Abstract
<?tight?>Discovering the correlations among variables of air quality data is challenging, because the correlation time series are long-lasting, multi-faceted, and information-sparse. In this article, we propose a novel visual representation, called Time-correlation-partitioning (TCP) tree, that compactly characterizes correlations of multiple air quality variables and their evolutions. A TCP tree is generated by partitioning the information-theoretic correlation time series into pieces with respect to the variable hierarchy and temporal variations, and reorganizing these pieces into a hierarchically nested structure. The visual exploration of a TCP tree provides a sparse data traversal of the correlation variations and a situation-aware analysis of correlations among variables. This can help meteorologists understand the correlations among air quality variables better. We demonstrate the efficiency of our approach in a real-world air quality investigation scenario.
- COLORBREWER 2.0. Retrieved from http://colorbrewer2.org/#type=sequential&scheme===Reds&n===3.Google Scholar
- Ayan Biswas, Soumya Dutta, Han-Wei Shen, and Jonathan Woodring. 2013. An information-aware framework for exploring multivariate data sets. Trans. Visual. Comput. Graph. 19, 12 (2013), 2683--2692. Google ScholarDigital Library
- Anna Lisa Bondi and Antonella Plaia. 2005. Weather variables and air pollution via hierarchical linear models. Stat. Environ. (2005), 237--240.Google Scholar
- U. D. Bordoloi and Han Wei Shen. 2005. View selection for volume rendering. In IEEE Visualization. 62.Google Scholar
- Stefan Bruckner and Torsten Möller. 2010. Isosurface similarity maps. In Computer Graphics Forum, Vol. 29. Wiley Online Library, 773--782. Google ScholarDigital Library
- Min Chen, Miquel Feixas, Ivan Viola, Anton Bardera, Han-Wei Shen, and Mateu Sbert. 2016. Information Theory Tools for Visualization. CRC Press. Google ScholarDigital Library
- Min Chen and Amos Golan. 2016. What may visualization processes optimize? Trans. Visual. Comput. Graph. 22, 12 (2016), 2619--2632. Google ScholarDigital Library
- Min Chen and Heike Jaenicke. 2010. An information-theoretic framework for visualization. Trans. Visual. Comput. Graph. 16, 6 (2010), 1206--1215. Google ScholarDigital Library
- Thomas M. Cover and Joy A. Thomas. 2012. Elements of Information Theory. John Wiley 8 Sons.Google Scholar
- Tuan Nhon Dang, Anushka Anand, and Leland Wilkinson. 2013. Timeseer: Scagnostics for high-dimensional time series. Trans. Visual. Comput. Graph. 19, 3 (2013), 470--483. Google ScholarDigital Library
- M. Demuzere, R. M. Trigo, Vila Guerau De Arellano, and N. P. M. Van Lipzig. 2009. The impact of weather and atmospheric circulation on O3 and PM10 levels at a rural mid-latitude site. Atmos. Chem. Phys. 9, 2009 (2009), 2695--2714.Google ScholarCross Ref
- Miquel Feixas, Esteve Del Acebo, Philippe Bekaert, and Mateu Sbert. 1999. An information theory framework for the analysis of scene complexity. In Computer Graphics Forum, Vol. 18. Wiley Online Library, 95--106.Google Scholar
- Yi Gu and Chaoli Wang. 2011. Transgraph: Hierarchical exploration of transition relationships in time-varying volumetric data. Trans. Visual. Comput. Graph. 17, 12 (2011), 2015--2024. Google ScholarDigital Library
- Steffen Hadlak, Heidrun Schumann, Clemens H. Cap, and Till Wollenberg. 2013. Supporting the visual analysis of dynamic networks by clustering associated temporal attributes. IEEE Trans. Visual. Comput. Graph. 19, 12 (2013), 2267--2276. Google ScholarDigital Library
- Martin Haidacher, Stefan Bruckner, Armin Kanitsar, and M. Eduard Gröller. 2008. Information-based transfer functions for multimodal visualization. In Proceedings of the 1st Eurographics conference on Visual Computing for Biomedicine. Eurographics Association, 101--108. Google ScholarDigital Library
