Hazard hotspots analysis from geospatial database using geospatial data mining technology

Due to the particular geographical location and geological condition, Taiwan suffers from many natural hazards which often cause series property damages and life losses. To reduce the damages and casualty, an effective real-time system for hazard prediction and mitigation is necessary. In this study, a case study for landslide hotspots and hazard factors investigation are analyzed in accordance with spatial data mining technology from massive spatial database. Many different kinds of geospatial data, such as the terrain elevation, land cover types, the distance to roads and rivers, geology maps, and monitoring rainfall data etc., are collected into the database for spatial autocorrelation and spatial regression analysis. In order to guarantee the data quality, the spatial data cleaning is essential to remove the noises, errors, outliers, and inconsistency hiding in the input spatial data sets. The experiment results show that the hot spot analysis exactly has the ability to indicate the hazards locations. In addition, the spatial relationship can be built using the geographically weight regression (GWR) model.