Encyclopedia of GIS

2017 Edition
| Editors: Shashi Shekhar, Hui Xiong, Xun Zhou

Patterns in Spatiotemporal Data

  • Hui Yang
  • Srinivasan Parthasarathy
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-17885-1_966

Synonyms

Definition

Spatio-temporal data refer to data that are both spatial and time-varying in nature, for instance, the data concerning traffic flows on a highway during rush hours. Spatio-temporal data are also being abundantly produced in many scientific domains. Examples include the datasets in computational fluid dynamics that describe the evolutionary behavior of vortices in fluid flows, and the datasets in bioinformatics that study the folding pathways of proteins from an initially string-like 3D structure to their respective native 3D structure.

One important issue in analyzing spatio-temporal data is to characterize the spatial relationship among spatial entities and, more importantly, to define how such a relationship evolves or changes over time. In the traffic flow example, one might be interested in identifying and monitoring the automobiles that are following one another...

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References

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  2. Ester M, Kriegel HP, Sander J (2001) Algorithms and applications for spatial data mining. Geographic data mining and knowledge discovery, research monographs. In: GIS Chapter 7Google Scholar
  3. Huang Y, Xiong H, Shekhar S, Pei J (2003) Mining confident co-location rules without a support threshold. In: Proceedings of the 2003 ACM symposium on applied computing, Melbourne (SAC’03). ACM Press, pp 497–501Google Scholar
  4. Koperski K, Han J (1995) Discovery of spatial association rules in geographic information databases. In: Proceedings of the 4th international symposium on advances in spatial databases (SSD’95), Portland. Springer, pp. 47–66Google Scholar
  5. Morimoto Y (2001) Mining frequent neighboring class sets in spatial databases. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco. ACM Press, pp 353–358Google Scholar
  6. Xiong H, Shekhar S, Huang Y, Kumar V, Ma X, Yoo JS (2004) A framework for discovering co-location patterns in data sets with extended spatial objects. In: SIAM international conference on data mining (SDM), Portland, Apr 2004Google Scholar
  7. Yang H, Parthasarathy S, Mehta S (2005) A generalized framework for mining spatio-temporal patterns in scientific data. In: Proceeding of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining (KDD’05). ACM Press, New York, pp 716–721Google Scholar
  8. Yang H, Parthasarathy S, Ucar D (2007) A spatio-temporal mining approach towards summarizing and analyzing protein folding trajectories. Algorithms Mol Biol 2(3)Google Scholar

Recommended Reading

  1. Mokbel MF, Ghanem TM, Aref WG. Spatio-temporal access methods. Technical report, Department of Computer Sciences, Purdue UniversityGoogle Scholar
  2. Neill DB, Moore AW, Sabhnani M, Daniel K (2005) Detection of emerging space-time clusters. In: Proceedings of SIGKDD 2005, Copenhagen, pp 218–227Google Scholar
  3. Rao CR, Suryawanshi S (1996) Statistical analysis of shape of objects based on landmark data. Proc Natl Acad Sci U S A 93(22):12132–12136zbMATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hui Yang
    • 1
  • Srinivasan Parthasarathy
    • 2
  1. 1.Department of Computer Science and EngineeringSan Francisco State UniversitySan Francisco, CAUSA
  2. 2.Department of Computer Science and EngineeringThe Ohio State UniversityColumbus, OHUSA