Altiparmak, F., Ferhatosmanoglu, H., Erdal, S., and Trost, D. C. (2006). In formation mining over heterogeneous and high-dimensional time-series data in clinical trials databases. IEEE Transactions on Information Technology in Biomedicine, 10(2):254-263. Annals of GIS 21 Andrienko, G. and Andrienko, N. (2008). Spatio-temporal aggregation for visual analysis of movements. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST 2008), pages 51-58. IEEE. Andrienko, G. and Andrienko, N. (2010). Poster: Dynamic time transformation for interpreting clusters of trajectories with space-time cube. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST 2010), pages 213-214. Andrienko, G., Andrienko, N., and Wrobel, S. (2007). Visual analytics tools for analysis of movement data. ACM SIGKDD Explorations Newsletter, 9(2):38- 46. Ankerst, M., Breunig, M. M., Kriegel, H.-P., and Sander, J. (1999). Optics: ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2):49-60. Ashbrook, D. and Starner, T. (2003). Using gps to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing, 7(5):275-286. Atev, S., Masoud, O., and Papanikolopoulos, N. (2006). Learning traffic patterns at intersections by spectral clustering of motion trajectories. In Proceedings of the IEEE Conference on Intelligent Robots and Systems, pages 4851-4856. IEEE. Atev, S., Miller, G., and Papanikolopoulos, N. (2010). Clustering of vehi cle trajectories. IEEE Transactions on Intelligent Transportation Systems, 11(3):647-657. Buchin, K., Buchin, M., and Wenk, C. (2006). Computing the fr´chet distance e between simple polygons in polynomial time. In Proceedings of the 22nd An nual Symposium on Computational Geometry, pages 80-87. ACM. Buchin, M., Dodge, S., and Speckmann, B. (2012). Context-aware similarity of trajectories. In Geographic information science, pages 43-56. Springer. Castro, P. S., Zhang, D., and Li, S. (2012). Urban traffic modelling and predic tion using large scale taxi gps traces. In Pervasive Computing, pages 57-72. Springer. Chen, L., Ozsu, M., and Oria, V. (2005). Robust and fast similarity search for moving object trajectories. In Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pages 491-502. ACM. Chen, W., Song, Y., Bai, H., Lin, C., and Chang, E. (2011). Parallel spectral clustering in distributed systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3):568-586. Demˇar, U. and Virrantaus, K. (2010). Space-time density of trajectories: ex s ploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24(10):1527-1542. Dodge, S., Weibel, R., and Forootan, E. (2009). Revealing the physics of move ment: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33(6):419- 434. Dodge, S., Weibel, R., and Lautensch¨tz, A. (2008). Towards a taxonomy of u movement patterns. Information Visualization, 7(3-4):240-252. Annals of GIS 22 Elnekave, S., Last, M., and Maimon, O. (2007). A compact representation of spatio-temporal data. In Proceedings of the International Conference on Data Mining Workshops (ICDM 2007), pages 601-606. IEEE. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algo rithm for discovering clusters in large spatial databases with noise. In Proceed ings of the 2nd International Conference on Knowledge Discovery and Data Mining, page 226. Amer Assn for Artificial. Gschwend, C. and Laube, P. (2012). In Proceedings of GISRUK 2012. H¨gerstrand, T. (1970). What about people in regional science? Papers in a Regional Science, 24(1):6-21. Halliday, D., Resnick, R., and Walker, J. (1997). Fundamentals of Physics. Wiley. Hu, W., Xie, D., Fu, Z., Zeng, W., and Maybank, S. (2007). Semantic based surveillance video retrieval. IEEE Transactions on Image Processing, 16(4):1168-1181. Jain, A. K., Murty, M. N., and Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys (CSUR), 31(3):264-323. Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R., and Wu, A. (2000). The analysis of a simple k-means clustering algorithm. In Proceedings of the 16th Annual Symposium on Computational Geometry, pages 100-109. ACM. Kapler, T. and Wright, W. (2005). Geotime information visualization. Informa tion Visualization, 4(2):136-146. Kraak, M. (2008). Geovisualization and time-new opportunities for the spacetime cube. Geographic Visualization: Concepts, Tools and Applications, pages 293-306. Kriegel, H.-P., Kr¨ger, P., Sander, J., and Zimek, A. (2011). Density-based o clustering. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Dis covery, 1(3):231-240. Landau, R., Werner, S., Auslander, G., Shoval, N., and Heinik, J. (2009). Atti tudes of family and professional care-givers towards the use of gps for tracking patients with dementia: an exploratory study. British Journal of Social Work, 39(4):670-692. Laube, P., Imfeld, S., and Weibel, R. (2005). Discovering relative motion pat terns in groups of moving point objects. International Journal of Geographical Information Science, 19(6):639-668. Millonig, A. and Gartner, G. (2011). Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools. Journal of Location Based Services, 5(1):3-21. Morris, B. and Trivedi, M. (2009). Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009 (CVPR 2009), pages 312-319. IEEE. Palma, A., Bogorny, V., Kuijpers, B., and Alvares, L. (2008). A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the 2008 ACM Symposium on Applied Computing, pages 863-868. ACM. Annals of GIS 23 Sakurai, Y., Yoshikawa, M., and Faloutsos, C. (2005). Ftw: fast similarity search under the time warping distance. In Proceedings of the 24th ACM SIGMOD- SIGACT-SIGART Symposium on Principles of Database Systems, pages 326- 337. ACM. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of the IEEE Symposium on Visual Languages, pages 336-343. IEEE. Song, Y., Chen, W., Bai, H., Lin, C., and Chang, E. (2008). Parallel spectral clustering. Machine Learning and Knowledge Discovery in Databases, pages 374-389. Van der Spek, S. (2010). Tracking tourists in historic city centres. Information and Communication Technologies in Tourism 2010, pages 185-196. Van Schaick, J. (2011). Timespace matters-exploring the gap between knowing about activity patterns of people and knowing how to design and plan urban areas and regions. Vlachos, M., Kollios, G., and Gunopulos, D. (2002). Discovering similar multi dimensional trajectories. In Proceedings of the 18th International Conference on Data Engineering, pages 673-684. IEEE. Wilson, C. (2008). Activity patterns in space and time: calculating representative hagerstrand trajectories. Transportation, 35(4):485-499. Zhang, Z., Huang, K., and Tan, T. (2006). Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In Proceedgings of the International Conference on Pattern Recognition (ICPR 2006), volume 3, pages 1135-1138. IEEE. Zheng, X., Zhong, T., and Liu, M. (2009). Modeling crowd evacuation of a building based on seven methodological approaches. Building and Environ ment, 44(3):437-445. Zheng, Y. and Zhou, X. (2011). Computing with spatial trajectories. Springer.