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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<strong>Spatial</strong> <strong>Pattern</strong> <strong>of</strong> <strong>Socio</strong>-<strong>economic</strong> <strong>L<strong>and</strong>slide</strong> <strong>Vulnerability</strong> <strong>and</strong><br />
<strong>its</strong> <strong>Spatial</strong> Prediction by means <strong>of</strong> GIS-Fuzzy Logic<br />
in Kayangan Catchment Indonesia<br />
Guruh SAMODRA 1,2 *, Guangqi CHEN 1 , Junun SARTOHADI 2 , Kiyonobu KASAMA 1 ,<br />
Danang Sri HADMOKO 1<br />
1 Civil <strong>and</strong> Structural Engineering Department, Kyushu University, Japan<br />
2 Environmental Geography Department, Universitas Gadjah Mada, Indonesia<br />
The objective <strong>of</strong> this research is to identify the spatial pattern <strong>and</strong> to explore the spatial distribution <strong>of</strong> socio<strong>economic</strong><br />
l<strong>and</strong>slide vulnerability in Kayangan Catchment. Household survey was employed in this research in<br />
order to construct several indicators <strong>of</strong> socio-<strong>economic</strong> l<strong>and</strong>slide vulnerability. It was conducted by transect<br />
walk <strong>and</strong> structured (closed question) interview. The information <strong>of</strong> <strong>economic</strong> <strong>and</strong> social vulnerability was<br />
taken from 112 respondents through multistage r<strong>and</strong>om sampling based on the settlement obtained from l<strong>and</strong>use<br />
map from Bakosurtanal (Survey <strong>and</strong> Mapping Agency <strong>of</strong> Indonesia). Photograph analysis was also used to<br />
accompany the vulnerability analysis <strong>of</strong> household survey. Mean centre, st<strong>and</strong>ard distance, directional<br />
distribution <strong>and</strong> average nearest neighbor analysis were employed in order to figure out the spatial pattern <strong>of</strong><br />
socio-<strong>economic</strong> l<strong>and</strong>slide vulnerability. Therefore the spatial pattern <strong>of</strong> vulnerability accompanied by<br />
knowledge driven expert judgment was employed to build fuzzy membership analysis. Fuzzy logic technique<br />
was applied in order to predict spatial distribution <strong>of</strong> overall settlement polygon in Kayangan Catchment.<br />
Nearest neighbor analysis <strong>of</strong> low, medium <strong>and</strong> high vulnerability level showed z-scores -4.52, -5.26 <strong>and</strong> -1.86<br />
respectively. It implied that the spatial distribution <strong>of</strong> each level <strong>of</strong> vulnerability was clustered. There were<br />
interlinked between natural feature, social behavior, <strong>economic</strong> level <strong>and</strong> vulnerability level. The fuzzy operator<br />
Or, Sum <strong>and</strong> high gamma value were successfully applied to predict spatial distribution <strong>of</strong> socio-<strong>economic</strong><br />
vulnerability level. The result coincided with the <strong>economic</strong>, social <strong>and</strong> ecological dimensions <strong>of</strong> livelihood in<br />
Kayangan Catchment.<br />
Key words: <strong>Socio</strong>-<strong>economic</strong>, <strong>L<strong>and</strong>slide</strong>, <strong>Vulnerability</strong>, <strong>Spatial</strong>, Prediction<br />
1. INTRODUCTION<br />
The recent increasing number <strong>of</strong> disaster dem<strong>and</strong>s a response for administrator <strong>and</strong> policy maker to<br />
minimize loss <strong>of</strong> life, damage property <strong>and</strong> people suffering. Several mitigation strategies <strong>and</strong> measures<br />
focusing on hazard have been addressed to overcome those objectives. However, it has not been fully<br />
effective due to the uniqueness <strong>of</strong> socio-<strong>economic</strong> community <strong>and</strong> environmental response to cope with<br />
disaster. It causes mitigation strategies <strong>and</strong> measures are inadequate to address the disaster as a threat.<br />
Recognizing broader spectrum <strong>of</strong> hazard <strong>and</strong> more appropriate framework analysis are necessary to<br />
identify the unique process <strong>of</strong> socio-<strong>economic</strong> <strong>and</strong> environmental response to natural hazard. <strong>Vulnerability</strong><br />
concept is widely proposed to overcome the drawbacks <strong>of</strong> limited <strong>and</strong> centered hazard analysis. As stated in<br />
the Hyogo Framework 2007 that vulnerability analysis are dem<strong>and</strong>ed for risk analysis dealing with natural<br />
