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

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4. RESULT AND DISCUSSION 4.1. Central Tendency and Spatial Pattern of Socio-economic Landslide Vulnerability Since the nature of people, social structure and culture of Kayangan Catchment were less influenced by the development of technology and modernization, the natural feature would more dominantly influence the way of life of the people in Kayangan Catchment. The area preference of building settlement was one example of social behavior mainly influenced by natural feature and condition. There were several options for a household to build a settlement, but there was limited option for low income household. Settlement type was often a reflection of welfare level. It was usually that low income household could not choose whether they would build their house in more gentle or steep topography. Therefore, low income household tend to build settlement based on their land given from their parent or ancestor. It was usually located in steeper, more remote and low accessibility. For high income household, there were several options to build a settlement. They usually build a settlement in relatively gentle topography and have good accessibility. Good accessibility was the most common reason where they would build a settlement. Even, with the reason of accessibility, they would build a settlement by excavating and cutting the slope into more gentle topography. Low income household living in low accessibility and hilly area had low economic ability of household to refinance the impact of natural disaster. They usually depend on the agricultural product and have low education level. Besides living in hilly area which was more prone to be landsliding, they also had high socio-economic vulnerability level. There were interlinked between natural feature, social behavior, economic level and vulnerability level. It would affect the spatial pattern of settlement in Kayangan Catchment. Figure 2 Sampling Distribution, Mean Center, Standard Distance (Left) and Directional Distribution (Right) of Socio-economic Vulnerability Spatial mean center (SMC) provides an analysis where a phenomenon tends to be centered. Figure 2 shows sampling location of socio-economic vulnerability level and their spatial mean centers. It is interesting to note that low vulnerability SMC tends to be located in the lower stream of Kayangan Catchment which has more gentle topography and nearby the center of trade, education and local government office location. In the other hand, the SMCs of medium and high level of vulnerability tend to be located in the upper part of Kayangan Catchment which has steeper topography. It means that socio-economic vulnerability is spatially associated with topography and socio-economic functional unit. Standard distance (SD) was employed to measure the distribution of socio-economic vulnerability deviate from their SMC. Figure 2 (left) shows the spatial SD circle of socio-economic vulnerability. The low vulnerability level was the highest deviation from the spatial mean and the high vulnerability level was the lowest one. However, high vulnerability and medium vulnerability had no significance difference. It implied that low vulnerability level locations were highly dispersed. High and medium vulnerability levels were not so much deviated from their mean centre. High and medium vulnerability levels were mainly located on the hilly area and the boundary of deviation was not exceeding the hilly area of Kayangan Catchment. 6 - 525 -
Since Kayangan Catchment form has unsymmetrical width, SD has not fitted to represent the directional deviation of socio-economic vulnerability. Directional distribution or Standard Deviational Ellipse (SDE) was employed to measure directional deviation of socio-economic vulnerability. The calculation of SDE made the trend of socio-economic vulnerability distribution more clear. It pointed out that low vulnerability level had highest deviation along the major axis and covered flat area. Medium vulnerability level SDE had longer major axis than high vulnerability level. It indicated that high vulnerability level tend to be concentrated on the hilly area. This implied that slope had significant role on socio-economic vulnerability in Kayangan Catchment. Nr. of Settlement Block 35 30 25 20 15 10 5 0 0 50 100 150 200 250 300 350 Distance to Road Nr. of Settlement Block 45 40 35 30 25 20 15 10 5 0 0 10 20 30 40 50 60 Slope (Degree) : Low Vulnerability : Medium Vulnerability : High Vulnerability Figure 3 the Vulnerability Level based on number of settlement towards distance to road and Slope Furthermore, nearest neighbor analysis was employed to analyze the spatial pattern of socioeconomic vulnerability. Nearest neighbor analysis of low, medium and high vulnerability level showed z-scores -4.52, -5.26 and -1.86 respectively. It implied that the spatial distribution of each level of vulnerability was clustered. Low level vulnerability household was clustered in more gentle area and high accessibility. However, low level vulnerability had higher tendency on deviation due to the high result of SD and SDE. In the other hand high level vulnerability household was clustered in the hilly and low degree of accessibility. Medium Vulnerability was also clustered in the hilly area but the accessibility is higher than low vulnerability level. Figure 3 showed the interlinked between vulnerability level and its environmental factor i.e. slope and accessibility (distance to road). Later, the trend of the interlinked between vulnerability level and its environmental was employed as an input of fuzzy membership in fuzzy logic technique. 4.2. Spatial Landslide Vulnerability Prediction by means of Fuzzy Logic Technique Spatial pattern analysis with household survey described the distribution of socio-economic vulnerability level in Kayangan Catchment quite well. However, the vulnerability level location obtained from household survey could not be described spatially on each settlement block due to the limitation of the samples. Fuzzy logic technique based on the slope and distance to road membership function was introduced to extrapolate vulnerability degree in each settlement block. Fuzzy membership function (Figure 3) and the fuzzy operator (Eqs. 1, 4 and 5) were generated to predict the socio-economic vulnerability level in Kayangan Catchment. It was computed for 6 cases i.e. Fuzzy operator Or, Sum, Gamma 0.975, gamma 0.9, gamma 0.8 and gamma 0.7. The result of computation was mapped in order to interpret the spatial distribution of socio-economic vulnerability in Kayangan Catchment. Socio-economic vulnerability map was represented by the possibility value between 0 and 1. The value of 0 indicated the full impossibility vulnerable and the value of 1 indicated the full possibility of being vulnerable. Thus, an area given as a value approaching 1 was indicated higher socio-economic vulnerability level. The final maps were presented in Figure 4 representing spatial distribution of socio-economic vulnerability level in Kayangan Catchment. 7 - 526 -
Page 1 and 2: Spatial Pattern of Socio-economic L
Page 3 and 4: Figure 1 Location of Study Area, Ka
Page 5: example, based on the spatial patte
Page 9 and 10: There were significant different am

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

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