Predicting Locations Using Map Similarity(PLUMS): A Framework for Spatial Data Mining

Spatial data mining is a process to discover interesting, potentially useful and high utility 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 spatial data sets. The current approach towards solving spatial data mining problems is to use classical data mining tools after ``materializing'' spatial relationships. However, the key property of spatial data is that of spatial autocorrelation. Like temporal data, spatial data values are influenced by values in their immediate vicinity. Ignoring spatial autocorrelation in the modeling process leads to results which are a poor-fit and unreliable. In this paper we will propose PLUMS(Predicting Locations Using Map Similarity), a new approach for supervised spatial data mining problems. PLUMS searches the space of solutions using a map-similarity measure which is more appropriate in the context of spatial data. We will show that compared to state-of-the-art spatial statistics approaches, PLUMS achives comparable accuracy but at a fraction of the computational cost. Furthermore, PLUMS provides a general framework for specializing other data mining techniques for mining spatial data.