Spatial Pattern of Socio-economic Landslide Vulnerability and its Spatial Prediction by means of GIS-Fuzzy Logic in Kayangan Catchment Indonesia

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1 Spatial Pattern of Socio-economic Landslide Vulnerability and its Spatial Prediction by means of GIS-Fuzzy Logic in Kayangan Catchment Indonesia Guruh SAMODRA 1,2 *, Guangqi CHEN 1, Junun SARTOHADI 2, Kiyonobu KASAMA 1, Danang Sri HADMOKO 1 1 Civil and Structural Engineering Department, Kyushu University, Japan 2 Environmental Geography Department, Universitas Gadjah Mada, Indonesia The objective of this research is to identify the spatial pattern and to explore the spatial distribution of socioeconomic landslide vulnerability in Kayangan Catchment. Household survey was employed in this research in order to construct several indicators of socio-economic landslide vulnerability. It was conducted by transect walk and structured (closed question) interview. The information of economic and social vulnerability was taken from 112 respondents through multistage random sampling based on the settlement obtained from landuse map from Bakosurtanal (Survey and Mapping Agency of Indonesia). Photograph analysis was also used to accompany the vulnerability analysis of household survey. Mean centre, standard distance, directional distribution and average nearest neighbor analysis were employed in order to figure out the spatial pattern of socio-economic landslide vulnerability. Therefore the spatial pattern of vulnerability accompanied by knowledge driven expert judgment was employed to build fuzzy membership analysis. Fuzzy logic technique was applied in order to predict spatial distribution of overall settlement polygon in Kayangan Catchment. Nearest neighbor analysis of low, medium and high vulnerability level showed z-scores -4.52, and respectively. It implied that the spatial distribution of each level of vulnerability was clustered. There were interlinked between natural feature, social behavior, economic level and vulnerability level. The fuzzy operator Or, Sum and high gamma value were successfully applied to predict spatial distribution of socio-economic vulnerability level. The result coincided with the economic, social and ecological dimensions of livelihood in Kayangan Catchment. Key words: Socio-economic, Landslide, Vulnerability, Spatial, Prediction 1. INTRODUCTION The recent increasing number of disaster demands a response for administrator and policy maker to minimize loss of life, damage property and people suffering. Several mitigation strategies and measures focusing on hazard have been addressed to overcome those objectives. However, it has not been fully effective due to the uniqueness of socio-economic community and environmental response to cope with disaster. It causes mitigation strategies and measures are inadequate to address the disaster as a threat. Recognizing broader spectrum of hazard and more appropriate framework analysis are necessary to identify the unique process of socio-economic and environmental response to natural hazard. Vulnerability concept is widely proposed to overcome the drawbacks of limited and centered hazard analysis. As stated in the Hyogo Framework 2007 that vulnerability analysis are demanded for risk analysis dealing with natural disasters and the societal consequences of disaster (UNISDR, 2007). Nowadays, vulnerability becomes central concept in disaster research and mitigation strategies (Menoni, et al., 2012). Recent development and debate of vulnerability analysis related to terminology (Costa and Cropp, 2012), concept (Wolf, 2012), methodological, robustness and legitimacy (Kaynia, et al., 2008; Fekete, 2011) are documented well. In addition, spatial data analysis with GIS techniques and procedure has become major * guruh.samodra@gmail.com

