The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - May (2012 vol.18)
pp: 675-688
G. Adrienko , Fraunhofer Inst. for Intell. Anal. & Inf. Syst. (IAIS), St. Augustin, Germany
N. Adrienko , Fraunhofer Inst. for Intell. Anal. & Inf. Syst. (IAIS), St. Augustin, Germany
M. Mladenov , Fraunhofer Inst. for Intell. Anal. & Inf. Syst. (IAIS), St. Augustin, Germany
M. Mock , Fraunhofer Inst. for Intell. Anal. & Inf. Syst. (IAIS), St. Augustin, Germany
C. Politz , Fraunhofer Inst. for Intell. Anal. & Inf. Syst. (IAIS), St. Augustin, Germany
ABSTRACT
Events that happened in the past are important for understanding the ongoing processes, predicting future developments, and making informed decisions. Important and/or interesting events tend to attract many people. Some people leave traces of their attendance in the form of computer-processable data, such as records in the databases of mobile phone operators or photos on photo sharing web sites. We developed a suite of visual analytics methods for reconstructing past events from these activity traces. Our tools combine geocomputations, interactive geovisualizations, and statistical methods to enable integrated analysis of the spatial, temporal, and thematic components of the data, including numeric attributes and texts. We also support interactive investigation of the sensitivity of the analysis results to the parameters used in the computations. For this purpose, statistical summaries of computation results obtained with different combinations of parameter values are visualized in a way facilitating comparisons. We demonstrate the utility of our approach on two large real data sets, mobile phone calls in Milano during 9 days and flickr photos made on British Isles during 5 years.
INDEX TERMS
statistical analysis, data visualisation, geography, history, mobile computing, past event reconstruction, place history identification, activity traces, eye, computer-processable data, database, mobile phone operator, photo sharing Web sites, visual analytics method, geocomputation, interactive geovisualization, statistical method, spatial component, temporal component, thematic component, interactive investigation, statistical summary, Flickr photos, Time series analysis, History, Visual analytics, Area measurement, Databases, Data visualization, scale effect., Keywords—Event detection, spatiotemporal data, time series analysis, scalable visualization, geovisualization, visual analytics, sensitivity analysis
CITATION
G. Adrienko, N. Adrienko, M. Mladenov, M. Mock, C. Politz, "Identifying Place Histories from Activity Traces with an Eye to Parameter Impact", IEEE Transactions on Visualization & Computer Graphics, vol.18, no. 5, pp. 675-688, May 2012, doi:10.1109/TVCG.2011.153
REFERENCES
[1] N. Andrienko and G. Andrienko, Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer-Verlag, 2006.
[2] G. Andrienko, N. Andrienko, M. Mladenov, M. Mock, and C. Poelitz, "Discovering Bits of Place Histories from People's Activity Traces," Proc. IEEE Symp. Visual Analytics Science and Technology (VAST), pp. 59-66, 2010.
[3] G. Andrienko and N. Andrienko, "Visual Exploration of the Spatial Distribution of Temporal Behaviours," Proc. Ninth Int'l Conf. Information Visualisation (IV), pp. 799-806, 2005.
[4] N. Andrienko and G. Andrienko, "Spatial Generalization and Aggregation of Massive Movement Data," IEEE Trans. Visualization and Computer Graphics, vol. 17, no. 2, pp. 205-219, Feb. 2011.
[5] M. Basseville and I. Nikiforov, Detection of Abrupt Changes: Theory and Application. Prentice-Hall, 1993.
[6] E. Billauer, "Peakdet: Peak Detection Using MATLAB," http://www.billauer.co.ilpeakdet.htm, Feb. 2010.
[7] P. Buono, A. Aris, C. Plaisant, A. Khella, and B. Shneiderman, "Interactive Pattern Search in Time Series," Proc. SPIE Visualization and Data Analysis, vol. 5669, pp. 175-186, 2005.
[8] P. Buono, C. Plaisant, A. Simeone, A. Aris, B. Shneiderman, G. Shmueli, and W. Jank, "Similarity-Based Forecasting with Simultaneous Previews: A River Plot Interface for Time Series Forecasting," Proc. 11th Int'l Conf. Information Visualization (IV), 2007.
[9] M. Elfeky, W. Aref, and A. Elmagarmid, "Periodicity Detection in Time Series Databases," IEEE Trans. Knowledge and Data Eng., vol. 17, no.7, pp. 875-887, July 2005.
[10] S. Fortune, "A Sweepline Algorithm for Voronoi Diagrams," Proc. Second Ann. Symp. Computational Geometry, pp. 313-322, 1986.
[11] F. Girardin, F. Fiore, C. Ratti, and J. Blat, "Leveraging Explicitly Disclosed Location Information to Understand Tourist Dynamics: A Case Study," J. Location Based Services, vol. 2, no. 1, pp. 41-56, 2008.
[12] M.F. Goodchild, "Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0," Int'l J. Spatial Data Infrastructures Research, vol. 2, pp. 24-32, 2007.
[13] T. Hägerstrand, "What about People in Regional Science?," Papers, Regional Science Assoc., vol. 24, pp. 7-21, 1970.
[14] M. Hao, H. Janetzko, P. Sharma, U. Dayal, D. Keim, and M. Castellanos, "Visual Prediction of Time Series," Proc. IEEE Visual Analytics Science and Technology (VAST), pp. 229-230, 2008.
[15] H. Hochheiser and B. Shneiderman, "Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration," Information Visualization, vol. 3, no. 1, pp. 1-18, 2004.
[16] Y. Ivanov, C. Wren, A. Sorokin, and I. Kaur, "Visualizing the History of Living Spaces," IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1153-1160, Nov./Dec. 2004.
[17] P. Jankowski, N. Andrienko, G. Andrienko, and S. Kisilevich, "Discovering Landmark Preferences and Movement Patterns from Photo Postings," Trans. GIS, vol. 4, no. 6, pp. 833-852, 2010.
[18] Y. Kawahara and M. Sugiyama, "Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation," Proc. SIAM Int'l Conf. Data Mining, 2009.
[19] D. Keim, G. Andrienko, J.-D. Fekete, C. Görg, J. Kohlhammer, and G. Melancon, "Visual Analytics: Definition, Process, and Challenges," Information Visualization Human-Centered Issues and Perspectives, A. Kerren, J.T. Stasko, J.-D. Fekete, and C. North, eds., vol. 4950 of LNCS State-of-the-Art Survey, Springer, pp. 154-175, 2008.
[20] S. Kisilevich, F. Mansmann, and D. Keim, "P-DBSCAN: A Density Based Clustering Algorithm for Exploration and Analysis of Attractive Areas Using Collections of Geo-tagged Photos," Proc. First Int'l Conf. and Exhibition on Computing for Geospatial Research and Application (COM.Geo '10), 2010.
[21] S. Openshaw, The Modifiable Areal Unit Problem. Geo Books, 1984.
[22] D. Peuquet, Representations of Space and Time. Guilford, 2002.
[23] R. Pulselli, P. Romano, C. Ratti, and E. Tiezzi, "Computing Urban Mobile Landscapes through Monitoring Population Density Based on Cell-Phone Chatting," Int'l J. Design and Nature and Ecodynamics, vol. 3, no. 2, pp. 121-134, 2008.
[24] M. Small and K. Judd, "Detecting Periodicity in Experimental Data Using Linear Modeling Techniques," Physical Rev. E, vol. 59, no. 2, pp. 1379-1385, Feb. 1999.
[25] B. Tversky, J.B. Morrison, and M. Bétrancourt, "Animation: Can It Facilitate?," Int'l J. Human-Computer Studies, vol. 57, no. 4, pp. 247-262, 2002.
[26] J.J. van Wijk and E.R. van Selow, "Cluster and Calendar Based Visualization of Time Series Data," Proc. IEEE Information Visualization (InfoVis '99), pp. 4-9, 1999.
[27] D. Yankov, E. Keogh, J. Medina, B. Chiu, and V. Zordan, "Detecting Time Series Motifs under Uniform Scaling," Proc. 13th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD), 2007.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool