A Review on Spatial Data Mining Methods and Applications

Spatial Data Mining (SDM) is a complex phenomenon as it deals with data that represents both spatial and non-spatial correlations in spatial databases. SDM extracts latent and implicit trends in spatial data to acquire business intelligence which support expert decision making. Spatial database is very vast as it can hold the spatial objects spread across the globe. Mining such databases have plethora of real world utilities such as discovering cancer clusters, crime hotspots, warming of oceans, traffic risk analysis, agriculture land grading, analyzing forest extent changes to mention few. In order to achieve this various techniques are used. This paper focuses on reviewing various spatial data mining techniques and their applications. The essence of SDM is to have applications pertaining to pattern families such as location prediction, spatial interactions, and hotspots.