Skip to main content
SpringerLink
Log in

Computing and visualizing popular places

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures, analyzing professional sport matches or better understanding wildlife. A well-known collection of patterns which can occur for a subset of trajectories of moving objects exists. In this paper, we study the popular places pattern, that is, locations that are visited by many moving objects. We consider two criteria, strong and weak, to establish either the exact number of times that an object has visited a place during its complete trajectory or whether it has visited the place, or not. To solve the problem of reporting popular places, we introduce the popularity map. The popularity of a point is a measure of how many times the moving objects of a set have visited that point. The popularity map is the subdivision, into regions, of a plane where all the points have the same popularity. We propose different algorithms to efficiently compute and visualize popular places, the so-called popular regions and their schematization, by taking advantage of the parallel computing capabilities of the graphics processing units. Finally, we provide and discuss the experimental results obtained with the implementation of our algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. 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 

  2. Andrienko, GL, Andrienko NV (2008) A visual analytics approach to exploration of large amounts of movement data. In: Sebillo M, Vitiello G, Schaefer G (eds) VISUAL, lecture notes in computer science, vol 5188, Springer, pp 1–4

  3. Benkert M, Djordjevic B, Gudmundsson J, Wolle T (2010) Finding popular places. Int J Comput Geom Appl (IJCGA) 20(1):19–42

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  5. Bogorny V, Shekhar S (2010) Spatial and spatio-temporal data mining. In: Proceedings of IEEE international conference on data mining ICDM’2010, p 1217

  6. Coll N, Fort M, Madern N, Sellarès JA (2007) Multi-visibility maps of triangulated terrains. Int J Geogr Inf Sci 21(10):1115–1134

    Article  Google Scholar 

  7. Dartmouth College (2008) CRAWDAD. http://crawdad.cs.dartmouth.edu/. Accessed Apr 2013

  8. Dodge S, Weibel R, Lautenschutz AK (2008) Towards a taxonomy of movement patterns. Inf Vis 7(3–4):240–252

    Article  Google Scholar 

  9. Fang W, Lu M, Xiao X, He B, Luo Q (2009) Frequent itemset mining on graphics processors. In: DaMoN ’09: proceedings of the fifth international workshop on data management on new hardware. ACM, New York, NY, USA, pp 34–42

  10. Fort M, Sellarès JA, Valladares N (2010) Computing popular places using graphics processors. In: Proceedings of SSTDM’10 in cooperation with IEEE ICDM’10, IEEE Computer Society, pp 233–241

  11. Fort M, Sellarès JA, Valladares N (2010) Computing popularity maps with graphics hardware. In: Proceedings of the 27th European workshop on computational geometry, pp 233–240

  12. Gajentaan A, Overmars M (1995) On a class of \(O(n^2)\) problems in computational geometry. Comput Geom Theory Appl 5:165–185

    Article  MATH  MathSciNet  Google Scholar 

  13. Giannotti F, Nanni M, Pedreschi D, Pinelli F (2007) Trajectory pattern mining. In: Proceedings of 13th ACM SIGKDD, Sant Jose, California, USA, pp 330–339

  14. Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice-Hall, Inc., Englewood Cliffs, NJ

  15. Gudmundsson J, van Kreveld M, Speckmann B (2004) Efficient detection of motion patterns in spatio-temporal data sets. In: Pfoser D, Cruz IF, Ronthaler M (eds) GIS. ACM, New York, pp 250–257

  16. Gudmundsson J, van Kreveld MJ (2006) Computing longest duration flocks in trajectory data. In: de By RA, Nittel S (eds) GIS. ACM, New York, pp 35–42

  17. Gudmundsson J, van Kreveld MJ, Speckmann B (2007) Efficient detection of patterns in 2D trajectories of moving points. GeoInformatica 11(2):195–215

    Article  Google Scholar 

  18. Laube P, Imfeld S, Weibel R (2005) Discovering relative motion patterns in groups of moving point objects. Int J Geogr Inf Sci 19(6):639–668

