I appreciate the title of JMonterey’s blog! Spatial data mining, as described in the article by Shekhar et al., seems exactly like extracting precious resources out of the underground or a kind of ‘homogeneous’ set of data. The article gives great examples of the type of ‘gold’ that we can get from the data mining processes and it’s applications (e.g.: crime, safety, floods,…). The article points out again to me that the techniques and methods of data mining depend on what is the application and what is the type of information that the researcher is looking for. Remember the example of data mining in the article starts from the question related to bird-nesting habits. This implies choosing the right set of data for our question and establishing where we are going to find the information we are looking for.
I’m left with the question of time… The ‘gold’ or outliers are spatial objects with non-spatial characteristics that differ from their neighbors’ characteristics. But what is a neighbor? I’m wondering where the notion of time fits in the data mining models, because two spatial neighbors could have a very distant relationship if we consider time, processes, change and interactions (recall the notion of absolute space/relative space in Marceau’s article on the problem of scale).
S_Ram