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Quality Measures for Visual Point Clustering in Geospatial Mapping

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10181)

Abstract

Visualizing large amounts of point data in a way that resembles the density of the distribution is a complex problem if the size of the drawing area is constrained. Naïvely drawing points on top of each other leads to occlusion and therefore a loss of information. An intuitive approach is combining close points as clusters that resemble their size as well as their geographic location. However, traditional clustering algorithms are not designed for visual clusterings rather than minimizing an error function independent of a graphical representation. This paper introduces measures for the quality of circle representations based on clustering outputs. Our experimental evaluation revealed that all methods had weaknesses regarding at least one of these criteria.

Keywords

  • Geographic visualization
  • Point clustering
  • Evaluation

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Notes

  1. 1.

    Our counting also considers the case where a bucket is only partially contained by a circle. In this case we interpolate the count by the fraction of its contained part.

  2. 2.

    commons.apache.org/proper/commons-math/.

  3. 3.

    www.opencv.org.

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Acknowledgement

This work was supported by DFG grant no. SE 553/7-2.

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Correspondence to Christian Beilschmidt .

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Beilschmidt, C., Fober, T., Mattig, M., Seeger, B. (2017). Quality Measures for Visual Point Clustering in Geospatial Mapping. In: Brosset, D., Claramunt, C., Li, X., Wang, T. (eds) Web and Wireless Geographical Information Systems. W2GIS 2017. Lecture Notes in Computer Science(), vol 10181. Springer, Cham. https://doi.org/10.1007/978-3-319-55998-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-55998-8_10

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