A relational perspective on spatial data mining

Remote sensing and mobile devices nowadays collect a huge amount of spatial data, which have to be analysed in order to discover interesting information about economic, social and scientific problems. However, the presence of a spatial dimension adds some problems to data mining tasks. The geometrical representation and relative positioning of spatial objects implicitly define spatial relationships, whose efficient computation requires a tight integration of the data mining system with the spatial DBMS. The interaction between spatially close objects causes different forms of autocorrelation, whose effect should be considered to improve the predictive accuracy of induced models and patterns. Units of analysis are typically composed of several spatial objects with different properties and their structure cannot be easily accommodated by classical double entry tabular data. In the paper, a way is shown to face these problems when a (multi-)relational data mining approach is considered for spatial data analysis. Moreover, the challenges that spatial data mining poses on current relational data mining methods are presented.

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