Measuring and Profiling Social Vulnerability to Natural Disaster in Indonesia in 2019

Yuliagnis Transver Wijaya; Ian Tryaldi  Halim

doi:10.20956/j.v19i1.21686

Authors

Yuliagnis Transver Wijaya Politeknik Statistika STIS
Ian Tryaldi Halim BPS Kabupaten Jeneponto

DOI:

https://doi.org/10.20956/j.v19i1.21686

Keywords:

Indonesia, K-Means Clustering, Natural Disasters, Social Vulnerability, SoVI

Abstract

Nowadays, natural hazards are often seen from the nature perspective only. However, it is necessary to know not only about the hazards, but also the community resilience to prepare for, respond to, and recover from disasters based on the social characteristics which are called social vulnerability. This study provides the identification of social vulnerability to natural hazards condition and characterization of the dominant factors at the district level in Indonesia using secondary data. The principal component analysis (PCA) is used to reduce 13 district-level variables into 4 components that represents the driving factors of social vulnerability. The results of PCA are used to quantify the social vulnerability level of the districts in Indonesia using social vulnerability index (SoVI), followed by the deeper exploration of social vulnerability problem using K-Means Clustering. The SoVI and cluster results were mapped by using QGIS to identify the social vulnerability at districts level. The research shows that most districts in Indonesia are at a low-level vulnerability. The districts with low vulnerability are spread in the Sumatera and Kalimantan area. However, there are 43 Districts in Eastern Indonesia are in a high-level vulnerability. These districts also suffer many problems, such low sosioeconomic status. The results of this study support not only the previous social vulnerability studies but also the government as the policymakers by setting priority regions and allocating the policies according to main social vulnerability problem of each district, especially in the most vulnerable regions.

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References

Birkmann, J., Kienberger, S., & Alexander, D. E., 2014. Introduction Vulnerability: A key determinant of risk and its importance for risk management and sustainability. In Assessment of Vulnerability to Natural Hazards: A European Perspective. Elsevier Inc. https://doi.org/10.1016/B978-0-12-410528-7.02001-4

Borden, K. A., Schmidtlein, M. C., Emrich, C. T., Piegorsch, W. W., & Cutter, S. L., 2007. Vulnerability of U.S. cities to environmental hazards. Journal of Homeland Security and Emergency Management, 4(2). https://doi.org/10.2202/1547-7355.1279

Burton, C. G., Rufat, S., & Tate, E., 2018. Social {Vulnerability}: {Conceptual} {Foundations} and {Geospatial} {Modeling}. Cambridge University Press, December, 37.

Chen, W., Cutter, S. L., Emrich, C. T., & Shi, P., 2013. Measuring social vulnerability to natural hazards in the Yangtze River Delta region, China. International Journal of Disaster Risk Science, 4(4), 169–181. https://doi.org/10.1007/s13753-013-0018-6

Cutter, S. L., 1996. Vulnerability to environmental hazards. Progress in Human Geography, 20(4), 529–539. https://doi.org/10.1177/030913259602000407

Cutter, S. L., Boruff, B. J., & Shirley, W. L., 2003. Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261. https://doi.org/10.1111/1540-6237.8402002

de Loyola Hummell, B. M., Cutter, S. L., & Emrich, C. T., 2016. Social Vulnerability to Natural Hazards in Brazil. International Journal of Disaster Risk Science, 7(2), 111–122. https://doi.org/10.1007/s13753-016-0090-9

Dintwa, K. F., Letamo, G., & Navaneetham, K., 2019. Measuring social vulnerability to natural hazards at the district level in Botswana. Jamba: Journal of Disaster Risk Studies, 11(1), 1–11. https://doi.org/10.4102/JAMBA.V11I1.447

Dwyer, A., Zoppou, C., & Nielsen, O., 2004. Indicator selection. In Quantifying social vulnerability: a methodology for identifying those at risk to natural hazards. http://www.ga.gov.au/webtemp/image_cache/GA4267.pdf

Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B., 2020. A Social Vulnerability Index for Disaster Management. Journal of Homeland Security and Emergency Management, 8(1). https://doi.org/10.2202/1547-7355.1792

Flanagan, B. E., Hallisey, E. J., Adams, E., & Lavery, A., 2018. Measuring community vulnerability to natural and anthropogenic hazards: The Centers for Disease Control and Prevention’s social vulnerability index. Journal of Environmental Health, 80(10), 34–36.