disasters <strong>and</strong> the societal consequences <strong>of</strong> disaster (UNISDR, 2007). Nowadays, vulnerability becomes<br />
central concept in disaster research <strong>and</strong> mitigation strategies (Menoni, et al., 2012).<br />
Recent development <strong>and</strong> debate <strong>of</strong> vulnerability analysis related to terminology (Costa <strong>and</strong> Cropp, 2012),<br />
concept (Wolf, 2012), methodological, robustness <strong>and</strong> legitimacy (Kaynia, et al., 2008; Fekete, 2011) are<br />
documented well. In addition, spatial data analysis with GIS techniques <strong>and</strong> procedure has become major<br />
*Email: guruh.samodra@gmail.com<br />
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tool <strong>and</strong> trending topic in order to figure out place-based vulnerability analysis. However, there are several<br />
major considerations should be underlined in the development <strong>of</strong> spatial vulnerability analysis. Scale<br />
analysis <strong>and</strong> the robustness <strong>of</strong> method are important aspect to employ GIS technique in vulnerability<br />
assessment.<br />
The scale analysis <strong>of</strong> vulnerability assessment is usually driven by the availability <strong>of</strong> data <strong>and</strong> usually<br />
applied in site specific location or over to the large area. It was usually based on the analysis <strong>of</strong> secondary<br />
traditional vulnerability data based on administrative unit i.e. age, gender, literacy, <strong>and</strong> population. The main<br />
drawback <strong>of</strong> this procedure is the unmatched <strong>of</strong> administrative data <strong>and</strong> the boundary <strong>of</strong> study area. For<br />
instance, the problem arises when dem<strong>and</strong>ed on the analysis <strong>of</strong> catchment area in which the boundaries <strong>of</strong><br />
catchment is oversize or undersize with the administrative unit. Due to lack <strong>of</strong> data on the household<br />
characteristic, the vulnerability per settlement area is also difficult to be estimated by administrative based<br />
data. The choice in the scale analysis <strong>of</strong> observation <strong>and</strong> <strong>its</strong> spatial unit is necessary in spatial vulnerability<br />
assessment (Fekete, 2010).<br />
To overcome the limitation <strong>of</strong> administrative data, the socio-<strong>economic</strong> vulnerability data should be<br />
collected by direct survey (primary data). Those are only available obtained through census or <strong>and</strong><br />
community based methods. However, data collected using house-to-house surveys <strong>and</strong> community based<br />
methods have limited efficiency <strong>and</strong> transferability (Ebert et al., 2009). Medium scale vulnerability<br />
assessment based on catchment analysis focused on household level is challenging in order to breakdown the<br />
drawbacks <strong>of</strong> traditional spatial vulnerability assessment. It will be very valuable to explain the spatial<br />
interdependencies <strong>of</strong> l<strong>and</strong>slide phenomenon in the framework <strong>of</strong> environmental system. Sampling technique<br />
was proposed in order to overcome timely <strong>and</strong> costly census procedure. However, the limitation <strong>of</strong> the<br />
sampling technique is related to the generalization <strong>of</strong> spatial unit attribute. Limited sample will only describe<br />
a small portion <strong>of</strong> whole spatial unit attribute in the entire the study area. Generalization <strong>of</strong> the sample data<br />
should fulfill the requirement <strong>of</strong> vulnerability information in each spatial unit in the whole study area. It<br />
requires an underst<strong>and</strong>ing <strong>of</strong> relationship between vulnerability information <strong>and</strong> the environment factors<br />
affecting them. <strong>Spatial</strong> data analysis focusing on detecting pattern is a capable tool to underst<strong>and</strong> processes<br />
which are responsible for observed patterns (Fischer, 2002).<br />
<strong>Spatial</strong> pattern analysis focuses on the describing space as an important role to derive socio-<strong>economic</strong><br />
process in the space-time context. It is powerful analysis to recognize phenomenon related to the spatial<br />
interaction among observed field. Household sample is derived to draw inferences about underlying process<br />
