Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method

References

• Ahearn, S. C., S. Dodge, A. Simcharoen, G. Xavier, and J. L. D. Smith. 2016. “A Context-Sensitive Correlated Random Walk: A New Simulation Model for Movement.” International Journal of Geographical Information Science 1–17. doi:10.1080/13658816.2016.1224887.
   Web of Science ®Google Scholar
• Andrienko, G., N. Andrienko, and M. Heurich. 2011. “An Event-Based Conceptual Model for Context-Aware Movement Analysis.” International Journal of Geographical Information Science 25: 1347–1370. doi:10.1080/13658816.2011.556120.
   Web of Science ®Google Scholar
• Aviation Weather Center. 2016. Accessed 10 December 2016. www.aviationweather.gov.
   Google Scholar
• Berndt, D. J., and J. Clifford. 1994. “Using Dynamic Time Warping to Find Patterns in Time Series.” KDD Workshop, Seattle, WA, 24 April 1994 359–370.
   Google Scholar
• Bogorny, V., C. Renso, A. R. De Aquino, F. De Lucca Siqueira, and L. O. Alvares. 2014. “CONSTAnT – A Conceptual Data Model for Semantic Trajectories of Moving Objects.” Transactions in GIS 18: 66–88. doi:10.1111/tgis.2014.18.issue-1.
   Web of Science ®Google Scholar
• Bruderlin, A., and L. Williams. 1995. “Motion Signal Processing.” Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, (SIGGRAPH '95), Susan G. Mair and Robert Cook (Eds.) ACM, New York, NY. 97-104. doi: 10.1145/218380.218421.
   Google Scholar
• Buchin, K., M. Buchin, J. Gudmundsson, M. Löffler, and J. Luo. 2011. “Detecting Commuting Patterns by Clustering Subtrajectories.” International Journal of Computational Geometry & Applications 21: 253–282. doi:10.1142/S0218195911003652.
   Web of Science ®Google Scholar
• Buchin, M., S. Dodge, and B. Speckmann. 2014. “Similarity of Trajectories Taking into Account Geographic Context.” Journal of Spatial Information Science 9: 101–124.
   Google Scholar
• Chavoshi, S. H., B. De Baets, T. Neutens, H. Ban, O. Ahlqvist, G. De Tré, and N. Van De Weghe. 2014. “Knowledge Discovery in Choreographic Data Using Relative Motion Matrices and Dynamic Time Warping.” Applied Geography 47: 111–124. doi:10.1016/j.apgeog.2013.12.007.
   Web of Science ®Google Scholar
• Chen, L., M. T. Özsu, and V. Oria. 2005. “Robust and Fast Similarity Search for Moving Object Trajectories.” Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, ACM, Baltimore, MD. 491–502. doi: 10.1145/1066157.1066213.
   Google Scholar
• Coro, G., P. Pagano, and A. Ellenbroek. 2014. “Comparing Heterogeneous Distribution Maps for Marine Species.” GIScience & Remote Sensing 51: 593–611. doi:10.1080/15481603.2014.959391.
   Web of Science ®Google Scholar
• Demšar, U., K. Buchin, F. Cagnacci, K. Safi, B. Speckmann, N. Van De Weghe, D. Weiskopf, and R. Weibel. 2015. “Analysis and Visualisation of Movement: An Interdisciplinary Review.” Movement Ecology 3: 1–24. doi:10.1186/s40462-015-0032-y.
   PubMed Web of Science ®Google Scholar
• Dey, A. K. 2001. “Understanding and Using Context.” Personal Ubiquitous Computing 5: 4–7. doi:10.1007/s007790170019.
   Web of Science ®Google Scholar
• Ding, H., G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh. 2008. “Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures.” Proceedings of the VLDB Endowment 1: 1542–1552. doi:10.14778/1454159.
   Google Scholar
• Dodge, S. 2011. “Exploring Movement Using Similarity Analysis. PhD diss., University of Zurich.
   Google Scholar
• Dodge, S., G. Bohrer, K. Bildstein, S. C. Davidson, R. Weinzierl, M. J. Bechard, D. Barber, et al. 2014. “Environmental Drivers of Variability in the Movement Ecology of Turkey Vultures (Cathartes Aura) in North and South America.” Philosophical Transactions of the Royal Society B: Biological Sciences 369: 20130195. doi:10.1098/rstb.2013.0195.