- Susan Havre, Beth Hetzler, and Lucy Nowell. 2000. ThemeRiver: Visualizing theme changes over time. In Proceedings of the IEEE Symposium on Information Visualization. 115--123. Google ScholarDigital Library
- K. Ito, G. D. Thurston, A. Nadas, and M. Lippmann. 2001. Monitor-to-monitor temporal correlation of air pollution and weather variables in the North-Central U.S. J. Exposure Anal. Environ. Epidemiol. 11, 1 (2001), 21--32.Google ScholarCross Ref
- Guangfeng Ji and Han-Wei Shen. 2006. Dynamic view selection for time-varying volumes. Trans. Visual. Comput. Graph. 12, 5 (2006), 1109--1116. Google ScholarDigital Library
- Teng-Yok Lee and Han-Wei Shen. 2009. Visualization and exploration of temporal trend relationships in multivariate time-varying data. Trans. Visual. Comput. Graph. 15, 6 (2009), 1359--1366. Google ScholarDigital Library
- Philip Levis, Nelson Lee, Matt Welsh, and David Culler. 2003. TOSSIM: Accurate and scalable simulation of entire TinyOS applications. In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems. Google ScholarDigital Library
- Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, and Daonna M. Nystrom. 2004. VizTree: A tool for visually mining and monitoring massive time-series databases. In Proceedings of the 30th International Conference on Very Large Data Bases-Volume 30. VLDB Endowment, 1269--1272. Google ScholarDigital Library
- Michael Ogawa and Kwan-Liu Ma. 2010. Software evolution storylines. In Proceedings of the 5th International Symposium on Software Visualization. ACM, 35--42. Google ScholarDigital Library
- Catherine Plaisant, Brett Milash, Anne Rose, Seth Widoff, and Ben Shneiderman. 1996. LifeLines: Visualizing personal histories. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 221--227. Google ScholarDigital Library
- Huamin Qu, Wing Yi Chan, Anbang Xu, Kai Lun Chung, Kai Hon Lau, and Ping Guo. 2007. Visual analysis of the air pollution problem in Hong Kong. Trans. Visual. Comput. Graph. 13, 6 (2007), 1408--1415. Google ScholarDigital Library
- P. S. Quinan and M. Meyer. 2016. Visually comparing weather features in forecasts. Trans. Visual. Comput. Graph. 22, 1 (2016), 389--398.Google ScholarDigital Library
- William Ribarsky, Zachary Wartell, and Wenwen Dou. 2012. Event structuring as a general approach to building knowledge in time-based collections. In Expanding the Frontiers of Visual Analytics and Visualization. Springer, London, 149--162.Google Scholar
- Jaume Rigau, Miquel Feixas, and Mateu Sbert. 2005. Shape complexity based on mutual information. In Proceedings of the International Conference Shape Modeling and Applications. IEEE, 355--360. Google ScholarDigital Library
- Thomas Schreiber. 2000. Measuring information transfer. Phys. Rev. Lett. 85, 2 (2000), 461.Google ScholarCross Ref
- R. Sharovsky, L. A. M. César, and J. A. F. Ramires. 2004. Temperature, air pollution, and mortality from myocardial infarction in Sao Paulo, Brazil. Brazil. J. Med. Biol. Res. 37, 11 (2004), 1651--1657.Google ScholarCross Ref
- Han-Wei Shen, Ling-Jen Chiang, and Kwan-Liu Ma. 1999. A fast volume rendering algorithm for time-varying fields using a time-space-partitioning (TSP) tree. In Proceedings of the Conference on Visualization: Celebrating Ten Years. IEEE Computer Society Press, 371--377. Google ScholarDigital Library
- Lei Shi, Qi Liao, Yuan He, Rui Li, Aaron Striegel, and Zhong Su. 2011. SAVE: Sensor anomaly visualization engine. In Proceedings of the IEEE Conference on Visual Analytics Science and Technology. 201--210.Google ScholarCross Ref
- Shigeo Takahashi, Issei Fujishiro, Yuriko Takeshima, and Tomoyuki Nishita. 2005. A feature-driven approach to locating optimal viewpoints for volume visualization. In IEEE Visualization. 495--502.Google Scholar
- Christian Tominski, James Abello, and Heidrun Schumann. 2004. Axes-based visualizations with radial layouts. In Proceedings of the ACM Symposium on Applied Computing. ACM, 1242--1247. Google ScholarDigital Library
- Martin Turon. 2005. MOTE-VIEW: A sensor network monitoring and management tool. In Proceedings of the 2nd IEEE Workshop on Embedded Networked Sensors. 11--17. Google ScholarDigital Library
- Jarke J. Van Wijk and Edward R. Van Selow. 1999. Cluster and calendar based visualization of time-series data. In Proceedings of the IEEE Symposium on Information Visualization. 4--9. Google ScholarDigital Library
- Péter Völgyesi, András Nádas, Xenofon Koutsoukos, and Ákos Lédecz. 2008. Air quality monitoring with SensorMap. In International Conference on Information Processing in Sensor Networks. 529--530. Google ScholarDigital Library
- Greg Ver Steeg and Aram Galstyan. 2012. Information transfer in social media. In Proceedings of the 21st International Conference on World Wide Web. ACM, 509--518. Google ScholarDigital Library
- Raul Vicente, Michael Wibral, Michael Lindner, and Gordon Pipa. 2011. Transfer entropy—A model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. 30, 1 (2011), 45--67. Google ScholarDigital Library
- Ivan Viola, Miquel Feixas, Mateu Sbert, and Meister Eduard Groller. 2006. Importance-driven focus of attention. Trans. Visual. Comput. Graph. 12, 5 (2006), 933--940. Google ScholarDigital Library
- Chaoli Wang and Han-Wei Shen. 2006. LOD map-a visual interface for navigating multiresolution volume visualization. Trans. Visual. Comput. Graph. 12, 5 (2006), 1029--1036. Google ScholarDigital Library
- Chaoli Wang and Han-Wei Shen. 2011. Information theory in scientific visualization. Entropy 13, 1 (2011), 254--273.Google ScholarCross Ref
- Chaoli Wang, Hongfeng Yu, Ray W. Grout, Kwan-Liu Ma, and Jacqueline H. Chen. 2011. Analyzing information transfer in time-varying multivariate data. In Proceedings of the IEEE Pacific Visualization Symposium. IEEE, 99--106. Google ScholarDigital Library
- Chaoli Wang, Hongfeng Yu, and Kwan-Liu Ma. 2008. Importance-driven time-varying data visualization. Trans. Visual. Comput. Graph. 14, 6 (2008), 1547--1554. Google ScholarDigital Library
- Jingyuan Wang, Robert Sisneros, and Jian Huang. 2013. Interactive selection of multivariate features in large spatiotemporal data. In Proceedings of the IEEE Pacific Visualization Symposium. 145--152.Google ScholarCross Ref
- Marc Weber, Marc Alexa, and Wolfgang Muller. 2001. Visualizing time-series on spirals. In Proceedings of the IEEE Symposium on Information Visualization. 7--14. Google ScholarDigital Library
- Lijie Xu, Teng-Yok Lee, and Han-Wei Shen. 2010. An information-theoretic framework for flow visualization. Trans. Visual. Comput. Graph. 16, 6 (2010), 1216--1224. Google ScholarDigital Library
- Li Yu, Aidong Lu, William Ribarsky, and Wei Chen. 2010. Automatic animation for time-varying data visualization. In Proceedings of the Computer Graphics Forum, Vol. 29. Wiley Online Library, 2271--2280.Google ScholarCross Ref
- Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. U-Air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1436--1444. Google ScholarDigital Library
- Yu Zheng, Furui Liu, and Hsun-Ping Hsieh. 2013. U-Air: When urban air quality inference meets big data. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1436--1444. Google ScholarDigital Library
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- Visual Exploration of Air Quality Data with a Time-correlation-partitioning Tree Based on Information Theory
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