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

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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 4 - 523 -
example, based on the spatial pattern analysis, the environmental factor of landslide vulnerability represented by an area could be areas on the map defined as areas highly vulnerable to landslide. In this paper, slope and distance to road were chosen as predictor variable to extrapolate the spatial socio-economic vulnerability of landslide. As a geographic phenomenon, vulnerability can be spatially referenced, analyzed and mapped using geospatial tools. Empirical evidence through frequency distribution diagram showed that two geography features i.e. slope and distance to road were play important role for defining the socio-economic vulnerability in Kayangan Catchment. The frequency distribution pattern of distance to road and slope toward the number of settlement in a given vulnerability degree was employed to determine fuzzy membership function. Thus, the final prediction of socio-economic vulnerability was calculated by fuzzy membership function and fuzzy operator. Fuzzy membership function is generated by relating map classes of environmental variable to membership value. The value of membership ranges from 0 to 1 representing the degree of certainty of membership. An area of environmental factor will be given 1 if it has certainly a member of a set or will be given 0 if it has not a member of a set. Values are given based on the spatial pattern and the relationship between the particular spatial data interest with the environmental variable. Different with regression or logistic regression technique which is mainly data driven approach, the fuzzy logic technique give an approach in which an expert can be freely control the weighting process. The spatial pattern analysis of socio-economic vulnerability samples towards its environmental factor predictor was employed to generate the membership function of each environmental predictor. In addition, it is also generated by function represented the relationship between environmental factor and socio economic vulnerability. Fuzzy membership linear representing the function of slope and fuzzy small representing the function of distance to road were employed in this research. Fuzzy operator was employed to combine or overlay the membership function map of each environmental factor. There are five operators on fuzzy technique as follows fuzzy and, fuzzy or, fuzzy product, fuzzy sum and fuzzy gamma operator. Fuzzy and operator sets the minimum value of the input membership as a result of overlay. It is defined as: µ combination = MIN (µ A, µ B, µ C, ) (1) In the other hand, fuzzy or operator sets the maximum value of the input membership as a result of overlay. It is defined as: µ combination = MAX (µ A, µ B, µ ) (2) The fuzzy product works with multiplying the input of membership. It is defined as: µ combination = (3) The fuzzy sum works with adding the input of membership. It is defined as: µ combination = (4) The fuzzy gamma is algebraic product of fuzzy sum and fuzzy product. It is defined as: µ combination = (Fuzzy sum) * (Fuzzy product) 1- (5) Where µ combination is the calculated fuzzy membership function, µ A is the membership value for map A represented by a membership value of a pixel and µ B is the membership value for map B and so on. µ i is the fuzzy membership function for the i-th map and i=1, 2, n ship will be similar to fuzzy In this paper, only were employed. Fuzzy and & fuzzy product were not employed in this research. Fuzzy and was not employed because of the type of the overlay would return the minimum value of the pixel membership. Fuzzy product works by multiplying each of the fuzzy membership value. The value of fuzzy product overlay is always less than the membership value of the input therefore the value was very small. Therefore, both of those techniques were difficult to predict the socio-economic vulnerability based on the fuzzy membership of environmental factor predictors. 5 - 524 -
Page 1 and 2: Spatial Pattern of Socio-economic L
Page 3: Figure 1 Location of Study Area, Ka
Page 7 and 8: Since Kayangan Catchment form has u
Page 9 and 10: There were significant different am

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

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