2 tool and trending topic in order to figure out place-based vulnerability analysis. However, there are several major considerations should be underlined in the development of spatial vulnerability analysis. Scale analysis and the robustness of method are important aspect to employ GIS technique in vulnerability assessment. The scale analysis of vulnerability assessment is usually driven by the availability of data and usually applied in site specific location or over to the large area. It was usually based on the analysis of secondary traditional vulnerability data based on administrative unit i.e. age, gender, literacy, and population. The main drawback of this procedure is the unmatched of administrative data and the boundary of study area. For instance, the problem arises when demanded on the analysis of catchment area in which the boundaries of catchment is oversize or undersize with the administrative unit. Due to lack of data on the household characteristic, the vulnerability per settlement area is also difficult to be estimated by administrative based data. The choice in the scale analysis of observation and its spatial unit is necessary in spatial vulnerability assessment (Fekete, 2010). To overcome the limitation of administrative data, the socio-economic vulnerability data should be collected by direct survey (primary data). Those are only available obtained through census or and community based methods. However, data collected using house-to-house surveys and community based methods have limited efficiency and transferability (Ebert et al., 2009). Medium scale vulnerability assessment based on catchment analysis focused on household level is challenging in order to breakdown the drawbacks of traditional spatial vulnerability assessment. It will be very valuable to explain the spatial interdependencies of landslide phenomenon in the framework of environmental system. Sampling technique was proposed in order to overcome timely and costly census procedure. However, the limitation of the sampling technique is related to the generalization of spatial unit attribute. Limited sample will only describe a small portion of whole spatial unit attribute in the entire the study area. Generalization of the sample data should fulfill the requirement of vulnerability information in each spatial unit in the whole study area. It requires an understanding of relationship between vulnerability information and the environment factors affecting them. Spatial data analysis focusing on detecting pattern is a capable tool to understand processes which are responsible for observed patterns (Fischer, 2002). Spatial pattern analysis focuses on the describing space as an important role to derive socio-economic process in the space-time context. It is powerful analysis to recognize phenomenon related to the spatial interaction among observed field. Household sample is derived to draw inferences about underlying process of socio-economic vulnerability and its environment based on the settlement block spatial unit. Average nearest neighbor analysis was employed in order to figure out the spatial pattern of socio-economic landslide vulnerability. Geographic distribution of mean centre analysis, standard distance and directional distribution were also employed in order to identify spatial tendency and spatial distribution of socio-economic vulnerability. It was intended to explore the interdependencies between socio-economic landslide vulnerability with its environmental factor. Then, the spatial pattern analysis was applied as an input of fuzzy membership technique to predict socio-economic landslide vulnerability in the whole settlement block of Kayangan Catchment. Therefore, the objective of this paper is to propose medium scale spatial vulnerability analysis based on the socio-economic indicators through fuzzy logic technique. 2. STUDY AREA The research area was conducted in Kayangan Catchment Kulon Progo, Yogakarta Indonesia (Figure 1). The area of Kayangan Catchment extends 4 sub-districts i.e. Girimulyo, Nanggulan, Samigaluh and Kaligesing. It is located in the middle Java Island and comprises 35 km 2. The average annual rainfall in Kayangan Catchment is 2478 mm. The highest rainfall intensity usually occurs from February to March with average monthly rainfall 426 mm. Landslide usually occurs in the month of November to April during wet season. Land use in the study area can be classified into bushes, rain fed paddy field, irrigated paddy field, people forest, settlement, and dry cultivated land. Kulon Progo area has been traditionally dominated by agricultural sector. It is also the second lowest economic growth and the welfare level has only 8.7% of total GDP among 5 regencies in Yogyakarta Province. Around 78% household working in agricultural sector and mostly (88%) are living in rural areas. With the limitation of economic development and infrastructure, % people are living below national poverty line (BPS, 2008). Due to inadequate access and resource to economic development, it may increase socio-economic vulnerability level when disaster occurs

