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Knowledge Discovery in Spatial Planning Data: A Concept for Cluster Understanding

  • Martin BehnischEmail author
  • Alfred Ultsch
Chapter
Part of the Geotechnologies and the Environment book series (GEOTECH, volume 13)

Abstract

The objective of this paper is to present a methodology for discovering comprehensible, valid, potentially innovative, and useful patterns, i.e., new knowledge, in multidimensional spatial data. Techniques from statistics, machine learning, and data mining are applied in consecutive logical steps to allow the visualization of results and the application of validation procedures at each stage. However, the approach does not end with a data cluster; rather, if such a valid cluster has been achieved, then the question is posed: “What do the clusters mean?”. Symbolic machine learning methods are employed to produce an explanation of the clusters in terms of rules employing an understandable subset of the high-dimensional data variables. This combined with canonical representatives of a cluster and consideration of the spatial distribution of the clusters lead to hypothesis on emergent data structures, that is, potential new knowledge. The approach is demonstrated on an exemplary data set of German urban districts featuring seven dimensions of land use.

Keywords

Knowledge discovery Data mining Cluster Spatial planning 

Notes

Acknowledgements

The authors acknowledge the yearly data provided by the Federal Agency for Cartography and Geodesy, which was crucial for the development of the land use monitoring. The Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) makes several spatial typologies available. The authors would like to thank the colleagues of the Leibniz Institute of Ecological Urban and Regional Development (IOER) for the indicator computation and the fruitful cooperation. Further, we cordially appreciate the remarks of the reviewers and editors for giving constructive and helpful comments to improve the quality of this chapter.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Leibniz Institute of Ecological Urban and Regional DevelopmentDresdenGermany
  2. 2.Department of Mathematics and Computer SciencePhilipps-University of MarburgMarburgGermany

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