Representation of Spatial Data Modeling Results Measles Diseases, Case Study in East Java Province

Anik Vega Vitianingsih, Achmad Choiron, Dwi Cahyono, Suyanto Suyanto

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Background of the study: Measles is a major cause of child death caused by a lack of immunization when a child is a baby.

Purpose: The discussion in this paper aims to describe the results of the analysis of the spatial data modeling of measles, knowing the percentage distribution of measles-prone areas, each district based on coverage on immunization status with good, average, fair and poor classification categories. The classification results include areas with good, average, fair, and poor immunization coverage status categories.

Method: The method used i.e. with to requirement gathering information data from the East Java health profile book in 2011-2016 for the measles attribute, a literature study to describe the parameter requirements based on the coverage of immunization status (infant immunization status, PD3I, epidemic, and nutritional status), and selection of artificial intelligent (AI) system methods that are in accordance with data behavior for the spatial data modeling process in the formulation of alternative preference values with a decision-making system that involves multi-criteria parameters (multiple attributes decision-making/MADM) with Simple Additive Weighting (SAW) method.

Findings: The alternative preference value Vi in the spatial data modeling process with the SAW method can be used as a mathematical model for the same data series behavior.

Conclusion: The results of the representation in the modeling of spatial data and this attribute data can be used as a reference for planning in the development of health care centers in areas with poor immunization status categories.


spatial analysis, spatial data modeling, measles, MADM.

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A. S. Fotheringham, C. Brundson, and M. C. (2010). Qualitative Geography : Perspectives on Spatial Data Analysis. In The Sage handbook of qualitative geography.

Almberg, E. S., Cross, P. C., Johnson, C. J., Heisey, D. M., & Richards, B. J. (2011). Modeling routes of chronic wasting disease transmission: Environmental prion persistence promotes deer population decline and extinction. PLoS ONE, 6(5).

Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical Modeling and Analysis for Spatial Data.

Dinas Kesehatan Provinsi Jawa Timur. (2012). Profil Kesehatan Provinsi Jawa Timur Th 2011.

Dinas Kesehatan Provinsi Jawa Timur. (2013). Profil Kesehatan Provinsi Jawa Timur Tahun 2012. Dinas Kesehatan Provinsi Jawa Timur.

Dinas Kesehatan Provinsi Jawa Timur. (2015a). Profil Kesehatan Provinsi Jawa Timur 2014. Retrieved from

Dinas Kesehatan Provinsi Jawa Timur. (2015b). Profil Kesehatan Provinsi Jawa Timur 2015. Dinas Kesehatan Provinsi Jawa Timur.

Dinas Kesehatan Provinsi Jawa Timur. (2017). Profil Kesehatan Provinsi Jawa Timur Tahun 2016. In Profil Kesehatan Provinsi Jawa Timur Tahun 2012. Retrieved from

Fitzpatrick, G., Ward, M., Ennis, O., Johnson, H., Cotter, S., Carr, M. J., … Fitzgerald, M. (2012). Use of a geographic information system to map cases of measles in real-time during an outbreak in Dublin, Ireland, 2011. Eurosurveillance, 17(49), 1–11.

Gao, S. (2010). Advanced Health Information Sharing With Web-Based Gis. (272), 272.

Guttman, L. (1944). A Basis for Scaling Qualitative Data. American Sociological Review, 9(2), 139.

Hadeler, K. P. (2011). Parameter identification in epidemic models. Mathematical Biosciences, 229(2), 185–189.

Ingridara, N., & Garna, H. (2017). Hubungan Usia , Status Gizi , dan Status Imunisasi dengan Kejadian Campak pada Anak Usia 0 – 5 Tahun di Rumah Sakit Umum Daerah Al-Ihsan Periode Januari 2016 – Mei 2017 Relationship Between Age , Nutritional Status , and Immunization Status with the Incid. Bandung Meeting on Global Medicine & Health (BaMGMH), 1(1), 49–54.

Laohasiriwong, W., Puttanapong, N., & Singsalasang, A. (2018). Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis. Geospatial Health, 13(1), 20–27.

N. Bhart, A. Djibo, M. J. Ferrari, R. F.Grais, A. J. Tatem, C. A. Mccabe, O. N. Bjornstad, B. T. G. (2010). Measles hotspots and epidemiological connectivity. Epidemiology and Infection, 138(9), 1308–1316.

Parker, R. N., & Asencio, E. K. (2009). GIS and Spatial Analysis for the Social Sciences: Coding, Mapping and Modeling. Political Justice and Religious Values.

Patanè, G., & Spagnuolo, M. (2016). Heterogenous Spatial Data: Fusion, Modeling, and Analysis for GIS Applications. Synthesis Lectures on Visual Computing, 8(2), 1–155.

T.C. Bailey, A. C. G. (1995). Interactive Spatial Data Analysis. In Interactive Spatial Data Analysis.

Timur, D. K. P. J. (2014). Profil Kesehatan Provinsi Jawa Timur 2013. Dinas Kesehatan Provinsi Jawa Timur.

Ulugtekin, N., Alkoy, S., Seker, D., & Goksel, C. (2006). Use of GIS in epidemiology: A case study in Istanbul. Journal of Environmental Science and Health - Part A Toxic/Hazardous Substances and Environmental Engineering, 41(9), 2013–2026.

Vitianingsih, A., Choiron, A., Cahyono, D., Umam, A., & Suyanto, S. (2019). Spatial Data Modeling on GIS for Classification of Measles-prone Region Using Multiple Attribute Decision Making. International Journal of Intelligent Engineering and Systems, 12(3), 97–107.

Vitianingsih, A. V., Cahyono, D., & Choiron, A. (2017). Analysis and design of web-geographic information system for tropical diseases-prone areas: A case study of East Java Province, Indonesia. 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), 255–260.

World Health Organization. (2017). Global Measles and Rubella Update June 2018. World Health Organization, (April), 6. Retrieved from

Zhu, B., Fu, Y., Liu, J., & Mao, Y. (2018). Spatial distribution of 12 class B notifiable infectious diseases in China: A retrospective study. PLoS ONE, 13(4), 1–17.

Zhu, Y., Xu, Q., Lin, H., Yue, D., Song, L., Wang, C., … Li, X. (2013). Spatiotemporal analysis of infant measles using population attributable risk in Shandong Province, 1999-2008. PLoS ONE, 8(11).


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