   PubMed Web of Science ®Google Scholar
• Dodge, S., G. Bohrer, R. Weinzierl, S. C. Davidson, R. Kays, D. Douglas, S. Cruz, J. Han, D. Brandes, and M. Wikelski. 2013. “The Environmental-Data Automated Track Annotation (Env-Data) System: Linking Animal Tracks with Environmental Data.” Movement Ecology 1: 3–14. doi:10.1186/2051-3933-1-3.
   PubMedGoogle Scholar
• Dodge, S., P. Laube, and R. Weibel. 2012. “Movement Similarity Assessment Using Symbolic Representation of Trajectories.” International Journal of Geographical Information Science 26: 1563–1588. doi:10.1080/13658816.2011.630003.
   Web of Science ®Google Scholar
• Dodge, S., R. Weibel, S. C. Ahearn, M. Buchin, and J. A. Miller. 2016. “Analysis of Movement Data.” International Journal of Geographical Information Science 30: 825–834. doi:10.1080/13658816.2015.1132424.
   Web of Science ®Google Scholar
• Dodge, S., R. Weibel, and E. Forootan. 2009. “Revealing the Physics of Movement: Comparing the Similarity of Movement Characteristics of Different Types of Moving Objects.” Computers, Environment and Urban Systems 33: 419–434. doi:10.1016/j.compenvurbsys.2009.07.008.
   Web of Science ®Google Scholar
• Dodge, S., R. Weibel, and P. Laube. 2011. “Trajectory Similarity Analysis in Movement Parameter Space.” Proceedings of GISRUK, Plymouth, UK, April 27–29.
   Google Scholar
• Dodge, S., R. Weibel, and A.-K. Lautenschütz. 2008. “Towards a Taxonomy of Movement Patterns.” Information Visualization 7: 240–252. doi:10.1057/palgrave.ivs.9500182.
   Web of Science ®Google Scholar
• Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth. 1996. “From Data Mining to Knowledge Discovery in Databases.” AI Magazine 17: 37.
   Web of Science ®Google Scholar
• Federal Aviation Administration Advisory Circular 120-108. 2011 Accessed 10 December 2016. www.faa.gov/documentLibrary/media/Advisory_Circular/AC%20120-108.pdf.
   Google Scholar
• FlightAware. 2016. Accessed 10 December 2016. www.flightaware.com.
   Google Scholar
• Furtado, A. S., D. Kopanaki, L. O. Alvares, and V. Bogorny. 2015. “Multidimensional Similarity Measuring for Semantic Trajectories.” Transactions in GIS. 20: 280-298. doi:10.1111/tgis.issue-2.
   Google Scholar
• Gariel, M., A. N. Srivastava, and E. Feron. 2011. “Trajectory Clustering and an Application to Airspace Monitoring.” IEEE Transactions on Intelligent Transportation Systems 12: 1511–1524. doi:10.1109/TITS.2011.2160628.
   Web of Science ®Google Scholar
• Ghisu, T., B. Arca, G. Pellizzaro, and P. Duce. 2015. “An Optimal Cellular Automata Algorithm for Simulating Wildfire Spread.” Environmental Modelling & Software 71: 1–14. doi:10.1016/j.envsoft.2015.05.001.
   Web of Science ®Google Scholar
• Gschwend, C. 2015. “Relating Movement to Geographic Context – Effects of Preprocessing, Relation Methods and Scale.” PhD diss., University of Zurich.
   Google Scholar
• Guo, D., X. Zhu, H. Jin, P. Gao, and C. Andris. 2012. “Discovering Spatial Patterns in Origin-Destination Mobility Data.” Transactions in GIS 16: 411–429. doi:10.1111/tgis.2012.16.issue-3.
   Web of Science ®Google Scholar
• Güting, R. H., and M. Schneider. 2005. Moving Objects Databases. San Francisco, CA:Elsevier.
