• International Journal of Technology (IJTech)
  • Vol 13, No 4 (2022)

Communicating the High Susceptible Zone of COVID-19 and its Exposure to Population Number through a Web-GIS Dashboard for Indonesia Cases

Communicating the High Susceptible Zone of COVID-19 and its Exposure to Population Number through a Web-GIS Dashboard for Indonesia Cases

Title: Communicating the High Susceptible Zone of COVID-19 and its Exposure to Population Number through a Web-GIS Dashboard for Indonesia Cases
Supriatna, Faris Zulkarnain, Ardiansyah, Nurrokhmah Rizqihandari, Jarot Mulyo Semedi, Satria Indratmoko, Nurul Sri Rahatiningtyas, Triarko Nurlambang, Muhammad Dimyati

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Cite this article as:
Supriatna, Zulkarnain, F., Ardiansyah, Rizqihandari, N., Semedi, J.M., Indratmoko, S., Rahatiningtyas, N.S., Nurlambang, T., Dimyati, M., 2022. Communicating the High Susceptible Zone of COVID-19 and its Exposure to Population Number through a Web-GIS Dashboard for Indonesia Cases. International Journal of Technology. Volume 13(4), pp. 706-716

Supriatna Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Faris Zulkarnain Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Ardiansyah Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Nurrokhmah Rizqihandari Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Jarot Mulyo Semedi Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Satria Indratmoko Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Nurul Sri Rahatiningtyas Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Triarko Nurlambang Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Muhammad Dimyati Departement of Geography, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia
Email to Corresponding Author

Communicating the High Susceptible Zone of COVID-19 and its Exposure to Population Number through a Web-GIS Dashboard for Indonesia Cases

The Medical Geographic Information System (Medical GIS) application during the COVID-19 pandemic crisis has become influential in communicating disease surveillance for health practitioners and society. The Johns Hopkins University has extensively used a well-known Web-GIS dashboard to track the COVID-19 cases since January 22 and illustrates the location and number of confirmed COVID-19 cases. Unfortunately, the dashboard particularly for Indonesian cases is only represented by one point (dot map) placed on the centroid of the Indonesian archipelago. Further research can fill the gap in downscaling the geographical location data of COVID-19 cases to the cities or even the village level in Indonesia and communicating the susceptible zoning to society. We uplift the point COVID-19 cases data to susceptible zoning gathered from official COVID-19 government websites, process it using Geographic Information System analysis, and communicate it to society through a Web-GIS dashboard. Five datasets, i.e., population data, administrative boundary, Landsat 8 OLI satellite imagery, COVID-19 cases geographic location, transportation infrastructure, and crowded places location, are used to analyze the susceptible area. Due to different standard data sources from each province in Indonesia, we only present provinces in Java Island with complete COVID-19 cases data on villages-scale. The technical challenges and future improvement in developing the national dashboard of Web-GIS-based susceptibility dashboard are also discussed. The dashboard information would further add some essential information for society to explore their zone status in adapting to the “New Normal” using the SICOVID-19 dashboard from their computers or gadgets during the pandemic crisis.

