Tobler’s Law of Geography is central to spatial data mining. The purpose of knowledge discovery in databases is to identify clusters of similar attributes and find links with the distribution of other attributes in the same areas. Using decision tree algorithms, spatial data systems and their associated neighborhood graphs can be classified, and rules can be concluded from the results. The four generic tasks introduced in the beginning of the article are not addressed later on. Identifying deviation from an expected pattern is presented as central to KDD as well, but an algorithm for this doesn’t appear to be discussed.
The article remains strictly concentrated on the implications of KDD algorithms on spatial database systems and computer systems. Little relation is made to non-spatial database systems, even though many of the algorithms presented are based on non-spatial decision-tree algorithms.
I’m sure that patterns can be detected in human attributes of nodes in a social network. Since distance along an edge is so crucial to spatial classification, do non-physical edges quantified in other ways perform similarly in the creation of human “neighborhoods”? When patterns are deviated from, can conclusions be drawn as easily about social networks?
“Neighborhood indices” are important sources of knowledge that can drastically reduce the time of a database query. Creating spatial indices requires some knowledge of a spatial hierarchy. Spatial hierarchies are clear-cut in political representations of geography. As pointed out in the article, often the influence of centers (i.e. cities) is not restricted to political demarcations. These algorithmically created neighborhood indices may present interesting results to urban planners and geographers, who often have difficulty delineating the extent of influence of cities. beyond their municipal borders.