Assessment of Regional Disaster Resilience by using Social Vulnerability Index
A paper for International Conference on Regional Development, Environment and Infrastructures
Institute of Technology Bandung (ITB), Bandung, Indonesia, June 18 – 19, 2009Assessment of Regional Disaster Resilience by using Social
Vulnerability Index Authors: Pungky Utami (MSc Alumni, University of Bristol, e-mail: [email protected] ) Saut Sagala (PhD Student, Kyoto University, e-mail: [email protected] ) Aria Mariany (Centre for Disaster Mitigation, ITB, e-mail: [email protected] ) AbstractThe occurrence of recent natural disasters in Indonesia highlights the needs to achieve regional disaster resilience. Regional disaster resilience measures how quick and strong a region or a system can recover to its normal situation prior to occurrence of disasters. An immediate recovery time implies higher resilience and at the same time represents lower vulnerability. In addition, recovery also needs to address critical infrastructures involved in re-building a region after a disaster. However, within a region not all sub-regions have the same capability to deal with and to recover from disaster impacts. Such differences could be due to different level of vulnerability and existence of preparedness, resources and facilities to mitigate. To assess the differences of disaster resilience level within a region, this paper proposes the use of social vulnerability index (SoVI). Taking four regencies affected by Mt. Merapi as the study area (Sleman, Magelang, Boyolali and Klaten regencies), this study argues the importance of SoVI in determining regional preparedness. Having a SoVI, ones can prioritize what sort of preparedness activities that can be carried out prior to an occurrence of a disaster. The advantages of SoVI approach are due to the extensive use of complex indicators, quantitative measurements and transformability into spatial use. Thus, SoVI is highly relevant with regional development and thus needs to be taken into account for a disaster prone region. In this study we performed a number of dynamic indicators used to develop a SoVI. The final results illustrate and map various levels of social vulnerability index among the sub-regions. Recommendation related with methodology and improvement of regional disaster resilience is presented in this paper.
Keywords: disaster resilience; Merapi; region; social vulnerability index
1. Introduction
Indonesia is known as one of the countries where various types of natural hazards often collide with vulnerable situation bringing both localized and regionwide recurring disasters. With respect to the resilience of the country against natural disasters, it is important to understand the capacities of the regions to cope with the disaster and external disturbances (Adger et al., 2005). Ronan and Johnston pointed out that “resilience is linked to how well a community (a city or a region) can bounce back after a major disaster”. Thus, the concept of resilience is often linked with the concept of vulnerability on the one hand and preparedness or capacity to recover on the other hand (Klein et al., 2003).
The current rapid demographical and social change such as population growth and economic instability may aggravate the people’s susceptibility to the impacts of disasters, resulting in possible greater losses of lives and properties. With regards to disaster research in general, Cutter et al (2003) emphasized that although substantial efforts have been put into the complex work of characterising and mapping of physical vulnerability, it appears that the social aspects of vulnerability as a component of potential risk have not been much explored yet in as much detail. This paper examines the regional disaster resilience based on the social vulnerability index which was developed by Cutter et al (2003). To provide a brief discussion on social vulnerability we first discuss the literature on this issue. Furthermore, we discuss the case of Mt. Merapi where the concept of social vulnerability is applied.
2. Concept of Social Vulnerability Index and Regional Disaster Resilience
Due to difficulties in quantifying the social vulnerability, Cutter et al (2003) proposed the construction of Social Vulnerability Index (SoVI) as a basis for local officials, emergency managers, and planners to add to their action plan on disaster response in order to allocate the necessary resources in the events of disasters to the right targets at the right location (Cutter et al., 2003; UCHSC, 2004; Boruff et al., 2005; Boruff and Cutter, 2007). To obtain a better understanding on the importance of social vulnerability in a wider perspective, this study refers to Cutter (1996) who introduced the hazards-of-place concept. The term “place vulnerability” involves the physical vulnerability and the characteristics of the people and places that make them less resistant and resilient toward the disaster impacts (Cutter et al., 2000; Cutter and Emrich, 2006). The model of hazards-of-place concept is presented in Figure 1.
