More Web Proxy on the site http://driver.im/

article

Reporting flock patterns

Authors:

Joachim Gudmundsson,

Florian Hübner,

Thomas WolleAuthors Info & Claims

Computational Geometry: Theory and Applications, Volume 41, Issue 3

Pages 111 - 125

https://doi.org/10.1016/j.comgeo.2007.10.003

Published: 01 November 2008 Publication History

Abstract

Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns, where a pattern can be any configuration of some moving objects in a certain area and/or during a certain time period. One of the most common spatio-temporal patterns sought after is flocks. A flock is a large enough subset of objects moving along paths close to each other for a certain pre-defined time. We give a new definition that we argue is more realistic than the previous ones, and by the use of techniques from computational geometry we present fast algorithms to detect and report flocks. The algorithms are analysed both theoretically and experimentally.

References

[1]

Al-Naymat, G., Chawla, S. and Gudmundsson, J., Dimensionality reduction for long duration and complex spatio-temporal queries. In: Proceedings of the 22nd ACM Symposium on Applied Computing, ACM. pp. 393-397.

[2]

Arya, S. and Mount, D.M., Approximate range searching. Computational Geometry---Theory and Applications. v17 i3--4. 135-152.

[3]

Balme, G. and Hunter, L., Mortality in a protected leopard population, phinda private game reserve, South Africa: A population in decline?. Ecological Journal. v6.

[4]

Bern, M., Eppstein, D. and Teng, S.-H., Parallel construction of quadtrees and quality triangulations. International Journal of Computational Geometry and Applications. v9 i6. 517-532.

[5]

Dettki, H., Ericsson, G. and Edenius, L., Real-time moose tracking: An internet based mapping application using GPS/GSM-collars in Sweden. Alces. v40. 13-21.

[6]

D. Eppstein, M.T. Goodrich, J.Z. Sun, The skip quadtree: A simple dynamic data structure for multidimensional data, in: Proceedings of the 21st ACM Symposium on Computational Geometry, 2005, pp. 296--305

Digital Library

[7]

In: Frank, A.U., Raper, J.F., Cheylan, J.-P. (Eds.), Life and Motion of Spatial Socio-economic Units, Taylor & Francis, London.

[8]

J. Gudmundsson, M. van Kreveld, Computing longest duration flocks in trajectory data, in: Proceedings of the 14th ACM Symposium on Advances in GIS, 2006, pp. 35--42

[9]

Gudmundsson, J., van Kreveld, M. and Speckmann, B., Efficient detection of motion patterns in spatio-temporal sets. GeoInformatica. v11 i2. 195-215.

[10]

Heurich, M., Löttker, P., Stache, A., Baierl, F. and Kiener, H., Der Luchs im Bergwaldökosystem. AFZ---Der Wald. v10. 530-531.

[11]

S. Iwase, H. Saito, Tracking soccer player using multiple views, in: Proceedings of the IAPR Workshop on Machine Vision Applications (MVA02), 2002, pp. 102--105

[12]

G. Kollios, S. Sclaroff, M. Betke, Motion mining: Discovering spatio-temporal patterns in databases of human motion, in: Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2001

[13]

Laube, P. and Imfeld, S., Analyzing relative motion within groups of trackable moving point objects. In: Egenhofer, M.J., Mark, D.M. (Eds.), Lecture Notes in Computer Science, vol. 2478. Springer, Berlin. pp. 132-144.

[14]

Laube, P., van Kreveld, M. and Imfeld, S., Finding REMO---detecting relative motion patterns in geospatial lifelines. In: Fisher, P.F. (Ed.), Developments in Spatial Data Handling: Proceedings of the 11th International Symposium on Spatial Data Handling, Springer, Berlin. pp. 201-214.

[15]

In: Metha, D.P., Sahni, S. (Eds.), Chapman & Hall/CRC Computer and Information Science Series, Chapman & Hall/CRC.

[16]

In: Miller, H.J., Han, J. (Eds.), Geographic Data Mining and Knowledge Discovery, Taylor & Francis, London.

[17]

Roddick, J.F., Hornsby, K. and Spiliopoulou, M., An updated bibliography of temporal, spatial, and spatio-temporal data mining research. In: Roddick, J.F., Hornsby, K. (Eds.), Lecture Notes in Artificial Intelligence, vol. 2007. Springer, Berlin. pp. 147-163.

[18]

Shim, C.-B. and Chang, J.-W., A new similar trajectory retrieval scheme using k-warping distance algorithm for moving objects. In: Lecture Notes in Computer Science, vol. 2762. Springer, Berlin. pp. 433-444.

