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From Spatial-Temporal Cluster Relationships to Lifecycles: Framework and Mobility Applications

Spatial-temporal data analysis relates to the application of data analysis techniques to data where space and time are both relevant. Usually, the results of these techniques are used to classify or predict a phenomenon, but little attention is given to the explanation of how such phenomenon happened. For example, one may predict that a sporting event will happen at a particular location and date, but little is known about the indications that such event will happen (e.g. a higher number of vehicles on certain streets, parking lots becoming full, large number of vehicles going to supermarkets, or a sudden drop in pedestrian and vehicular traffic movement when the match starts). In this paper, we report on our ongoing work on using spatial-temporal cluster relationships to identify cluster lifecycles. These lifecycles are a series of stages through which a cluster passes during its lifetime, much like a human lifecycle of birth, growth, reproduction, and death. We focus on the identification of cluster lifecycle stages, namely start, expand, shrink, and end, and on their use to predict spatial-temporal phenomena, such as traffic congestion, human events, or animal movement.