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Spatiotemporal Modeling and Analysis—Introduction and Overview

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Abstract

Over the past five to seven years the analysis of trajectory data has established itself as an independent research discipline within the area of data mining. In this article we provide an overview on data characteristics, state-of-the-art preprocessing and analysis methods of trajectory data. We conclude the article with a collection of challenges that arise due to the increasing variety of spatiotemporal data sources and which have to be solved for the application of spatiotemporal analysis methods in practice.

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References

  1. Allen JF (1984) Towards a general theory of action and time. Artif Intell 23(2):123–154

    Article  MATH  Google Scholar 

  2. Alvares LO, Bogorny V, Kuijpers B, de Macedo JAF, Moelans B, Vaisman A (2007) A model for enriching trajectories with semantic geographical information. In: Proc of the 15th annual ACM international symposium on advances in geographic information systems (GIS’07). ACM, New York, pp 1–8

    Chapter  Google Scholar 

  3. Andersson M, Gudmundsson J, Laube P, Wolle T (2008) Reporting leaders and followers among trajectories of moving point objects. Geoinformatica 12(4):497–528

    Article  Google Scholar 

  4. Andrienko G, Andrienko N, Wrobel S (2007) Visual analytics tools for analysis of movement data. SIGKDD Explor Newsl 9(2):38–46

    Article  Google Scholar 

  5. Andrienko N, Andrienko G, Pelekis N, Spaccapietra S (2008) Basic concepts of movement data. In: Giannotti F, Pedreschi D (eds) Mobility, data mining and privacy. Springer, Berlin, Chap 1

    Google Scholar 

  6. Andrienko G, Andrienko N, Rinzivillo S, Nanni M, Pedreschi D, Giannotti F (2009) Interactive visual clustering of large collections of trajectories. In: Proc of the IEEE symposium on visual analytics science and technology (VAST’09). IEEE, New York, pp 3–10

    Chapter  Google Scholar 

  7. Benetis R, Jensen CS, Karciauskas G, Saltenis S (2006) Nearest and reverse nearest neighbor queries for moving objects. VLDB J 15(3):229–249

    Article  Google Scholar 

  8. Benkert M, Gudmundsson J, Hübner F, Wolle T (2008) Reporting flock patterns. Comput Geom 41(3):111–125

    Article  MathSciNet  MATH  Google Scholar 

  9. Bonchi F, Lakshmanan LV, Wang H (2011) Trajectory anonymity in publishing personal mobility data. SIGKDD Explor Newsl 13(1):30–42

    Article  Google Scholar 

  10. Buchin M, Driemel A, van Kreveld M, Sacristán V (2010) An algorithmic framework for segmenting trajectories based on spatio-temporal criteria. In: Proc of the 18th SIGSPATIAL international conference on advances in geographic information systems (ACM GIS’10). ACM, New York, pp 202–211

    Google Scholar 

  11. Chen R, Fung BCM, Desai BC (2011) Differentially private trajectory data publication. CoRR abs/1112.2020

  12. Claramunt C, Jiang B (2000) A representation of relationships in temporal spaces. In: Innovations in GIS VII: geocomputation. Taylor & Francis, London, pp 41–53

    Google Scholar 

  13. Claramunt C, Jiang B (2001) An integrated representation of spatial and temporal relationships between evolving regions. Geogr Syst 3:411–428

    Article  Google Scholar 

  14. Dwork C (2006) Differential privacy. In: Proc of the 33rd international colloquium on automata, languages and programming (ICALP’06). Lecture notes in computer science. Springer, Berlin, pp 1–12

    Chapter  Google Scholar 

  15. Egenhofer MJ (1991) Reasoning about binary topological relations. In: Günther O, Schek HJ (eds) Proc of the 2nd international symposium on advances in spatial databases (SSD). Springer, Berlin, pp 143–160

    Google Scholar 

  16. Forlizzi L, Güting RH, Nardelli E, Schneider M (2000) A data model and data structures for moving objects databases. In: Proc of the 2000 ACM SIGMOD international conference on management of data (SIGMOD’00). ACM, New York, pp 319–330

    Chapter  Google Scholar 

  17. Giannotti F, Nanni M, Pedreschi D (2006) Efficient mining of temporally annotated sequences. In: Proc of the 6th SIAM international conference on data mining (SDM’06). SIAM, Philadelphia, pp 346–357