   Google Scholar
• Hurter, C., R. Alligier, D. Gianazza, S. Puechmorel, G. Andrienko, and N. Andrienko. 2014. “Wind Parameters Extraction from Aircraft Trajectories.” Computers, Environment and Urban Systems 47: 28–43. doi:10.1016/j.compenvurbsys.2014.01.005.
   Web of Science ®Google Scholar
• Izakian, Z., M. S. Mesgari, and A. Abraham. 2016. “Automated Clustering of Trajectory Data Using a Particle Swarm Optimization.” Computers, Environment and Urban Systems 55: 55–65. doi:10.1016/j.compenvurbsys.2015.10.009.
   Web of Science ®Google Scholar
• Laube, P. 2009. Progress in Movement Pattern Analysis. The Netherlands: IOS Press.
   Google Scholar
• Laube, P. 2014. Computational Movement Analysis. Cham: Springer International Publishing AG.
   Google Scholar
• Lin, D. 1998. “An Information-Theoretic Definition of Similarity.” In Proceedings of the Fifteenth International Conference on Machine Learning (ICML '98), Jude W. Shavlik (Ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, 296–304.
   Google Scholar
• Little, J. J., and Z. Gu. 2001. “Video Retrieval by Spatial and Temporal Structure of Trajectories.” Proceedings of the SPIE Storage and Retrieval for Media Databases. San Jose, CA, 20 January 2001 545–552.
   Google Scholar
• Liu, H., and M. Schneider. 2012. “Similarity Measurement of Moving Object Trajectories.” In Proceedings of the Third ACM SIGSPATIAL International Workshop on GeoStreaming. Redondo Beach, CA: ACM.
   Google Scholar
• Long, J. A., and T. A. Nelson. 2013. “A Review of Quantitative Methods for Movement Data.” International Journal of Geographical Information Science 27: 292–318. doi:10.1080/13658816.2012.682578.
   Web of Science ®Google Scholar
• Lymperopoulos, I., and J. Lygeros. 2010. “Sequential Monte Carlo Methods for Multi-Aircraft Trajectory Prediction in Air Traffic Management.” International Journal of Adaptive Control and Signal Processing 24: 830–849. doi:10.1002/acs.1174.
   Web of Science ®Google Scholar
• Mcintosh, J., and M. Yuan. 2005. “Assessing Similarity of Geographic Processes and Events.” Transactions in GIS 9: 223–245. doi:10.1111/tgis.2005.9.issue-2.
   Google Scholar
• Nathan, R., W. M. Getz, E. Revilla, M. Holyoak, R. Kadmon, D. Saltz, and P. E. Smouse. 2008. “A Movement Ecology Paradigm for Unifying Organismal Movement Research.” Proceedings of the National Academy of Sciences 105: 19052–19059. doi:10.1073/pnas.0800375105.
   PubMed Web of Science ®Google Scholar
• Parent, C., S. Spaccapietra, C. Renso, G. Andrienko, G. Andrienko, V. Bogorny, M. L. Damiani, et al. 2013. “Semantic Trajectories Modeling and Analysis.” ACM Computing Surveys 45: 1–32. doi:10.1145/2501654.2501656.
   Web of Science ®Google Scholar
• Parizeau, M., and R. Plamondon. 1990. “A Comparative Analysis of Regional Correlation, Dynamic Time Warping, and Skeletal Tree Matching for Signature Verification.” IEEE Transactions on Pattern Analysis and Machine Intelligence 12: 710–717. doi:10.1109/34.56215.
   Web of Science ®Google Scholar
• Park, J., and D.-H. Jang. 2016. “Application of MK-PRISM for Interpolation of Wind Speed and Comparison with Co-Kriging in South Korea.” GIScience & Remote Sensing 53: 421–443. doi:10.1080/15481603.2016.1192373.
   Web of Science ®Google Scholar
• Purves, R. S., P. Laube, M. Buchin, and B. Speckmann. 2014. “Moving beyond the Point: An Agenda for Research in Movement Analysis with Real Data.” Computers, Environment and Urban Systems 47: 1–4. doi:10.1016/j.compenvurbsys.2014.06.003.
   Web of Science ®Google Scholar
• Qiu, B., Z. Wang, Z. Tang, Z. Liu, D. Lu, C. Chen, and N. Chen. 2016. “A Multi-Scale Spatiotemporal Modeling Approach to Explore Vegetation Dynamics Patterns under Global Climate Change.” GIScience & Remote Sensing 1–18. doi:10.1109/LGRS.2016.2633622.
   Web of Science ®Google Scholar
• Ranacher, P., and K. Tzavella. 2014. “How to Compare Movement? A Review of Physical Movement Similarity Measures in Geographic Information Science and Beyond.” Cartography and Geographic Information Science 41: 286–307. doi:10.1080/15230406.2014.890071.
   PubMed Web of Science ®Google Scholar
• Sakoe, H., and S. Chiba. 1978. “Dynamic Programming Algorithm Optimization for Spoken Word Recognition.” IEEE Transactions on Acoustics, Speech, and Signal Processing 26: 43–49. doi:10.1109/TASSP.1978.1163055.
   Google Scholar
• Siła-Nowicka, K., J. Vandrol, T. Oshan, J. A. Long, U. Demšar, and A. S. Fotheringham. 2016. “Analysis of Human Mobility Patterns from GPS Trajectories and Contextual Information.” International Journal of Geographical Information Science 30: 881–906. doi:10.1080/13658816.2015.1100731.
   Web of Science ®Google Scholar
• Spaccapietra, S., C. Parent, M. L. Damiani, J. A. De Macedo, F. Porto, and C. Vangenot. 2008. “A Conceptual View on Trajectories.” Data & Knowledge Engineering 65: 126–146. doi:10.1016/j.datak.2007.10.008.
   Web of Science ®Google Scholar
• Spaccapietra, S., C. Parent, and L. Spinsanti. 2013. “Trajectories and Their Representations.” In Mobility Data: Modeling, Management, and Understanding, edited by C. Renso, S. Spaccapietra, and E. Zima´ Nyi. New York, NY: Cambridge University.
   Google Scholar
• Vahidnia, M. H., and A. A. Alesheikh. 2014. “Plain Move Predicate and Its Consistency Concerning the Moving Agents in a Network.” International Journal of Geographical Information Science 28: 2145–2177. doi:10.1080/13658816.2014.912281.
   Web of Science ®Google Scholar
• Xia, Y., G.-Y. Wang, X. Zhang, G.-B. Kim, and H.-Y. Bae. 2010. “Research of Spatio-Temporal Similarity Measure on Network Constrained Trajectory Data.” In Rough Set and Knowledge Technology: 5th International Conference, RSKT 2010, Beijing, China, October 15–17, 2010, edited by J. Yu, S. Greco, P. Lingras, G. Wang, and A. Skowron. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Xiao, X., Y. Zheng, Q. Luo, and X. Xie. 2014. “Inferring Social Ties between Users with Human Location History.” Journal of Ambient Intelligence and Humanized Computing 5: 3–19. doi:10.1007/s12652-012-0117-z.
   Web of Science ®Google Scholar
• Yuan, F., C. Wang, and M. Mitchell. 2014. “Spatial Patterns of Land Surface Phenology Relative to Monthly Climate Variations: US Great Plains.” GIScience & Remote Sensing 51: 30–50. doi:10.1080/15481603.2014.883210.
   Web of Science ®Google Scholar
• Yuan, Y., and M. Raubal. 2012. “Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data.” In Geographic Information Science: 7th International Conference, GIScience 2012, edited by N. Xiao, M.-P. Kwan, M. F. Goodchild, and S. Shekhar. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Yuan, Y., and M. Raubal. 2014. “Measuring Similarity of Mobile Phone User Trajectories– a Spatio-Temporal Edit Distance Method.” International Journal of Geographical Information Science 28: 496–520. doi:10.1080/13658816.2013.854369.
   Web of Science ®Google Scholar
• Zhang, C., J. Han, L. Shou, J. Lu, and T. L. La Porta. 2014. “Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories.” Proceedings of the VLDB Endowment 7: 769–780. doi:10.14778/2732939.
   Google Scholar
• Zhou, F., and F. De La Torre. 2012. “Generalized Time Warping for Multi-Modal Alignment of Human Motion.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, June 16–21, 1282–1289.
   Google Scholar