No URL ">



Infected cases and suspect cases of covid-19 are increasing more and more daily. This increment happens either in whole regions of Indonesia and DKI Jakarta as a capital city. The purpose of this research is to seek the pattern in spatial of Covid-19 incidence with 3 different periods of before, during, and after large-scale social restriction, and to identify the influence of the presence of the elderly and other factors. One of the scopes of this study is the presence of the elderly because the elderly population is considered as influencing the increase of Covid-19 incidence. The analysis method used in this research is spatial analysis. Novel findings show that spatial pattern change in 3 periods of observation where clusterization of Covid-19 is more intensive, the presence of elderly is a more significant influence to the transmission of Covid-19. Also, there are spatial effects towards the influence of elderly to the spread of Covid-19. The other variables such as the number of traditional markets and population density initially insignificant turn out to be significant in the second and third period.


Covid-19, Elderly Population, Spatial Analysis, social restriction

Bahasa Abstract


Kasus terinfeksi dan kasus suspek Covid-19 semakin hari semakin meningkat. Peningkatan ini terjadi baik di seluruh wilayah Indonesia maupun di DKI Jakarta sebagai ibu kota. Tujuan penelitian ini adalah untuk mencari pola penularan secara spasial dengan 3 periode indikator penularan yang berbeda, dan mengidentifikasi pengaruh keberadaan lansia dan faktor lainnya serta analisis spasial. Salah satu ruang lingkup penelitian ini adalah keberadaan lansia karena populasi lansia dianggap rentan terhadap Covid-19. Metode analisis yang digunakan dalam penelitian ini adalah regresi spasial. Temuan baru menunjukkan bahwa perubahan pola spasial dalam 3 periode pengamatan dimana klasterisasi Covid-19 lebih intensif, keberadaan lansia lebih berpengaruh signifikan terhadap penularan Covid-19. Selain itu, terdapat efek spasial terhadap pengaruh lansia terhadap penyebaran Covid-19. Variabel lain seperti jumlah pasar tradisional dan kepadatan penduduk yang awalnya tidak signifikan berubah menjadi signifikan pada periode kedua dan ketiga.



Ahmadi, M.; Sharifi, A.; Dorosti, S.; Ghoushchi, S.J.; Ghanbari N. (2020). Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Science of the Total Environment 729, 138705. https://doi.org/10.1016/j.scitotenv.2020.138705

Allcott, H.; Boxell, L.; Conway, J.; Gentzkow, M.; Thaler, M.; Yang, D. (2020). Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic. Journal of Public Economics 191, 104254. https://doi.org/10.1016/j.jpubeco.2020.104254

Anselin, L. (1999). Spatial Econometrics. Regional Science and Urban Economics, 1–30. https://doi.org/10.1016/j.regsciurbeco.2006.11.009

Anselin, Luc and Rey, S Joseph. (2014). Modern Spatial Econometrics in Practice: A Guide to GeoDaSpace and PySAL. GeoDa Press LLC.

Badr, H.S.; Du, H.; Marshall, M.; Dong, E.; Squire, M.M.; Gardner, L.M.. (2020). Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study. Lancet Infect Dis; 20: 1247–54. https://doi.org/10.1016/S1473-3099(20)30553-3

Bashir, M.F.; Ma, B.; Bilal; Komal, B.; Bashir, M.A.; Tan, D.; Bashir, M. (2020). Correlation between climate indicators and COVID-19 pandemic in New York, USA. Science of the Total Environment 728, 138835. https://doi.org/10.1016/j.scitotenv.2020.138835

Bonacini, L.; Gallo, G.; Patriarca, F. (2021). Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures. Journal of Population Economics volume 34, pages 275–301, https://doi.org/10.1007/s00148-020-00799-x

Briz-Redón, Á.; Serrano-Aroca, Á. (2020). A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain. Science of the Total Environment 728, 138811, https://doi.org/10.1016/j.scitotenv.2020.138811

Chang, H.-Y.; Tang, W.; Hatef, E.; Kitchen, C.; Weiner, J.P.; Kharrazi, H. (2021). Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States. BMC Public Health, 21:1140 https://doi.org/10.1186/s12889-021-11149-1

Chow, T.E.; Choi, Y.; Yang, M.; Mills, D.; Yue, R. (2021). Geographic pattern of human mobility and COVID-19 before and after Hubei lockdown. Annals of GIS, 27:2, 127-138, https://doi.org/10.1080/19475683.2020.1841828

COVID-19 Task Force. (2021). Covid-19 monitoring data (in Bahasa) https://covid19.go.id/peta-sebaran-covid19 Accessed 24 June 2021

Cressie, N. (1991) Statistics for spatial data. John Wiley & Sons, New York.

Elfriede, F.; Silalahi, S. (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 volume 20, Article number: 1053, https://doi.org/10.1186/s12913-020-05896-x.

