Ester et al. (1997) propose basic operations used for knowledge discovery in databases (KDD) for spatial database systems. They do so with an emphasis on the utility of neighbourhood graphs and neighbourhood indices for KDD. When the programming language began to bleed into the article it was clear that maybe some of the finer points would be lost on me. I was reminded of the discussion of whether or not it’s critical that every concept in GIScience is accessible to every GIS user. I’m convinced that in order for GIS users to practice critical reflexivity in their use of queries within a database, they ultimately need to understand the fundamentals of the operations they utilize. After making it through the article, I can say that Ester et al. could explain these principles to a broader audience reasonably well. I’ll have to echo the sentiments of previous posts that it would have been interesting to see more discussion of this, but perhaps it’s beyond the scope of this article.
Maybe it’s because we’re now into our 9th week of GIScience discourse, but I felt that the authors did a particularly good job of situating spatial data mining–which, despite its name, might appear more closely related to the field of computer science at a glance–within the realm of GIScience. Tobler’s Law even makes an appearance on page 4! It’s an interesting thought that GIScientists might have more to contribute to computation beyond the handling of explicitly spatial data. For instance, Ester et al. point to spatial concept hierarchies that can be applied to both spatial and non-spatial attributes. You can imagine how spatial association rules conceived by spatial scientists might then lend themselves the handling of non-spatial data as well.