Scalable analysis of movement data for extracting and exploring significant places

Gennady Andrienko 1 Natalia Andrienko 1 Christophe Hurter 2 Salvatore Rinzivillo 3 Stefan Wrobel 1
1 IAIS - Institute for Intelligent Analysis and Information Systems
2 LII - ENAC - Laboratoire d'Informatique Interactive
3 KDD Lab
Abstract : Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: (1) event extraction from trajectories; (2) extraction of relevant places based on event clustering; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large datasets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales.
Keywords : trajectory context data mining visualization cities and towns time series analysis image color analysis spatiotemporal clustering movement trajectories spatiotemporal data spatial events spatial clustering
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Gennady Andrienko, Natalia Andrienko, Christophe Hurter, Salvatore Rinzivillo, Stefan Wrobel. Scalable analysis of movement data for extracting and exploring significant places. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2013, 19 (7), pp 1078-1094. ⟨10.1109/TVCG.2012.311⟩. ⟨hal-01021632⟩

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