    Google Scholar 

  18. Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proc of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’07). ACM, New York, pp 330–339

    Chapter  Google Scholar 

  19. Güting RH, Schneider M (2005) Moving objects databases. Morgan Kaufmann, San Mateo

    Google Scholar 

  20. Hägerstrand T (1970) What about people in regional science? Pap Reg Sci Assoc 24:7–21

    Article  Google Scholar 

  21. Hecker D, Körner C, Stange H, Schulz D, May M (2011) Modeling micro-movement variability in mobility studies. In: Geertman S, Reinhardt W, Toppen F (eds) Advancing geoinformation science for a changing world. Lecture notes in geoinformation and cartography. Springer, Berlin, pp 121–140

    Chapter  Google Scholar 

  22. Hwang SY, Liu YH, Chiu JK, Lim EP (2005) Mining mobile group patterns: a trajectory-based approach. In: Proc of the 9th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD’05). Lecture notes in computer science, vol 3518. Springer, Berlin, pp 713–718

    Google Scholar 

  23. Kang J, Yong HS (2010) Mining spatio-temporal patterns in trajectory data. J Inf Process Syst 6(4):521–536

    Google Scholar 

  24. Körner C (2012) Modeling visit potential of geographic locations based on mobility data. PhD thesis, University of Bonn

  25. Laasonen K (2005) Clustering and prediction of mobile user routes from cellular data. In: Proc of 9th European conference on principles and practice of knowledge discovery in databases (PKDD’05). Springer, Berlin, pp 569–576

    Google Scholar 

  26. Laube P, Imfeld S (2002) Analyzing relative motion within groups of trackable moving point objects. In: Proc of the 2nd international conference on geographic information science (GIScience’02). Springer, London, pp 132–144

    Google Scholar 

  27. Lei PR, Shen TJ, Peng WC, Su IJ (2011) Exploring spatial-temporal trajectory model for location prediction. In: Proceedings of the 2011 IEEE 12th international conference on mobile data management (MDM’11). IEEE Computer Society, Washington, pp 58–67

    Chapter  Google Scholar 

  28. Liang B, Haas ZJ (2003) Predictive distance-based mobility management for multidimensional PCS networks. IEEE/ACM Trans Netw 11(5):718–732

    Article  Google Scholar 

  29. Liao L, Fox D, Kautz H (2007) Extracting places and activities from GPS traces using hierarchical conditional random fields. Int J Robot Res 26(1):119–134

    Article  Google Scholar 

  30. Liou SC, Huang YM (2005) Trajectory predictions in mobile networks. Int J Inf Technol 11(11):109–122

    Google Scholar 

  31. Marketos G, Frentzos E, Ntoutsi I, Pelekis N, Raffaetà A, Theodoridis Y (2008) Building real-world trajectory warehouses. In: Proc of the seventh ACM international workshop on data engineering for wireless and mobile access (MobiDE’08). ACM, New York, pp 8–15

    Chapter  Google Scholar 

  32. Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) WhereNext: a location predictor on trajectory pattern mining. In: Proc of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’09). ACM, New York, pp 637–646

    Chapter  Google Scholar 

  33. Monreale A, Andrienko G, Andrienko N, Giannotti F, Pedreschi D, Rinzivillo S, Wrobel S (2010) Movement data anonymity through generalization. Trans Data Priv 3(2):91–121

    MathSciNet  Google Scholar 

  34. Muckell J, Hwang JH, Lawson CT, Ravi SS (2010) Algorithms for compressing GPS trajectory data: an empirical evaluation. In: Proc of the 18th SIGSPATIAL international conference on advances in geographic information systems (ACM GIS’10). ACM, New York, pp 402–405

    Google Scholar 

  35. Nanni M, Pedreschi D (2006) Time-focused clustering of trajectories of moving objects. J Intell Inf Syst 27(3):267–289

    Article  Google Scholar 

  36. Nanni M, Kuijpers B, Körner C, May M, Pedreschi D (2008) Spatiotemporal data mining. In: Giannotti F, Pedreschi D (eds) Mobility, data mining and privacy. Springer, Berlin, Chap 10

    Google Scholar 

  37. Panagiotakis C, Pelekis N, Kopanakis I, Ramasso E, Theodoridis Y (2011) Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans Knowl Data Eng 24:1328–1343

