International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 703–709, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-703-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 703–709, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-703-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Sep 2018

19 Sep 2018

QUANTITATIVE ASSESSMENT OF SOCIAL VULNERABILITY FOR LANDSLIDE DISASTER RISK REDUCTION USING GIS APPROACH (CASE STUDY: CILACAP REGENCY, PROVINCE OF CENTRAL JAVA, INDONESIA)

A. P. Wijaya and J.-H. Hong A. P. Wijaya and J.-H. Hong
  • Dept. of Geomatics, National Cheng Kung University, Tainan, Taiwan

Keywords: Quantitative assessment, Social vulnerability, Disaster risk reduction, Education, Unemployment

Abstract. Social vulnerability is an important aspect in determining the level of disaster risk in a region. Social vulnerability index (SoVI) is influenced by several supporting factors, such as age, gender, health, education, etc. When different sets of parameters are considered, the SoVI analyzed results are likely to be also different from one to another. In this paper, we will discuss the quantitative assessments of SoVI based on two different models. The first model, proposed by Frigerio et al. (2016), is used to analyze the spatial diversity of social vulnerability due to seismic hazards in Italy. The second model is based on the regulations of the head of the National Disaster Management Agency (BNPB) No. 2 of 2012. GIS is used to present and compare the results of the two selected models. In additive impact factor on the SoVI is also done. The result is that there are regions that belong to the same class on both models such as Pemalang, there are regions that enter in different classes on both models such as Cilacap. The result also shows the model of Frigerio et al. (2016) is more representative than the BNPB model (2012) by additionally considering the education and unemployment factors in determining the SoVI, while the BNPB model (2012) only includes internal factors such as age, gender. By considering education and unemployment factors, we get more detailed conditions about society from social vulnerability.