Abstract
One of the initial phases in the applications dealing with data processing on GPS trajectory data is to generate the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The sequence is constructed by labeling each of the GPS observations of the trajectory using the ID of their intersecting Geometries of Interest (GOIs). In this paper, we enhance the performance of the state-of-the-art scheme for constructing the GOIs of a mobile object by proposing a data aggregation and outlier detection method. Our experimental results using geometric similarity metrics show that our improved GOI construction method outperforms the baseline methods by constructing the GOIs remarkably more geometrically similar to the real world GOIs. The geometric similarity metrics are only applicable when we have access to the geometries of the real world GOIs (ground truth). To be able to analyse the performance of the GOI extraction methods in environments which we do not have access to the ground truth, we propose two useful spatio-temporal metrics to measure the quality of GOIs based on the quality of the generated SVLs based on them. Our experimental results show that these two metrics are able to discriminate between the results of our different outlier detection methods and select the best scheme without using any external knowledge about the geometries of the real world GOIs.
Similar content being viewed by others
References
Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7(5):275–286
Bhattacharya T, Kulik L, Bailey J (2012) Extracting significant places from mobile user GPS trajectories: a bearing change based approach. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, ACM, New York, NY, USA, SIGSPATIAL ’12, pp 398–401
Bhattacharya T, Kulik L, Bailey J (2015) Automatically recognizing places of interest from unreliable GPS data using spatio-temporal density estimation and line intersections. Pervasive Mob Comput 19:86–107
De Maio C, Fenza G, Loia V, Orciuoli F (2016) Unfolding social content evolution along time and semantics. Future Gener Comput Syst. doi:10.1016/j.future.2016.05.039
Ester M (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. AAAI Press, Portland, pp 226–231
Fenza G, Fischetti E, Furno D, Loia V (2011) A hybrid context aware system for tourist guidance based on collaborative filtering. In: Fuzzy Systems (FUZZ), 2011 IEEE International Conference, pp 131–138
Filzmoser P, Garrett RG, Reimann C (2005) Multivariate outlier detection in exploration geochemistry. Comput Geosci 31(5):579–587
Gidófalvi G, Dong F (2012) When and where next: individual mobility prediction. In: MobiGIS, pp 57–64
Gupta M, Gao J, Aggarwal C, Han J (2014) Outlier detection for temporal data. Synth Lect Data Mining Knowl Discov 5(1):1–129
Handcock RN, Swain DL, Bishop-Hurley GJ, Patison KP, Wark T, Valencia P, Corke P, ONeill CJ (2009) Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing. Sensors 9(5):3586–3603
Hariharan R, Toyama K (2004) Project lachesis: parsing and modeling location histories. In: Egenhofer M, Freksa C, Miller H (eds) Geographic information science. Lecture notes in computer science, vol 3234. Springer, Berlin, Heidelberg, pp 106–124
Howell D (2002) Statistical methods for psychology. Duxbury/Thomson Learning, North Scituate
Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47(260):583–621
Li Z, Han J, Ji M, Tang LA, Yu Y, Ding B, Lee JG, Kays R (2011) Movemine: mining moving object data for discovery of animal movement patterns. ACM TIST 2(4):37–57
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Le Cam LM, Neyman J (eds) Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol 1. University of California Press, Berkeley, CA, USA, pp 281–297
Mahalanobis PC (1936) On the generalized distance in statistics. Proceedings of the National Institute of Sciences (Calcutta) 2:49–55
Miller J (2009) Fastest path analysis in a vehicle-to-infrastructure intelligent transportation system architecture. In: Intelligent Vehicles Symposium, 2009 IEEE, pp 1125–1130
Miller J, Horowitz E (2007) Freesim—a free real-time freeway traffic simulator. In: Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE, pp 18–23
Min W, Wynter L (2011) Real-time road traffic prediction with spatio-temporal correlations. Transp Res Part C Emerg Technol 19(4):606–616
Mousavi SM, Harwood A, Karunasekera S, Maghrebi M (2016) Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data. J Ambient Intell Human Comput. doi:10.1007/s12652-016-0400-5
Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM Symposium on Applied Computing, ACM, New York, NY, USA, SAC ’08, pp 863–868
R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org (ISBN 3-900051-07-0)
Scellato S, Musolesi M, Mascolo C, Latora V, Campbell AT (2011a) Nextplace: a spatio-temporal prediction framework for pervasive systems., Proceedings of the 9th international conference on Pervasive computing, Pervasive ′11. Springer-Verlag, Berlin, Heidelberg
Scellato S, Musolesi M, Mascolo C, Latora V, Campbell AT (2011b) Nextplace: a spatio-temporal prediction framework for pervasive systems. In: Pervasive, pp 152–169
Si H, Wang Y, Yuan J, Shan X (2010) Mobility prediction in cellular network using hidden markov model. In: Consumer Communications and Networking Conference (CCNC), 2010 7th IEEE, pp 1–5
Smucker MD, Allan J, Carterette B (2007) A comparison of statistical significance tests for information retrieval evaluation. In: Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, ACM, New York, NY, USA, CIKM ’07, pp 623–632
Song L, Deshpande U, Kozat U, Kotz D, Jain R (2006) Predictability of wlan mobility and its effects on bandwidth provisioning. In: INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings, pp 1–13
Xiao X, Zheng Y, Luo Q, Xie X (2010) Finding similar users using category-based location history. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, New York, NY, USA, GIS ’10, pp 442–445
Xiao X, Zheng Y, Luo Q, Xie X (2014) Inferring social ties between users with human location history. J Ambient Intell Human Comput 5(1):3–19
Xue AY, Zhang R, Zheng Y, Xie X, Huang J, Xu Z (2013) Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: IEEE International Conference on Data Engineering (ICDE 2013), IEEE
Ye Y, Zheng Y, Chen Y, Feng J, Xie X (2009) Mining individual life pattern based on location history. In: Mobile Data Management: Systems, Services and Middleware, 2009. MDM ’09. Tenth International Conference, pp 1–10
Zhang K, Hutter M, Jin H (2009) A new local distance-based outlier detection approach for scattered real-world data. In: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Springer-Verlag, Berlin, Heidelberg, PAKDD ’09, pp 813–822
Zheng Y, Chen Y, Li Q, Xie X, Ma WY (2010) Understanding transportation modes based on GPS data for web applications. ACM Trans Web 4(1):1:1–1:36
Zhou C, Frankowski D, Ludford P, Shekhar S, Terveen L (2004) Discovering personal gazetteers: An interactive clustering approach. In: Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, ACM, New York, NY, USA, GIS ’04, pp 266–273
Zimek A, Schubert E, Kriegel HP (2012) A survey on unsupervised outlier detection in high-dimensional numerical data. Stat Anal Data Mining 5(5):363–387
Acknowledgements
We would like to acknowledge the financial support that we received from Data61 during this research project.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mousavi, S.M., Harwood, A., Karunasekera, S. et al. Enhancing the quality of geometries of interest (GOIs) extracted from GPS trajectory data using spatio-temporal data aggregation and outlier detection. J Ambient Intell Human Comput 9, 173–186 (2018). https://doi.org/10.1007/s12652-016-0426-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-016-0426-8