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Modelling Higher Dimensional Data for GIS Using Generalised Maps

  • Ken Arroyo Ohori
  • Hugo Ledoux
  • Jantien Stoter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7971)

Abstract

Real-world phenomena have traditionally been modelled in 2D/3D GIS. However, powerful insights can be gained by integrating additional non-spatial dimensions, such as time and scale. While this integration to form higher-dimensional objects is theoretically sound, its implementation is problematic since the data models used in GIS are not appropriate. In this paper, we present our research on one possible data model/structure to represent higher-dimensional GIS datasets: generalised maps. It is formally defined, but is not directly applicable for the specific needs of GIS data, e.g. support for geometry, overlapping and disconnected regions, holes, complex handling of attributes, etc. We review the properties of generalised maps, discuss needs to be modified for higher-dimensional GIS, and describe the modifications and extensions that we have made to generalised maps. We conclude with where this research fits within our long term goal of a higher dimensional GIS, and present an outlook on future research.

Keywords

Geographic Information System High Dimensional Data Geographic Information System Data Constrain Delaunay Triangulation Analytical Geographic Information System 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ken Arroyo Ohori
    • 1
  • Hugo Ledoux
    • 1
  • Jantien Stoter
    • 1
  1. 1.Delft University of TechnologyThe Netherlands

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