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Geographic Information System (GIS) for Landslide Prone Prediction

 Geographic Information System (GIS) for Landslide Prone Prediction


Geographic Information System abbreviated as GIS (English: Geographic Information System) is a special information system that manages data that has spatial information (spatial reference). The spatial information that is sought is to get a picture of the situation of the earth's surface about the earth's surface that is needed to be able to answer or solve a problem that is contained in the surface of the earth concerned. The series of activities include collecting, structuring, processing, analyzing, and presenting data or facts that exist or are contained in a certain earth surface space. The data / facts that exist on the face of the earth are often referred to as fact / geographic data or spatial data / facts. The results of the analysis are called geographic information or spatial information. In other words, GIS is a series of activities for collecting, structuring, processing and analyzing data / facts or spatial data in order to obtain spatial information to be able to answer or solve a problem in a certain earth surface space. This analysis function is carried out using spatial data and attribute data in GIS to answer various questions developed from existing data into a relevant problem, the analysis function intended is the function of processing and analyzing spatial data and attributes, in simplifying the various analysis groups there are 4 categories. namely: data calling / classification / measuring functions, overlapping functions, neighbor functions and network / linkage functions (Arifin et., al. 2006). Landslide prone areas in Solok Regency, West Sumatra Province were analyzed based on overlapping or overlaying maps of slopes, rainfall, geology and soil. For each of these field variables, the highest weight is given a value of 5 (five) and the lowest is given a weight of 1 (one). The assumption used is that the higher the weight value given, the greater the influence of the field variables in influencing a landslide event (BAPPEDA, 2010).