This paper presented the primary tools in which to affect data mining on a set of data. The tangible results found as a result of data mining were not new to me, I believe it is something that many budding GI scientists engage with at the beginning of their education. I remember engaging with learning and training data from other classes, typically in the form of geolocating.
I found that the hidden data sets emerging from these analyses poses a very interesting insight into our epistemology of data sets. With learning and training data it seems that we’re engaging with a very basic form of machine learning. I am intrigued by the opportunities this faces with a more open form of data. I can imagine that with more open data sources, the machine learning aspects could learn from other data sets and gain more insight within hidden data. I wonder if our treatment of data and rights will come into discussion in the future. I’d be interested in know in what forums these conversations are taking place.
As a whole, all of these techniques seem to provide a very valuable tool. To extrapolate meaning from disparate forms of data, such as by clustering, determining outliers and figuring out co-locational rules can be an extremely insightful tool for a lot of disciplines in the social and physical sciences. Taking a rudimentarily psychological lens, I find it interesting how much of these techniques assume a behaviouralist understanding of spatial processes, in which they interact in rational ways with each other as part of a greater whole. The fact that they take interest in outliers seems to factor in the irrationality of some processes. I would also be interested in knowing where the research on that is headed.