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Encyclopedia of GIS pp 1–12Cite as

Constraint Databases and Data Interpolation

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Synonyms

Contraint relations; Data approximation; Delaunay triangulation; Fourier series; Inverse distance weighting; Nearest neighbors; Shape function; Spatial interpolation; Spatiotemporal interpolation; Splines; Trend surfaces

Definition

Constraint databases generalize relational databases by finitely representing infinite relations. In the constraint data model, each attribute is associated with an attribute variable, and the value of an attribute in a relation is specified implicitly using constraints. Compared with the traditional relational databases, constraint databases offer an extra layer of data abstraction, which is called the constraint level (Revesz, 2002). It is the constraint level that makes it possible for computers to use finite number of tuples to represent infinite number of tuples at the logical level.

It is very common in GIS that sample measurements are taken only at a set of points. Interpolation is based on the assumption that things that are close to one...

Keywords

  • Shape Function
  • West Nile Virus
  • Voronoi Diagram
  • Inverse Distance Weighting
  • Spatial Interpolation

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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Constraint Databases and Data Interpolation, Fig. 1
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Constraint Databases and Data Interpolation, Fig. 9

References

  • Gao J, Revesz P (2006) Voting prediction using new spatiotemporal interpolation methods. In: Proceedings of the seventh annual international conference on digital government research, San Diego

    CrossRef  Google Scholar 

  • Li J, Narayanan R, Revesz P (2003) A shape-based approach to change detection and information mining in remote sensing. In: Chen CH (ed) Frontiers of remote sensing information processing. WSP, Singapore/River Edge, pp 63–86

    CrossRef  Google Scholar 

  • Li L (2003) Spatiotemporal interpolation methods in GIS. Ph.D thesis, University of Nebraska-Lincoln, Lincoln

    Google Scholar 

  • Li L, Li Y, Piltner R (2004) A new shape function based spatiotemporal interpolation method. In: Proceedings of the first international symposium on constraint databases 2004. Lecture notes in computer science, vol 3074. Springer, Berlin/Heidelberg/New York, pp 25–39

    Google Scholar 

  • Li L, Revesz P (2002) A comparison of spatio-temporal interpolation methods. In: Proceedings of the second international conference on GIScience 2002. Lecture notes in computer science, vol 2478. Springer, Berlin, Heidelberg, New York, pp 145–160

    Google Scholar 

  • Li L, Revesz P (2004) Interpolation methods for spatio-temporal geographic data. J Comput Environ Urban Syst 28(3):201–227

    CrossRef  Google Scholar 

  • Li L, Zhang X, Piltner R (2006) A spatiotemporal database for ozone in the conterminous U.S. In: Proceedings of the thirteenth international symposium on temporal representation and reasoning, Budapest. IEEE, pp 168–176

    Google Scholar 

  • Preparata FP, Shamos MI (1985) Computational geometry: an introduction. Springer, Berlin/Heidelberg/New York

    CrossRef  MATH  Google Scholar 

  • Revesz P (2002) Introduction to constraint databases. Springer, New York

    MATH  Google Scholar 

  • Revesz P, Li L (2002) Constraint-based visualization of spatial interpolation data. In: Proceedings of the sixth international conference on information visualization. IEEE Press, London/England, pp 563–569

    CrossRef  Google Scholar 

  • Revesz P, Li L (2002) Representation and querying of interpolation data in constraint databases. In: Proceedings of the second national conference on digital government research. Los Angeles, California, pp 225–228

    Google Scholar 

  • Revesz P, Li L (2003) Constraint-based visualization of spatiotemporal databases. In: Advances in geometric modeling, chapter 20. Wiley, England, pp 263–276

    Google Scholar 

  • Revesz P, Wu S (2006) Spatiotemporal reasoning about epidemiological data, Artificial Intelligence in Medicine, 38(2):157–170

    CrossRef  Google Scholar 

  • Shepard D (1968) A two-dimensional interpolation function for irregularly spaced data. In: 23nd national conference ACM, Las Vegas. ACM, pp 517–524

    Google Scholar 

  • Zienkiewics OC, Taylor RL (2000) Finite element method. The basis, vol 1. Butterworth Heinemann, London

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Sciences, Georgia Southern University, Statesboro, GA, USA

    Lixin Li

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  1. Lixin Li
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Correspondence to Lixin Li .

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Editors and Affiliations

  1. Minneapolis, Minnesota, USA

    Shashi Shekhar

  2. Rutgers Business School, Newark, New Jersey, USA

    Hui Xiong

  3. The University of Iowa Tippie College of Business, Iowa City, Iowa, USA

    Xun Zhou

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Li, L. (2014). Constraint Databases and Data Interpolation. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-23519-6_188-2

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  • DOI: https://doi.org/10.1007/978-3-319-23519-6_188-2

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