Creating Representations for Continuously Moving Regions from Observations

  • Erlend Tøssebro
  • Ralf Hartmut Güting
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2121)


Recently there is much interest in moving objects databases, and data models and query languages have been proposed offering data types such as moving point and moving region together with suitable operations. In contrast to most earlier work on spatio-temporal databases, a moving region can change its shape and extent not only in discrete steps, but continuously. Examples of such moving regions are oil spills, forest fires, hurricanes, schools of fish, spreads of diseases, or armies, to name but a few.

Whereas the database will contain a “temporally complete” representation of a moving region in the sense that for any instant of time the current extent and shape can be retrieved, the original information about the object moving around in the real world will most likely be a series of observations (“snapshots”). We consider the problem of constructing the complete moving region representation from a series of snapshots. We assume a model where a region is represented as a set of polygons with polygonal holes. A moving region is represented as a set of slices with disjoint time intervals, such that within each slice it is a region whose vertices move linearly with time. Snapshots are also given as sets of polygons with polygonal holes. We develop algorithms to interpolate between two snapshots, going from simple convex polygons to arbitrary polygons. The implementation is available on the Web.


Line Segment Convex Hull Moving Segment Convex Polygon Moving Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    M. H. Böhlen, R. H. Güting, M. Erwig, C. S. Jensen, N. A. Lorentzos, M. Schneider, and M. Vazirgiannis, A Foundation for Representing and Querying Moving Objects. ACM Transactions on Database Systems 25:1 (2000), pp. 1–42.Google Scholar
  2. 2.
    T. S. Cheng and S. K. Gadia, A Pattern Matching Language for Spatio-Temporal Databases. In Proc. ACM Conf. on Information and Knowledge Management, pp. 288–295. 1994.Google Scholar
  3. 3.
    J. Chomicki and P. Revesz, Constraint-Based Interoperability of Spatio-Temporal Databases. In Proc. 5th Int. Symp. on Large Spatial Databases, pp. 142–161, Berlin, Germany, 1997.Google Scholar
  4. 4.
    J. Chomicki and P. Revesz, A Geometric Framework for Specifying Spatiotemporal Objects. In Proc. 6th Int. Workshop on Temporal Representation and Reasoning (TIME), pp. 41–46, 1999.Google Scholar
  5. 5.
    L. Forlizzi, R. H. Güting, E. Nardelli, and M. Schneider, A Data Model and Data Structures for Moving Objects Databases. Proc. ACM SIGMOD Int. Conf. on Management of Data, Dallas, Texas, pp. 319–330, 2000.Google Scholar
  6. 6.
    R. L. Graham, An Efficient Algorithm for Determining the Convex Hull of a Finite Planai Set. Information Processing Letters 1 (1972), pp. 132–133.zbMATHCrossRefGoogle Scholar
  7. 7.
    D. J. Peuquet and N. Duan, An Event-Based Spatiotemporal Data Model (ESTDM) for Temporal Analysis of Geographical Data. Int. Journal of Geographical Information Systems 9:1 (1995), pp. 7–24.CrossRefGoogle Scholar
  8. 8.
    F. P. Preparata and M. I. Shamos, Computational Geometry: An Introduction. Springer-Verlag, New York, 1985.Google Scholar
  9. 9.
    T. W. Sederberg and E. Greenwood: A Physically Based Approach to 2-D Shape Blending. Computer Graphics (Proc. ACM SIGGRAPH) 26:2 (1992), pp 25–34.CrossRefGoogle Scholar
  10. 10.
    A. P. Sistla, O. Wolfson, S. Chamberlain and S. Dao: Modeling and Querying Moving Objects. Proc. Int. Conf. on Data Engineering, pp. 422–432, 1997.Google Scholar
  11. 11.
    E. Tossebro and R. H. Güting, Creating Representations for Continuously Moving Regions from Observations. FernUniversität Hagen, Informatik-Report, in preparation, 2001.Google Scholar
  12. 12.
    M. F. Worboys, A Unified Model for Spatial and Temporal Information. The Computer Journal 37:1 (1994), pp. 25–34.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Erlend Tøssebro
    • 1
  • Ralf Hartmut Güting
    • 2
  1. 1.Department of Computer ScienceNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Praktische Informatik IVFernuniversitä HagenHagenGermany

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