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, I., 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, I., 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.
| Original language | English |
|---|---|
| Pages (from-to) | 77-85 |
| Number of pages | 9 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 42 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2018 Sep 19 |
| Event | ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change - Delft, Netherlands Duration: 2018 Oct 1 → 2018 Oct 5 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Geography, Planning and Development
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Quantitative assessment of social vulnerability for landslide disaster risk reduction using gis approach (case study : Cilacap regency, province of central Java, Indonesia). / Wijaya, Arwan Putra; Hong, Jung-Hong.
In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Vol. 42, No. 4, 19.09.2018, p. 77-85.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Quantitative assessment of social vulnerability for landslide disaster risk reduction using gis approach (case study
T2 - Cilacap regency, province of central Java, Indonesia)
AU - Wijaya, Arwan Putra
AU - Hong, Jung-Hong
PY - 2018/9/19
Y1 - 2018/9/19
N2 - 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, I., 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, I., 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.
AB - 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, I., 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, I., 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85056190203&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLII-4-703-2018
DO - 10.5194/isprs-archives-XLII-4-703-2018
M3 - Conference article
VL - 42
SP - 77
EP - 85
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - 4
ER -