<strong>of</strong> socio-<strong>economic</strong> vulnerability <strong>and</strong> <strong>its</strong> environment based on the settlement block spatial unit. Average<br />
nearest neighbor analysis was employed in order to figure out the spatial pattern <strong>of</strong> socio-<strong>economic</strong> l<strong>and</strong>slide<br />
vulnerability. Geographic distribution <strong>of</strong> mean centre analysis, st<strong>and</strong>ard distance <strong>and</strong> directional distribution<br />
were also employed in order to identify spatial tendency <strong>and</strong> spatial distribution <strong>of</strong> socio-<strong>economic</strong><br />
vulnerability. It was intended to explore the interdependencies between socio-<strong>economic</strong> l<strong>and</strong>slide<br />
vulnerability with <strong>its</strong> environmental factor. Then, the spatial pattern analysis was applied as an input <strong>of</strong> fuzzy<br />
membership technique to predict socio-<strong>economic</strong> l<strong>and</strong>slide vulnerability in the whole settlement block <strong>of</strong><br />
Kayangan Catchment. Therefore, the objective <strong>of</strong> this paper is to propose medium scale spatial vulnerability<br />
analysis based on the socio-<strong>economic</strong> indicators through fuzzy logic technique.<br />
2. STUDY AREA<br />
The research area was conducted in Kayangan Catchment Kulon Progo, Yogakarta Indonesia (Figure 1).<br />
The area <strong>of</strong> Kayangan Catchment extends 4 sub-districts i.e. Girimulyo, Nanggulan, Samigaluh <strong>and</strong><br />
Kaligesing. It is located in the middle Java Isl<strong>and</strong> <strong>and</strong> comprises 35 km 2 . The average annual rainfall in<br />
Kayangan Catchment is 2478 mm. The highest rainfall intensity usually occurs from February to March with<br />
average monthly rainfall 426 mm. <strong>L<strong>and</strong>slide</strong> usually occurs in the month <strong>of</strong> November to April during wet<br />
season. L<strong>and</strong> use in the study area can be classified into bushes, rain fed paddy field, irrigated paddy field,<br />
people forest, settlement, <strong>and</strong> dry cultivated l<strong>and</strong>.<br />
Kulon Progo area has been traditionally dominated by agricultural sector. It is also the second lowest<br />
<strong>economic</strong> growth <strong>and</strong> the welfare level has only 8.7% <strong>of</strong> total GDP among 5 regencies in Yogyakarta<br />
Province. Around 78% household working in agricultural sector <strong>and</strong> mostly (88%) are living in rural areas.<br />
With the limitation <strong>of</strong> <strong>economic</strong> development <strong>and</strong> infrastructure, 40.31 % people are living below national<br />
poverty line (BPS, 2008). Due to inadequate access <strong>and</strong> resource to <strong>economic</strong> development, it may increase<br />
socio-<strong>economic</strong> vulnerability level when disaster occurs.<br />
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Figure 1 Location <strong>of</strong> Study Area, Kayangan Catchment, Indonesia.<br />
3. METHODOLOGY<br />
3.1. <strong>Vulnerability</strong> Indicators<br />
<strong>Vulnerability</strong> indicators play an important role to vulnerability analysis. It should be in line with the<br />
<br />
characteristics <strong>of</strong> person or group in terms <strong>of</strong> their capacity to anticipate, cope with, resist <strong>and</strong> recover from<br />
<br />
with the three pillars <strong>of</strong> sustainable development which is pointed out to reduce vulnerability in the context<br />
<strong>of</strong> social, <strong>economic</strong> <strong>and</strong> environmental spheres (Birkmann, 2006). It explains that the vulnerability is<br />
interlinked with the individual, household or community ability to recover from disaster by strengthening the<br />
exposure, coping capacity <strong>and</strong> resilience. The complex nature <strong>of</strong> people, household, social structure <strong>and</strong><br />
culture are amongst the important features that determine how well selected indicators represent socio<strong>economic</strong><br />
l<strong>and</strong>slide vulnerability. Therefore several indicators were employed to describe exposure, coping<br />
capacity <strong>and</strong> resilience in Kayangan Catchment.<br />
Exposure is derived from building material, construction type, building age, infrastructures, <strong>and</strong> building<br />
location. Coping capacity described as capability to cope with disaster is derived from preparedness,<br />
