research-article

Storygraph: extracting patterns from spatio-temporal data

Authors:

Ayush Shrestha,

Yi ZhaoAuthors Info & Claims

IDEA '13: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

Pages 95 - 103

https://doi.org/10.1145/2501511.2501525

Published: 11 August 2013 Publication History

Abstract

Analysis of spatio-temporal data often involves correlating different events in time and location to uncover relationships between them. It is also desirable to identify different patterns in the data. Visualizing time and space in the same chart is not trivial. Common methods includes plotting the latitude, longitude and time as three dimensions of a 3D chart. Drawbacks of these 3D charts include not being able to scale well due to cluttering, occlusion and difficulty to track time in case of clustered events. In this paper we present a novel 2D visualization technique called Storygraph which provides an integrated view of time and location to address these issues. We also present storylines based on Storygraph which show movement of the actors over time. Lastly, we present case studies to show the applications of Storygraph.

References

[1]

G. Adrienko, N. Adrienko, M. Mladenov, M. Mock, and C. Politz. Identifying place histories from activity traces with an eye to parameter impact. IEEE Transactions on Visualization and Computer Graphics, 18(5):675--688, May 2012.

Digital Library

[2]

W. Aigner, S. Miksch, W. Muller, H. Schumann, and C. Tominski. Visualizing time-oriented data - a systematic view. Computers and Graphics, 31(3):401--409, 2007.

Digital Library

[3]

W. Aigner, S. Miksch, W. Muller, H. Schumann, and C. Tominski. Visual methods for analyzing time-oriented data. IEEE Transactions on Visualization and Computer Graphics, 14(1):47--60, jan.-feb. 2008.

Digital Library

[4]

G. Andrienko, N. Andrienko, and S. Wrobel. Visual analytics tools for analysis of movement data. ACM SIGKDD Explorations Newsletter, 9(2):38--46, 2007.

Digital Library

[5]

N. Andrienko, G. Andrienko, and P. Gatalsky. Visualization of spatio-temporal information in the internet. In Proceedings of 11th International Workshop on Database and Expert Systems Applications, pages 577--585, 2000.

Digital Library

[6]

A. O. Artero, M. C. F. de Oliveira, and H. Levkowitz. Uncovering clusters in crowded parallel coordinates visualizations. In Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on, pages 81--88. IEEE, 2004.

Digital Library

[7]

A. Asgary, A. Ghaffari, and J. Levy. Spatial and temporal analyses of structural fire incidents and their causes: A case of Toronto, Canada. Fire Safety Journal, 45(1):44--57, 2010.

[8]

C. Brunsdon, J. Corcoran, and G. Higgs. Visualising space and time in crime patterns: A comparison of methods. Computers, Environment and Urban Systems, 31(1):52--75, 2007.

[9]

U. Demsar and K. Virrantaus. Space-time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24(10):1527--1542, 2010.

Digital Library

[10]

D. Fisher, A. Hoff, G. Robertson, and M. Hurst. Narratives: A visualization to track narrative events as they develop. In Proceedings of IEEE Symposium on Visual Analytics Science and Technology, pages 115--122, oct. 2008.

[11]

A. Fredrikson, C. North, C. Plaisant, and B. Shneiderman. Temporal, geographical and categorical aggregations viewed through coordinated displays: a case study with highway incident data. In Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management, NPIVM '99, pages 26--34, New York, NY, USA, 1999. ACM.

Digital Library

[12]

P. Gatalsky, N. Andrienko, and G. Andrienko. Interactive analysis of event data using space-time cube. In Proceedings. Eighth International Conference on Information Visualisation, pages 145--152, july 2004.

Digital Library

[13]

Z. Geng, Z. Peng, R. Laramee, R. Walker, and J. Roberts. Angular histograms: Frequency-based visualizations for large, high dimensional data. IEEE Transactions on Visualization and Computer Graphics, 17(12):2572--2580, dec. 2011.

Digital Library

[14]

T. Gschwandtner, W. Aigner, K. Kaiser, S. Miksch, and A. Seyfang. CareCruiser: Exploring and visualizing plans, events, and effects interactively. In Proceedings of IEEE Pacific Visualization Symposium (PacificVis), pages 43--50, march 2011.

