Spatial data mining relies on the geographic attributes of the data to uncover spatial relationships within the dataset leading to knowledge discovery. No doubt that if implemented successfully, then it has contributed to developing spatial theories, and contributing to geographic research. It’s a science!
The authors contend that with the increasing contribution of volunteered geographic information, GPS and LBS technology provides new research directions for the field. Though there is an abundance spatial data, and borrowing from Beth’s theme of critical GIS – we have to be mindful that those that can/do contribute is not representative an entire population. Bias is inherent in obtaining data from these sources because certain groups of people have been known to contribute more, certain locales can be headlined more often, and there is a large group of individuals who are disenfranchised because they do not have access to such technologies and completely sidelined. The authors concern themselves with developing the right questions and methodologies to solicit the answers, but is the data appropriate to answer the questions being asked?
Though there are several techniques to begin the spatial data mining process, how does the user decide which technique is appropriate for their analysis? Given that the end user may not be well versed in spatial data mining and nuances that exist within different types of techniques, different results will be generated by different rule sets, classifications. Since spatial data mining is a multidisciplinary field, who should be ultimately responsible for teaching the theories and methodologies?
-tranv