Spatiotemporal analysis of sensor logs using growth ring maps

IEEE Transactions on Visualization and Computer Graphics

Volumn 15, Issue 6, 2009, Pages 913-920

  Bak, Peter ^a   Mansmann, Florian ^a   Janetzko, Halldor ^a   Keim, Daniel A ^a  

^a UNIVERSITY OF KONSTANZ   (Germany)

Author keywords

animal behavior; dense pixel displays; spatiotemporal visualization; visual analytics

Indexed keywords

ALZHEIMER; ANIMAL BEHAVIOR; CAPACITY PLANNING; DATA SETS; DENSE PIXEL DISPLAYS; GROWTH RINGS; HIER-ARCHICAL CLUSTERING; MULTI-DIMENSIONAL SCALING; RESEARCH FIELDS; SENSOR LOGS; SENSOR POSITION; SPATIAL ANALYSIS; SPATIAL DATA; SPATIAL LOCATION; SPATIO-TEMPORAL DATA; SPATIOTEMPORAL ANALYSIS; SPATIOTEMPORAL VISUALIZATION; TEMPORAL ANALYSIS; TEMPORAL PROPERTY; THREE-DIMENSIONAL SOLUTIONS; TRANSGENICS; TRANSITION MATRICES; TWO-DIMENSIONAL MAP; VISUAL ANALYTICS; VISUALIZATION TECHNIQUE;

ANIMALS; COLOR; DATA VISUALIZATION; MULTI AGENT SYSTEMS; SENSOR NODES; SENSORS; TELECOMMUNICATION EQUIPMENT; VISUALIZATION;

PIXELS;

ALZHEIMER DISEASE; ANIMAL; ANIMAL BEHAVIOR; ARTICLE; BEHAVIOR; BIOLOGY; CLUSTER ANALYSIS; COMPUTER GRAPHICS; DISEASE MODEL; FEMALE; MALE; METHODOLOGY; MOUSE; PHYSIOLOGY; TIME; TRANSGENIC ANIMAL;

ALZHEIMER DISEASE; ANIMALS; ANIMALS, GENETICALLY MODIFIED; BEHAVIOR, ANIMAL; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER GRAPHICS; DISEASE MODELS, ANIMAL; FEMALE; MALE; MICE; SPATIAL BEHAVIOR; TIME FACTORS;

EID: 70350628987     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2009.182     Document Type: Conference Paper

References (26)

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. (Pubitemid 44500053)
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. Psy-chometrika, 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.