    Article  Google Scholar 

  38. Pelekis N, Andrienko G, Andrienko N, Kopanakis I, Marketos G, Theodoridis Y (2011) Visually exploring movement data via similarity-based analysis. J Intel Inf Syst 1–49

  39. Pelekis N, Frentzos E, Giatrakos N, Theodoridis Y (2011) HERMES: a trajectory db engine for mobility-centric applications. Int J Knowl-Based Organ, in press

  40. Rinzivillo S, Pedreschi D, Nanni M, Giannotti F, Andrienko N, Andrienko G (2008) Visually driven analysis of movement data by progressive clustering. Inf Vis 7(3):225–239

    Article  Google Scholar 

  41. Saltenis S, Jensen CS, Leutenegger ST, Lopez MA (2000) Indexing the positions of continuously moving objects. In: Proc of the 2000 ACM SIGMOD international conference on management of data (SIGMOD’00). ACM, New York, pp 331–342

    Chapter  Google Scholar 

  42. Samarati P (2001) Protecting respondents’ identities in microdata release. IEEE Trans Knowl Data Eng 13:1010–1027

    Article  Google Scholar 

  43. Schuessler N, Axhausen KW (2009) Processing raw data from global positioning systems without additional information. Transp Res Rec 2105:28–36

    Article  Google Scholar 

  44. Schulz D, Bothe S, Körner C (2012) Human mobility from GSM data—a valid alternative to GPS? In: Proc of the mobile data challenge workshop

    Google Scholar 

  45. Shekhar S, Raju VR, Celik M (2009) Spatial and spatio-temporal data mining: recent advances. In: Kargupta H, Han J, Yu P, Motwani R, Kumar V (eds) Next generation of data mining. Chapman & Hall/CRC, London, Chap 26

    Google Scholar 

  46. Stopher PR (2009) Collecting and processing data from mobile technologies. In: Transport survey methods—keeping up with a changed world. Emerald Group Publishing Limited, Bingley, Chap 21

    Google Scholar 

  47. Tao Y, Papadias D (2002) Time-parameterized queries in spatio-temporal databases. In: Proc of the 2002 ACM SIGMOD international conference on management of data (SIGMOD’02). ACM, New York, pp 334–345

    Chapter  Google Scholar 

  48. Tao Y, Papadias D, Sun J (2003) The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Proc of 29th international conference on very large data bases (VLDB’03). Morgan Kaufmann, San Mateo, pp 790–801

    Google Scholar 

  49. Wachowicz M, Ong R, Renso C, Nanni M (2011) Finding moving flock patterns among pedestrians through collective coherence. Int J Geogr Inf Sci 25(11):1849–1864

    Article  Google Scholar 

  50. Wang Y, Lim EP, Hwang SY (2003) On mining group patterns of mobile users. In: Proc of the 14th international conference on database and expert systems applications (DEXA’03). Springer, Berlin, pp 287–296

    Google Scholar 

  51. Yan Z, Chakraborty D, Parent C, Spaccapietra S, Aberer K (2011) SeMiTri: a framework for semantic annotation of heterogeneous trajectories. In: Proc of the 14th international conference on extending database technology (EDBT’11). ACM, New York, pp 259–270

    Google Scholar 

  52. Yan Z, Giatrakos N, Katsikaros V, Pelekis N, Theodoridis Y (2011) SeTraStream: semantic-aware trajectory construction over streaming movement data. In: Proc of the 12th international symposium on advances in spatial and temporal databases (SSTD’11), pp 367–385

    Chapter  Google Scholar 

  53. Ying JJC, Lee WC, Weng TC, Tseng VS (2011) Semantic trajectory mining for location prediction. In: Proc of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems (ACM GIS’11). ACM, New York, pp 34–43

    Google Scholar 

  54. Zhou C, Frankowski D, Ludford P, Shekhar S, Terveen L (2007) Discovering personally meaningful places: an interactive clustering approach. ACM Trans Inf Syst 25(3)

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Correspondence to Christine Körner.

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Körner, C., May, M. & Wrobel, S. Spatiotemporal Modeling and Analysis—Introduction and Overview. Künstl Intell 26, 215–221 (2012). https://doi.org/10.1007/s13218-012-0215-2

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  • DOI: https://doi.org/10.1007/s13218-012-0215-2

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