Spatial data mining as a tool for improving geographical models

Abstract

Spatial data mining is a new and rapidly developing technique for analyzing geographical data. In this master's thesis, the usability of the technique is examined for the improvement of an existing geographical model regarding rescue operations. The main focus of spatial data mining is set on the discovery of interesting patterns of information embedded in large geographical databases. Due to its ability to operate without a previously formulated hypothesis. spatial data mining is becoming a popular tool for spatial data analyzes.

After a short explanation of the best known spatial data mining techniques, this thesis concentrates on association rule mining in more detail. Discovered spatial association rules may detect useful relationships among spatially distributed objects. Once the relations are identified, the existing spatial model can be extended by the variables with strongest relations to the modeled phenomenon.

The behavior of association rule mining is studied by applying it on sample data representing incident locations within the Helsinki city center. The core data is provided by the Fire and Rescue department in Espoo. To observe interaction of the incident with its neighbourhood, information of geographical objects situated within the study area is obtained from the SeutuCD geographical database.

Although spatial data mining does not yet belong to the most commonly used spatial data analyzes, it was found effective for detecting strong relationships among geographical objects.