knowledge <strong>of</strong> l<strong>and</strong>slide, age <strong>and</strong> gender. Resilience which is generally described as an ability <strong>of</strong> household to<br />
recover from disaster is derived from income, saving <strong>and</strong> building architecture representing the <strong>economic</strong><br />
welfare <strong>of</strong> a household. The indicators <strong>of</strong> <strong>economic</strong> welfare were intended to explore the <strong>economic</strong> ability <strong>of</strong><br />
household to refinance the impact <strong>of</strong> natural disaster. Several closed questions were employed to gain indepth<br />
information <strong>of</strong> preparedness <strong>and</strong> knowledge <strong>of</strong> l<strong>and</strong>slide. For instance as preparedness <strong>of</strong> evacuation<br />
route, emergency needs, important phone number, important document, participation <strong>of</strong> l<strong>and</strong>slide<br />
education/socialization, etc.<br />
The sampling technique <strong>and</strong> data mining <strong>of</strong> socio-<strong>economic</strong> l<strong>and</strong>slide vulnerability were conducted by<br />
household survey using observation, photograph analysis <strong>and</strong> interview. Household survey was conducted<br />
by transect walk <strong>and</strong> structured (closed question) interview by using questionnaire. Photograph analysis <strong>of</strong><br />
building was also used to accompany the analysis <strong>of</strong> household survey to portray the building architecture.<br />
Samples <strong>of</strong> 112 respondents were successfully aided to derive socio-<strong>economic</strong> vulnerability. Weighting<br />
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multiple indicators <strong>of</strong> exposure, coping capacity <strong>and</strong> resilience was applied to derive final consideration <strong>of</strong><br />
l<strong>and</strong>slide vulnerability. Finally, it was classified into low, medium <strong>and</strong> high level <strong>of</strong> vulnerability (Table 1).<br />
Table 1 <strong>L<strong>and</strong>slide</strong> <strong>Vulnerability</strong> Indicators<br />
No. <strong>Vulnerability</strong> Exposure Indicators Coping Capacity Indicators Resilience Indicators<br />
Classification<br />
1. Low 1. Building Material:<br />
traditional brick with<br />
reinforced concrete column<br />
1. Age: dominated by 15-<br />
64 years old<br />
2. Gender: dominated by<br />
1. Income:<br />
>1.500.000 IDR<br />
2. Saving: yes<br />
2. Construction type:<br />
engineered-semi engineered<br />
male<br />
3. Preparedness: well<br />
3. Building<br />
material:<br />
3. Building age: (10 m- 50m)<br />
timber<br />
3. High 1. Building Material: bamboo<br />
<strong>and</strong> timber<br />
2. Construction type: non<br />
engineered<br />
3. Building age: >30 years<br />
4. Infrastructure (distance to<br />
road): low accessibility (>50<br />
m)<br />
1. Age: dominated by 64<br />
2. Gender: dominated by<br />
female<br />
3. Preparedness: poorly<br />
prepared<br />
4. Knowledge <strong>of</strong> l<strong>and</strong>slide:<br />
no understood<br />
1. Income: <<br />
750.000<br />
2. Saving: no<br />
3. Building<br />
material: bamboo<br />
<strong>and</strong> timber<br />
3.2. <strong>Spatial</strong> <strong>Pattern</strong> Analysis<br />
<strong>Spatial</strong> pattern analysis is employed to describe exploratory point sampling data <strong>of</strong> socio-<strong>economic</strong><br />
vulnerability. The data comprises the spatial coordinate data represented in two dimensionally space <strong>and</strong> the<br />
categorical level <strong>of</strong> socio-<strong>economic</strong> vulnerability. Nearest neighborhood, spatial mean centre, st<strong>and</strong>ard<br />
distance <strong>and</strong> directional distribution (st<strong>and</strong>ard deviation ellipse) were employed to show the proximity <strong>of</strong><br />
particular spatial point location in relation with <strong>its</strong> environmental spheres.<br />
Nearest neighborhood technique was employed in order to explore whether the point <strong>of</strong> categorical<br />
socio-<strong>economic</strong> vulnerability looked to be clustered or dispersed visually. It measures the ratio between the<br />
observed distances <strong>of</strong> each point toward <strong>its</strong> nearest point neighbor <strong>and</strong> the expected mean distance for the<br />
point given in a r<strong>and</strong>om pattern. If the ratio is less than 1, it means that the exploratory point sampling seems<br />