Figure 1. The hazards-of-place model of vulnerability (Cutter, 1996) According to the model, the place vulnerability is “the interaction of both social and biophysical vulnerability“ and it may “reduce or enhance both risk and mitigation” (Cutter, 1996, p.533). Thus, this study adopts the term “place vulnerability” in assessing the relationship between the social vulnerability of Merapi proximal community and the biophysical vulnerability, represented by the volcanic hazard map. However, due to the absence of other social vulnerability measures, such as capacity and mitigation initiatives, cultural aspect, and community perception, as well as the smooth gradient in the present volcanic hazard zonation, the vulnerability of neighbouring communities measured in this study do not present absolute values and therefore, is termed as “relative vulnerability.” The term social vulnerability represents partly “social inequalities” and partly “place inequalities” resulting from the characteristics of communities and the built environment (Cutter et al., 2003). Social vulnerability may include (but is not limited to) the socioeconomic status of individuals or groups of people, demographic characteristics, perception and attitude towards hazards and risks, social networks, access to capital and resources, physically weak individuals, cultural beliefs, access to basic infrastructure services, and access to political power and representation (Cutter et al., 2003; Wisner et al., 2004).
Cutter et al (2003) synthesized forty-two socioeconomic and built environment variables as observed through research literature and took the county level in the United States as their unit of analysis. The variables were then reduced, by using principal component analysis, to eleven independent factors that represented the social vulnerability of the people explaining 76.4% of the variance among all counties. It is in the context of both social inequalities and place inequalities where the regional disaster resilience lies. In this framework, disasters should not only be seen with more emphasis on the occurrence of a hazard (physical vulnerability) but also how prepared or resourceful a region or a sub-region may cope with. Therefore, in a regional disaster resilience concept, it is important to measure the preparedness of the sub-regions within a region in dealing with (natural) disasters. To measure this we refer to the concept of social vulnerability index proposed by Cutter et al (2003) as this index has capacity to differentiate and provide a comparison among areas in relation to natural disaster issues.
3. Study area
Merapi volcano is taken as the case study in this research, which is regarded as one of the most active and densely populated volcanoes in Indonesia. It lies approximately 30 km north from Yogyakarta, a city with a population of 442,209 in 2006 (BKPM, 2006). Merapi volcano administratively lies in four different regencies, they are: Sleman regency in Yogyakarta province, Klaten regency, Boyolali regency, and Magelang regency in Central Java province. A number of sub-districts are more prone than others to the threat of pyroclastic flows posed by Merapi eruption. However, the people living within these areas have benefited from the fertile soils for agriculture, sand for quarrying, stone for masons and water reservoirs for daily needs (The Panos Institute, 2002). According to 2002 Merapi hazard map, fifty-five village districts that are within the hazard zones of pyroclastic flows are taken as care study for this research.
4. Methodology
Initially, the analysis used indicators and variables that have been proposed in previous studies on social vulnerability [Cutter, Boruff, and Shirley (2003); Boruff and Cutter (2007); and others] After observing the spurious correlation and examining its relevance to the case study, there were 14 socioeconomic variables (Table 1) available for further analysis, collected from Village Potential Statistics (PODES) 2006 and primary survey of local volcanological agency in 2007.
The unit analysis in this study was at village level. Therefore all indicators in this research were then calculated at the village level. To obtain the SoVI, two steps of analyses were carried out. The first step was variable reduction and then followed by the calculation of social and place vulnerability index as explained in the following section.
4.1 Variable reduction
The analysis uses Principle Component Analysis (PCA) to reduce correlated variables into several uncorrelated appropriate components using varimax rotation and the eigenvalues greater than 1. Five components with eigenvalues greater than 1 were extracted, then used to measure the social vulnerability of each district. Furthermore, the resulting components are examined on what they broadly present and how the may influence social vulnerability.
