The article “Spatial Data Mining, by Shashi Shekhar, explains what data mining is and how it has made great strides across various categories such as location prediction, spatial outlier detection, co-location mining, and clustering. Data mining is finding meaningful patterns or information in data from a large data set that would otherwise have been imperceptible. This can be done in many ways, using either statistical tools or modeling, or a combination of both. The modeling usually takes a training set of data, and applies it to a testing set of data in order to build the model. One of the classic challenges of data mining is to take into account the spatial autocorrelation and spatial heterogeneity during this process.
The main unsolved problems of spatial data mining lie in the intersection of geospatial data and information technology. As GIScientists, this relates directly to many of the other subjects that we study. As is often the case in more modern applications of this science, we are limited more in methodology than by the technology itself. The advantage to a concept such as this is that patterns may emerge that nobody had previously considered, as opposed to doing statistical tests on hypothesized meanings of datasets. This technology opens a whole new world of possible advances in knowledge, both relating to GIScience and otherwise.
Pointy McPolygon