Digital Library

[15]

Guardian.co.uk. Afghanistan war logs 2003-2010, July 2010.

[16]

T. Hägerstrand et al. Time-geography: focus on the corporeality of man, society, and environment. The science and praxis of complexity, pages 193--216, 1985.

[17]

M. Harrower and C. Brewer. Colorbrewer. org: an online tool for selecting colour schemes for maps. Cartographic Journal, The, 40(1):27--37, 2003.

[18]

K. Hinum, S. Miksch, W. Aigner, S. Ohmann, C. Popow, M. Pohl, and M. Rester. Gravi++: Interactive information visualization of highly structured temporal data. Workshop IDAMAP 2005 Intelligent Data Analysis in Medicine and Pharmacology, pages 67--72, 2005.

[19]

T.-H. Hsieh, J.-J. J. Chen, L.-H. Chen, P.-T. Chiang, and H.-Y. Lee. Time-course gait analysis of hemiparkinsonian rats following 6-hydroxydopamine lesion. Behavioural Brain Research, 222(1):1--9, 2011.

[20]

W. Javed, B. McDonnel, and N. Elmqvist. Graphical perception of multiple time series. IEEE Transactions on Visualization and Computer Graphics, 16(6):927--934, nov.-dec. 2010.

Digital Library

[21]

M. Jern and J. Franzen. GeoAnalytics - exploring spatio-temporal and multivariate data. In Proceedings of Tenth International Conference on Information Visualization, pages 25--31, july 2006.

Digital Library

[22]

T. Kapler and W. Wright. Geotime information visualization. Information Visualization, 4(2):136--146, 2005.

Digital Library

[23]

M. Krstajic, E. Bertini, and D. Keim. CloudLines: Compact display of event episodes in multiple time-series. IEEE Transactions on Visualization and Computer Graphics, 17(12):2432--2439, dec. 2011.

Digital Library

[24]

T. V. Landesberger, S. Bremm, N. Andrienko, G. Adrienko, and M. Tekusova. Visual analytics for categoric spatio-temporal data. In Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST 2012), pages 183--192, October 2012.

Digital Library

[25]

M. Lanzenberger, S. Miksch, and M. Pohl. The Stardinates - visualizing highly structured data. In Proceedings of Seventh International Conference on Information Visualization (IV 2003), pages 47--52, july 2003.

Digital Library

[26]

M. Lanzenberger, S. Miksch, and M. Pohl. Exploring highly structured data: a comparative study of stardinates and parallel coordinates. In Proceedings. Ninth International Conference on Information Visualisation, pages 312--320, july 2005.

Digital Library

[27]

U. Lao. Lao national unexploded ordnance programme, 2011.

[28]

R. Maciejewski, S. Rudolph, R. Hafen, A. Abusalah, M. Yakout, M. Ouzzani, W. Cleveland, S. Grannis, and D. Ebert. A visual analytics approach to understanding spatiotemporal hotspots. IEEE Transactions on Visualization and Computer Graphics, 16(2):205--220, march-april 2010.

Digital Library

[29]

A. Murray, Y. Liu, S. Rey, and L. Anselin. Exploring movement object patterns. The Annals of Regional Science, pages 1--14, 2012. 10.1007/s00168-011-0459-z.

[30]

D. Orellana, A. K. Bregt, A. Ligtenberg, and M. Wachowicz. Exploring visitor movement patterns in natural recreational areas. Tourism Management, 33(3):672--682, 2012.

[31]

C. Plug, J. C. Xia, and C. Caulfield. Spatial and temporal visualisation techniques for crash analysis. Accident Analysis and Prevention, 43(6):1937--1946, 2011.

[32]

L. N. U. O. Programme. Annual report 2007, 2008.

[33]

S. Rinzivillo, D. Pedreschi, M. Nanni, F. Giannotti, N. Andrienko, and G. Andrienko. Visually driven analysis of movement data by progressive clustering. Information Visualization, 7(3-4):225--239, 2008.

Digital Library

[34]

Y. Tanahashi and K.-L. Ma. Design considerations for optimizing storyline visualizations. Visualization and Computer Graphics, IEEE Transactions on, 18(12):2679--2688, 2012.

Digital Library

[35]

C. Tominski, P. Schulze-Wollgast, and H. Schumann. 3D information visualization for time dependent data on maps. In Proceedings. Ninth International Conference on Information Visualisation, pages 175--181, july 2005.

Digital Library

[36]

E. R. Tufte. Beautiful evidence, volume 23. Graphics Press Cheshire, CT, 2006.

Digital Library

[37]

K. Vrotsou, J. Johansson, and M. Cooper. ActiviTree: Interactive visual exploration of sequences in event-based data using graph similarity. IEEE Transactions on Visualization and Computer Graphics, 15(6):945--952, nov.-dec. 2009.

Digital Library

[38]

Wikileaks. Cablegate's cables, 2010.

[39]

J. Zhao, F. Chevalier, E. Pietriga, and R. Balakrishnan. Exploratory analysis of time-series with ChronoLenses. IEEE Transactions on Visualization and Computer Graphics, 17(12):2422--2431, dec. 2011.

Digital Library

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Baumgartl TPetzold MWunderlich MHohn MArchambault DLieser MDalpke AScheithauer SMarschollek MEichel VMutters NConsortium HLandesberger T(2021)In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in HospitalsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303043727:2(711-721)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030437
Romanowski AChaniecki ZKoralczyk AWoźniak MNowak AKucharski PJaworski TMalaya MRózga PGrudzień K(2020)Interactive Timeline Approach for Contextual Spatio-Temporal ECT Data InvestigationSensors10.3390/s2017479320:17(4793)Online publication date: 25-Aug-2020
https://doi.org/10.3390/s20174793
Kucher KMartins RParadis CKerren A(2020)StanceVis Prime: visual analysis of sentiment and stance in social media textsJournal of Visualization10.1007/s12650-020-00684-5Online publication date: 25-Aug-2020
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Index Terms

Storygraph: extracting patterns from spatio-temporal data
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
    2. Shape modeling
      1. Parametric curve and surface models

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Published In

cover image ACM Conferences

IDEA '13: Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

August 2013

104 pages

ISBN:9781450323291

DOI:10.1145/2501511

Editors:
Duen Horng Chau
Georgia Tech
,
Jilles Vreeken
University of Antwerp
,
Matthijs van Leeuwen
KU Leuven
,
Christos Faloutsos
Carnegie Mellon University

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 August 2013

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KDD' 13

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11, 2013

Illinois, Chicago

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IDEA '13 Paper Acceptance Rate 11 of 25 submissions, 44%;

Overall Acceptance Rate 11 of 25 submissions, 44%

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Cited By

Baumgartl TPetzold MWunderlich MHohn MArchambault DLieser MDalpke AScheithauer SMarschollek MEichel VMutters NConsortium HLandesberger T(2021)In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in HospitalsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303043727:2(711-721)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030437
Romanowski AChaniecki ZKoralczyk AWoźniak MNowak AKucharski PJaworski TMalaya MRózga PGrudzień K(2020)Interactive Timeline Approach for Contextual Spatio-Temporal ECT Data InvestigationSensors10.3390/s2017479320:17(4793)Online publication date: 25-Aug-2020
https://doi.org/10.3390/s20174793
Kucher KMartins RParadis CKerren A(2020)StanceVis Prime: visual analysis of sentiment and stance in social media textsJournal of Visualization10.1007/s12650-020-00684-5Online publication date: 25-Aug-2020
https://doi.org/10.1007/s12650-020-00684-5
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Murenin INovikova EVolkov A(2017)Uncovering interaction patterns between employees of the organization2017 XX IEEE International Conference on Soft Computing and Measurements (SCM)10.1109/SCM.2017.7970710(746-749)Online publication date: May-2017
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Lukasczyk JMaciejewski RGarth CHagen HAli MHuang YGertz MRenz MSankaranarayanan J(2015)Understanding hotspotsProceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/2820783.2820817(1-10)Online publication date: 3-Nov-2015
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Tanahashi YHsueh CMa K(2015)An Efficient Framework for Generating Storyline Visualizations from Streaming DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2015.239277121:6(730-742)Online publication date: 1-Jun-2015
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Shrestha AZhu YManandhar K(2014)NetTimeView: Applying Spatio-temporal Data Visualization Techniques to DDoS Attack AnalysisAdvances in Visual Computing10.1007/978-3-319-14249-4_34(357-366)Online publication date: 2014
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