Spatial data mining is a process to discover interesting and potentially useful spatial patterns embedded in spatial databases. Efficient tools for extracting information from spatial data sets can be of importance to organizations which own, generate and manage large geo-spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools after "materializing" spatial relationships and assuming independence between different data points. However, classical data mining methods often perform poorly on spatial data sets which have high spatial auto-correlation. In this paper we will review spatial statistical techniques which can effectively model the notion of spatial-autocorrelation and apply it to the problem of predicting bird nest locations in a marshland.
Chawla, Sanjay; Shekhar, Shashi; WuLi, Wei; Ozesmi, Uygar.
Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest Locations.
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