Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visualizing interchange patterns in massive movement data
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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In this paper, we present a new visual way of exploring state sequencesin large observational time-series. A key advantage of our method is that it can directly visualizehigher-order state transitions.A standard first order state transitionis a sequence of two states that are linked by a transition.A higher-orderstate transition is a sequence of three or more states where thesequence of participating states are linked together by consecutive firstorder state transitions.Our method extends the current state-graph exploration methods by employinga two dimensional graph, in which higher-order state transitions arevisualized as curved lines.All transitions are bundled into thicksplines, so that the thickness of an edge represents the frequency of instances.The bundling between two states takes into account the state transitionsbefore and after the transition.This is done in such a way that it formsa continuous representation in which any subsequence of the timeseries isrepresented by a continuous smooth line.The edge bundles in these graphscan be explored interactively through our incremental selection algorithm.We demonstrate our method with an application in exploring labeledtime-series data from a biological survey, where a clustering hasassigned a single label to the data at each time-point.In thesesequences, a large number of cyclic patterns occur, which in turn arelinked to specific activities.We demonstrate how our method helps tofind these cycles, and how the interactive selection process helps to findand investigate activities.