Table 1 Socioeconomic variables and its vulnerability concept Label Socioeconomic variable Concept of vulnerability PRCTFEMALE Percentage of females Gender PRCTFARMHH Percentage of farming households Livelihood POPFARMLBR Number of population working as farm labour Livelihood PRCTFARMLANDS Percentage of farmlands Livelihood SMALLINDSTRY Number of home/small-scale industries Livelihood
Number of health facilities per 1,000 of HEALTHFAC population Health services PRCTHHHEALTHS Percentage of poor households receiving free ERV health care services Health services PRCTPOORHH Percentage of poor households Socioeconomic status PERMHOUSE Number of permanent houses Socioeconomic status
Percentage of households with electric
PRCTHHELECT lighting Development/infrastructure
PRCTDISABLED Percentage of disabled people Disabilities AVRGPERHH Average number of persons per household Family structureILLITERATE Number of illiterate people Education POPDNSTY Population density Population growth Source: Adapted from Boruff and Cutter (2007)
4.2 Calculation of social and place vulnerability index
50 Mranggen
0.77
10 Ngargomulyo -0.82 29 Sumber
0.09
47 Sewukan
0.81
11 Samiran -0.76 30 Sudimoro 0.1
48 Mangunsoko
0.82
12 Ketep -0.7 31 Kendalsari 0.14
49 Suroteleng 0.83
13 Kaliurang -0.67 32 Glagah Harjo
0.24
1.15
9 Sidorejo -0.87
14 Wonolelo -0.55 33 Merdiko Rejo 0.26
51 Mriyan
1.16
15 Kamongan -0.53 34 Ngargosoko 0.26
52 Polengan 1.6
16 Purwo Binangun -0.35 35 Lencoh
0.28
53 Srumbung 1.94
17 Ngablak -0.35 36 Candi Binangun 0.29 54 Cluntang 2.18
18 Pandanretno -0.3 37 Krogowanan
0.34
55 Tegalrandu 2.47
19 Balerante -0.24
28 Pucanganom 0.06 46 Wates
Following are the derived components which are used to construct the social vulnerability index for each village districts.
Table 2 Component score and loadings used to construct the social vulnerability index Component Scaling method Percentage explained variance Component 1: Access to services and welfare Inverse
0.47
17.75 Component 2: Livelihood Absolute
11.4 Component 3: Socioeconomic dependence None
10.62 Component 4: Health services and special needs population None
9.52 Component 5: Population growth None
8.86 Source: Adapted from Boruff et al (2005)
The components’ score needs rescaling in order to ensure that negative values representing low social vulnerability and vice versa. Upon rescaling the components’ values, the total components’ score is summed to create the Social Vulnerability Index (SoVI) score. The score is classified into five levels ranging from the value < -1.5 indicating low social vulnerability to the value > +1.5 indicating high social vulnerability.
Table 3 Ranking of SoVI in Merapi proximal village districts No Village Name SV
Score No Village Name SV
Score No Village Name SV
Score
1 Hargo Binangun -2.54 20 Nglumut -0.18
38 Dukun
2 Kemiren -1.97
8 Umbul Harjo -0.96
21 Krinjing -0.16 39 Kalibening 0.48
3 Kepuh Harjo -1.95
22 Sawangan -0.14 40 Tlogolele 0.53
4 Wukir Sari -1.77
23 Wonodoyo -0.12 41 Keningar 0.54
5 Wono Kerto -1.68
24 Jrakah -0.05 42 Sengi
0.56
6 Paten -1.31
25 Panggang -0.04 43 Banyudono
0.62
7 Giri Kerto -1.27
26 Klakah -0.03 44 Kapuhan 0.66
27 Tegalmulyo -0.02 45 Ngadipuro 0.67 The inspection of the raw data of each village district is made in order to verify the result and it is found that most socially vulnerable village districts have substantial differences in the statistical data compared to other village districts. The highly socially vulnerable village districts in general have significant figures on population density, percentage of farming households, percentage of poor households, and average number of persons per household. However, it is essential to note that although percentage of disabled people is the second most important contributor to social vulnerability, the raw data show that there is little variation among village districts, with none of them demonstrates extreme figures. Upon the construction of social vulnerability index, we later developed the place vulnerability index through the multiplication of the social vulnerability score with the weighted average of each hazard zone and the portion of the overlapped areas. Each hazard zone is given a value from 1 to 4, assigned to non-hazard zone, hazard zone I, hazard zone
II, and hazard zone III, respectively. The hazard zone is weighted from 1 to 4 respectively indicating higher hazard values while the portion of the overlapped areas is derived from the affected areas per total area. The place vulnerability score is normalized to avoid zero value vulnerability by adding each village district’s social vulnerability score with the absolute value of the minimum score minus 1. Therefore, the minimum total vulnerability score will be +1 ranging to the upper end. Place vulnerability was calculated based on the relationship between normalized social vulnerability index with the portion of village district and the values of hazard zone (Eq. 1). j = n
PVIndex = NSoVI a h i i j j ∑ j = 1
(Eq.1) where: PVIi = place vulnerability index of village district i NSoVIi = normalized social vulnerability index of village district i n = number of hazard zone category (including non-hazard zone)
≤ a ≤ j
1 aj = portion of village district i lies within hazard zone j ( ) hj = hazard value of hazard zone j The map shows that the vast majority of village districts lie within Merapi hazard zones II and III show extremely low to average levels of social vulnerability representing 70.9% of fifty-five village districts in total. The most socially vulnerable population residing in four village districts represent 7.3 % of the total that are randomly distributed.
Figure 2. Relative social vulnerability index of Merapi proximal village districts (Utami, 2009)
In order to identify the location of socially vulnerable population within the zones prone to pyroclastic flow hazard, an overlay technique for superimposing the social vulnerability map and volcanic hazard map was used (Figure 2). To appropriately present the place vulnerability score based on each administrative unit, the vulnerability score for each hazard zone in one village district is added to produce the total score. This was done as one village district might be located in two or more hazard zones. To revisit the idea of place vulnerability used in this study, it is merely the combination of social vulnerability and the physical vulnerability. Using the equal interval to classify the place vulnerability score, five classes of the place vulnerability score ranges from 1.92 at lower end to 18.21 at upper end were created (Table 4).
Table 4. Ranking of place vulnerability in Merapi proximal village districts No Village
12 Kepuh Harjo 4.99 31 Banyudono 7.23
13.56
50 Sengi
7.46
13 Jrakah 5.06 32 Pandanretno
13.52
49 Krinjing
12.98
51 Mangunsoko 14.58
48 Ngargosoko
30 Purwo Binangun 7.12
11 Merdiko Rejo 4.84
47 Kalibening 12.62
10 Kendalsari 4.78 29 Suroteleng 6.98
12.33
46 Wates
14 Kamongan 5.34 33 Mranggen 7.79
15 Mriyan 5.37 34 Nglumut
9 Ngadipuro 4.48
36 Srumbung 9.35
vulnerability score of the village district within each hazard zone. From the visualisation for every hazard zone, it is obvious that there is not much difference between the place vulnerability and the social vulnerability map due to lack of spatial details in the volcanic hazard map. For example, in panel III the majority of the village districts have average score of place vulnerability despite their adjacent distance to the summit of the volcano. In Panel II, there are more vulnerable areas although the risk is lower than in the summit. In addition, the visualisation of place vulnerability in Panel I is different from the other two zones as this study does not include the entire areas within hazard zone I. In general, despite the effort of assigning a weight to each hazard zone when calculating the place vulnerability index, it is apparent that the smooth gradient of hazard zonation fails to represent a definite measure of the place vulnerability.
5.73 The place vulnerability map (Figure 3) shows the comparison of the relative place
19 Lencoh
55 Tegalrandu 18.21
9.92
18 Kemiren 5.53 37 Klakah
54 Keningar 16.32
17 Candi Binangun 5.52
8.84
14.81
53 Sewukan
8.92
35 Paten
16 Panggang 5.4
14.66
52 Tlogolele
28 Kapuhan 6.98
11.88
Name PV Score No Village
39 Dukun
4 Ketep
40 Cluntang 10.47
5.92
22 Pucanganom
3.44
3 Hargo Binangun
10.26
21 Wonodoyo 5.8
23 Giri Kerto 6.04
2 Wonolelo 3.03
38 Kaliurang 9.99
5.76
20 Krogowanan
1 Wukir Sari 1.92
Score
Name PV Score No Village Name PV
3.74
41 Ngargomulyo
45 Sumber
43 Ngablak
6.9
27 Umbul Harjo
8 Sudimoro 4.16
44 Balerante 11.77
26 Sidorejo 6.51
7 Samiran 4.01
11.24
6.27
10.85
25 Tegalmulyo
3.93
6 Wono Kerto
11
42 Glagah Harjo
24 Polengan 6.21
5 Sawangan 3.85
However, the present research confirms that the integration of physical and social attributes in vulnerability assessment produce “spatial differences” and influence the assessment of place vulnerability (Boruff et al., 2005, p.939), regardless of the flat gradient of the place vulnerability map produced in this study.
Figure 3. Place vulnerability of each hazard zone of Merapi volcano (Utami, 2009)
5. Result
The findings show that the vast majority of village districts lie within Merapi hazard zones
II and III show extremely low to average levels of relative social vulnerability representing 70.9% of fifty-five village districts in total. About 29.1% have average to extremely high level of relative social vulnerability are randomly distributed around the flanks, but mostly is leading toward the southwestern part of the volcano.
Four village districts, i.e. Polengan, Srumbung, Cluntang, and Tegalrandu are more socially vulnerable compared to other cases. The underlying reasons for each village district are different, but the combination of several variables has made them socially vulnerable. Polengan, Srumbung, and Tegalrandu village districts are located in the southwestern part of Merapi flanks, while Cluntang is located separately on the eastern flank. The least socially vulnerable village districts are evenly distributed on the western flanks and the rest of the village districts with moderate scores are geographically widespread. It is imperative to note that the social vulnerability scores of the village districts derived from the present study are relative to the situation where current data are used. Moreover, this term is used given the lack of baseline data contributing to social vulnerability, such as perception to volcanic hazards and hazard knowledge of the neighbouring communities available for this study. Based on the rank of the scores, Tegalrandu village district is identified as the most socially and physically vulnerable, followed by Keningar. Geographically, Tegalrandu is located within the first danger zone while Keningar is located within the forbidden zone. In addition, approximately twelve out of fifty-five village districts are considered socially and physically vulnerable and the distribution of relative place vulnerability scores in all village districts appears to follow similar pattern of the hazard zonation, particularly for the pyroclastic flow hazard, toward the southwestern flanks. Three socially vulnerable village districts, i.e. Cluntang, Glagah Harjo, and Balerante are exceptionally separated from the other areas.
6. Discussion
The method developed by Cutter et al (2003) offers consistency and simplicity in measuring social vulnerability. Derived from widely used vulnerability indicators in most hazards literature and have been tested within multi-hazards perspective in the United States and the Caribbean, this method provides robust indicators representing socioeconomic characteristics of a vulnerable population. In our case in Mt. Merapi, this approach also seems to be capable in measuring the social vulnerability index. This is supported by the availability of statistical data used to construct the social vulnerability index. It is also shown by the results of maps of relative social vulnerability and place vulnerability.
However, referring to the application in the present study, there are some limitations to this method. First, there is a mix of conflicting component loadings which cannot be solved by only observing the sign of the loadings in the rescaling technique. Therefore, to solve the problem that emerged in this study, instead of manipulating the sign of loadings, an attempt to investigate the nature of variables in each component has been made to decide whether the variables indicate high or low social vulnerability. To verify the interpretation, inspection of the raw data to examine the relation between the value of each variable and its component score is necessary. The finding confirms that this method works well in ensuring that positive values indicating high social vulnerability and vice versa, as suggested by the original method. Second, the small number of cases has created few difficulties in processing the dataset since PCA requires large sample size. Earlier, the unit of analysis in this study was the sub-districts but due to a sample size problem (only eleven sub-districts), the cases were reduced to the village level, resulting in a greater number of cases (fifty-five village districts taken as the cases). In fact, the same method has been applied with a limited number of cases (below fifteen) in the case of Caribbean nations (Boruff and Cutter, 2007) but no further problems appeared (Bryan Boruff, email communication) which is confusing as far as the PCA calculation is concerned. It appears that the results are more statistically reliable when based on a large number of cases, which also influence the quality of the mapping and the precision of the index calculation.
The interesting part of the finding is that the socially vulnerable people do not necessarily reside within hazard zones II or III, taking the example of Cluntang village district, where only small parts of the area are within these hazard zones. This finding suggests that hazard is not the only (main) aspect in determining the vulnerability and resilience of a region. Other factors (existence of resources and other capacities) may be more important in the final result of social vulnerability index. This finding is similar to the ideas by Wisner et al (2004) in which the vulnerability and coping capacities of the communities that matter a lot. However, there is a possibility that the socially vulnerable population live within those two hazard zones. This explains why the areas within hazard zone III, for example, have relatively low to average place vulnerability score although their physical locations are considered very vulnerable. Due to the fact that there is a lack of spatial hazard gradient in the current volcanic hazard map, the place vulnerability map tends to have slightly different variability to the social vulnerability map. Although none of the village districts has an extreme value in both social and physical vulnerability, the result of this study confirms that the inclusion of social aspects of vulnerability incorporated with physical aspects produces spatial variability in the distribution of place vulnerability. This could later serve as a basis to augment the vulnerability assessment framework which may lead toward a more accurate and reliable vulnerability assessment. Overall, the main positives of the present research and few ideas on how the results of the study can be utilised include the following: 1) the creation of an index using village-level statistical data encourages the small area statistics-based vulnerability research, where in Indonesia, are now increasing in demand but still few in terms of numbers, as shown by the absence of the small area statistics data in Indonesia for certain themes on disaster-related variables; 2) the result of this research demonstrates the vital needs for the revision of volcanic hazard zonation taking into account that the vulnerability is dynamic and may change over time, therefore the hazard zonation needs to be adjusted according to the current condition; 3) the creation of a vulnerability index, with further refinement on the variables, may be useful in carrying out a rapid assessment, particularly in the study area, should a volcanic eruption occurs. Based on the previous experiences in Indonesia in recent years, following the occurrence of a disaster, a rapid reconnaissance study to assess the needs of the affected communities is held by the local government officials or international organisations. In this case, the index may be useful as a preliminary guidance for the assessment, thus the assessment team can quickly identify which areas that need to be prioritised and what kind of assistance should be provided before going further to the field for a baseline survey. Last but not least, the maps, with further refinement, may be used to identify the priority for distribution of resources and can be considered in the local planning process, either for land use plan or for sector plan; determine post-disaster actions, such as the establishment of rehabilitation and relocation sites that are safe enough from the hazard impacts; and design mitigation initiatives and long-term development plan in disaster prone areas.
Yet, there are some drawbacks which need to be taken into account while carrying a similar approach. This approach depends much on the quality and availability of the data. It also depends on the unit analysis that we are dealing with. However, an observation by Sagala and Okada (in review) suggest that a study in Mt. Merapi needs to be carried out at a hamlet (dusun) level instead of village since a village may be located at several hazard zones, as we described earlier. Yet, the absence of data at a hamlet level did not allow us to carry out the analysis at that precision.
7. Conclusion
The paper has examined the regional disaster resilience in Mt. Merapi using the social vulnerability index (SoVI). The results indicate that the regional disaster resilience does not depend on mainly the distance with the volcano as the sources of hazards but also with the capacities of the villages or communities in the villages. The research findings reveal the underlying indicators and variables that put the society in a socially vulnerable situation, particularly in the relations to disaster occurrences. Furthermore, this research is able to visually illustrate the geographical variability of the socially vulnerable village districts based on their relative place vulnerability scores.
Taking into consideration the limitations highlighted in this research, some areas for improvement can be considered for future research as follow: 1) The combination between census-based data and qualitative survey-based data would provide the complete picture of the social characteristics of society in detail and therefore would lead to more accurate and valid assessment of existing social vulnerability; 2) A thorough assessment of the capacity- building initiatives as well as latest updates of the local government policies on risk reduction initiatives is necessary. However, in relation to this present study, it puts emphasis more on vulnerability rather than risks, therefore these capacity measures are not much explored; 3) The inclusion of areas within the entire hazard zones together with provision of relevant physical variables would provide a comprehensive assessment of the social characteristics of the people which are posed by threats from different types of hazards and therefore would be useful in determining the overall vulnerability index; and 4) To cope with the data sources problem, it is advisable to use more than one data source as supplement as well as a comparison, provided they have equivalent level of analysis and time period. 5). The current indicators that were applied in this research mainly refers to those of earlier applied at a developed or more developed nations. Further important research that needs to be carried out is on the decisions of which indicators are suitable in the context of a developing like Indonesia. This could be done by doing several scenarios and getting the scenarios which fit best with the model.
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Merapi Volcano, Indonesia. Unpublished Master thesis, Department of Earth Sciences, University of Bristol, United Kingdom. Wisner, B, Blaikie, P., Cannon, T. and Davis, I., (2004) At Risk: Natural Hazards, People’s Vulnerability and Disasters. Routledge: London. Appendix 1. Vulnerability indicators and variables used in the research Indicator Description Possible variables Data availability Reason for inclusion/rejection (in the case of Merapi) mobility concerns which also represents the age dependency ratio. Gladwin and Children and elderly may require extra care due to their mobility constraints or Variables selected for inclusion 2000 census data and the other census data used in this study. There are differences in number of population between the Age (2000) homes. Peacock (1997) added that the elderly also tend to be reluctant to leave their Percentage of elderly > 65 Population Census The use of 2000 Census data will cause the exclusion of Sources: O’Brien & Mileti (1992), Hewitt (1997), Ngo (2001), Cutter et al Percentage of children < 15 Population Census Percentage of median age Population Census population - 2000 - years old 2000 - years old 2000 several variables from 2006 data Reason same as above Reason same as above Gender Sources: Fothergill (1996), Morrow & Enarson (1996), Hewitt (1997), Enarson The ability to absorb losses and enhance resilience to hazard impacts. Wealth Peacock et al (2000) population 2000/PODES 2006 Percentage of females & Morrow (1998), Enarson & Scanlon (1999), Morrow & Phillips (1999), Percentage of female Population Census Women can have a more difficult time during recovery than men, often due to Percentage of male Population Census - sector-specific employment, lower wages, and family care responsibilities. population 2000/PODES 2006 The exclusion is due to the reason that the indicator has been The number of permanent houses variable reflects the socio- The gender issue is essential to be included in the social presented by the percentage of female population vulnerability assessment Socio-economic status enables communities to absorb and recover from losses more quickly due to Number of permanent Primary survey economic situation of population which contributes to their
insurance, social safety nets, and entitlement programs. houses BPPTK 2007 Number of permanent houses economic resilience
Lower education and high illiteracy rate constrain the ability to understand Stanford (1998), Puente (1999), Cutter et al (2000), Peacock et al (2000) households PODES 2006 households to increasing social vulnerability Sources: Burton et al (1993), Blaikie et al (1994), Hewitt (1997), Bolin & Percentage of poor Percentage of poor resources and cope with the hazard impacts, which contribute Illiteracy rate influences the process of communicating the Poor households require support to be able to access the Education lower socioeconomic status Number of illiterate people BPPTK 2007 Number of illiterate people attainment due to socio-economic status warning information and access to recovery information as well as representing Primary survey warning and information as well as reflects the education Number of population > 5 2000 census data and the other census data used in this study. years old by educational Population Census There are differences in number of population between the The use of 2000 Census data will cause the exclusion of - several variables from 2006 data Sources: Heinz Center for Science, Economics, and the Environment (2000) attainment 2000 Family structure Source: Blaikie et al (1994), Morrow (1999), Puente (1999), Heinz Center for Average number of persons Average number of persons Science, Economics, and the Environment (2000), Morrow (2000) per household PODES 2006 per household responsibilities and care for family members at the same time Families with large number of dependents or single-parent households often have limited finances to outsource care for dependents, and thus must do work Families with many dependants tend to face greater obstacles in responding to an emergency Disabilities Special needs populations (infirm, institutionalized, transient, homeless), while difficult to identify and measure, are disproportionately affected during District in Figures during recovery. They also need extra assistance in order to respond to disaster Source: Tobin & Ollenburger (1993), Morrow (1999) disasters and, because of their invisibility in communities, mostly ignored Disabled persons Percentage of disabled people hazard impacts thus affecting the level of social vulnerability 2006/PODES 2006 Disabled people require additional support in coping with the of the population Table continued14
Health services will lengthen immediate relief and longer-term recovery from disasters.
Indicator Description Possible variables Data availability Reason for inclusion/rejection (in the case of Merapi)
important post-event sources of relief. The lack of proximate medical services per 1,000 of population 1,000 of population process from disasters
Health care providers, including physicians, nursing homes, and hospitals, are Number of health facilities Number of health facilities per The provision of health facilities supports the recovery Percentage of poor Percentage of poor PODES 2006 Variables selected for inclusion The provision of free health care services is considered as Infrastructure Source: Morrow (1999), Hewitt (1997), Heinz Center for Science, Economics, households receiving free PODES 2006 households receiving free The loss of infrastructure will severely affect the communities' life due to lack and the Environment (2000) health care services health care services Environment (2000) The potential loss of employment following a disaster exacerbates the number Source: Platt (1995), Heinz Center for Science, Economics, and the with electric lighting electric lighting attainment of infrastructure services of financial resources to rebuild Percentage of households Percentage of households with The provision of electric lighting for households reflects the PODES 2006 capacity thus decreasing vulnerability Unemployment of unemployed workers in a community, contributing to a slower Number of unemployed District in Figures recovery from the disaster. persons 2006 Source: Mileti (1999) populations. Counties experiencing rapid growth lack available quality housing, and the Source: Morrow (1999), Puente (1999), Cutter et al (2000), Heinz Center for District in Figures District in Figures 2006 - social services network may not have had time to adjust to increased Natality rate Therefore, it is decided not to take this variable into There are mix of data sources with inconsistent figures. Data not available for the entire case studies - consideration Population growth Science, Economics, and the Environment (2000) Migration rate - Reason same as above Out migration - Reason same as above Mortality rate In migration District in Figures District in Figures District in Figures 2006 2006 2006 2006 Reason same as above - Reason same as above - Renters sufficient shelter options when lodging becomes People that rent do so because they are either transient or do not have the about financial aid during recovery. In the most extreme cases, renters lack uninhabitable or too costly to afford. financial resources for home ownership. They often lack access to information Number of population living Population Census 2000 census data and the other census data used in this study. Population density Population density in their own homes 2000 The use of 2000 Census data will cause the exclusion of - 2000/PODES 2006 vulnerability Population Census High density of population will increase the level of social There are differences in number of population between the several variables from 2006 data Occupation/ Source: Morrow (1999), Heinz Center for Science, Economics, and the Number of population living Population Census Environment (2000) in rented homes 2000 and the Environment (2000) industries industries alternative livelihood other than farming Sources: Hewitt (1997), Puente (1999), Heinz Center for Science, Economics, Number of home/small-scale Number of home/small-scale This variable is used to observe the households which have impacted by a hazard event due to indirect impact (related to employment loss) Some occupations which involve resource extraction, may be severely PODES 2006 Percentage of farming Percentage of farming This variable is used to observe the households which households households depend on farming as their main livelihood PODES 2006 - Reason same as above Livelihood Percentage of farmlands PODES 2006 Percentage of farmlands working as farm labour working as farm labour of social vulnerability within a community Number of population Number of population The type of livelihood is one factor that determines the level PODES 2006 This variable reflects the land use of the areas which could indicate the dependency of communities in farming Source: Adapted from Cutter et al (2003) and The Heinz Centre for Science, Economics, and the Environment (2002).15