to be clustered pattern. In the other h<strong>and</strong>, dispersed pattern is described by the ratio more than 1.<br />
Central feature analysis estimates the average central point location <strong>of</strong> a set <strong>of</strong> particular spatial point<br />
location. It measures the mean centre <strong>of</strong> x-coordinate <strong>and</strong> the mean centre <strong>of</strong> y-coordinate. Mean centre<br />
analysis is useful to show the concentration <strong>of</strong> each categorical socio-<strong>economic</strong> l<strong>and</strong>slide. Therefore, the<br />
tendency <strong>of</strong> the spatial distribution can also be addressed as well. St<strong>and</strong>ard distance describes the<br />
compactness or spatial deviation <strong>of</strong> the point analysis toward <strong>its</strong> central features. It seems likely to be<br />
st<strong>and</strong>ard deviation in traditional statistic. In GIS platform, it is represented by circle feature which has the<br />
radius equal to the st<strong>and</strong>ard distance value. Directional distribution is rather similar with st<strong>and</strong>ard distance<br />
circle. St<strong>and</strong>ard distance circle calculate the st<strong>and</strong>ard distance simultaneously in the x <strong>and</strong> y direction.<br />
However, st<strong>and</strong>ard distance circle cannot pose the directional bias in point distribution. In the directional<br />
distribution, the x <strong>and</strong> y direction is calculated separately. It calculates the st<strong>and</strong>ard deviation <strong>of</strong> the x <strong>and</strong> y<br />
coordinates from the mean centre. The result is axes <strong>of</strong> an ellipse or st<strong>and</strong>ard deviation <strong>of</strong> ellipse which is<br />
drawn as an ellipse feature. Thus, by those techniques, the spatial characteristic <strong>of</strong> categorical point socio<strong>economic</strong><br />
vulnerability such as central tendency, dispersion <strong>and</strong> directional trends can be posed easily.<br />
3.3. Fuzzy Logic Technique<br />
The fuzzy logic technique was employed in order to consider the environmental factor <strong>of</strong> socio<strong>economic</strong><br />
l<strong>and</strong>slide vulnerability represented in a map as members <strong>of</strong> a set. It defines the design <strong>of</strong> a<br />
membership function that expresses the degree <strong>of</strong> membership in respect to the attribute <strong>of</strong> interest. For<br />
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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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4. RESULT AND DISCUSSION<br />
4.1. Central Tendency <strong>and</strong> <strong>Spatial</strong> <strong>Pattern</strong> <strong>of</strong> <strong>Socio</strong>-<strong>economic</strong> <strong>L<strong>and</strong>slide</strong> <strong>Vulnerability</strong><br />
Since the nature <strong>of</strong> people, social structure <strong>and</strong> culture <strong>of</strong> Kayangan Catchment were less influenced by<br />
the development <strong>of</strong> technology <strong>and</strong> modernization, the natural feature would more dominantly influence the<br />
way <strong>of</strong> life <strong>of</strong> the people in Kayangan Catchment. The area preference <strong>of</strong> building settlement was one<br />
example <strong>of</strong> social behavior mainly influenced by natural feature <strong>and</strong> condition. There were several options<br />
for a household to build a settlement, but there was limited option for low income household. Settlement<br />
type was <strong>of</strong>ten a reflection <strong>of</strong> welfare level. It was usually that low income household could not choose<br />
whether they would build their house in more gentle or steep topography. Therefore, low income household<br />
tend to build settlement based on their l<strong>and</strong> given from their parent or ancestor. It was usually located in<br />
steeper, more remote <strong>and</strong> low accessibility. For high income household, there were several options to build a<br />
settlement. They usually build a settlement in relatively gentle topography <strong>and</strong> have good accessibility. Good<br />
accessibility was the most common reason where they would build a settlement. Even, with the reason <strong>of</strong><br />
accessibility, they would build a settlement by excavating <strong>and</strong> cutting the slope into more gentle topography.<br />
Low income household living in low accessibility <strong>and</strong> hilly area had low <strong>economic</strong> ability <strong>of</strong> household<br />
