Abstract
Visual analytics combines advanced visualisation methods with intelligent analysis techniques in order to explore large data sets whose complexity, underlying structure and inherent dynamics are beyond what traditional visualisation techniques can handle. The ultimate goal is to expose relevant patterns and relationships from the data, since not everything can be exposed easily through intelligent analysis techniques. On the contrary, the human eye can outperform algorithms in grasping and interpreting subtle patterns, provided it is supported by intelligent visualisations.
In this paper, we propose three novel visual analytics techniques for analysing spatio-temporal data. First, we present a fingerprinting technique for discovering and rapidly interpreting temporal and recurring patterns by use of circular heat maps. Next, we present a technique supporting comparisons in time or space by use of circular heat map subtraction. Finally, we propose a technique enabling to characterise and get insights of the temporal behaviour of the phenomenon under study by use of label maps.
The potential of the proposed approach to reveal interesting patterns is demonstrated in a case study using traffic data, originating from multiple inductive loops in the Brussels-Capital Region, Belgium.
This research was subsidised by the Brussels-Capital Region - Innoviris and received funding from the Flemish Government (AI Research Program).
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Data source: energy consumption at Arizona State University (ASU), https://www.kaggle.com/pdnartreb/asu-buildings-energy-consumption/activity.
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Dhont, M., Tsiporkova, E., Tourwé, T., González-Deleito, N. (2020). Visual Analytics for Extracting Trends from Spatio-temporal Data. In: Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R., Ifrim, G. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2020. Lecture Notes in Computer Science(), vol 12588. Springer, Cham. https://doi.org/10.1007/978-3-030-65742-0_9
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DOI: https://doi.org/10.1007/978-3-030-65742-0_9
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