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Quantitative Assessment of Social Vulnerability For The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4, 2018 ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change”, 1–5 October 2018, Delft, The Netherlands QUANTITATIVE ASSESSMENT OF SOCIAL VULNERABILITY FOR LANDSLIDE DISASTER RISK REDUCTION USING GIS APPROACH (Case study: Cilacap Regency, Province of Central Java, Indonesia) Arwan Putra Wijaya 1, Jung-Hong Hong 1* 1 Dept. of Geomatics, National Cheng Kung University, Tainan, Taiwan – [email protected], [email protected] Commission IV, WG IV/3 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, 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. 1. INTRODUCTION 1.1 Background To support the analysis of social vulnerability, the following perspectives are considered in this paper. The first is the According to UNISDR (2017), vulnerability is the characteristics historical disaster events occurred in the region, as the the determined by physical, social, economic and environmental or number of casualties and economic loss may rise with the number processes which increase the susceptibility of an individual, a and scale of the disasters. The second is the land use and people's community, assets or systems to the impacts of hazards livelihood. If these two factors work well, the unemployment rate (Preventionweb team, 2017). Regions with high vulnerability can be effectively reduced. The final consideration is industry, as have lower capacity of resilience and are less likely to “bounce a solid and growing local industry can absorb a lot of manpower, back” after hazard strikes. To correctly use the vulnerability reduce the number of unemployed people, as well as reduce the assessment data, it is important to build a thorough understanding social vulnerability. More detailed discussion can be found in about the factors that have impacts on the analysed outcomes, section 3. such that area with higher level of vulnerability can be correctly identified and proper actions can be taken. 1.2 Study area Quantitative assessment helps to provide a reference to analyze Central Java, one of 34 provinces in Indonesia, ranks fifth in the vulnerability of individual administrative units, as well as the population density in the country, has a population density of spatial patterns for the whole region or even country. Various 1037 people / km2, after Yogyakarta which has a population models have been proposed over the years. In Indonesia, the density of 1155 people / km2. According to the Central Bureau of provisions on the determination of social vulnerability have been Statistics (BPS) data in 2017, the unemployment rate in Central included in the Regulation of the Head of National Agency for Java is 4.57%, while the average unemployment rate for the Disaster Management (BNPB) Number 2 of 2012. The social country is 5.13%. From the past history, Central Java is a region vulnerability is measured by several factors, including population under many types of hazard threats, such as floods, landslides, density, dependency ratio, sex ratio, disability ratio, and poverty volcanic eruptions, earthquakes, etc. Among them, floods and ratio. The social vulnerability model proposed by Frigerio, I., et landslides are the two most frequent types of disasters al (2016), on the other hand, considers factors of age, experienced by the people of Central Java. Landslides often occur employment, education, and anthropization. As the two models after heavy rains that last for several days. Flood occurs when the may generate different outcomes due to the different choices of facility and infrastructure cannot handle the accumulated rainfall. factors, the comparison of their results may further provide more According to the vulnerability of land movement zone map from in-depth knowledge about the quantitative assessment of social the Department of Energy and Mineral Resources (ESDM) of vulnerability. Central Java province in 2014, several areas, including Cilacap, * Corresponding author This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-4-703-2018 | © Authors 2018. CC BY 4.0 License. 703 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4, 2018 ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change”, 1–5 October 2018, Delft, The Netherlands Brebes, Banyumas, Kebumen, Purbalingga, and Wonosobo, are data warehouse of the 14th population and housing census that prone to the threat of the hazard because this region belongs to describes the demographic and social structure of the population the mountain area in Central Java. The mountains include Mount residing in Italy. Dieng, Mount Slamet, Mount Merbabu, and Mount Sindoro. Variables Indicators Impact on social According to Aquinus (2018) in the media TrubusNews, during vulnerability the year 2017 Central Java was experiencing landslides as much The rate of Age Increase as 1091 times. Then continue in 2018, several major landslide children <14 years events in Central Java have caused human causalties. For The rate of elderly example, five people were killed and dozens more injured in >65 years Brebes in February 2018. In Purbalingga, four people were killed Dependency ratio and a dozen missing around the same time. While the media Elderly index mainly focuses on the number of people killed or injured, how Female labor force those survived people deal with the after-disaster lives should employed Employment Decrease also receive equivalent attentions. For more details about the Labor force condition of mass movement vulnerability in Central Java can be employed seen in Figure 1. Unemployment Increase rate Commuting rate Index of high Education Decrease education Index of low Increase education Population density Anthropization Urbanized index Increase for residential use Crowding index Residential Quality residential property Decrease Ethnicity Foreign resident Increase Table 2. The indicators and variables of social vulnerability (Frigerio, I., et al, 2016) Figure 1. Map of mass movement prone zone in Central java © For easier analysis and comparison, the value of each indicator is Department of Energy and Mineral Resources (ESDM) of calculated following equation (1): Central Java province Copyright 2018 ( ) = (1) 2. METHODOLOGY Where, Zij = normalized value of different social vulnerable 2.1 Data source factors (j) in the certain assessed area (i); Xij = value of different social vulnerable factor (j) in The data used in this study is mainly collected from the Central the certain assessed area (i); Bureau of Statistics (BPS) of Central Java Province, supported Mj = average value of certain social vulnerable by data from the Regional Development Planning Agency factor (j); (BAPPEDA) and the Department of Energy and Mineral SDj = standard deviation of certain social vulnerable Resources (ESDM) in Central Java. The selected datasets are factor (j); listed in Table 1. i = different area of assessment j = different social vulnerable factors Data type Generate data Demographic Population density The calculated results are later used to compute SoVI following Dependency ratio equation (2): Sex ratio Healthy ∑ Disability ratio = (2) Poverty The poor ratio Education Education rate Where, Manpower Unemployment rate SoVIi = Social Vulnerability Index of the assessed area (i); Table 1. The kinds of data used in determining social Zij = normalized value of different social vulnerable vulnerability factors (j) in the certain assessed area (i); N = number of social vulnerable factors 2.2 Model of Frigerio, I., et al. (2016) j = different social vulnerable factors The research of Frigerio, I., et al. (2016) aimed to identify the spatial variability of social vulnerability for seismic hazard in According to the findings of Frigerio et al., not all of indicators Italy. The indicators proposed for measuring the social are used for assessing the social vulnerability because only four vulnerability are
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