Skip to main content

Advertisement

Log in

Efficient and stable circular cartograms for time-varying data by using improved elastic beam algorithm and hierarchical optimization

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

The circular cartogram, also known as the famous DorlingMap, is widely used to visualize geographical statistics by representing geographical regions as circles. However, all existing approaches for circular cartograms are only designed for static data. While applying these approaches for time-varying data, the circle locations in each circular cartogram are recomputed separately and will result in low efficiency and low visual stability between sequential circle cartograms. To generate visually stable circular cartograms for time-varying data efficiently, we propose a novel approach by improving the elastic beam algorithm with a hierarchical optimization strategy. First, the time-varying data at different time points are grouped using a hierarchical clustering method based on their similarity, and a hierarchy is then built for their corresponding circular cartograms. Second, we generate intermediate circle locations level by level for clusters of circular cartograms according to the built hierarchy with an improved elastic beam algorithm iteratively. The elastic beam algorithm is improved in its proximity graph construction and force computation by considering that the algorithm will be applied to displace circles in a cluster of circular cartograms. The iterative process stops until we obtain satisfactory circular cartograms for each time point. The evaluation results indicate that the proposed approach can achieve a higher quality (184.85%↑ and 265.69%↑) on visual stability, and a higher efficiency (58.54%↑ and 73.96%↑) with almost the same quality on overlap avoidance and relation maintenance by comparing to the existing approaches. Project website: https://github.com/TrentonWei/DorlingMap.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Bostock M, Ogievetsky V, Heer J (2011) D3 data-driven documents. IEEE Trans vis Comput Graphics 17(12):2301–2309

    Article  Google Scholar 

  • Buchin K, Speckmann B, Verdonschot S (2012) Evolution strategies for optimizing rectangular cartograms. In: International conference on geographic information science. Springer, Berlin, pp 29–42

  • Castells M, Blackwell C (1998) The information age: economy, society and culture: the rise of the network society. Environ Plann B Plann Des 25(1):631–636

    Google Scholar 

  • Chi MT, Lin SS, Chen SY, Lin CH, Lee TY (2015) Morphable word clouds for time-varying text data visualization. IEEE Trans Visual Comput Graphics 21(12):1415–1426

    Article  Google Scholar 

  • Chrisman NR (1997) Cartogram projections of planar polygon networks. Harvard Laboratory for Computer Graphics and Spatial Analysis, Harvard University

  • Dorling DFL (1996) Area cartograms: their use and creation. In: Concepts and techniques in modern geography series, Environmental Publications, University of East Anglia

  • Dougenik JA, Chrisman NR, Niemeyer DR (1985) An algorithm to construct continuous area cartograms. Prof. Geographer 37(1):75–81

    Article  Google Scholar 

  • Gastner MT, Newman MEJ (2004) Diffusion-based method for producing density-equalizing maps. Proc Natl Acad Sci 101(20):7499–7504

    Article  MathSciNet  MATH  Google Scholar 

  • Gastner MT, Seguy V, More P (2018) Fast flow-based algorithm for creating density-equalizing map projections. Proc Natl Acad Sci 115(10):E2156–E2164

    Article  MathSciNet  MATH  Google Scholar 

  • Guo Q, Wei Z, Wang Y, Wang L (2017) The method of extracting spatial distribution characteristics of buildings combined with feature classification and proximity graph. Acta Geodaetica Et Cartogr Sin 46(5):631–638

    Google Scholar 

  • Heilmann R, Keim D, Panse C, Sips M (2004) Recmap: rectangular map approximations. In: IEEE symposium on information visualization, IEEE, pp.33–40

  • Inoue R (2011) A new construction method for circle cartograms. Cartogr Geogr Inf Sci 38(2):146–152

    Article  Google Scholar 

  • Jackel CB (1997) Using ArcView to create contiguous and noncontiguous area cartograms. Cartogr Geogr Inf Syst 24(2):101–109

    Google Scholar 

  • Kronenfeld BJ (2018) Manual construction of continuous cartograms through mesh transformation. Cartogr Geogr Inf Sci 45(1):76–94