to refinance the impact <strong>of</strong> natural disaster. They usually depend on the agricultural product <strong>and</strong> have low<br />
education level. Besides living in hilly area which was more prone to be l<strong>and</strong>sliding, they also had high<br />
socio-<strong>economic</strong> vulnerability level. There were interlinked between natural feature, social behavior,<br />
<strong>economic</strong> level <strong>and</strong> vulnerability level. It would affect the spatial pattern <strong>of</strong> settlement in Kayangan<br />
Catchment.<br />
Figure 2 Sampling Distribution, Mean Center, St<strong>and</strong>ard Distance (Left)<br />
<strong>and</strong> Directional Distribution (Right) <strong>of</strong> <strong>Socio</strong>-<strong>economic</strong> <strong>Vulnerability</strong><br />
<strong>Spatial</strong> mean center (SMC) provides an analysis where a phenomenon tends to be centered. Figure 2<br />
shows sampling location <strong>of</strong> socio-<strong>economic</strong> vulnerability level <strong>and</strong> their spatial mean centers. It is interesting<br />
to note that low vulnerability SMC tends to be located in the lower stream <strong>of</strong> Kayangan Catchment which<br />
has more gentle topography <strong>and</strong> nearby the center <strong>of</strong> trade, education <strong>and</strong> local government <strong>of</strong>fice location.<br />
In the other h<strong>and</strong>, the SMCs <strong>of</strong> medium <strong>and</strong> high level <strong>of</strong> vulnerability tend to be located in the upper part <strong>of</strong><br />
Kayangan Catchment which has steeper topography. It means that socio-<strong>economic</strong> vulnerability is spatially<br />
associated with topography <strong>and</strong> socio-<strong>economic</strong> functional unit.<br />
St<strong>and</strong>ard distance (SD) was employed to measure the distribution <strong>of</strong> socio-<strong>economic</strong> vulnerability<br />
deviate from their SMC. Figure 2 (left) shows the spatial SD circle <strong>of</strong> socio-<strong>economic</strong> vulnerability. The low<br />
vulnerability level was the highest deviation from the spatial mean <strong>and</strong> the high vulnerability level was the<br />
lowest one. However, high vulnerability <strong>and</strong> medium vulnerability had no significance difference. It implied<br />
that low vulnerability level locations were highly dispersed. High <strong>and</strong> medium vulnerability levels were not<br />
so much deviated from their mean centre. High <strong>and</strong> medium vulnerability levels were mainly located on the<br />
hilly area <strong>and</strong> the boundary <strong>of</strong> deviation was not exceeding the hilly area <strong>of</strong> Kayangan Catchment.<br />
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Since Kayangan Catchment form has unsymmetrical width, SD has not fitted to represent the directional<br />
deviation <strong>of</strong> socio-<strong>economic</strong> vulnerability. Directional distribution or St<strong>and</strong>ard Deviational Ellipse (SDE)<br />
was employed to measure directional deviation <strong>of</strong> socio-<strong>economic</strong> vulnerability. The calculation <strong>of</strong> SDE<br />
made the trend <strong>of</strong> socio-<strong>economic</strong> vulnerability distribution more clear. It pointed out that low vulnerability<br />
level had highest deviation along the major axis <strong>and</strong> covered flat area. Medium vulnerability level SDE had<br />
longer major axis than high vulnerability level. It indicated that high vulnerability level tend to be<br />
concentrated on the hilly area. This implied that slope had significant role on socio-<strong>economic</strong> vulnerability in<br />
Kayangan Catchment.<br />
Nr. <strong>of</strong> Settlement Block<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
0 50 100 150 200 250 300 350<br />
Distance to Road<br />
Nr. <strong>of</strong> Settlement Block<br />
45<br />
40<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
0 10 20 30 40 50 60<br />
Slope (Degree)<br />
: Low <strong>Vulnerability</strong> : Medium <strong>Vulnerability</strong> : High <strong>Vulnerability</strong><br />
Figure 3 the <strong>Vulnerability</strong> Level based on number <strong>of</strong> settlement towards distance to road <strong>and</strong> Slope<br />
Furthermore, nearest neighbor analysis was employed to analyze the spatial pattern <strong>of</strong> socio<strong>economic</strong><br />