COVID-19; Population exposure; Susceptible area; Web-GIS dashboard


    World Health Organization (WHO) officially announced Severe Acute Respiratory Syndrome Corona Virus-2 (SARS-CoV-2) or COVID-19 as a pandemic on March 11, 2020. In response to this pandemic event, the health authority and many world health researchers made several web-based dashboards to monitor the spreading of COVID-19 in near-real-time (Dong et al., 2020; Morettini et al., 2020; Zhou et al., 2020; Wissel et al., 2020; Bernasconi & Grandi, 2021). Location-based data of confirmed cases, deaths, and recovered patients are urgently needed to accurately measure the extent and severity of COVID-19 and assess the response’s effectiveness (Franch-Pardo et al., 2020; Rosenkrantz et al., 2021). 
    During a pandemic event, the open geo-location COVID-19 database supplies information about the addition of new cases, the rate of death, and the recovery rate that understand the pandemic dynamics. It also provides reliable spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control (Zhao et al., 2020; Bogoch et al., 2020). Some research also shows that geographical location data of pandemic cases can play a role in communicating the risk of transmission and even in evaluating the policy to deal with the outbreak, especially when the data is in near-real-time (Fang et al., 2008; Tatem et al., 2012; Kraemer et al., 2020; Kamel-Boulos & Geraghty, 2020). A device tracking and managing the spread of COVID-19 also needs innovation and contribution from the engineering perspective (Berawi et al., 2020). Moreover, the combination of geographical information big data with other relevant data sets, such as transportation hubs and the area where people gather, will provide additional insight into the potential local transmission region (Zhou et al., 2020; Costa et al., 2020).
    A web-based map dashboard of world COVID-19 that was launched on January 22 by the Center for Systems Science and Engineering (CSSE) at The Johns Hopkins University illustrates rapid visualization of the confirmed case location of pandemic information (Dong et al., 2020). Unfortunately, it only shows Indonesian cases on the country centroid which is unavailing information for Indonesian. In early March, the Indonesia Institute of Science survey revealed that more than 60% of the respondent needed at least sub-district level infected case information, and 97% agreed that the government should expose 14 days of history mobilization of infected patients (Cahyadi, 2020). Considering patients’ privacy while revealing the spatial data can be manageable by aggregating it and anonymizing it in the right way to protect privacy would take some burden off.
    On the national level, the Indonesian government, through its National COVID-19 Task Force, develops a web-based COVID-19 dashboard with data visualization on the provincial level. While several local governments have been visualizing different administration boundary levels and formats, some local governments share detailed data and deliver interactive web mapping. However, some local governments also visualize the data on a static map. It is unfavorable information for a population that lives and travels inter-administrative boundaries. They must visit multiple websites to be well informed about their neighbourhood's susceptibility. One compact open web-based map dashboard that compiles the COVID-19 case on the village level is needed to supply the information. To address this issue, we develop a Web-GIS dashboard that integrates all of the COVID-19 case data from various provincial governments. To fill the gap of this issue, we develop a dashboard with sensitive zone information to inform the user directly from their smartphone using the geo-location application. 


The development of a Web-GIS dashboard that can provide detailed information on COVID-19 case data is urgent because of the very dynamic nature of the disease every day. The dynamic nature of COVID-19 requires the government to be able to take policy quickly and strategically. This research is the first study on a COVID-19 information system application that integrates a COVID-19 dataset with different standards and formats into one complete data. The development of SICOVID-19 Dashboard is able to find use of the different formats of COVID-19 statistics and population estimation model to deliver new information which, is the prone area that is not available in other COVID-19 dashboards. The SICOVID-19 dashboard seeks to present the distribution of village-based cases and model the estimated number of people who have the potential to be exposed to COVID-19. The result for the spatial pattern of the susceptibility shows that high susceptibility to COVID-19 is higher closer to the city center with high road density and vice versa. The access option of the SICOVID-19 dashboard through the web interface and mobile interface makes it easier for the public to find information about their region’s vulnerability to COVID-19. It is hoped that the data presented in the SICOVID-19 dashboard could provide a more in-depth insight into the threat of COVID-19 in Indonesia and as a reference for society to adapting the “New Normal” way of life. 


    This study was funded by the Consortium of Ministry of Research and Technology/National Research and Innovation Agency and Indonesia Endowment Fund for Education, Ministry of Finance, The Republic of Indonesia. We want to thank PT Infimap Geospatial Sistem for providing the People in Pixels data and undergraduate students from the Department of Geography, Faculty of Mathematics and Natural Science, Universitas Indonesia, for their work in updating the location of COVID-19 cases daily, ESRI Indonesia for providing us ArcGIS Online facility, and Badan Nasional Penanggulangan Bencana for reviewing our dashboard.


