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

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.
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