vulnerability. Nearest neighbor analysis <strong>of</strong> low, medium <strong>and</strong> high vulnerability level<br />
showed z-scores -4.52, -5.26 <strong>and</strong> -1.86 respectively. It implied that the spatial distribution <strong>of</strong> each<br />
level <strong>of</strong> vulnerability was clustered. Low level vulnerability household was clustered in more gentle<br />
area <strong>and</strong> high accessibility. However, low level vulnerability had higher tendency on deviation due<br />
to the high result <strong>of</strong> SD <strong>and</strong> SDE. In the other h<strong>and</strong> high level vulnerability household was clustered<br />
in the hilly <strong>and</strong> low degree <strong>of</strong> accessibility. Medium <strong>Vulnerability</strong> was also clustered in the hilly<br />
area but the accessibility is higher than low vulnerability level. Figure 3 showed the interlinked<br />
between vulnerability level <strong>and</strong> <strong>its</strong> environmental factor i.e. slope <strong>and</strong> accessibility (distance to<br />
road). Later, the trend <strong>of</strong> the interlinked between vulnerability level <strong>and</strong> <strong>its</strong> environmental was<br />
employed as an input <strong>of</strong> fuzzy membership in fuzzy logic technique.<br />
4.2. <strong>Spatial</strong> <strong>L<strong>and</strong>slide</strong> <strong>Vulnerability</strong> Prediction by means <strong>of</strong> Fuzzy Logic Technique<br />
<strong>Spatial</strong> pattern analysis with household survey described the distribution <strong>of</strong> socio-<strong>economic</strong><br />
vulnerability level in Kayangan Catchment quite well. However, the vulnerability level location<br />
obtained from household survey could not be described spatially on each settlement block due to<br />
the limitation <strong>of</strong> the samples. Fuzzy logic technique based on the slope <strong>and</strong> distance to road<br />
membership function was introduced to extrapolate vulnerability degree in each settlement block.<br />
Fuzzy membership function (Figure 3) <strong>and</strong> the fuzzy operator (Eqs. 1, 4 <strong>and</strong> 5) were generated<br />
to predict the socio-<strong>economic</strong> vulnerability level in Kayangan Catchment. It was computed for 6<br />
cases i.e. Fuzzy operator Or, Sum, Gamma 0.975, gamma 0.9, gamma 0.8 <strong>and</strong> gamma 0.7. The<br />
result <strong>of</strong> computation was mapped in order to interpret the spatial distribution <strong>of</strong> socio-<strong>economic</strong><br />
vulnerability in Kayangan Catchment. <strong>Socio</strong>-<strong>economic</strong> vulnerability map was represented by the<br />
possibility value between 0 <strong>and</strong> 1. The value <strong>of</strong> 0 indicated the full impossibility vulnerable <strong>and</strong> the<br />
value <strong>of</strong> 1 indicated the full possibility <strong>of</strong> being vulnerable. Thus, an area given as a value<br />
approaching 1 was indicated higher socio-<strong>economic</strong> vulnerability level. The final maps were<br />
presented in Figure 4 representing spatial distribution <strong>of</strong> socio-<strong>economic</strong> vulnerability level in<br />
Kayangan Catchment.<br />
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(a)<br />
(b)<br />
(c)<br />
(d)<br />
(e<br />
(e)<br />
Figure 4 <strong>Socio</strong>-<strong>economic</strong> <strong>Vulnerability</strong> Map based on Fuzzy Operator Or (a), Sum (b), Gamma 0975 (c), gamma 09 (d),<br />
gamma 08 (e), gamma 07 (f)<br />
(f)<br />
8<br />
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There were significant different amongst cases <strong>of</strong> fuzzy operators. Fuzzy membership <strong>and</strong><br />
fuzzy operator play important role to generate final map <strong>of</strong> socio-<strong>economic</strong> l<strong>and</strong>slide vulnerability.<br />
It implied that fuzzy operator Or, Sum <strong>and</strong> high gamma value represented better result than fuzzy<br />
operator product, fuzzy operator <strong>and</strong> <strong>and</strong> fuzzy operator with low gamma value. Since the fuzzy<br />
operator product, <strong>and</strong> <strong>and</strong> low gamma value working with returning the minimum value <strong>of</strong> the pixel<br />
membership <strong>and</strong> the result was always less than the membership value <strong>of</strong> the input, the result <strong>of</strong> the<br />
map will be low vulnerable in the whole area. Thus, fuzzy operator product, fuzzy operator <strong>and</strong> <strong>and</strong><br />
fuzzy operator with low gamma value were not included in the analysis.<br />
