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