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Minimizing Misrepresentation in Spatial Data Visualizations

Video Type: Case Study

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Summary

Dr. Reka Solymosi, PhD, a lecturer of quantitative methods at the University of Manchester and fellow of the Software Sustainability Institute, discusses four data visualization techniques—choropleth maps, cartograms, hexagrams, and grid maps—and their use in minimizing misrepresentation of data.

Resources

Resources

References
References

Further Reading

Smith, MJ, Goodchild, MF, & Longley, PA(2009).Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools. Matador,

Further Reading

Harris, R., (2017).Hexograms: Better maps of area-based data. https://rpubs.com/profrichharris/hexograms

Journal Article

Brewer, C., (2006).Basic mapping principles for visualising cancer data using geographic information systems. American Journal of Preventative Medicine, 30(2s),s25-s36.

Journal Article

Langton, SH, & Solymosi, R(2019).Cartograms, hexagrams and regular grids: Minimising misrepresentation in spatial data visualisations. Environment and Planning B, 48(2),348-357. http://dx.doi.org/https://doi.org/10.1177/2399808319873923

Conference Proceedings

Harris, R, Charlton, M, Brunsdon, C, & et al, (2017).Tackling the curse of cartograms. Proceedings of the 25th GIS research UK conference, http://huckg.is/gisruk2017/GISRUK_2017_paper_29.pdf

Journal Article

Harris, R, Charlton, M, Brunsdon, C, & et al, (2017).Balancing visibility and distortion: Remapping the results of the 2015 UK general election. Environment and Planning A, 49(9),1945-1947. http://dx.doi.org/https://doi.org/10.1177/0308518X17708439

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