Digital Library

[19]

Sumpter, N. and Bulpitt, A.J., Learning spatio-temporal patterns for predicting object behaviour. Image Vision and Computing. v18 i9. 697-704.

[20]

Verhein, F. and Chawla, S., Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases. In: Lecture Notes in Computer Science, vol. 3882. Springer, Berlin. pp. 187-201.

Digital Library

Cited By

Andrienko NAndrienko GArtikis AMantenoglou PRinzivillo S(2024)Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time SeriesIEEE Computer Graphics and Applications10.1109/MCG.2024.337985144:3(14-29)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1109/MCG.2024.3379851
Kim WMémoli F(2024)Extracting Persistent Clusters in Dynamic Data via Möbius InversionDiscrete & Computational Geometry10.1007/s00454-023-00590-171:4(1276-1342)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00454-023-00590-1
van Mulken MSpeckmann BVerbeek K(2023)Density Approximation for Moving GroupsAlgorithms and Data Structures10.1007/978-3-031-38906-1_45(675-688)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-38906-1_45
Show More Cited By

Index Terms

Reporting flock patterns
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Shape modeling
      1. Parametric curve and surface models
      2. Volumetric models
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

Recommendations

Reporting Leaders and Followers among Trajectories of Moving Point Objects

Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. ...
Reporting leadership patterns among trajectories
SAC '07: Proceedings of the 2007 ACM symposium on Applied computing

Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. ...
Reporting flock patterns
ESA'06: Proceedings of the 14th conference on Annual European Symposium - Volume 14

Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns. One of the most common spatio-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computational Geometry: Theory and Applications

Computational Geometry: Theory and Applications Volume 41, Issue 3

November, 2008

137 pages

ISSN:0925-7721

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2007.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2008

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

42
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Andrienko NAndrienko GArtikis AMantenoglou PRinzivillo S(2024)Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time SeriesIEEE Computer Graphics and Applications10.1109/MCG.2024.337985144:3(14-29)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1109/MCG.2024.3379851
Kim WMémoli F(2024)Extracting Persistent Clusters in Dynamic Data via Möbius InversionDiscrete & Computational Geometry10.1007/s00454-023-00590-171:4(1276-1342)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00454-023-00590-1
van Mulken MSpeckmann BVerbeek K(2023)Density Approximation for Moving GroupsAlgorithms and Data Structures10.1007/978-3-031-38906-1_45(675-688)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-38906-1_45
Yang YLi LZhang LLiu GLiu Z(2022)Analysis of moving cluster with scene constraints for group behavior pattern miningNeural Computing and Applications10.1007/s00521-022-07433-934:21(18545-18560)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s00521-022-07433-9
Zhao BLiu XJia JJi GTan SYu Z(2020)A Framework for Group Converging Pattern Mining using Spatiotemporal TrajectoriesGeoinformatica10.1007/s10707-020-00404-z24:4(745-776)Online publication date: 1-Oct-2020
https://dl.acm.org/doi/10.1007/s10707-020-00404-z
Kim WMémoli F(2020)Spatiotemporal Persistent Homology for Dynamic Metric SpacesDiscrete & Computational Geometry10.1007/s00454-019-00168-w66:3(831-875)Online publication date: 2-Feb-2020
https://dl.acm.org/doi/10.1007/s00454-019-00168-w
Chen TZhang YTuo YWang W(2020)Detecting Abnormal Congregation Through the Analysis of Massive Spatio-Temporal DataWeb and Big Data10.1007/978-3-030-60290-1_30(376-390)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-60290-1_30
Wiratma Lvan Kreveld MLöffler MStaals F(2019)An Experimental Evaluation of Grouping Definitions for Moving EntitiesProceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3347146.3359346(89-98)Online publication date: 5-Nov-2019
https://dl.acm.org/doi/10.1145/3347146.3359346
Chatzigeorgakidis GSkoutas DPatroumpas KPalpanas TAthanasiou SSkiadopoulos S(2019)Local Pair and Bundle Discovery over Co-Evolving Time SeriesProceedings of the 16th International Symposium on Spatial and Temporal Databases10.1145/3340964.3340982(160-169)Online publication date: 19-Aug-2019
https://dl.acm.org/doi/10.1145/3340964.3340982
Theodoropoulos GTritsarolis ATheodoridis Y(2019)EvolvingClusters: Online Discovery of Group Patterns in Enriched Maritime DataMultiple-Aspect Analysis of Semantic Trajectories10.1007/978-3-030-38081-6_5(50-65)Online publication date: 16-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-38081-6_5
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents