Spatial Pattern of Socio-economic Landslide Vulnerability and its ...

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tool and trending topic in order to figure out place-based vulnerability analysis. However, there are several major considerations should be underlined in the development of spatial vulnerability analysis. Scale analysis and the robustness of method are important aspect to employ GIS technique in vulnerability assessment. The scale analysis of vulnerability assessment is usually driven by the availability of data and usually applied in site specific location or over to the large area. It was usually based on the analysis of secondary traditional vulnerability data based on administrative unit i.e. age, gender, literacy, and population. The main drawback of this procedure is the unmatched of administrative data and the boundary of study area. For instance, the problem arises when demanded on the analysis of catchment area in which the boundaries of catchment is oversize or undersize with the administrative unit. Due to lack of data on the household characteristic, the vulnerability per settlement area is also difficult to be estimated by administrative based data. The choice in the scale analysis of observation and its spatial unit is necessary in spatial vulnerability assessment (Fekete, 2010). To overcome the limitation of administrative data, the socio-economic vulnerability data should be collected by direct survey (primary data). Those are only available obtained through census or and community based methods. However, data collected using house-to-house surveys and community based methods have limited efficiency and transferability (Ebert et al., 2009). Medium scale vulnerability assessment based on catchment analysis focused on household level is challenging in order to breakdown the drawbacks of traditional spatial vulnerability assessment. It will be very valuable to explain the spatial interdependencies of landslide phenomenon in the framework of environmental system. Sampling technique was proposed in order to overcome timely and costly census procedure. However, the limitation of the sampling technique is related to the generalization of spatial unit attribute. Limited sample will only describe a small portion of whole spatial unit attribute in the entire the study area. Generalization of the sample data should fulfill the requirement of vulnerability information in each spatial unit in the whole study area. It requires an understanding of relationship between vulnerability information and the environment factors affecting them. Spatial data analysis focusing on detecting pattern is a capable tool to understand processes which are responsible for observed patterns (Fischer, 2002). Spatial pattern analysis focuses on the describing space as an important role to derive socio-economic process in the space-time context. It is powerful analysis to recognize phenomenon related to the spatial interaction among observed field. Household sample is derived to draw inferences about underlying process of socio-economic vulnerability and its environment based on the settlement block spatial unit. Average nearest neighbor analysis was employed in order to figure out the spatial pattern of socio-economic landslide vulnerability. Geographic distribution of mean centre analysis, standard distance and directional distribution were also employed in order to identify spatial tendency and spatial distribution of socio-economic vulnerability. It was intended to explore the interdependencies between socio-economic landslide vulnerability with its environmental factor. Then, the spatial pattern analysis was applied as an input of fuzzy membership technique to predict socio-economic landslide vulnerability in the whole settlement block of Kayangan Catchment. Therefore, the objective of this paper is to propose medium scale spatial vulnerability analysis based on the socio-economic indicators through fuzzy logic technique. 2. STUDY AREA The research area was conducted in Kayangan Catchment Kulon Progo, Yogakarta Indonesia (Figure 1). The area of Kayangan Catchment extends 4 sub-districts i.e. Girimulyo, Nanggulan, Samigaluh and Kaligesing. It is located in the middle Java Island and comprises 35 km 2 . The average annual rainfall in Kayangan Catchment is 2478 mm. The highest rainfall intensity usually occurs from February to March with average monthly rainfall 426 mm. Landslide usually occurs in the month of November to April during wet season. Land use in the study area can be classified into bushes, rain fed paddy field, irrigated paddy field, people forest, settlement, and dry cultivated land. Kulon Progo area has been traditionally dominated by agricultural sector. It is also the second lowest economic growth and the welfare level has only 8.7% of total GDP among 5 regencies in Yogyakarta Province. Around 78% household working in agricultural sector and mostly (88%) are living in rural areas. With the limitation of economic development and infrastructure, 40.31 % people are living below national poverty line (BPS, 2008). Due to inadequate access and resource to economic development, it may increase socio-economic vulnerability level when disaster occurs. 2 - 521 -
Figure 1 Location of Study Area, Kayangan Catchment, Indonesia. 3. METHODOLOGY 3.1. Vulnerability Indicators Vulnerability indicators play an important role to vulnerability analysis. It should be in line with the characteristics of person or group in terms of their capacity to anticipate, cope with, resist and recover from with the three pillars of sustainable development which is pointed out to reduce vulnerability in the context of social, economic and environmental spheres (Birkmann, 2006). It explains that the vulnerability is interlinked with the individual, household or community ability to recover from disaster by strengthening the exposure, coping capacity and resilience. The complex nature of people, household, social structure and culture are amongst the important features that determine how well selected indicators represent socioeconomic landslide vulnerability. Therefore several indicators were employed to describe exposure, coping capacity and resilience in Kayangan Catchment. Exposure is derived from building material, construction type, building age, infrastructures, and building location. Coping capacity described as capability to cope with disaster is derived from preparedness, knowledge of landslide, age and gender. Resilience which is generally described as an ability of household to recover from disaster is derived from income, saving and building architecture representing the economic welfare of a household. The indicators of economic welfare were intended to explore the economic ability of household to refinance the impact of natural disaster. Several closed questions were employed to gain indepth information of preparedness and knowledge of landslide. For instance as preparedness of evacuation route, emergency needs, important phone number, important document, participation of landslide education/socialization, etc. The sampling technique and data mining of socio-economic landslide vulnerability were conducted by household survey using observation, photograph analysis and interview. Household survey was conducted by transect walk and structured (closed question) interview by using questionnaire. Photograph analysis of building was also used to accompany the analysis of household survey to portray the building architecture. Samples of 112 respondents were successfully aided to derive socio-economic vulnerability. Weighting 3 - 522 -
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