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Spatiotemporal Analysis of Sensor Logs using Growth Ring Maps
November/December 2009 (vol. 15 no. 6)
pp. 913-920
Peter Bak, University of Konstanz
Florian Mansmann, University of Konstanz
Halldor Janetzko, University of Konstanz
Daniel Keim, University of Konstanz
Abstract—Spatiotemporal analysis of sensor logs is a challenging research field due to three facts: a) traditional two-dimensional maps do not support multiple events to occur at the same spatial location, b) three-dimensional solutions introduce ambiguity and are hard to navigate, and c) map distortions to solve the overlap problem are unfamiliar to most users. This paper introduces a novel approach to represent spatial data changing over time by plotting a number of non-overlapping pixels, close to the sensor positions in a map. Thereby, we encode the amount of time that a subject spent at a particular sensor to the number of plotted pixels. Color is used in a twofold manner; while distinct colors distinguish between sensor nodes in different regions, the colors’ intensity is used as an indicator to the temporal property of the subjects’ activity. The resulting visualization technique, called Growth Ring Maps, enables users to find similarities and extract patterns of interest in spatiotemporal data by using humans’ perceptual abilities. We demonstrate the newly introduced technique on a dataset that shows the behavior of healthy and Alzheimer transgenic, male and female mice. We motivate the new technique by showing that the temporal analysis based on hierarchical clustering and the spatial analysis based on transition matrices only reveal limited results. Results and findings are cross-validated using multidimensional scaling. While the focus of this paper is to apply our visualization for monitoring animal behavior, the technique is also applicable for analyzing data, such as packet tracing, geographic monitoring of sales development, or mobile phone capacity planning.

[1] E. Bertini and G. Santucci, Give chance a chance: modeling density to enhance scatter plot quality through random data sampling. Information Visualization, 5 (2): 95–110, 2006.
[2] U. Brandes and C. Pich Eigensolver methods for progressive multidimensional scaling of large data. In Proceedings of the 16th International Symposium on Graph Drawing (GD'06), pages 42–53, 2007.
[3] F. Brian, and J. Pritchard., Visualisation of historical events using lexis pencils. In Case Studies of Visualization in the Social Sciences, 1997.
[4] J. V. Carlis and J. A. Konstan., Interactive visualization of serial periodic data. In UIST '98: Proc. 11th annual ACM Symp. on User interface software and technology, pages 29–38. ACM, 1998.
[5] D. Dorling, A. Barford, and M. Newman., Worldmapper: The world as you've never seen it before. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 757, 2006.
[6] G. Ellis and A. Dix., A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics, 13 6: 1216–1223, 2007.
[7] N. Goertz, L. Lewejohann, M. Tomm, O. Ambree, K. Keyvani, W. Paulus, and N. Sachser., Effects of environmental enrichment on exploration, anxiety, and memory in female tgcrnd8 alzheimer mice. Behavioural Brain Research, 191, 2008.
[8] L. Gygax, G. Neisen, and H. Bollhalder., Accuracy and validation of a radar-based automatic local position measurement system for tracking dairy cows in free-stall barns. In Computers and electronics in agriculture, pages 22–33, 2007.
[9] M. Harrower and C. Brewer., Colorbrewer. org: an online tool for selecting colour schemes for maps. Cartographic Journal, 40 1: 27–37, 2003.
[10] H. Hochheiser and B. Shneiderman., Dynamic query tools for time series data sets: Timebox widgets for interactive exploration. Information Visualization, 3 1: 1–18, 2004.
[11] Y. Ivanov, C. Wren, A. Sorokin, and I. Kaur, Visualizing the history of living spaces. IEEE Transactions on Visualization and Computer Graphics, 13 6: 1153–1160, 2007.
[12] M. W. J. Yang and E. Rundensteiner., Hierarchical Exploration of Large Multivariate Data Sets. F. Post, G. Nielson, G.-P. Bonneau (Eds.): Data Visualization: The State of the Art 2003: 201-212, 2003.
[13] T. Kapler, and W. Wright., Geotime information visualization. In Proc. IEEE Symp. on Information Visualization, pages 25–32, 2004.
[14] D. A. Keim, M. Ankerst, and H.-P. Kriegel., Recursive pattern: A technique for visualizing very large amounts of data. In Proc. 6th Conf. on Visualization, pages 279–286, 1995.
[15] D. A. Keim, M. C. Hao, U. Dayal, and M. Hsu., Pixel bar charts: a visualization technique for very large multi-attribute data sets. Information Visualization, 1 1: 20–34, 2002.
[16] M. J. Kraak, The space-time cube revisited from a geovisualization perspective. Proc. 21st Int. Cartographic Conf., 2003.
[17] M. Kritzler, L. Lewejohann, and A. Krueger, Analysing movement and behavioural patterns of laboratory mice in a semi natural environment based on data collected via rfid-technology. In Behaviour Monitoring and Interpretation, pages 17–28, 2007.
[18] L. Lewejohann, N. Reefmann, P. Widmann, O. Ambre, A. Herring, K. Keyvani, W. Paulus, and N. Sachser., Transgenic alzheimer mice in a semi-naturalistic environment: More plaques, yet not compromised in daily life. Behavioural Brain Research, 2009 − in press.
[19] C. Panse, M. Sips, D. Keim, and S. North., Visualization of geo-spatial point sets via global shape transformation and local pixel placement. IEEE Transactions on Visualization and Computer Graphics, 12 5: 749–756, 2006.
[20] T. Saito, H. Miyamura, M. Yamamoto, H. Saito, Y. Hoshiya, and T. Kaseda., Two-Tone Pseudo Coloring: Compact Visualization for One-Dimensional Data. In Proc. IEEE Symp. on Information Visualization, pages 173–180, 2005.
[21] C. Tominski, J. Abello, and H. Schumann., Axes-based visualizations with radial layouts. In Proc. ACM Symp. on Applied computing, pages 1242–1247, 2004.
[22] C. Tominski, P. Schulze-Wollgast, and H. Schumann., 3D Information Visualization for Time Dependent Data on Maps. In Proc. Int. Conf. on Information Visualisation, pages 175–181, 2005.
[23] W. S. Torgerson., Multidimensional scaling: I. Theory and method. Psychometrika, 17 4: 401–419, 1952.
[24] J. J. Van Wijk and E. R. Van Selow, Cluster and calendar based visualization of time series data. In Proc. IEEE Symp. on Information Visualization, pages 4–9. IEEE Computer Society, 1999.
[25] J. Ward., Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc, (58): 236–244, 1963.
[26] T. Zhang, R. Ramakrishnan, and M. Livny., Birch: an efficient data clustering method for very large databases. SIGMOD Rec., 25 (2), 1996.

Index Terms:
spatiotemporal visualization, visual analytics, animal behavior, dense pixel displays
Peter Bak, Florian Mansmann, Halldor Janetzko, Daniel Keim, "Spatiotemporal Analysis of Sensor Logs using Growth Ring Maps," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 6, pp. 913-920, Nov.-Dec. 2009, doi:10.1109/TVCG.2009.182
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