Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods

We present an approach to the process of constructing knowledge through str uctured exploration of large spatiotemporal data sets. First, we introduce our problem context and define both Geographic Visualization (GVis) and Kno wledge Discovery in Databases (KDD), the source domains for methods being i ntegrated. Next, we review and compare recent GVis and KDD developments and consider the potential for their integration, emphasizing that an iterativ e process with user interaction is a central focus for uncovering interest and meaningful patterns through each. We then introduce an approach to desi gn of an integrated GVis-KDD environment directed to exploration and discov ery in the context of spatiotemporal environmental data. The approach empha sizes a matching of GVis and KDD meta-operations. Following description of the GVis and KDD methods that are linked in our prototype system, we presen t a demonstration of the prototype applied to a typical spatiotemporal data set. We conclude by outlining, briefly, research goals directed toward more complete integration of GVis and KDD methods and their connection to tempo ral GIS.