3 Figure 1 Location of Study Area, Kayangan Catchment, Indonesia. 3. METHODOLOGY 3.1. Vulnerability Indicators Vulnerability indicators play an important role to vulnerability analysis. It should be in line with the characteristics of person or group in terms of their capacity to anticipate, cope with, resist and recover from with the three pillars of sustainable development which is pointed out to reduce vulnerability in the context of social, economic and environmental spheres (Birkmann, 2006). It explains that the vulnerability is interlinked with the individual, household or community ability to recover from disaster by strengthening the exposure, coping capacity and resilience. The complex nature of people, household, social structure and culture are amongst the important features that determine how well selected indicators represent socioeconomic landslide vulnerability. Therefore several indicators were employed to describe exposure, coping capacity and resilience in Kayangan Catchment. Exposure is derived from building material, construction type, building age, infrastructures, and building location. Coping capacity described as capability to cope with disaster is derived from preparedness, knowledge of landslide, age and gender. Resilience which is generally described as an ability of household to recover from disaster is derived from income, saving and building architecture representing the economic welfare of a household. The indicators of economic welfare were intended to explore the economic ability of household to refinance the impact of natural disaster. Several closed questions were employed to gain indepth information of preparedness and knowledge of landslide. For instance as preparedness of evacuation route, emergency needs, important phone number, important document, participation of landslide education/socialization, etc. The sampling technique and data mining of socio-economic landslide vulnerability were conducted by household survey using observation, photograph analysis and interview. Household survey was conducted by transect walk and structured (closed question) interview by using questionnaire. Photograph analysis of building was also used to accompany the analysis of household survey to portray the building architecture. Samples of 112 respondents were successfully aided to derive socio-economic vulnerability. Weighting

4 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 Classification Exposure Indicators Coping Capacity Indicators Resilience Indicators 1. Low 1. Building Material: traditional brick with reinforced concrete column 1. Age: dominated by years old 2. Gender: dominated by 1. Income: > IDR 2. Saving: yes 2. Construction type: engineered-semi engineered male 3. Preparedness: well 3. Building material: 3. Building age: (<10 years) prepared traditional brick 4. Infrastructure (distance to road): high accessibility 4. Knowledge of landslide: highly understood with reinforced concrete column (<10 m) 2. Medium 1. Building Material: traditional brick combined with bamboo and timber 1. Age: no domination 2. Gender: no domination 3. Preparedness: 1. Income: Saving: no 2. Construction type: semi engineered-non engineered Moderately prepared 4. Knowledge of landslide: 3. Building material: 3. Building age: > 10 years 20 years slightly understood traditional brick with bamboo and 4. Infrastructure (distance to road): medium accessibility (>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 <15 and >64 2. Gender: dominated by female 3. Preparedness: poorly prepared 4. Knowledge of landslide: no understood 1. Income: < 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 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

5 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

6 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

7 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 Distance to Road Nr. of Settlement Block 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, and 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 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

8 (a) (b) (c) (d) (e (e) Figure 4 Socio-economic Vulnerability Map based on Fuzzy Operator Or (a), Sum (b), Gamma 0975 (c), gamma 09 (d), gamma 08 (e), gamma 07 (f) (f)

9 There were significant different amongst cases of fuzzy operators. Fuzzy membership and fuzzy operator play important role to generate final map of socio-economic landslide vulnerability. It implied that fuzzy operator Or, Sum and high gamma value represented better result than fuzzy operator product, fuzzy operator and and fuzzy operator with low gamma value. Since the fuzzy operator product, and and low gamma value working with returning the minimum value of the pixel membership and the result was always less than the membership value of the input, the result of the map will be low vulnerable in the whole area. Thus, fuzzy operator product, fuzzy operator and and fuzzy operator with low gamma value were not included in the analysis. Figure 5 (a) Socio-economic Vulnerability Map of Kayangan Catchment (b) High Accessibility and Centre of Activities(c) Low Vulnerability Household (d) Bad Road Condition Showing Low Accessibility (e) High Vulnerability Household in Hilly area The result map of fuzzy operator Or, Sum and high gamma value tend to have similar trend describing socio-economic vulnerability level. High vulnerability level was located in the middle part of Kayangan Catchment in which covered by steep topography and low accessibility. The result of field observation showed that the settlement pattern of high vulnerability level tend to be clustered and was characterized by semi-permanent building constructed by non-engineered bamboo and timber. The clustered pattern would depend on the natural resource. A single clustered village was usually located close to water resource (spring water). It was due to water supply in the hilly area which fully depends on the availability of spring water. It is sometime also used to irrigate rain fed

