., Tao, L., & Guo, X., 2021. The Spatial Clustering Analysis of COVID-19 and Its Associated Factors in Mainland China at the Prefecture Level. Science of the Total Environment, 2021.
Liu, Y., Watson, S.C., Gettings, J.R., Lund, R.B., Nordone, S.K., Yabsley, M.J., & McMahan, C.S., 2017. A Bayesian Spatio-Temporal Model for Forecasting Anaplasma Species Seroprevalence in Domestic Dogs within the Contiguous United States. PloS one, 12(7).
Mo, C., Tan, D., Mai, T., Bei, C., Qin, J., Pang, W., & Zhang, Z., 2020. An Analysis of Spatiotemporal Pattern for COVID-19 in China Based on Space-Time Cube. J Med Virol, 92(9), pp.1587–1595.
Moran, P.A.P., 1950. Notes on Continuous Stochastic Phenomena. Biometrika, 37(1–2), pp.17–23.
Parvin, F., Ali, S.A., Hashmi, S.N.I., & Ahmad, A., 2021. Spatial Prediction and Mapping of the COVID-19 Hotspot in India Using Geostatistical Technique. Spatial Information Research, 29(4), pp.479–494.
Pourghasemi, H.R., Pouyan, S., Heidari, B., Farajzadeh, Z., Fallah Shamsi, S.R., Babaei, S., Khosravi , R., Etemadi, M., Ghanbarian, G., Farhadi, A., Safaeian, R., Heidari, Z., Tarazkar, M.H., Tiefenbacher, J.P., Azmi, A., & Sadeghian, F., 2020. Spatial Modeling, Risk Mapping, Change Detection, and Outbreak Trend Analysis of Coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020). International Journal of Infectious Diseases, 98, pp.90–108.
Purwanto, P., Utaya, S., Handoyo, B., Bachri, S., Astuti, I.S., Utomo, K.S., & Aldianto, Y.E., 2021. Spatiotemporal Analysis of COVID-19 Spread with Emerging Hotspot Analysis and Space–Time Cube Models in East Java, Indonesia. ISPRS International Journal of Geo-Information, 10.
Raymundo, C E., Oliveira, M.C., de Araujo Eleuterio, T., André, S.R., da Silva, M.G., da Silva Queiroz, E.R., & de Andrade Medronho, R., 2021. Spatial Analysis of COVID-19 Incidence and the Sociodemographic Context in Brazil. PLoS ONE, 16, pp.1–16.
Ristiantri, Y.R.A., Susiloningtyas, D., Shidiq, I.P.A., Syetiawan, A., & Azizah, F.N., 2022. Multi-criteria Decision Analysis for Readiness of COVID-19 Referral Hospital in Jakarta. IOP Conference Series: Earth and Environmental Science, 1039(1), pp.12022.
Rodríguez, O.F.-, Gustafsson, P.E., & Sebastián, M.S., 2021. Spatial Clustering and Contextual Factors Associated with Hospitalisation and 19 in Sweden: a Deaths due to COVID Geospatial Nationwide Ecological Study. BMJ Global Health, 6(e006247), pp.1–9.
Syetiawan, A., Harimurti, M., & Prihanto, Y., 2022. A spatiotemporal Analysis of COVID-19 Transmission in Jakarta, Indonesia for Pandemic Decision Support. Geospatial Health, 17(s1), pp.1–13.
Wangping, J., Ke, H., Yang, S., Wenzhe, C., Shengshu, W., Shanshan, Y., Jianwei, W., Fuyin, K., Penggang, T., Jing, L., Miao, L., & Yao, H.,2020. Extended SIR Prediction of the Epidemics Trend of COVID-19 in Italy and Compared With Hunan, China. Frontiers in Medicine, 7(169).
Yuwono, T., Sigit, Prahasta, E., Pramono, D., & Jantarto, D., 2015. Aplikasi Sistem Informasi Geografis Untuk Keselamatan Penyelaman Menggunakandata Kedalaman, Temperatur Dan Jenis Dasar Laut (Studi Kasus Di Perairan Teluk Ambon). Jurnal Chart Datum, 1(2), pp.115–120.
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