Aldila, D., Khosnaw, S.H.A., Safitri, E., Anwar, Y.R., Bakry, A.R.Q., Samiadji, B.M., Anugerah, D.A., Alfarizi, M.F.G.H., Ayulani, I.D., Salim, S.N., 2020. A Mathematical Study on the Spread Of COVID-19 Considering Social Distancing and Rapid Assessment: The Case of Jakarta, Indonesia. Chaos, Solitons & Fractals, Volume 139, p. 110042

Agrawal, S., Gupta, R.D., 2017. Web-GIS and its Architecture: A Review. Arabian Journal of Geoscience, Volume 10(23), pp. 1–13

Alirol, E., Getaz, L., Stoll, B., Chappuis, F., Loutan, L., 2011. Urbanization and Infectious Diseases in a Globalized World. The Lancet Infectious Diseases. Volume 11(2), pp 131–141

Ardiansyah, A., Hernina, R., Suseno, W., Zulkarnain, F., Yanidar, R., Rokhmatuloh, R., 2018. Percent of Building Density (PBD) of Urban Environment: A Multi-Index Approach-Based Study in DKI Jakarta Province. Indonesian Journal of Geography, Volume 50(2), pp. 154–161

Atkinson, P.J., Unwin, D.J., 2002. Density and Local Attribute Estimation of an Infectious Disease using Mapinfo. Computers and Geosciences, Volume 28(9), pp. 1095–1105

Berawi, M.A., 2020.  Empowering Healthcare, Economic, and Social Resilience during Global

Pandemic Covid-19. International Journal of Technology, Volume 11(3), pp. 436–439

Berawi, M.A., Suwartha, N., Kusrini, E., Yuwono, A.H., Harwahyu, R., Setiawan, E.A., Yatmo, Y.A., Atmodiwirjo, P., Zagloel, Y.T., Suryanegara, M., Putra, N., Budiyanto, M.A., Whulanza, Y., 2020. Tackling the COVID-19 Pandemic: Managing the Cause, Spread, and Impact. International Journal of Technology, Volume 11(2), pp. 209–214

Bernasconi, A., Grandi, S., 2021. A Conceptual Model for Geo-Online Exploratory Data Visualization: The Case of The COVID-19 Pandemic. Information, Volume 12(2), pp. 69–95

Bhatia, T.S., Singh, H., Litoria, P.K., Pateriya, B., 2019. GIS Based Dashboard Development using Operations Dashboard for ArcGIS. International Journal of Computer Science and Telecommunications, Volume 10(4), pp. 24–26

Bogoch, I.I., Watts, A., Thomas-Bachli, A., Huber, C., Kraemer, M.U.G., Khan, K., 2020. Potential for Global Spread of a Novel Coronavirus from China. Journal of Travel Medicine, Volume 27(2), pp. 1–3

Cahyadi, R., 2020. Temuan Survei Keterbukaan Informasi Pasien Positif COVID-19 (Findings of the Information Disclosure Survey of Positive COVID-19 Patients). Lembaga Ilmu Pengetahuan Indonesia. Available online at http://lipi.go.id/berita/temuan-survei--keterbukaan-informasi-pasien-positif-covid-19/21983, Accessed on May 5, 2020

Cai, Q., Rushton, G., Bhaduri, B., 2012. Validation Tests of an Improved Kernel Density Estimation Method for Identifying Disease Clusters. Journal of Geographical Systems, Volume 14(3), pp. 243–264

Chen, S., Yang, J., Yang, W., Wang, C., Bärnighausen, T., 2020. COVID-19 Control in China During Mass Population Movements at New Year. The Lancet, Volume 395(10266), pp. 764–766

Chong, K.C., Cheng, W., Zhao, S., Ling, F., Mohammad, K.N., Wang, M., Zee, B.C.Y, Wei, L., Xiong, X., Liu, H., Wang, J., Chen, E., 2020. Transmissibility of Coronavirus Disease 2019 (COVID-19) in Chinese Cities with Different Transmission Dynamics Of Imported Cases. PeerJ, Volume 8, pp. 1–15

Costa, J.P., Grobelnik, M., Fuart, F., Stopar, L., Epelde, G., Fischaber, S., Poliwoda, P., Rankin, D., Wallace, J., Black, M., Bond, R., Mulvenna, M., Weston, D., Carlin, P., Bilbao, R., Nikolic, G., Shi, X., De Moor, B., Pikkarainen, M., Pääkkönen, J., Staines, A., Connolly, R., Davis, P., 2020. Meaningful Big Data Integration for a Global COVID-19 Strategy. IEEE Computational Intelligence Magazine, Volume 15(4), pp. 51–61