10 paddy field. Agricultural activity was dominant activity in the hilly area. Almost 60% of the respondents in the hilly area worked in agricultural sector. The second type of settlement pattern was clustered linear village settlement around road. It was usually characterized by engineered-semi engineered traditional brick with reinforced concrete column. Most of them were working in service sector and categorized as medium to high household income. They were mostly educated people and had more capability to cope with disaster. Besides of livestock as a saving system, they also saved their money in the bank. This social behavior was totally different with the people living in the hilly and remote area that had livestock only for their saving. This type of settlement pattern was concentrated in the western part and south-eastern part of Kayangan Catchment which were dominated by low topography. This place can be considered as a central economic, education, and local government activity. Those were also classified as low to medium socio-economic vulnerability level. Both spatial settlement patterns were illustrated in Figure CONCLUSION Spatial pattern analysis had play important role to analyze socio-economic vulnerability in the rural area. Classified as a rural area, Kayangan Catchment is less experienced with agricultural modernization, industrialization and deindustrialization, and commodification of rural landscapes. Thus, it reflects the societal character of rural behavior including social structure and culture. 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 socio-economic vulnerability in Kayangan Catchment. Determining environmental factors influencing socio-economic vulnerability was very helpful to predict spatial distribution of socio-economic vulnerability through fuzzy logic technique. Fuzzy membership function is generated by relating map classes of environmental variable to membership value. The fuzzy operator Or, Sum and high gamma value were successfully applied to predict spatial distribution of socio-economic vulnerability level. The result coincided with the economic, social and ecological dimensions of livelihood in Kayangan Catchment. REFERENCES (UNISDR) United Nations International Strategy for Disaster Reduction Secretariat Hyogo Framework for Action : Building the Resilience of Nations and Communities to Disasters. United Nations International Strategy for Disaster Reduction Secretariat, Geneva. Birkmann, J Measuring Vulnerability to Natural Hazards: towards Disaster Resilient Sicieties. United Nation University Press, Tokyo. BPS (Badan Pusat Statistik) Kulon Progo Regency in Figures BPS. Yogyakarta Costa, L and Kropp J, P Linking components of Vulnerability in Theoretic Frameworks and Case Studies. Sustain Sci DOI /s Ebert, A., Kerle, N., & Stein, A Urban Social Vulnerability Assessment with Physical Proxies and Spatial Metrics derived from Air- and Spaceborne Imagery and GIS Data. Nat Hazards 48: Fekete, A Spatial Disaster Vulnerability and Risk Assessment: Challenges in Their Quality and Acceptance. Nat Hazards 61: Fekete, A., Damm, M., Birkmann, J Scales as Challenge for Vulnerability Assessment. Nat Hazards 55 (3): Fischer, M. M Spatial Analysis in Geography. International Encyclopedia of the Social & Behavioral Sciences Kaynia, A. M., Papathoma-Kohle, M., Neuhauser, B., Ratzinger, K., Wenzel, H., Medina-Cetina, Z Probabilistic Assessment of Vulnerability to Landslide: Application to the Village of Lichtenstein, Baden-Wurttemberg, Germany. Engineering Geology 101: Kuhlicke, C., Scolobig, A., Tapsell, S., Steinfuhrer, A., De Marchi, B Contextualizing Social Vulnerability: Finding from case Studies Across Europe. Nat Hazards 58: Menoni, S., Molinari, D., Parker, D., Ballio, F., Tapsell Assessing Multifaceted Vulnerability and Resilience in order to Design risk-mitigation Strategies. Nat Hazards DOI /s Wolf, S Vulnerability and Risk: Comparing Assessment Approaches. Nat Hazards 61: