Exploring Geographical Data with Spatio-Visual Data Mining

  • Urška Demšar
  • Jukka M. Krisp
  • Olga Křemenová


Efficiently exploring a large spatial dataset with the aim of forming a hypothesis is one of the main challenges for information science. This study presents a method for exploring spatial data with a combination of spatial and visual data mining. Spatial relationships are modeled during a data pre-processing step, consisting of the density analysis and vertical view approach, after which an exploration with visual data mining follows. The method has been tried on emergency response data about fire and rescue incidents in Helsinki.


Data Mining Incident Density Proximity Surface Spatial Data Mining Parallel Coordinate Plot 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahonen-Rainio P (2005) Visualization of geospatial metadata for selecting geographic datasets. PhD Thesis, Helsinki University of Technology, HelsinkiGoogle Scholar
  2. Andrienko G, Andrienko N, Gatalsky P (2004) Visual Mining of Spatial Time Series Data. In: Proc of the 8th European Conf on Principles and Practice of Knowledge Discovery in Databases, PKDD 2004 (= LNCS 3202). Springer, Berlin Heidelberg New York, pp 524–527Google Scholar
  3. Ankerst M (2000) Visual Data Mining. PhD Thesis, Ludwig-Maximilans-Universität, MünchenGoogle Scholar
  4. Chawla S, Shekhar S, Wu W, Ozesmi U (2001) Modelling spatial dependencies for mining geospatial data. In: Miller HJ, Han J (eds) Geographic Data Mining and Knowledge Discovery. Taylor & Francis, London, pp 131–159Google Scholar
  5. Demšar U (2004) Exploring geographical metadata by automatic and visual data mining. Licenciate Thesis, Royal Institute of Technology (KTH), StockholmGoogle Scholar
  6. Edsall RM (2003) The parallel coordinate plot in action: design and use for geographic visualization. Computational Statistics & Data Analysis 43:605–619CrossRefGoogle Scholar
  7. Ester M, Kriegel H-P, Sander J (1997) Spatial Data Mining: A Database Approach. In: Proc of the 5th Int Symp on Large Spatial Databases, SSD 1997, Berlin, GermanyGoogle Scholar
  8. Estivill-Castro V, Lee I (2001) Data Mining Techniques for Autonomous Exploration of Large Volumes of Geo-referenced Crime Data. In: Proc of 6th Int Conf on Geocomputation, Brisbane, AustraliaGoogle Scholar
  9. Fayyad U, Grinstein GG, Wierse A (eds) (2002) Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann Publishers, San FranciscoGoogle Scholar
  10. Gahegan M, Takatsuka M, Wheeler M, Hardisty F (2002) Introducing GeoVISTA Studio: an integrated suite of visualization and computational methods for exploration and knowledge construction in geography. Computers, Environment and Urban Systems 26:267–292CrossRefGoogle Scholar
  11. Grünfeld K (2005) Visualization, integration and analysis of multi-element geochemical data. PhD Thesis, Royal Institute of Technology (KTH), StockholmGoogle Scholar
  12. Hand D, Mannila H, Smyth P (2001) Principles of Data Mining. The MIT Press, Cambridge, MassachusettsGoogle Scholar
  13. Inselberg A (1999) Multidimensional detective. In: Card SK, MacKinley JD, Shneiderman B (eds) Using Vision to Think, Readings in Information Visualization. Morgan Kaufmann, San FranciscoGoogle Scholar
  14. Joost S, The Econogene Consortium (2005) Combining biotechnologies and GIScience to contribute to sheep and goat genetic resources conservation. In: Proc of FAO Int Congress: The Role of Biotechnology, Turin, Italy, pp 109–116Google Scholar
  15. Karasová V (2005) Spatial data mining as a tool for improving geographical models. Master Thesis, Helsinki University of Technology, HelsinkiGoogle Scholar
  16. Keim DA, Ward M (2003) Visualization. In: Berthold M, Hand DJ (eds) Intelligent Data Analysis, 2nd ed. Springer, Berlin Heidelberg, pp 403–428Google Scholar
  17. Keim DA, Panse C, Sips M, North SC (2004) Pixel based visual data mining of geo-spatial data. Computers & Graphics 28:327–344CrossRefGoogle Scholar
  18. Keim DA, Panse C, Sips M (2005) Information Visualization: Scope, Techniques and Opportunities for Geovisualization. In: Dykes J, MacEachren AM, Kraak M-J (eds) Exploring Geovisualization. Elsevier and International Cartographic Association, AmsterdamGoogle Scholar
  19. Klein P (2005) TheCircleSegmentView: A User Centered, Meta-data Driven Approach for Visual Query and Filtering. PhD Thesis, Universität Konstanz, KonstanzGoogle Scholar
  20. Koperski K, Han J (1995) Discovery of Spatial Association Rules in Geographic Information Databases. In: Proc of the 4th Int Symp on Large Spatial Databases, Portland, Maine, USAGoogle Scholar
  21. MacEachren AM, Dai X, Hardisty F, Guo D, Lengerich G (2003) Exploring High-D Spaces with Multiform Matrices and Small Multiples. In: Proc of the Int Symp on Information Visualization, Seattle, pp 31–38Google Scholar
  22. Malerba D, Esposito F, Lisi F A (2001) Mining Spatial Association Rules in Census Data. In: Proc of the Joint Conf on New Techniques and Technologies for Statistics and Exchange of Technology and Know-how, Crete, GreeceGoogle Scholar
  23. Miller JH, Han J (2001) Geographic Data Mining and Knowledge Discovery: an overview. In: Miller HJ, Han J (eds) Geographic Data Mining and Knowledge Discovery. Taylor & Francis, London, pp 3–32Google Scholar
  24. O’Sullivan D, Unwin DJ (2003) Geographic information analysis. John Wiley & Sons Inc., New JerseyGoogle Scholar
  25. Robinson AC, Chen J, Lengerich EJ, Meyer HG, MacEachren AM (2005) Combining Usability Techniques to Design Geovisualization Tools for Epidemiology. In: Proc of the Auto-Carto 2005, Las VegasGoogle Scholar
  26. Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, LondonGoogle Scholar
  27. Takatsuka M, Gahegan M (2002) GeoVISTA Studio: a codeless visual programming environment for geoscientific data analysis and visualization. Computers & Geosciences 28:1131–1144CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Urška Demšar
    • 1
  • Jukka M. Krisp
    • 2
  • Olga Křemenová
    • 2
  1. 1.GeoinformaticsRoyal Institute of Technology (KTH)StockholmSweden
  2. 2.Cartography and GeoinformaticsHelsinki University of TechnologyHelsinkiFinland

Personalised recommendations