# mcnabb19multivariate

# Multivariate Maps — A Glyph-Placement Algorithm to Support Multivariate Geospatial Visualization

Concept

The multivariate representation of data on maps is still considered an unsolved problem, McNabb and Laramee present a novel glyph placement algorithm to support multivariate maps [1].

The authors identify four major challenges for representing geo-spatial data on existing geo-spatial visualizations:

  1. Size perceivability: sizes of glyphs and areas on a map are not easily perceivable.
  2. Visualization of multivariate geo-spatial data: geo-spatial designs such as choropleths, cartograms, symbol maps etc. fail to depict multivariate data.
  3. Occlusion: glyphs on a map often overlap and are over-plotted.
  4. Glyph placement: existing solutions to address occlusion often de-couples glyphs from their original geo-spatial regions they are intended to represent.

The authors address these challenges by introducing a scale-aware map that supports dynamic modification to the level-of-detail shown via zooming and custom scaling options. The map is integrated with glyphs which are scale-aware and couple with their geo-spatial contexts.

Implementation

Figure 1 shows a comparison between a traditional map with glyphs placed at the centroids on the left, and a scale-aware map presented in this paper on the right.

The scale-aware map is occlusion-free, glyphs are designed to depict more details of the data by using outline, size and shadow as hidden indicators. User options are provided to customize the glyph design, filter and focus+context. The map also supports a smooth and fluid transition between different levels of detail.

The algorithm is evaluated with three case studies: diseases prevalence rate on England’s Clinical Commissioning Groups map, USA counties map with average income over 40 years, and Electoral Divisions map of the Republic of Ireland with population distribution data.

Related Work

The authors thoroughly review related work in aggregation techniques [2][3][4][5], cartograms [6][7][8][9][10][11], multivariate maps [12][13][14][15][16][17][18][19][20][21][22] and glyph placement. [23][24][25][26][27].

Data Characteristics

  • Data source: Public Health England, U.S. Department of Commerce and Ordnance Survey Ireland
  • Size: Unknown
  • Spatial dimensionality: 2D
  • Temporal dimensionality: static
  • Type: multivariate
  • Libraries used: C++

Visualization Techniques

  • Glyph scaling and placement
  • Scale-aware map with multivariate support

Papers Cited

43

Years Spanned

1934-2017

Application Domain

  • Geospatial Visualization of multivariate data

Reference

  1. McNabb, L., & Laramee, R. S. (2019). Multivariate Maps-A Glyph-placement algorithm to support multivariate geospatial visualization. Information (Switzerland), 10(10), 302. https://doi.org/10.3390/info10100302, ↩︎

  2. Jänicke, S., Heine, C., Stockmann, R., & Scheuermann, G. (2012). Comparative visualization of geospatial-temporal data. GRAPP 2012 IVAPP 2012 - Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Information Visualization Theory and Applications. https://doi.org/10.5220/0003833406130625 ↩︎

  3. Rohrdantz, C.; Krstajic, M.; El Assady, M.; Keim, D. What is Going On? How Twitter and Online News Can Work in Synergy to Increase Situational Awareness. In Proceedings of the 2nd IEEE Workshop on Interactive Visual Text Analytics Task-Driven Analysis of Social Media, Seattle, WA, USA, 15 October 2012. ↩︎

  4. Jo, J.; Vernier, F.; Dragicevic, P.; Fekete, J.D. A Declarative Rendering Model for Multiclass Density Maps. IEEE Trans. Vis. Comput. Graph. 2019, 25, 470–480. ↩︎

  5. Guo, D. Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). Int. J. Geogr. Inf. Sci. 2008, 22, 801–823. ↩︎

  6. Slingsby, A.; Wood, J.; Dykes, J. Treemap cartography for showing spatial and temporal traffic patterns. J. Maps 2010, 6, 135–146. ↩︎

  7. Slingsby, A.; Dykes, J.; Wood, J. Rectangular hierarchical cartograms for socio-economic data. J. Maps 2010, 6, 330–345. ↩︎

  8. Tong, C.; Roberts, R.; Laramee, R.S.; Berridge, D.; Thayer, D. Cartographic Treemaps for Visualization of Public Healthcare Data. In Computer Graphics and Visual Computing (CGVC), Manchester, UK.; Wan, T.R., Vidal, F., Eds.; The Eurographics Association, 2017. doi:10.2312/cgvc.20171276. ↩︎