Hamdan, O. F. (2019). Rasio Tenaga Pendidik, Rasio Tenaga Kesehatan, Dan Capaian Pembangunan Manusia Di Indonesia Dalam Analisis Spasial. Jurnal Ekonomi Dan Kebijakan Publik Indonesia, 6(2), 155–172. https://doi.org/10.24815/ekapi.v6i2.15346

Iaccarino, G.; Grassi, G.; Borghi, C.; Ferri, C.; Salvetti, M.; Volpe, M. (2020). Age and Multimorbidity Predict Death Among COVID-19 Patients. Hypertension, 76:00-00. https://doi.org/10.1161/HYPERTENSIONAHA.120.15324

Imam, Z.; Halalau, A.; Odish, F.; Gill, I.; O’Connor, D.; Armstrong, J.; Vanood, A.; Ibironke O.; Hanna, A.; Ranski, A. (2020). Older age and comorbidity are independent mortality predictors in a large cohort of 1305 COVID-19 patients in Michigan, United States. J Intern Med; 288: 469–476. https://doi.org/10.1111/joim.13119

Jakarta Government Communication, Informatics, and Statistics Department. (2020). PEMPROV DKI TERAPKAN PSBB EFEKTIF MULAI 10 APRIL 2020, KECUALI SEJUMLAH SEKTOR (in Bahasa) https://jakarta.go.id/artikel/konten/6238/pemprov-dki-terapkan-psbb-efektif-mulai-10-april-2020-kecuali-sejumlah-sektor Accessed 24 June 2021

Jakarta Government Health Department. (2021). Monitoring Data of Covid-19 in DKI Jakarta. https://corona.jakarta.go.id/id/data-pemantauan. Accessed 24 June 2021

Kivi, M.; Hansson, I.; Bjälkebring, P. (2021). Up and About: Older Adults’ Well-being During the COVID-19 Pandemic in a Swedish Longitudinal Study. The Journals of Gerontology: Series B, Volume 76, Issue 2, Pages e4–e9. https://doi.org/10.1093/geronb/gbaa084

Lai, C.C.; Shih, T.P.; Ko, W.C.; Tang, H.J.; Hsueh, P.R. (2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int. J. Antimicrob. Agents, 105924. https://doi.org/10.1016/j.ijantimicag.2020.105924

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 Volume 60, Issue 1, Pages e41-e44 https://doi.org/10.1016/j.jpainsymman.2020.03.041

Lee, J.Y.; Kim, H.A.; Huh, K.; Hyun, M.; Rhee, J.-Y.; Jang, S.; Kim, J.-Y.; Peck, K.R.; Chang, H.-H. (2020). Risk Factors for Mortality and Respiratory Support in Elderly Patients Hospitalized with COVID-19 in Korea. J Korean Med Sci. 15;35(23):e223. https://doi.org/10.3346/jkms.2020.35.e223

Lee, M.; Zhao, J.; Sun, Q.; Pan, Y.; Zhou, W.; Xiong, C.; Zhang, L. (2020) Human mobility trends during the early stage of the COVID-19 pandemic in the United States. PLoS ONE 15(11): e0241468. https://doi.org/10.1371/journal.pone.0241468

Liu, Y.; Mao, B.; Liang, S.; Yang, J.-W.; Lu, H.-W.; Chai, Y.-H.; Wang, L.; Zhang, L.; Li, Q.-H.; Zhao, L.; He, Y.; Gu, X.-L.; Ji, X.-B.; Li, L.; Jie, Z.-J.; Li, Q.; Li, X.-Y.; Lu, H.-Z.; Zhang, W.-H.; Song, Y.-L.; Qu, J.-M.; Xu, J. F. (2020). Association between age and clinical characteristics and outcomes of COVID-19. European Respiratory Journal, 55: 2001112. https://doi.org/10.1183/13993003.01112-2020

Ma, Y.; Zhao, Y.; Liu, J.; He, X.; Wang, B.; Fu, S.; Yan, J.; Niu, J.; Zhou, J.; Luo, B. (2020). Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Science of the Total Environment 724, 138226. https://doi.org/10.1016/j.scitotenv.2020.138226

Maged N.K.B.; Estella, M.G. (2020). Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics. Int J Health Geogr 19, 8 https://doi.org/10.1186/s12942-020-00202-8

Ministry of Health. (2020). Peraturan Menteri Kesehatan Republik Indonesia No. 9 Tahun 2020 (in Bahasa). http://hukor.kemkes.go.id/uploads/produk_hukum/PMK_No__9_Th_2020_ttg_Pedoman_Pembatasan_Sosial_Berskala_Besar_Dalam_Penanganan_COVID-19.pdf Accessed 24 June 2021

Mofijur, M.; Fattah, I.M.R.; Islam, A.B.M.S.; Uddin, M.N.; Rahman, S.M.A.; Chowdhurry, M.A.; Alam, Md.A.; Uddin, Md.A. (2020). Relationship between Weather Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh. Sustainability, 12, 8319; https://doi.org/10.3390/su12208319