Guillard-Goncąlves, C., Cutter, S. L., Emrich, C. T., & Zêzere, J. L., 2015. Application of Social Vulnerability Index (SoVI) and delineation of natural risk zones in Greater Lisbon, Portugal. Journal of Risk Research, 18(5), 651–674. https://doi.org/10.1080/13669877.2014.910689

Kaiser, H. F., & Rice, J., 1974. Little Jiffy, Mark Iv. Educational and Psychological Measurement, 34(1), 111–117. https://doi.org/10.1177/001316447403400115

Lin, W. Y., & Hung, C. T., 2016. Applying spatial clustering analysis to a township-level social vulnerability assessment in Taiwan. Geomatics, Natural Hazards and Risk, 7(5), 1659–1676. https://doi.org/10.1080/19475705.2015.1084542

Maharani, Y. N., Lee, S., & Ki, S. J., 2016. Social vulnerability at a local level around the Merapi volcano. International Journal of Disaster Risk Reduction, 20(October), 63–77. https://doi.org/10.1016/j.ijdrr.2016.10.012

Rabby, Y. W., Hossain, M. B., & Hasan, M. U., 2019. Social vulnerability in the coastal region of Bangladesh: An investigation of social vulnerability index and scalar change effects. International Journal of Disaster Risk Reduction, 41, 101329. https://doi.org/10.1016/j.ijdrr.2019.101329

Roncancio, D. J., Cutter, S. L., & Nardocci, A. C., 2020. Social vulnerability in Colombia. International Journal of Disaster Risk Reduction, 50(March), 101872. https://doi.org/10.1016/j.ijdrr.2020.101872

Siagian, T. H., Purhadi, P., Suhartono, S., & Ritonga, H., 2014. Social vulnerability to natural hazards in Indonesia: Driving factors and policy implications. Natural Hazards, 70(2), 1603–1617. https://doi.org/10.1007/s11069-013-0888-3

Singh, S. R., Eghdami, M. R., & Singh, S., 2014. The Concept of Social Vulnerability : A Review from Disasters Perspectives. International Journal of Interdisciplinary and Multidisciplinary Studies, 1(6), 71–82. http://www.ijims.com

Talakua, M. W., Leleury, Z. A., & Talluta, A. W., 2017. Acluster Analysis By Using K-Means Method for Grouping of District/City in Maluku Province Industrial Based on Indicators of Maluku Development Index in 2014. Barekeng : Jurnal Ilmu Matematika Dan Terapan, 11(2), 119–128.

Tate, E., 2013. Uncertainty Analysis for a Social Vulnerability Index. Annals of the Association of American Geographers, 103(3), 526–543. https://doi.org/10.1080/00045608.2012.700616

Wiley, A. J., 2003. An Introduction to Multivariate Statistical Analysis Third Edition. In Data Handling in Science and Technology (Vol. 2, Issue C). https://doi.org/10.1016/S0922-3487(08)70234-X

Wisner, B., Blaikie, P., Cannon, T., & Davis, I., 1994. At Risk. In At Risk (Issue January). https://doi.org/10.4324/9780203428764

Wood, N. J., Burton, C. G., & Cutter, S. L., 2010. Community variations in social vulnerability to Cascadia-related tsunamis in the U.S. Pacific Northwest. Natural Hazards, 52(2), 369–389. https://doi.org/10.1007/s11069-009-9376-1

Zhou, Y., Li, N., Wu, W., & Wu, J., 2014. Assessment of provincial social vulnerability to natural disasters in China. Natural Hazards, 71(3), 2165–2186. https://doi.org/10.1007/s11069-013-1003-5