Physical Sciences and Mathematics Commons^™

Space-Time Hierarchical Clustering For Identifying Clusters In Spatiotemporal Point Data, David S. Lamb, Joni Downs, Steven Reader

School of Geosciences Faculty and Staff Publications

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., space–time hierarchical clustering, incorporates location, time, and attribute …