Analysis and prediction of natural disaster using spatial data mining technique

Data mining, also called knowledge-discovery in databases (KDD), is the process of automatically searching large volumes of data for patterns using specific DM technique. Goal of the data mining process is to extract information from a data set and transform it to an user understandable structure. Spatial data mining is the application of data mining methods to spatial data. Goal of Spatial data mining is to find patterns in data with respect to Geography. Data mining offers great potential benefits for GIS ( Geographic Information System) based decision making. Spatial databases mainly store two types of data: raster data (satellite/aerial digital images) and vector data (points, lines, polygons). Need of Spatial database. To store and query data that represents objects defined in a geometric space. To handle more complex structures such as 3D objects, topological coverage's, linear networks, etc. Some of its Issues and Challenges are described here: (1) The unique characteristic of spatial datasets requires significant modification of data mining techniques so that they can exploit the rich spatial and temporal relationships and patterns embedded in the datasets. (2) The attributes of neighboring patterns may have significant influence on a pattern and should be considered. (3)Visualization of the spatial patterns, scalability of data mining methods, data structures to represent and efficiently index spatial datasets are also challenging issues. (4) Spatial and temporal relationships like distance, topology, direction, before and after are information bearing. They need to be considered in spatiotemporal data analysis and mining.