Abstract
Extraction of interesting knowledge from large spatial databases is an important task in the development of spatial database systems. Spatial data mining is the branch of data mining that deals with spatial (location) data. Analyzing the huge amount (usually tera-bytes) of spatial data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics etc. is, however, a very difficult task. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially the area of analyzing spatial data. We provide some examples for effective visualizations of spatial data in important application areas such as consumer analysis, e-mail traffic analysis, and census demographics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
I. Advizor Solutions. Visual insight in3d (February 2003), http://www.advizorsolutions.com/
Ankerst, M., Keim, D.A., Kriegel, H.-P.: Recursive pattern: A technique for visualizing very large amounts of data. In: Proc. Visualization 1995, Atlanta, GA, pp. 279–286 (1995)
Ankerst, M., Keim, D.A., Kriegel, H.-P.: Circle segments: A technique for visually exploring large multidimensional data sets. In: Visualization 1996, Hot Topic Session, San Francisco, CA (1996)
Becker, R.A., Eick, S.G., Wilks, A.R.: Visualizing network data. IEEE Transactions on Visualization and Computer Graphics 1(1), 16–28 (1995)
Card, S., Mackinlay, J., Shneiderman, B.: Readings in Information Visualization. Morgan Kaufmann, San Francisco (1999)
Chernoff, H.: The use of faces to represent points in k-dimensional space graphically. Journal Amer. Statistical Association 68, 361–368 (1973)
Dent, B.D.: Cartography: Thematic Map Design, 4th edn., ch. 10. William C. Brown, Dubuque (1996)
Dougenik, J.A., Chrisman, N., Niemeyer, D.R.: An algorithm to construct continuous area cartograms. The Professional Geographer 37(1), 75–81 (1985)
Eick, S.G., Wills, G.J.: Navigating large networks with hierarchies. In: Proc. IEEE Conf. Visualization, October 25-29, pp. 204–210 (1993)
ESRI. Arc view (February, 2003), http://www.esri.com/software/arcgis/arcview/index.html
Geisler, G.: Making information more accessible: A survey of information, visualization applications and techniques (February 2003), http://www.ils.unc.edu/~geisg/info/infovis/paper.html
Gusein-Zade, S., Tikunov, V.: A new technique for constructing continuous cartograms. Cartography and Geographic Information Systems 20(3), 66–85 (1993)
Gusein-Zade, S., Tikunov, V.: Map transformations. Geography Review 9(1), 19–23 (1995)
Homepage, S.M.: Sgi mineset (February 2002), http://www.sgi.com/software/mineset.html
Inselberg, A., Dimsdale, B.: Parallel coordinates: A tool for visualizing multidimensional geometry. In: Proc. Visualization 1990, San Francisco, CA, pp. 361–370 (1990)
Johnson, B., Shneiderman, B.: Treemaps: A space-filling approach to the visualization of hierarchical information. In: Proc. Visualization 1991 Conf., pp. 284–291 (1991)
Keim, D.: Visual exploration of large databases. Communications of the ACM 44(8), 38–44 (2001)
Keim, D., Koutsofios, E., North, S.C.: Visual exploration of large telecommunication data sets. In: Proc. Workshop on User Interfaces In Data Intensive Systems (Invited Talk), Edinburgh, UK, pp. 12–20 (1999)
Keim, D., Koutsofios, E., North, S.C.: Visual exploration of large telecommunication data sets. In: Proc. Workshop on User Interfaces In Data Intensive Systems (Invited Talk), Edinburgh, UK, pp. 12–20 (1999)
Keim, D., Ward, M.: Visual Data Mining Techniques, Book Chapter in: Intelligent Data Analysis, an Introduction by D. Hand and M. Berthold., 2nd edn. Springer, Heidelberg (2002)
Keim, D.A., Herrmann, A.: The gridfit algorithm: An efficient and effective approach to visualizing large amounts of spatial data. In: IEEE Visualization, Research Triangle Park, NC, pp. 181–188 (1998)
Keim, D.A., North, S.C., Panse, C.: Cartodraw: A fast algorithm for generating contiguous cartograms. Trans. on Visualization and Computer Graphics, Information Visualization Research Group, AT&T Laboratories, Florham Park (March 2003)
Keim, D.A., North, S.C., Panse, C., Schneidewind, J.: Efficient cartogram generation: A comparison. In: InfoVis 2002, IEEE Symposium on Information Visualization, Boston, Massachusetts, pp. 33–36 ( October 2002)
Keim, D.A., North, S.C., Panse, C., Schneidewind, J.: Visualpoints contra cartodraw. Palgrave Macmillan – Information Visualization (March 2003)
Kocmoud, C.J., House, D.H.: Continuous cartogram construction. In: Proceedings IEEE Visualization, pp. 197–204 (1998)
Kraak, M.-J., Ormeling, F., Kroak, M.-J.: Cartography: Visualization of Spatial Data. Addison-Wesley Pub Co., Reading (1996)
MacEachren, M.: How Maps Work: Presentation, Visualization, and Design. The Guilford Press, New York (1995)
NCSA. Visualization study of the nsfnet (February 2003), http://archive.ncsa.uiuc.edu/SCMS/DigLib/text/technology/Visualization-Study-NSFNET-Cox.html
Pickett, R.M.: Visual Analyses of Texture in the Detection and Recognition of Objects. Academic Press, London (1970)
Pickett, R.M., Grinstein, G.G.: Iconographic displays for visualizing multidimensional data. In: Proc. IEEE Conf. on Systems, Man and Cybernetics, pp. 514–519. IEEE Press, Piscataway (1988)
Raisz, E.: Principles of Cartography. McGraw-Hill, New York (1962)
Schumann, H., Müller, W.: Visualisierung: Grundlagen und allgemeine Methoden. Springer, Heidelberg (2000)
Selvin, S., Merrill, D., Schulman, J., Sacks, S., Bedell, L., Wong, L.: Transformations of maps to investigate clusters of disease. Social Science and Medicine 26(2), 215–221 (1988)
Shneiderman, B.: Tree visualization with treemaps: A 2D space-filling approach. ACM Transactions on Graphics 11(1), 92–99 (1992)
Shneiderman, B.: The eye have it: A task by data type taxonomy for information visualizations. Visual Languages (1996)
Slocum, T.A.: Thematic cartography and visualization. Prentice Hall, Upper Saddle River (1999)
Spence, B.: Information Visualization. Pearson Education Higher Education publishers, UK (2000)
Tobler, W.: Cartograms and cartosplines. In: Proceedings of the 1976 Workshop on Automated Cartography and Epidemiology, pp. 53–58 (1976)
Tobler, W.: Pseudo-cartograms. The American Cartographer 13(1), 40–43 (1986)
Walter, J., Ritter, H.: On interactive visualization of high-dimensional data using the hyperbolic plane. In: Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 123–131 (2002)
Ward, M.O.: Xmdvtool: Integrating multiple methods for visualizing multivariate data. In: Proc. Visualization 1994, Washington, DC, pp. 326–336 (1994)
Ware, C.: Information Visualization: Perception for Design. Morgen Kaufman, San Francisco (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Keim, D.A., Panse, C., Sips, M. (2003). Visual Data Mining of Large Spatial Data Sets. In: Bianchi-Berthouze, N. (eds) Databases in Networked Information Systems. DNIS 2003. Lecture Notes in Computer Science, vol 2822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39845-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-540-39845-5_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20111-3
Online ISBN: 978-3-540-39845-5
eBook Packages: Springer Book Archive