Advertisement

GeoInformatica

, Volume 4, Issue 4, pp 403–418 | Cite as

Temporal Interpolation of Spatially Dynamic Object

  • Wei Zhang
  • Gary J. Hunter
Article

Abstract

During the past decade there has been increasing research into the temporal component of geographic information systems (GIS). Most of the research has focused on the treatment of discrete changes in spatial objects, for example in cadastral parcels. However less attention has been paid to the treatment of continuous change which occurs primarily in dynamic objects found in the natural environment, for example in seasonal coastal changes. In order to represent these objects appropriately in a GIS, which by their inherent structure tend to treat the real-world in a discrete manner, temporal interpolation techniques are required. The aim of this paper is to discuss the techniques available for handling the temporal interpolation of spatially dynamic objects, with particular emphasis on changes to their geometric properties. The paper also proposes a range of interpolation methods for three key variants of geometric change.

spatial data dynamic objects geometric change temporal interpolation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. Clifford and D.S. Warren. “Formal Semantics for Time in Databases,” ACM Transactions on Database Systems, Vol. 8(2):214–254, 1983.Google Scholar
  2. 2.
    R. Laurini and D. Thompson. Fundamentals of Spatial Information Systems. Academic Press: London,1992.Google Scholar
  3. 3.
    K. Brassel, F. Bucher, E.-M. Stephan, and A. Vckovski. “Completeness,” in S. C. Guptill and J. L. Morrison (Eds.), Elements of Spatial Data Quality. Elsevier, New York 81–108, 1995.Google Scholar
  4. 4.
    G. Ariav. “A Temporally Oriented Data Model,” ACM Transactions on Database Systems, Vol. 11(4):499–527, 1986.Google Scholar
  5. 5.
    G. Langran. Time in Geographic Information Systems. Taylor & Francis: London, 1992.Google Scholar
  6. 6.
    D. J. Peuquet and N. Duan. “An event-based spatio-temporal data model (ESTDM) for temporal analysis of geographical data,” International Journal of Geographical Information Systems, Vol. 9(1):7–24, 1995.Google Scholar
  7. 7.
    Y. Shahar. “Knowledge-Based temporal interpolation”. Proceedings of 4th International Workshop on Temporal Representation and Reasoning, Los Alamitos, California, 102–111, 1997.Google Scholar
  8. 8.
    M. Fahle and G. Bachmann. “Better performance through amblyopic than through normal eyes,” Vision Research, Vol. 136(13):1939–44, 1996.Google Scholar
  9. 9.
    A. Beller, T. Giblin, K.V. Le, and S. Litz. “A temporal GIS prototype for global change research.” Proceedings of the GIS/LIS 91 Conference, Atlanta, Georgia, 752–765, 1991.Google Scholar
  10. 10.
    H. Mitasova, L. Mitas, W.M. Brown, D.P. Gerdes, I. Kosinovsky, and T., Baker. “Modelling spatially and Temporally Distributed Phenomena: New Methods and Tools for GRASS GIS,” International Journal of Geographical Information Systems, Vol. 9(4):433–446, 1995.Google Scholar
  11. 11.
    J.P. Cole and C.A.M. King. Quantitative Geography. John Wiley: 1968.Google Scholar
  12. 12.
    A. Galton. “Space, time and movement,” in O. Stock (Ed.), Spatial and Temporal Reasoning, Kluwer Academic, Dordrecht, 321–352, 1997.Google Scholar
  13. 13.
    A.G. Cohn, N.M. Gotts, Z. Cui, D.A. Randell, B. Bennett, and J.M. Gooday. “Exploiting temporal continuity in qualitative spatial calculi,” in M.J. Egenhofer and R.G. Golledge (Eds.), Spatial and Temporal Reasoning in Geographical Information Systems. Oxford University Press: 5–24, 1998.Google Scholar
  14. 14.
    M.J. Egenhofer and K.K. Al-Taha. “Reasoning about Gradual Changes of Topological Relationship,” in Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, Lecture Notes in Computer Science 639, 196–219, 1992.Google Scholar
  15. 15.
    E.H. Isaaks and R.M. Srivastava. An Introduction to Applied Geostatistics. Oxford University Press: 1989.Google Scholar
  16. 16.
    M. Yuan. “Representing Spatio-temporal Processes to Support Knowledge Discovery in GIS Database,” Proceedings of the 8th International Symposium on Spatial Data Handling, Vancouver, Canada, 431–440, 1998.Google Scholar
  17. 17.
    D.F. Watson. Contouring: A Guide to the Analysis and Display of Spatial Data. Pergamon: 1992.Google Scholar
  18. 18.
    M. Erwig, R.H. Guting, M. Schneider, and M. Vazirgiannis. “Spatio-temporal data types: An approach to modeling and querying moving objects in database,” GeoInformatica, Vol.3(3):269–296, 1999.Google Scholar
  19. 19.
    C. Claramunt, and M. Theriault. “Managing time in GIS: An event-oriented approach,” in J. Clifford and A. Tuzhilin (Eds.), Recent Advances in Temporal Databases: Proceedings of International Workshop on Temporal Databases, Springer: 1995.Google Scholar
  20. 20.
    A.U. Frank. “Different types of “times” in GIS,” in M.J. Egenhofer and R.G. Golledge (Eds.), Spatial and Temporal Reasoning in Geographic Information Systems. Oxford University Press: 40–62, 1998.Google Scholar
  21. 21.
    C. Claramunt. “Managing time in GIS: An event-oriented approach.” Recent Advances in Temporal Databases: Proceedings of International Workshop on Temporal Databases, Zurich, Switzerland, 21–42, 1995.Google Scholar
  22. 22.
    N.W.J. Hazelton. “Integrating time, dynamic modelling and GIS: Development of 4-D GIS”. Unpublished Ph.D Dissertation, The University of Melbourne, Melbourne, Australia, 1991.Google Scholar
  23. 23.
    T. Cheng and M. Molenaar. “A process-oriented spatio-temporal data model to support physical environmental modeling.” Proceedings of the 8th International Symposium on Spatial Data Handling, Vancouver, Canada, 418–430, 1998.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Wei Zhang
    • 1
  • Gary J. Hunter
    • 2
  1. 1.Department of GeomaticsThe University of MelbourneAustralia
  2. 2.Department of GeomaticsThe University of MelbourneAustralia

Personalised recommendations