INTEGRATION OF GEOLOGICAL MULTI-HAZARD RISK MAP INTO SPATIAL PATTERN OF BANDUNG BARAT REGENCY SPATIAL PLAN

• Main Author: Adi Minarno, Pandu
• Format: Theses
• Language: Indonesia
• Subjects: Perencanaan wilayah
• Online Access: https://digilib.itb.ac.id/gdl/view/76907
• Institution: Institut Teknologi Bandung

The Spatial Pattern Plan Map in the Regional Spatial Planning document (RTRW) is the distribution of spatial allotment of an area which includes spatial allotment for protection function (kawasan lindung) and spatial allotment for cultivation function (kawasan budidaya). Indonesia as a country with various potential natural hazards makes country's disaster risk quite high, including the risk of geological disasters. This paper examines the extent of disaster risk aspects has taken into account within the preparation of Spatial Pattern in the existing RTRW. This paper aims to combine the Geological Multi-Hazard Risk Map with the Spatial Pattern of the RTRW using several types of spatial analysis approaches in Geographic Information Systems. The research location is in Bandung Barat Regency, Jawa Barat. This area was chosen as the research location because it has the potential for various geological hazards, namely from earthquakes, landslides, and volcanoes. In Bandung Barat Regency, there are sub-districts with relatively high population densities which increase vulnerability to disasters. Disaster risk is the level of potential loss of life, injury, or damage or destruction of assets that may occur in a place, community, or even community in a certain period. The interaction between the level of hazard and the level of vulnerability produces a level of disaster risk. With the ever-evolving knowledge regarding hazards as well as population patterns, as well as socio-economic development, disaster risks can be mapped and assessed. The risk map is compiled from overlayed the hazard and vulnerability maps. To create a representative and detailed risk map, spatial-based hazard data and vulnerability data with a good resolution are needed. Visualizing and compiling a geological multi-hazard map is a challenge itself, because the hazard units and levels are different for each type of geological hazard map. A conversion process is needed for each geological hazard map to become a map with a uniform hazard level range (0 to 1). This map is called a geological hazard index map, which will later be combined with other geological hazard index maps, to form a Geological Multi-Hazard Map. However, the limitations of spatial data make the method of converting hazard maps into hazard index maps is different to each other. The Earthquake Hazard Index Map was generated using the Earthquake Hazard Indexiv Map method generated using spatial analysis fuzzy logic which makes continuous pattern value of earthquake hazard levels. Landslide Hazard Index Map is generated using spatial analysis overlay and also scoring from expert interview. Meanwhile, the Volcano Hazard Index Map was generated using spatial analysis fuzzy logic, buffering, and also scoring from expert interview data. These three hazard index maps are combined to form one Geological Multi-Hazard Map. This map is generated using the highest position spatial analysis, which means that the displayed hazard index value is the highest hazard index value at each location/pixel, not the average of each hazard index. In another part of risk, vulnerability, this aspect is composed of four elements, namely social, physical, economic, and environmental. The data from each of these elements consists of several indicators with their respective levels of vulnerability. Then this data will be processed and classified into a uniform value range (0 to 1), which will produce a vulnerability map for each geological hazard. The weight of each element for each hazard vulnerability map will be different from one another. Then the three maps will be overlayed into one Geological Multi-Hazard Vulnerability Map by adjusting the characteristics for each type of geological hazard. Spatial data limitations were also found in the preparation of this vulnerability map. Social vulnerability data, namely population density and poverty are still on an administrative basis, not on a spatial basis. The spatial analysis fuzzy logic was significantly used in the hazard and vulnerability analysis in this study, namely in the preparation of hazard index maps and multi-hazard vulnerability maps, which is a novelty in the disaster risk research in Indonesia. The Geological Multi-Hazard Risk Map is generated from a combination of the Geological Multi-Hazard Map and the Geological Multi-Hazard Vulnerability Map. Then this risk map is integrated with the Spatial Pattern Plan Map of Bandung Barat Regency specifically for built-up areas, such as settlements, government buildings, markets, and industry area. This integration process is focused on builtup areas because these are areas where people are dominant in their activities and are also more vulnerable to disasters due to the presence of these buildings. This integration process produces a built-up area that has a high disaster risk. It is hoped that this area will become the concern of the Bandung Barat Government in the spatial pattern evaluation process, the process of monitoring building quality in more detail, and can also be used as a priority for community capacity building programs in dealing with disasters.