Using Open Source Tools for Spatial Temporal Querying and Knowledge Discovery from Moving Object Data
Geospatial information overload has become an issue in recent years. It is fuelled in part by the widespread availability of mobility data from a variety of sources, such as ubiquitous mobile computing devices, geographic positioning systems and traces from digital map interactions. The article describes a data analysis technique for extracting knowledge from mobility data. Data from mouse movements over digital maps were analysed for their spatial-temporal content to reveal user behavior. Although the trajectories are from mouse movements in Human-Computer Interaction domain, they can also serve as a proxy for physical trajectories in the real world. The article presents the methodology to reduce information overload and convert raw trajectory data into useful knowledge. This geographicknowledgediscoveryprocesswasrealisedusing Secondo, a highly specialised open source tool that allows developingspecific spatio-temporalqueriestoanalysetrajectories. The results indicate that Secondo can be intelligently exploited for identifying specific movement patterns and behavior and ultimately extractknowledgewhichcanbeusedinpersonalisedwebmaps,spatial recommender systems, event detection and crime monitoring tasks.
Dix, A., Finlay, J. and Abowd, G. (2004), Human-computer interaction, Prentice Hall.
Han, J. (2005), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
Morris, B. and Trivedi, M. (2009), Learning trajectory patterns by clustering: Experimental studies and comparative evaluation, in ‘IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009.’ pp. 312 –319.
Ballatore, A., Tahir, A., McArdle, G. and Bertolotto, M. (2011), “A comparison of open source geospatial technologies for web mapping”, International Journal of Web Engineering and Technology, Vol. 6, Inderscience Publishers, pp. 354–374.
Tahir, A., McArdle, G. and Bertolotto, M. (2011), Visualising user interaction history to identify web map usage patterns, in ‘14th AGILE International Conference on Geographic Information Science, Advancing Geo-information Science for a Changing World’, Utrecht, The Netherlands.
Guting, R. H., Almeida, V., Ansorge, D., Behr, T., Ding, Z., Hose, T., Hoffmann, F., Spiekermann, M. and Telle, U. (2005), Secondo: An extensible DBMS platform for research prototyping and teaching, in ‘Data Engineering, 2005. ICDE 2005. Proceedings. IEEE, pp. 1115–1116.
Vieira, M. R., Bakalov, P. and Tsotras, V. J. (2009), On-line discovery of flock patterns in spatio-temporal data, in ‘Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems’, ACM, pp. 286–295.
Nanni, M., Trasarti, R., Renso, C., Giannotti, F. and Pedreschi, D. (2010), Advanced knowledge discovery on movement data with the geopkdd system, in ‘Proceedings of the 13th International Conference on Extending Database Technology’, ACM, pp. 693–696.
Guting, R., Behr, T. and Duntgen, C. (2013), Trajectory databases, in ‘Mobility Data: Modeling, Management and Understanding’, Cambridge University Press, pp. 43–62.
Pelekis, N., Theodoridis, Y., Vosinakis, S. and Panayiotopoulos, T. (2006), Hermes – a framework for location-based data management, in ‘Advances in Database Technology-EDBT 2006’, Springer, pp. 1130–1134.
Gerasimos, M., Maria, L. D., Nikos, P., Yannis, T. and Zhixian, Y. (2013), Trajectory collection and reconstruction, in ‘Mobility Data: Modeling, Management and Understanding’, Cambridge University Press, Cambridge, pp. 23–42.
Gudmundsson, J., van Kreveld, M. and Speckmann, B. (2004), Efficient detection of motion patterns in spatio-temporal data sets, in ‘Proceedings of the 12th annual ACM international workshop on Geographic Information Systems’, ACM, pp. 250– 257.
Thomas, J. and Cook, K. (2006), “A visual analytics agenda”, Computer Graphics and Applications, IEEE, Vol. 26, IEEE, pp. 10–13.
Andrienko, G., Andrienko, N., Voss, H. and Michael, P. (2003), “Gis for everyone: the commongis project and beyond”, Maps and the Internet, Elsevier Science, Oxford, pp. 131–146.
Shekhar, S., Gunturi, V., Evans, M. R. and Yang, K. (2012), Spatial big-data challenges intersecting mobility and cloud computing, in ‘Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access’, ACM, pp. 1–6.
Reichenbacher, T. and Swienty, O. (2007), Attention-guiding geovisualisation, in ‘Proceedings of the 10th AGILE International Conference on Geographic Information Science, 8th-11th May, Aalborg University, Denmark’.
Ooms, K. and De Maeyer, P. (2015), Georeferencing eye tracking data on interactive cartographic products, in ‘Proceedings of the 27th International Cartographic Conference’.
Jacob, R. and Karn, K. S. (2003), “Eye tracking in human-computer interaction and usability research: Ready to deliver the promises”, Mind , Vol. 2, Citeseer, p. 4.
Giannotti, F. and Pedreschi, D. (2008), Mobility, data mining and privacy: Geographic knowledge discovery, Springer.
Tahir, A., McArdle, G. and Bertolotto, M. (2012), Identifying specific spatial tasks through clustering and geovisual analysis, in ‘Geoinformatics (GEOINFORMATICS), 2012 20th International Conference on’, IEEE, pp. 1–6.
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ISSN (Print): 2070-9900 ISSN (Online): 2411-6319