Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial information to produce business intelligence or other results. This requires specific techniques and resources to get the geographical data into relevant and useful formats.
Challenges involved in spatial data mining include identifying patterns or finding objects that are relevant to the questions that drive the research project. Analysts may be looking in a large database field or other extremely large data set in order to find just the relevant data, using GIS/GPS tools or similar systems.
One interesting thing about the term "spatial data mining" is that it is generally used to talk about finding useful and non-trivial patterns in data. In other words, just setting up a visual map of geographic data may not be considered spatial data mining by experts. The core goal of a spatial data mining project is to distinguish the information in order to build real, actionable patterns to present, excluding things like statistical coincidence, randomized spatial modeling or irrelevant results. One way analysts may do this is by combing through data looking for "same-object" or "object-equivalent" models to provide accurate comparisons of different geographic locations.