Figure 5 (a) <strong>Socio</strong>-<strong>economic</strong> <strong>Vulnerability</strong> Map <strong>of</strong> Kayangan Catchment (b) High Accessibility <strong>and</strong> Centre <strong>of</strong><br />
Activities(c) Low <strong>Vulnerability</strong> Household (d) Bad Road Condition Showing Low Accessibility<br />
(e) High <strong>Vulnerability</strong> Household in Hilly area<br />
The result map <strong>of</strong> fuzzy operator Or, Sum <strong>and</strong> high gamma value tend to have similar trend<br />
describing socio-<strong>economic</strong> vulnerability level. High vulnerability level was located in the middle<br />
part <strong>of</strong> Kayangan Catchment in which covered by steep topography <strong>and</strong> low accessibility. The<br />
result <strong>of</strong> field observation showed that the settlement pattern <strong>of</strong> high vulnerability level tend to be<br />
clustered <strong>and</strong> was characterized by semi-permanent building constructed by non-engineered<br />
bamboo <strong>and</strong> timber.<br />
The clustered pattern would depend on the natural resource. A single clustered village was<br />
usually located close to water resource (spring water). It was due to water supply in the hilly area<br />
which fully depends on the availability <strong>of</strong> spring water. It is sometime also used to irrigate rain fed<br />
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paddy field. Agricultural activity was dominant activity in the hilly area. Almost 60% <strong>of</strong> the<br />
respondents in the hilly area worked in agricultural sector.<br />
The second type <strong>of</strong> settlement pattern was clustered linear village settlement around road. It was<br />
usually characterized by engineered-semi engineered traditional brick with reinforced concrete<br />
column. Most <strong>of</strong> them were working in service sector <strong>and</strong> categorized as medium to high household<br />
income. They were mostly educated people <strong>and</strong> had more capability to cope with disaster. Besides<br />
<strong>of</strong> livestock as a saving system, they also saved their money in the bank. This social behavior was<br />
totally different with the people living in the hilly <strong>and</strong> remote area that had livestock only for their<br />
saving. This type <strong>of</strong> settlement pattern was concentrated in the western part <strong>and</strong> south-eastern part<br />
<strong>of</strong> Kayangan Catchment which were dominated by low topography. This place can be considered as<br />
a central <strong>economic</strong>, education, <strong>and</strong> local government activity. Those were also classified as low to<br />
medium socio-<strong>economic</strong> vulnerability level. Both spatial settlement patterns were illustrated in<br />
Figure 5.<br />
5. CONCLUSION<br />
<strong>Spatial</strong> pattern analysis had play important role to analyze socio-<strong>economic</strong> vulnerability in the rural area.<br />
Classified as a rural area, Kayangan Catchment is less experienced with agricultural modernization,<br />
industrialization <strong>and</strong> deindustrialization, <strong>and</strong> commodification <strong>of</strong> rural l<strong>and</strong>scapes. Thus, it reflects the<br />
societal character <strong>of</strong> rural behavior including social structure <strong>and</strong> culture. Since the nature <strong>of</strong> people, social<br />
structure <strong>and</strong> culture <strong>of</strong> Kayangan Catchment were less influenced by the development <strong>of</strong> technology <strong>and</strong><br />
modernization, the natural feature would more dominantly influence the socio-<strong>economic</strong> vulnerability in<br />
Kayangan Catchment. Determining environmental factors influencing socio-<strong>economic</strong> vulnerability was very<br />
helpful to predict spatial distribution <strong>of</strong> socio-<strong>economic</strong> vulnerability through fuzzy logic technique. Fuzzy<br />
membership function is generated by relating map classes <strong>of</strong> environmental variable to membership value.<br />
The fuzzy operator Or, Sum <strong>and</strong> high gamma value were successfully applied to predict spatial distribution<br />
<strong>of</strong> socio-<strong>economic</strong> vulnerability level. The result coincided with the <strong>economic</strong>, social <strong>and</strong> ecological<br />
dimensions <strong>of</strong> livelihood in Kayangan Catchment.<br />
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