Definition
Spatiotemporal visualization uses a set of tools and techniques to explore and explain changes of information in space and time, aiming to convey specific insights in large and complex data sets to humans (Zhong et al. 2012).
Introduction
Our world is saturated with spatiotemporal data. Spatiotemporal visualization plays an important role in the discovery and understanding of the inherent nature of data. Zhong et al. (2012) defined spatiotemporal visualization as a set of tools and techniques to explore changes of information in space and time, aiming to convey specific insights in large and complex data sets to humans. Researchers have created numerous applications and techniques to visualize spatiotemporal data as efficiently and effectively as possible. Space-time cube (Gatalsky et al. 2004), small multiples (Tufte 2001), the choropleth map (Nöllenburg 2007), and stacked area charts (Hao et al. 2005...
References
Baloian, N., Zurita, G.: Achieving better usability of software supporting learning activities of large groups. Inf. Syst. Front. 18, 125–144 (2016). https://doi.org/10.1007/s10796-015-9580-3
Bandyopadhyay, S., Giannella, C., Maulik, U., Kargupta, H., Liu, K., Datta, S.: Clustering distributed data streams in peer-to-peer environments. Inf. Sci. 176, 1952–1985 (2006). https://doi.org/10.1016/j.ins.2005.11.007
Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2008)
Chang, J.S.-K., Lei, W.T., Wei, S., Promann, M., Ma, Y.A., Chen, Y.V., Qian, Z.C.: SolarWheels: an interactive situation awareness visual display for large-scale computer networks. In: Proceedings of IEEE Conference on Visual Analytics Science and Technology. Atlanta: IEEE Computer Society Press. (2013)
Chen, V.Y., Razip, A.M., Ko, S., Qian, C.Z., Ebert, D.S.: Multi-aspect visual analytics on large-scale high-dimensional cyber security data. Inf. Vis. 14, 62–75 (2015). https://doi.org/10.1177/1473871613488573
Cook, K.A., Thomas, J.J.: Illuminating the Path: The Research and Development Agenda for Visual Analytics. Pacific Northwest National Laboratory (PNNL), Richland (2005)
Eick, S.G., Karr, A.F.: Visual scalability. J. Comput. Graph. Stat. 11, 22–43 (2002). https://doi.org/10.1198/106186002317375604
Eick, S.G., Steffen, J.L., Sumner, E.E., Jr.: Seesoft-a tool for visualizing line oriented software statistics. IEEE Trans. Softw. Eng. 18, 957–968 (1992). https://doi.org/10.1109/32.177365
Gatalsky, P., Andrienko, N., Andrienko, G.: Interactive analysis of event data using space-time cube. In: Proceedings of Eighth International Conference on Information Visualisation. IV 2004, London, UK. pp. 145–152 (2004)
Gretarsson, B., Bostandjiev, S., O’Donovan, J., Höllerer, T.: WiGis: a framework for scalable web-based interactive graph visualizations. In: Proceedings of the 17th International Conference on Graph Drawing. pp. 119–134. Springer, Berlin/Heidelberg (2010)
Guo, C., Xu, S., Yu, J., Zhang, H., Wang, Q., Xia, J., Zhang, J., Chen, Y.V., Qian, Z.C., Wang, C., Ebert, D.: Dodeca-rings map: interactively finding patterns and events in large geo-temporal data. In: IEEE Conference on Visual Analytics Science and Technology (VAST), Paris, France. pp. 353–354 (2014)
Gutwin, C., Fedak, C.: Interacting with big interfaces on small screens: a comparison of fisheye, zoom, and panning techniques. In: Proceedings of Graphics Interface. pp. 145–152. Canadian Human-Computer Communications Society, School of Computer Science, University of Waterloo, Waterloo (2004)
Hao, M.C., Dayal, U., Keim, D.A., Schreck, T.: Importance-driven visualization layouts for large time series data. In: IEEE Symposium on Information Visualization INFOVIS 2005, Minneapolis, MN. pp. 203–210 (2005)
Havre, S., Hetzler, B., Nowell, L.: ThemeRiver: visualizing theme changes over time. In: IEEE Symposium on Information Visualization, InfoVis 2000, Salt Lake City, Utah. pp. 115–123 (2000)
Keim, D.A.: Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans. Vis. Comput. Graph. 6, 59–78 (2000). https://doi.org/10.1109/2945.841121
Keim, D.A., Kriegel, H.-P.: Visualization techniques for mining large databases: a comparison. IEEE Trans. Knowl. Data Eng. 8, 923–938 (1996). https://doi.org/10.1109/69.553159
