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An efficient aggregation and overlap removal algorithm for circle maps

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Abstract

The visualization of spatial data is becoming increasingly important in science, business and many other areas. There are two main reasons for this: First, the amount of spatial data is growing continuously, making it impossible for people to manually process the data in raw form. Secondly, users have very high demands on the interactive processing of big spatial data in visual form. For instance in geography, data often corresponds to a large number of point observations that should be displayed on a constrained screen with limited resolution. This causes two crucial problems: drawing a lot of points is expensive at runtime and leads to a loss of information due to an overloaded and occluded visualization. In this paper we present a new efficient visualization algorithm that avoids these problems by aggregating point data into a set of non-overlapping circles with the following properties: (i) they follow the distribution of the data, (ii) they represent the cardinality of the underlying point subset by the circle area, (iii) they reveal hot spots while simultaneously keeping outliers, and (iv) the number of circles is typically much smaller than the number of points. Based on a quadtree, our algorithm computes the circles in linear time with respect to the number of points. Experimental results confirm its excellent runtime and quality in comparison to competitors.

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    https://www.w3.org/TR/css-values-4/, visited December 2, 2018

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    https://maps.google.com

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    Global Biodiversity Information Facility, https://www.gbif.org

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Acknowledgments

This work has been supported by the Deutsche Forschungsgemeinschaft (DFG) under grant no. SE 553/7-2.

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Correspondence to Christian Beilschmidt.

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Beilschmidt, C., Mattig, M., Fober, T. et al. An efficient aggregation and overlap removal algorithm for circle maps. Geoinformatica 23, 473–498 (2019). https://doi.org/10.1007/s10707-019-00342-5

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Keywords

  • Point aggregation
  • Spatial visualization
  • Big spatial point data