Detection of dynamic activity patterns at a collective level from large-volume trajectory data

International Journal of Geographical Information Science

Volumn 28, Issue 5, 2014, Pages 946-963

  Scholz, Ruojing W ^a   Lu, Yongmei ^a  

^a TEXAS STATE UNIVERSITY   (United States)

Author keywords

activity hot spots; GPS trajectory; life cycle of hot spots; space time activity patterns

Indexed keywords

GPS; SATELLITE DATA; SATELLITE IMAGERY; TRAJECTORY; URBAN DEVELOPMENT; URBAN PLANNING;

CALIFORNIA; SAN FRANCISCO [CALIFORNIA]; UNITED STATES;

EID: 84899110438     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2013.869819     Document Type: Article

References (52)

1. Abraham, S. and Lal, P.S., 2010. Trajectory similarity of network constrained moving objects and applications to traffic security. In: H. Chen, et al., eds. Proceedings of Intelligence and security informatics: Pacific Asia workshop, PAISI 2010, 21 June 2010, Hyderabad. Berlin: Springer, 31-43.
2. Ahas, R., et al., 2010a. Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: case study with mobile positioning data. Transportation Research Part C, 18, 45-54.
3. Ahas, R., et al., 2010b. Using mobile positioning data to model locations meaningful to users of mobile phones. Journal of Urban Technology, 17 (1), 3-27.
4. Alvares, L.O., et al., 2007a. Dynamic modeling of trajectory patterns using data mining and reverse engineering. In: J. Grundy, et al., eds. Proceedings of tutorials, posters, panels and industrial contributions at the 26th international conference on conceptual modeling - ER 2007. Auckland, CRIPT, 83. Darlinghurst: ACS, 149-154.
5. Alvares, L.O., et al., 2007b. A model for enriching trajectories with sematic geographical information. In: Proceedings of the 15th international symposium on advances in Geographic Information Systems (ACM-GIS'07), 7-9 November 2007 Seattle, WA. New York, NY: ACM, 1-8.
6. Andrienko, G.G. and Andrienko, N.N., 2008. Spatio-temporal aggregation for visual analysis of movements. In: D. Ebert and T. Ertl, eds. 2008 IEEE symposium on visual analytics science & technology, 21-23 October 2008, Columbus, OH. Los Alamitos, CA: IEEE Computer Society, 51-58.
7. Anselin, L., 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27 (2), 93-115.
8. Anselin, L., 2005. Exploring spatial data with GeoDa™: a workbook [online]. GeoDa center for geospatial analysis and computation. Available from: https://geodacenter.asu.edu/system/files/geodaworkbook.pdf [Accessed 13 February 2013].
9. 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.
10. Brakatsoulas, S., Pfoser, D., and Tryfona, N., 2004. Modeling, storing, and mining moving object databases. In: Proceedings 8th international database engineering and applications symposium (IDEAS '04), 7-9 July 2004 Coimbra. IEEE, 68-77.
11. Buchin, K., et al., 2011. Detecting commuting patterns by clustering subtrajectories. International Journal of Computational Geometry & Applications, 21 (3), 253-282.
12. Calabrese, F., et al., 2011. Estimating origin-destination flows using mobile phone location Data. IEEE Pervasive Computing, 10 (4), 36-44.
13. Diggle, P.J. and Chetwynd, A.G., 1995. Second-order analysis of space-time clustering. Statistical Methods in Medical Research, 4 (2), 124-136.
14. Elnekave, S., Last, M., and Maimon, O., 2007. Incremental clustering of mobile objects. In: 2007 IEEE 23rd international conference on data engineering workshop, 15-20 April 2007 Istanbul, 585-592.
15. Fotheringham, A.S. and Wong, D.W.S., 1991. The modifiable areal unit problem in multivariate statistical analysis. Environment and Planning A, 23, 1025-1044.