Diouf, R., Sarr, E.N., Sall, O., Birregah, B., Bousso, M., Mbaye, S.N., 2019. Web Scraping: State-of-The-Art and Areas of Application. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 6040–6042

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.K., Warsilah, H., 2020. Review and Analysis of Current Responses to COVID-19 in Indonesia: Period of January to March 2020. Progress in Disaster Science, Volume 6, p. 100091

Dong, E., Du, H., Gardner, L., 2020. An Interactive Web-Based Dashboard to Track COVID-19 in Real Time. The Lancet Infectious Diseases, Volume 20(5), pp. 533–534

ESRI Indonesia, 2020. See How Universities Joins the Fight Against Covid-19. Available online at https://edu-smartcommunity.hub.arcgis.com/pages/education-program, Accessed on May 15, 2020

Fang, L.-Q., De-Vlas, S.J., Liang, S., Looman, C.W.N., Gong, P., Xu, B., Yan, L., Richardus, J.H., Cao, W.-C., 2008. Environmental Factors Contributing to the Spread of H5N1 Avian Influenza in Mainland China. PLoS ONE, Volume 3(5), p. e2268

Franch-Pardo, I., Napoletano, B.M., Rosete-Verge, F., Billa, L., 2020. Spatial Analysis and GIS in the Study of COVID-19. A Review. Science of the Total Environment, Volume 739, p. 140033

Gorbiano, M.I., 2020. Jokowi deploys TNI, police to enforce ‘new normal’. The Jakarta Post. Available online at https://www.thejakartapost.com/news/2020/05/26/jokowi-deploys-tni-police-to-enforce-new-normal.html, Accessed on May 29, 2020

Indonesian Medical Association, 2020. Pemetaan Zona Rawan COVID-19 (Mapping of COVID-19 Prone Zones). Available online at https://covid19.idionline.org/zona-rawan/, Accessed on May 15, 2020

Ivanka, N., 2020. Large-Scale Social Restrictions: What's Next? The Indonesian Journal of International Clinical Legal Education, Volume 2(2), pp. 201–214

Jarquin C, Arnold BF, Muñoz F, Lopez B, Cuéllar VM, Thornton A, Patel J, Reyes L, Roy SL, Bryan JP, McCracken JP, Colford JM., 2016. Population Density, Poor Sanitation, and Enteric Infections in Nueva Santa Rosa, Guatemala. Am J Trop Med Hyg. Volume 94(4):912-919.

Kamel-Boulos, M.N., Geraghty, E.M., 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. International Journal of Health Geographics, Volume 19(1), pp. 1–12

Kraemer, M. U. G., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D. M., Open COVID-19 Data Working Group, du Plessis, L., Faria, N.R., Li, R., Hanage, W. P., Brownstein, J. S., Layan, M., Vespignani, A., Tian, H., Dye, C., Pybus, O.G., Scarpino, S. V., 2020. The Effect of Human Mobility and Control Measures on the COVID-19 Epidemic in China. Science, Volume 368, pp. 493–497

Luan, H., Law, J., 2014. Web-GIS-Based Public Health Surveillance Systems: A Systematic Review. ISPRS International Journal of Geo-Information, Volume 3(2), pp. 481–506

Manessa, M.D.M., Kamil, R., Setiaji, S., Ningrum, I., Suseno, W., Rahmayanti, I., Zulkarnain, F., Ardiansyah, A., Lesminia, I., Tasdiq, R.H., Moe, I.R., 2020. A Spatial Time Series Forecasting for Mapping The Risk of COVID-19 Pandemic Over Bandung Metropolitan Area, West Java, Indonesia. In: Proceedings Earth Resources and Environmental Remote Sensing/GIS Applications XI; 115340P, SPIE Publisher, Volume 11534, pp. 137–148

Morettini, M., Sbrollini, A., Marcantoni, I., Burattini, L., 2020. COVID-19 in Italy: Dataset of the Italian Civil Protection Department. Data in Brief, Volume 30, p. 105526

Rosenkrantz, L., Schuurman, N., Bell., N., Amram, O., 2021. The Need for Giscience in Mapping COVID-19. Health & Place, Volume 67, p. 102389

Ruckthongsook, Warangkana & Tiwari, Chetan & Oppong, Joseph & Natesan, Prathiba. 2018. Evaluation of threshold selection methods for adaptive kernel density estimation in disease mapping. International Journal of Health Geographics. 17. 10.1186/s12942-018-0129-9.

Shim, E., Tariq, A., Choi, W., Lee, Y., Chowell, G., 2020. Transmission Potential and Severity of COVID-19 in South Korea. International Journal of Infectious Diseases, Volume 93, pp. 339–344

Singrodia, V., Mitra, A., Paul, S., 2019. A Review on Web Scrapping and Its Applications. In: 2019 International Conference on Computer Communication and Informatics (ICCCI), IEEE, pp. 1–6

Sohrabi, C., Alsafi, Z., O’Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Iosfidis, C., Agha, R., 2020. World Health Organization Declares Global Emergency: A Review Of The 2019 Novel Coronavirus (COVID-19). International Journal of Surgery, Volume 76, pp. 71–76

Tatem, A.J., Adamo, S., Bharti, N., Burgert, C. R., Castro, M., Dorelien, A., Fink, G., Linard, C., John, M., Montana, L. Montgomery, M. R., Nelson, A., Noor, A. M., Pindolia, D., Yetman, G., Balk, D., 2012. Mapping Populations at Risk: Improving Spatial Demographic Data for Infectious Disease Modeling and Metric Derivation. Population Health Metrics, Volume 10(8), pp. 1–14

Vlahov, D., Freudenberg, N., Proietti, F., Ompad, D., Quinn, A., Nandi, V., Galea, S., 2007. Urban as a Determinant of Health. Journal of Urban Health, Volume 84(1), pp. 16–26

Wibowo, A., Salleh, K.O., 2018. Land Cover Types and Their Effect on the Urban Heat Signature of University Campuses using Remote Sensing, International Journal of Technology, Volume 9(3), pp. 479–490

Wissel, B.D., Van-Camp, P. J., Kouril, M., Weis, C., Glauser, T. A., White, P. S., Kohane, I. S., Dexheimer, J. W., 2020.  An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time. Journal of the American Medical Informatics Association, Volume 27(7), pp. 1121–1125

Yang, Y., Shang, W., Rao, X., 2020. Facing the COVID?19 Outbreak: What Should We Know and What Could We Do? Journal of Medical Virology, Volume 92, pp. 536–537

Zhao, S., Lin, Q., Ran, J., Musa, S.S., Yang, G., Wang, W., Lou, Y., Gao, D., Yang, L., He, D., Wang, M.H., 2020. Preliminary Estimation of the Basic Reproduction Number of Novel Coronavirus (2019-Ncov) in China, from 2019 To 2020: A Data-Driven Analysis in The Early Phase of the Outbreak. International Journal of Infectious Diseases, Volume 92, pp. 214–217

Zhou, C., Su, F., Pei, T., Zhang, A., Du, Y., Luo, B., Cao, Z., Wang, J., Zhu, Y., Song, C., Chen, J., Xu, J., Li, F., Ma, T., Jiang, L., Jan, F., Yi, J., Hu, Y., Liao, Y., Xiao, H., 2020. COVID-19: Challenges to GIS with Big Data. Geography and Sustainability, Volume 1(1), pp. 77–87

Zulkarnain, F., Manessa, M. D. M., Suseno, W., Ardiansyah, A., Bakhtiar, R., Safaryanto, A.N., Widjaja, B. W., Rokhmatuloh, R., 2019. People in Pixels: Developing Remote Sensing-Based Geodemographic Estimation through Volunteered Geographic Information and Crowdsourcing. In: Remote Sensing Technologies and Applications in Urban Environments IV, Volume 11157, pp. 48–57