Lian, X., Chen, L., Yu, J.X.: Pattern matching over cloaked time series. In: IEEE 24th International Conference on Data Engineering, Cancun, Mexico. pp. 1462–1464 (2008)
MacEachren, A.M.: Cartography and GIS: extending collaborative tools to support virtual teams. Prog. Hum. Geogr. 25, 431–444 (2001). https://doi.org/10.1191/030913201680191763
Minnen, D., Isbell, C.L., Essa, I., Starner, T.: Discovering multivariate motifs using subsequence density estimation and greedy mixture learning. In: Proceedings of the 22nd National Conference on Artificial Intelligence – 1. pp. 615–620. AAAI Press, Vancouver, (2007)
Nöllenburg, M.: Geographic visualization. In: SpringerLink. pp. 257–294. Springer Berlin/Heidelberg (2007)
Promann, M., Ma, Y.A., Wei, S., Lei, W.T., Chang, J.S.-K., Qian, Z.C., Chen, Y.V.: SpringRain: an ambient information display. In: Proceedings of the Visual Analytics Science and Technology, Atlanta, Georgia. pp. 5–6 (2013)
Robertson, G., Ebert, D., Eick, S., Keim, D., Joy, K.: Scale and complexity in visual analytics. Inf. Vis. 8, 247–253 (2009). https://doi.org/10.1057/ivs.2009.23
Rønne Jakobsen, M., Hornbæk, K.: Sizing up visualizations: effects of display size in focus+context, overview+detail, and zooming interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 1451–1460. ACM, New York (2011)
Schafer, W.A., Ganoe, C.H., Carroll, J.M.: Supporting community emergency management planning through a geocollaboration software architecture. Comput. Support. Coop. Work CSCW. 16, 501–537 (2007). https://doi.org/10.1007/s10606-007-9050-7
Scheepens, R., Willems, N., van de Wetering, H van Wijk J.J.: Interactive visualization of multivariate trajectory data with density maps. In: IEEE Pacific Visualization Symposium, Hong Kong, China. pp. 147–154 (2011)
Smith, G., Czerwinski, M., Meyers, B., Robbins, D., Robertson, G., Tan, D.S.: FacetMap: a scalable search and browse visualization. IEEE Trans. Vis. Comput. Graph. 12, 797–804 (2006). https://doi.org/10.1109/TVCG.2006.142
Tominski, C., Schumann, H., Andrienko, G., Andrienko, N.: Stacking-based visualization of trajectory attribute data. IEEE Trans. Vis. Comput. Graph. 18, 2565–2574 (2012). https://doi.org/10.1109/TVCG.2012.265
Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (2001)
Wang, Z.J., Willett, P.: Joint segmentation and classification of time series using class-specific features. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34, 1056–1067 (2004). https://doi.org/10.1109/TSMCB.2003.819486
Wattenberg, M., Kriss, J.: Designing for social data analysis. IEEE Trans. Vis. Comput. Graph. 12, 549–557 (2006). https://doi.org/10.1109/TVCG.2006.65
Wei, S., Hu, K., Cheng, L., Tang, H., Du, W., Guo, C., Pan, C., Li, M., Yu, B., Li, X., Chen, Y.V., Qian, Z.C., Zhu, Y.M.: CrowdAnalyzer: a collaborative visual analytic system. In: IEEE Conference on Visual Analytics Science and Technology (VAST), Illinois, Chicago. pp. 177–178 (2015)
Wongsuphasawat, K., Guerra Gómez, J.A., Plaisant, C., Wang, T.D., Taieb-Maimon, M., Shneiderman, B.: LifeFlow: visualizing an overview of event sequences. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 1747–1756. ACM, New York (2011)
Zhong, C., Wang, T., Zeng, W., Arisona, S.M.: Spatiotemporal visualisation: a survey and outlook. In: Arisona, S.M., Aschwanden, G., Halatsch, J., and Wonka, P. Digital Urban Modeling and Simulation. pp. 299–317. Springer Berlin/Heidelberg (2012)
Zinsmaier, M., Brandes, U., Deussen, O., Strobelt, H.: Interactive level-of-detail rendering of large graphs. IEEE Trans. Vis. Comput. Graph. 18, 2486–2495 (2012). https://doi.org/10.1109/TVCG.2012.238
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Guo, C., Wei, S., Chen, Y. (2017). Scalable Techniques to Visualize Spatiotemporal Data. In: Lee, N. (eds) Encyclopedia of Computer Graphics and Games. Springer, Cham. https://doi.org/10.1007/978-3-319-08234-9_94-1
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DOI: https://doi.org/10.1007/978-3-319-08234-9_94-1
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