16. Gambs, S., Killijian, M.-O., and del Prado Cortez, M.N., 2010. GEPETO: a GEoPrivacy-Enhancing TOolkit. In: IEEE 24th international conference on advanced information networking & applications workshops (WAINA), 20-23 April 2010 Perth. IEEE, 1071-1076.
17. Gao, Y., et al., 2010. Algorithms for constrained k-nearest neighbor queries over moving object trajectories. Geoinformatica, 14 (2), 241-276.
18. Giannotti, F., et al., 2009. Mining mobility behavior from trajectory data. In: 2009 international conference on computational science and engineering, 29-31 August 2009 Vancouver, BC. 948-951.
19. Gidófalvi, G. and Pedersen, T., 2009. Mining long, sharable patterns in trajectories of moving objects. Geoinformatica, 13 (1), 27-55.
20. Gong, H., et al., 2012. A GPS/GIS method for travel mode detection in New York City. Computers, Environment and Urban Systems, 36 (2), 131-139.
21. Herring, R.J., 2010. Real-time traffic modeling and estimation with streaming probe data using machine learning. Thesis (PhD). University of California, Berkeley, CA.
22. Hu, J., et al., 2009. Dynamic modeling of urban population travel behavior based on data fusion of mobile phone positioning data and FCD. In: 2009 17th international conference on Geoinformatics, 12-14 August 2009 Fairfax, CA, 1-5.
23. Hu, H., et al., 2012. Pick-up tree based route recommendation from taxi trajectories. Web-Age Information Management: Lecture Notes in Computer Science, 7418, 471-483.
24. Hwang, R.-H., Hsueh, Y.-L., and Chung, H.-W., 2012. A novel time-obfuscated algorithm for trajectory privacy. In: 2012 international symposium on pervasive systems, algorithms and networks, 13-15 December 2012 San Marcos, TX. IEEE, 208-215.
25. Jeung, H., et al., 2010. Path prediction and predictive range querying in road network databases. The VLDB Journal, 19 (4), 585-602.
26. Knox, E.G. and Bartlett, M.S., 1964. The detection of space-time interactions. Journal of the Royal Statistical Society Series C (Applied Statistics), 13 (1), 25-30.
27. Kulldorff, M., 2010. SaTScan™ user guide for version 9.0 [online]. SaTScan. Available from: http://www.satscan.org/cgi-bin/satscan/register.pl/Current%20Version:%20SaTScan%20v9.1.1%20released%20March%209%202011.?todo=process_userguide_download [Accessed 10 February 2013].
28. Kulldorff, M., et al., 2005. A space-time permutation scan statistic for disease outbreak detection. PLoS medicine, 2 (3), 216-224.
29. Kwan, M.-P., 2000. Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transportation Research Part C, 8 (1), 185-203.
30. Kwan, M.-P., Xiao, N., and Ding, G., 2014. Assessing activity pattern similarity with multidimensional sequence alignment based on a multiobjective optimization evolutionary algorithm. Geographical Analysis (forthcoming).
31. Lee, D., Baek, S., and Bae, H., 2009. aCN-RB-tree: update method for spatio-temporal aggregation of moving object trajectory in ubiquitous environment. In: 2009 international conference on computational science and its applications (ICCSA 2009), 29 June-2 July 2009, Yongin. Los Alamitos, CA: IEEE Computer Society, 177-182.
32. Lee, J., Han, J., and Whang, K., 2007. Trajectory clustering: a partition-and-group framework. In: 2007 ACM SIGMOD international conference on management of data (SIGMOD' 07), 11-14 June 2007 Beijing. 1-12.
33. Li, X. and Lin, H., 2006. Indexing network-constrained trajectories for connectivity-based queries. International Journal of Geographical Information Science, 20 (3), 303-328.
34. Lin, B. and Su, J., 2008. One way distance: for shape based similarity search of moving object trajectories. Geoinformatica, 12 (2), 117-142.