    Article  Google Scholar 

  • Liu Y, Guo Q, Sun Y, Ma X (2014) A combined approach to cartographic displacement for buildings based on skeleton and improved elastic beam algorithm. PLoS ONE 9(12):e113953

    Article  Google Scholar 

  • Nusrat S, Kobourov S (2016) The state of the art in cartograms. Comput Graphics Forum 2(3):619–642

    Article  Google Scholar 

  • Protovis – Dorling Cartograms (2010). http://mbostock.github.io/protovis/ex/cartogram.html

  • Raisz E (1934) The rectangular statistical cartogram. Geogr Rev 24(3):292–296

    Article  Google Scholar 

  • Reyes Nunez JJ (2014) The use of cartograms in school cartography. Thematic Cartography for the Society, 327–339

  • Sondag M, Speckmann B, Verbeek K (2017) Stable treemaps via local moves. IEEE Trans Visual Comput Graphics 24(1):729–738

    Article  Google Scholar 

  • Speckmann B, Kreveld M, Florisson S (2006) A linear programming approach to rectangular cartograms. In: International symposium on spatial data handling (SDH’06)

  • Sun S (2013a) A fast free-form rubber-sheet algorithm for contiguous area cartograms. Int J Geogr Inf Sci 27(3):567–593

    Article  MathSciNet  Google Scholar 

  • Sun S (2013b) An optimized rubber-sheet algorithm for continuous area cartograms. Prof Geogr 65(1):16–30

    Article  Google Scholar 

  • Sun S (2020) Applying forces to generate cartograms: a fast and flexible transformation framework. Cartogr Geogr Inf Sci 47(5):381–399

    Article  MathSciNet  Google Scholar 

  • Tang W (2013) Parallel construction of large circular cartograms using graphics processing units. Int J Geogr Inf Sci 27(11):2182–2206

    Article  Google Scholar 

  • Teichgraeber H, Brandt AR (2018) Systematic comparison of aggregation methods for input data time series aggregation of energy systems optimization problems. Comput Aided Chem Eng 44:955–960

    Article  Google Scholar 

  • Van Kreveld M, Speckmann B (2007) On rectangular cartograms. Comput Geom 37(3): 175–187

  • Wei Z, Guo Q, Wang L, Yan F (2018a) On the spatial distribution of buildings for map generalization. Cartogr Geogr Inf Sci 45(6):539–555

    Article  Google Scholar 

  • Wei Z, Guo Q, Yan F, Wang Y (2018b) Backtracking method of coloring administrative maps considering visual perception rules. Acta Geodaetica Et Cartogr Sin 47(3):396–402

    Google Scholar 

  • Wei Z, Ding S, Xu W, Cheng L, Zhang S, Wang Y (2022) Circular cartograms via the elastic beam algorithm originated from cartographic generalization. https://arxiv.org/abs/2204.12645

  • Wolf EB (2005) Creating contiguous cartograms in ArcGIS 9. In: Proceedings of 2005 ESRI international user conference, San Diego, CA

  • Ying S, Dou X, Xu Y (2021) Visualization of the epidemic situation of COVID-19. J Geo-Information Sci 23(2):211–221

    Google Scholar 

  • Zhang X, Stoter J, Ai T, Kraak MJ, Molenaar M (2013) Automated evaluation of building alignments in generalized maps. Int J Geogr Inf Sci 27(8):1550–1571

    Article  Google Scholar 

Download references

Acknowledgements

The authors wish to thank Dr. Tingzhong Huang for his help in data collecting. This work was supported in part by a grant from National Natural Science Foundation of China, Grant Number [41871378].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhiwei Wei.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, Z., Xu, W., Ding, S. et al. Efficient and stable circular cartograms for time-varying data by using improved elastic beam algorithm and hierarchical optimization. J Vis 26, 351–365 (2023). https://doi.org/10.1007/s12650-022-00878-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-022-00878-z

Keywords

Navigation