    Article  Google Scholar 

  19. Laube P, van Kreveld M, Imfield S (2004) Finding REMO—detecting relative motion patterns in geospatial lifelines. Developments in spatial data handling: 11th international symposium on spatial data handling, pp 201–215

  20. Leite PJS, Teixeira JMXN, de Farias TSMC, Teichrieb V, Kelner J (2009) Massively parallel nearest neighbor queries for dynamic point clouds on the GPU. In: SBAC-PAD. IEEE Computer Society, pp 19–25

  21. Li X, Han J, Lee JG, Gonzalez H (2007) Traffic density-based discovery of hot routes in road networks. In: SSTD 2007, LNCS, vol 4605, pp 441–459

  22. Liao Z-H, Peng W-C (2012) Clustering spatial data with a geographic constraint: exploring local search. Knowl Inf Syst 31(1):153–170

    Article  Google Scholar 

  23. Lu E, Lee WC, Tseng V (2010) Mining fastest path from trajectories with multiple destinations in road networks. Knowl Inf Syst 1–29

  24. NVIDIA (2011) CUDA programming guide 4.0. Technical report, NVIDIA Corporation

  25. NVIDIA (2011) NVIDIA CUDA C programming best practices guide 4.0. Technical report, NVIDIA Corporation

  26. Ong R, Wachowicz M, Nanni M, Renso C (2010) From pattern discovery to pattern interpretation in movement data. In: ICDM’10, pp 527–534

  27. Owens JD, Luebke D, Govindaraju N, Harris M, Krger J, Lefohn AE, Purcell TJ (2007) A survey of general-purpose computation on graphics hardware. Comput Graph Forum 26(1):80–113

    Article  Google Scholar 

  28. Pelekis N, Kopanakis I, Kotsifakos EE, Frentzos E, Theodoridis Y (2011) Clustering uncertain trajectories. Knowl Inf Syst 28(1):117–147

    Article  Google Scholar 

  29. Rinzivillo S, Pedreschi D, Nanni M, Giannotti F, Andrienko NV, Andrienko GL (2008) Visually driven analysis of movement data by progressive clustering. Inf Vis 7(3–4):225–239

    Article  Google Scholar 

  30. Sacharidis D, Patroumpas Kl, Terrovitis M, Kantere V, Potamias M, Mouratidis K, Sellis T (2008) On-line discovery of hot motion paths. In: EDBT’08, March 2008, France, pp 392–402

  31. Segal M, Akeley K (1994) The design of the openGL graphics interface. Technical report, Silicon Graphics Computer Systems

  32. Siddiqi K, Pizer S (2008) Medial representations: mathematics. Algorithms and applications. Springer, Berlin

  33. Theodoridis Y (2011) R-Tree portal. http://www.chorochronog.org/. Accessed Apr 2013

  34. Tietbohl A, Bogorny V, Kuijpers B, Alvares LO (2008) A Clustering-based approach for discovering interesting places in trajectories, In: SAC’08. March 2008, Brazil

  35. Trasarti R, Pinelli F, Nanni M, Giannotti F (2011) Mining mobility user profiles for car pooling. In: KDD’011, pp 1190–1198

  36. Wilensky U (1999) NetLogo. http://ccl.northwestern.edu/netlogo/models/BirdBreeder. Accessed Apr 2013

Download references

Acknowledgments

We thank the reviewers for their suggestions and comments. Authors are partially supported by the Spanish MCI grant TIN2010-20590-C02-02.

Author information

Authors and Affiliations

  1. Informàtica i Matemàtica Aplicada, Universitat de Girona, Girona, Spain

    Marta Fort, J. Antoni Sellarès & Nacho Valladares

Authors
  1. Marta Fort
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Antoni Sellarès
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nacho Valladares
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nacho Valladares.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fort, M., Sellarès, J.A. & Valladares, N. Computing and visualizing popular places. Knowl Inf Syst 40, 411–437 (2014). https://doi.org/10.1007/s10115-013-0639-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-013-0639-5

Keywords

Search

Navigation