35. Liu, X. and Karimi, H.A., 2006. Location awareness through trajectory prediction. Computers, Environment and Urban Systems, 30 (6), 741-756.
36. Lu, Y. and Liu, Y., 2012. Pervasive location acquisition technologies: opportunities and challenges for geospatial studies. Computers, Environment and Urban Systems, 36 (2), 105-108.
37. Manso, J.A.R.C., et al., 2010. DB-SMoT: a direction-based spatio-temporal clustering method. In: 2010 5th IEEE international conference intelligent systems (IS), 7-9 July 2010 London, 114-119.
38. Masciari, E., 2009. A framework for trajectory clustering. In: N. Trigoni, A. Markham, and S. Nawaz, eds. Proceedings of geosensor networks: third international conference, GSN 2009, 13-14 July 2009, Oxford. Berlin: Springer, 102-111.
39. Palma, A.T., et al., 2008. A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing (SAC'08), 16-20 March 2008 Fortaleza. New York, NY: ACM, 863-868.
40. Pfoser, D. and Jensen, C.S., 2005. Trajectory indexing using movement constraints. GeoInformatica, 9 (2), 93-115.
41. Ripley, B.D., 1976. The second-order analysis of stationary point processes. Journal of Applied Probability, 13 (2), 255-266.
42. Roh, G. and Hwang, S., 2010. NNCluster: an efficient clustering algorithm for road network trajectories. In: H. Kitagawa, et al., eds. Proceedings of database systems for advanced applications: 15th international conference, DASFAA 2010, part II, 1-4 April 2010, Tsukuba. Berlin: Springer, 47-61.
43. Ruan, T., Wang, Z.Q., and Song, T., 2009. A collaborative car auto-navigation framework based on intelligent trajectory mining. In: J. Zhang, G. Li, and J.Y. Yang, eds. 2009 international joint conference on bioinformatics, systems biology and intelligent computing (IJCBS '09), 3-5 August 2009, Shanghai. Los Alamitos, CA: IEEE Computer Society, 591-596.
44. Scellato, S., et al., 2011. NextPlace: a spatio-temporal prediction framework for pervasive systems. Pervasive Computing: Lecture Notes in Computer Science, 6696, 152-169.
45. Shen, Y., Kwan, M.-P., and Chai, Y., 2013. Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China. Journal of Transport Geography, 32 (1), 1-11.
46. Shoval, N. and Isaacson, M., 2007. Sequence alignment as a method for human activity analysis in space and time. Annals of the Association of American Geographers, 97 (2), 282-297.
47. Wartenberg, D., 1985. Multivariate spatial correlation: a method for exploratory geographical analysis. Geographical Analysis, 17, 263-283.
48. Wei, L., et al., 2009. Exploring spatio-temporal features for traffic estimation on road networks. In: Advances in spatial & temporal databases: 11th international symposium (SSTD 2009), 8-10 July 2009 Aalborg, 399-404.
49. Yue, Y., et al., 2009. Mining time-dependent attractive areas and movement patterns from taxi trajectory data. In: 17th international conference on Geoinformatics, 12-14 August 2009 Fairfax, VA, 1-6.
50. Zhao, X. and Xu, W., 2009. A clustering-based approach for discovering interesting places in a single trajectory. In: 2009 second international conference on intelligent computation technology & automation (ICICTA '09), 10-11 October 2009 Zhangjiajie. IEEE, 429-432.
51. Zheng, Y., et al., 2008. Understanding mobility based on GPS data. In: Proceedings of the 10th international conference on ubiquitous computing (UbiComp'08), 21-24 September 2008 Seoul. New York, NY: ACM, 1-10.
52. Zheng, Y., et al., 2009. Mining interesting locations and travel sequences from GPS trajectories. In: 18th international world wide web conference (WWW 2009), 20-24 April 2009 Madrid. New York, NY: ACM, 1-10.