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

multiple indicators of exposure, coping capacity and resilience was applied to derive final consideration of
landslide vulnerability. Finally, it was classified into low, medium and high level of vulnerability (Table 1).
Table 1 Landslide Vulnerability Indicators
No. Vulnerability Exposure Indicators Coping Capacity Indicators Resilience Indicators
Classification
1. Low 1. Building Material:
traditional brick with
reinforced concrete column
1. Age: dominated by 15-
64 years old
2. Gender: dominated by
1. Income:
>1.500.000 IDR
2. Saving: yes
2. Construction type:
engineered-semi engineered
male
3. Preparedness: well
3. Building
material:
3. Building age: (10 m- 50m)
timber
3. High 1. Building Material: bamboo
and timber
2. Construction type: non
engineered
3. Building age: >30 years
4. Infrastructure (distance to
road): low accessibility (>50
m)
1. Age: dominated by 64
2. Gender: dominated by
female
3. Preparedness: poorly
prepared
4. Knowledge of landslide:
no understood
1. Income: <
750.000
2. Saving: no
3. Building
material: bamboo
and timber
3.2. Spatial Pattern Analysis
Spatial pattern analysis is employed to describe exploratory point sampling data of socio-economic
vulnerability. The data comprises the spatial coordinate data represented in two dimensionally space and the
categorical level of socio-economic vulnerability. Nearest neighborhood, spatial mean centre, standard
distance and directional distribution (standard deviation ellipse) were employed to show the proximity of
particular spatial point location in relation with its environmental spheres.
Nearest neighborhood technique was employed in order to explore whether the point of categorical
socio-economic vulnerability looked to be clustered or dispersed visually. It measures the ratio between the
observed distances of each point toward its nearest point neighbor and the expected mean distance for the
point given in a random pattern. If the ratio is less than 1, it means that the exploratory point sampling seems
to be clustered pattern. In the other hand, dispersed pattern is described by the ratio more than 1.
Central feature analysis estimates the average central point location of a set of particular spatial point
location. It measures the mean centre of x-coordinate and the mean centre of y-coordinate. Mean centre
analysis is useful to show the concentration of each categorical socio-economic landslide. Therefore, the
tendency of the spatial distribution can also be addressed as well. Standard distance describes the
compactness or spatial deviation of the point analysis toward its central features. It seems likely to be
standard deviation in traditional statistic. In GIS platform, it is represented by circle feature which has the
radius equal to the standard distance value. Directional distribution is rather similar with standard distance
circle. Standard distance circle calculate the standard distance simultaneously in the x and y direction.
However, standard distance circle cannot pose the directional bias in point distribution. In the directional
distribution, the x and y direction is calculated separately. It calculates the standard deviation of the x and y
coordinates from the mean centre. The result is axes of an ellipse or standard deviation of ellipse which is
drawn as an ellipse feature. Thus, by those techniques, the spatial characteristic of categorical point socioeconomic
vulnerability such as central tendency, dispersion and directional trends can be posed easily.
3.3. Fuzzy Logic Technique
The fuzzy logic technique was employed in order to consider the environmental factor of socioeconomic
landslide vulnerability represented in a map as members of a set. It defines the design of a
membership function that expresses the degree of membership in respect to the attribute of interest. For
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