  9. Tong, C.; McNabb, L.; Laramee, R.S.; Lyons, J.; Walters, A.; Berridge, D.; Thayer, D. Time-oriented Cartographic Treemaps for Visualization of Public Healthcare Data. In Computer Graphics and Visual Computing (CGVC), Manchester, UK.; Wan, T.R., Vidal, F., Eds.; The Eurographics Association, 2017. doi:10.2312/cgvc.20171273. ↩︎

  10. Beecham, R.; Slingsby, A.; Brunsdon, C. Locally-varying explanations behind the United Kingdom’s vote to leave the European Union. J. Spat. Inf. Sci. 2018, 2018, 117–136. ↩︎

  11. Nusrat, S.; Alam, M.J.; Scheidegger, C.; Kobourov, S. Cartogram visualization for bivariate geo-statistical data. IEEE Trans. Vis. Comput. Graph. 2018, 24, 2675–2688. ↩︎

  12. Minard, C.J. Carte figurative et approximative des quantites de viandes de boucherie envoyees sur pied par les departements et consommees a Paris, 1858; Republished in Palsky, G. “Des chiffres et des cartes-la cartographie quantitative au XIXe siècle, Paris, èditions du CTHS, coll.”; 1996. ↩︎

  13. Kahrl, W.L.; Bowen, W.A.; Brand, S.; Shelton, M.L.; Fuller, D.L.; Ryan, D.A. The California Water Atlas; State of California, USA. 1978. ↩︎

  14. Olson, J.M. Spectrally encoded two-variable maps. Ann. Assoc. Am. Geogr. 1981, 71, 259–276. ↩︎

  15. Dunn, R. A dynamic approach to two-variable color mapping. Am. Stat. 1989, 43, 245–252. ↩︎

  16. Brewer, C.; Campbell, A.J. Beyond graduated circles: Varied point symbols for representing quantitative data on maps. Cartogr. Perspect. 1998, 6–25. doi:10.14714/CP29.672. ↩︎

  17. Andrienko, N.; Andrienko, G. Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2006. doi:10.1007/3-540-31190-4. ↩︎

  18. Slocum, T.A.; McMaster, R.B.; Kessler, F.C.; Howard, H.H. Thematic Cartography and Geovisualization; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2009. ↩︎

  19. Bertin, J. Semiology of Graphics: Diagrams, Networks, Maps; University of Wisconsin Press: Madison, WI, USA, 1983. ↩︎

  20. Elmer, M.E. Symbol Considerations for Bivariate Thematic Mapping. Ph.D. Thesis, University of Wisconsin–Madison, Madison, WI, USA, 2012. ↩︎

  21. Kresse, W.; Danko, D.M. Springer Handbook of Geographic Information; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012. ↩︎

  22. Tsorlini, A.; Sieber, R.; Hurni, L.; Klauser, H.; Gloor, T. Designing a Rule-based Wizard for Visualizing Statistical Data on Thematic Maps. Cartogr. Perspect. 2017. doi:10.14714/CP86.1392. ↩︎

  23. Ward, M.O. Multivariate data glyphs: Principles and practice. In Handbook of Data Visualization; Springer: Berlin/Heidelberg, Germany, 2008; pp. 179–198. ↩︎

  24. Ropinski, T.; Preim, B. Taxonomy and usage guidelines for glyph-based medical visualization. In Proceedings of the SimVis, Magdeburg, Germany, 18–29 February 2008; pp. 121–138. ↩︎

  25. Chung, D.H.; Legg, P.A.; Parry, M.L.; Bown, R.; Griffiths, I.W.; Laramee, R.S.; Chen, M. Glyph sorting: Interactive visualization for multi-dimensional data. Inf. Vis. 2015, 14, 76–90. ↩︎

  26. Ward, M.O.; Lipchak, B.N. A visualization tool for exploratory analysis of cyclic multivariate data. Metrika 2000, 51, 27–37. doi:10.1007/s001840000042. ↩︎

  27. Ward, M.O. A taxonomy of glyph placement strategies for multidimensional data visualization. Inf. Vis. 2002, 1, 194–210. ↩︎

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