Pardo, I.F.; Napoletano, B.M.; Verges, F.R.; Billa, L. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of the Total Environment 739, 140033. https://doi.org/10.1016/j.scitotenv.2020.140033

PPID DKI Jakarta. (2020). Peraturan Gubernur Daerah Khusus Ibukota Jakarta Nomor 33 tahun 2020 (in Bahasa). https://ppid.jakarta.go.id/download/f21867235094ab005e3dc6537e1018a837022c9a98d371c3d05705b9bdf059ac65a6059436797b7e05f0c2cd837beb4cf9ac32a66b8820211625a75b6506f6c3e4J5jt2Yke12dXbPHIdEVYnm7EQ1ZiOvqKdvdCXqQq4pkjY5APAYE0NNIaTV522 Accessed 24 June 2021

Prata, D.N.; Rodrigues, W.; Bermejo, P.H. (2020). Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil. Science of the Total Environment 729, 138862. https://doi.org/10.1016/j.scitotenv.2020.138862

Putri, R.N. (2020). Indonesia dalam menghadapi pandemi covid-19 (in Bahasa). Jurnal Ilmiah Universitas Batanghari Jambi, 20(2), 705-709 https://doi.org/10.33087/jiubj.v20i2.1010

Röhr, S.; Reininghaus, U.; Heller, S.G.R. (2020). Mental wellbeing in the German old age population largely unaltered during COVID19 lockdown: results of a representative survey. BMC Geriatrics 20:489. https://doi.org/10.1186/s12877-020-01889-x

Şahin, M. (2020). Impact of weather on COVID-19 pandemic in Turkey. Science of the Total Environment 728, 138810. https://doi.org/10.1016/j.scitotenv.2020.138810

Sharma, P.; Singh, A.K.; Agrawal, B.; Sharma, A. (2020). Correlation between weather and COVID-19 pandemic in India: An empirical investigation. Journal of Public Affairs, 20:e2222 https://doi.org/10.1002/pa.2222

Singh, S.; Roy, D.; Sinha, K.; Parveen, S.; Sharma, G.; Joshi, G. (2020). Impact of COVID-19 and lockdown on mental health of children and adolescents: A narrative review with recommendations. Psychiatry Research 293, 113429 https://doi.org/10.1016/j.psychres.2020.113429

Statistics Indonesia, (2020). Total Population of DKI Jakarta (in Bahasa). https://jakarta.bps.go.id/backend/materi_ind/materiBrsInd-20210122142034.pdf Accessed on 30 June 2021

Tamagusko, T.; Ferreira, A. (2020). Data-Driven Approach to Understand the Mobility Patterns of the Portuguese Population during the COVID-19 Pandemic. Sustainability, 12, 9775; https://doi.org/10.3390/su12229775

Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234. https://doi.org/10.2307/143141

Tosepu, R.; Gunawan, J.; Effendy, D.S.; Ahmad, L.O.A.I.; Lestari, H.; Bahar, H.; Asfian, P. (2020). Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Science of the Total Environment 725, 138436, https://doi.org/10.1016/j.scitotenv.2020.138436

World Health Organization. (2020). Pneumonia of unknown cause — China: disease outbreak news. Geneva: World Health Organization, January 5, 2020 (https://www.who.int/emergencies/disease-outbreak-news/item/2020-DON229) Accessed 24 June 2021.

World Health Organization. (2021). Novel Coronavirus (2019-nCoV) situation reports (22.06.2021). https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports Accessed 24 June 2021

Xie, J.; Zhu, Y. (2020). Association between ambient temperature and COVID-19 infection in 122 cities from China. Science of the Total Environment 724, 138201. https://doi.org/10.1016/j.scitotenv.2020.138201

Xiong, C.; Hu, S.; Yang, M.; Luo, W.; Zhang, L. (2020). Mobile device data reveal the dynamics in a positive relationship between human mobility and COVID-19 infections. PNAS vol. 117 no. 44 pp. 27087–27089. https://doi.org/10.1073/pnas.2010836117

Zhang, J.; Feng, B.; Wu, Y.; Xu, P.; Ke, R.; Dong, N. (2021). The effect of human mobility and control measures on traffic safety during COVID-19 pandemic. PLoS ONE 16(3): e0243263. https://doi.org/10.1371/journal.pone.0243263

Zhou, Z.; Zhang, M.; Wang, Y.; Zheng, F.; Huang, Y.; Huang, K.; Yu, Q.; Cai, C.; Chen, D.; Tian, Y.; Lei, J.; Xiao, X.; Clercq, E.D.; Li, G.; Xie, Y.; Gong, G. (2020). Clinical characteristics of older and younger patients infected with SARS-CoV-2. AGING, Vol. 12, No. 12. https://doi.org/10.18632/aging.103535