The visualisation of spatial information is a powerful tool for researchers in urban analytics seeking to convey their findings to the wider research community and the public in an accessible way. Yet, even the most well-intentioned cartographer may introduce misrepresentation by mapping irregularly shaped and sized areas. This paper explores the extent to which different methods of visualising area-based data can remedy (or exacerbate) this misrepresentation by presenting results from a crowdsourced survey. Data from the 2016 European Union referendum at Local Authority level in England are visualised using four alternative methods (balanced cartogram, hexogram, hexagonal grid, square grid) and compared to a traditional choropleth map, in terms of people’s understanding of the authors’ intended message, through a crowdsourced survey questionnaire. Results indicate that mapping out original boundaries can introduce misrepresentation, which can be mitigated by using balanced cartograms and hexograms to improve the accuracy of visualisations.
Bailey (2018) geogrid: Turn geospatial polygons into regular or hexagonal grids. R package version 0.1.0.1. Available at: https://CRAN.R-project.org/package=geogrid (28 August 2019).
2.
BarrozoLVPérez-MachadoRPSmallC, et al. (2016)
Changing spatial perception: Dasymetric mapping to improve analysis of health outcomes in a megacity. Journal of Maps12(5): 1242–1247.
3.
BillgerMThuvanderLWästbergBS (2017)
In search of visualization challenges: The development and implementation of visualization tools for supporting dialogue in urban planning processes. Environment and Planning B: Urban Analytics and City Science44(6): 1012–1035.
4.
BorgoRAbdul-RahmanAMohamedF, et al. (2012)
An empirical study on using visual embellishments in visualization. IEEE Transactions on Visualization and Computer Graphics18(12): 2759–2768.
5.
BorgoRMicallefLBachB, et al. (2018)
June. Information visualization evaluation using crowdsourcing. Computer Graphics Forum37(3): 573–595.
6.
BorkinMABylinskiiZKimNW, et al. (2016)
Beyond memorability: Visualization recognition and recall. IEEE Transactions on Visualization and Computer Graphics22(1): 519–528.
7.
BrewerCAPickleL (2002)
Evaluation of methods for classifying epidemiological data on choropleth maps in series. Annals of the Association of American Geographers92(4): 662–681.
8.
ColtekinA (2016)
Book review of Dimitris Ballas, Danny Dorling and Benjamin Hennig, the social atlas of Europe. Environment and Planning B: Planning and Design43(6): 1162–1164.
9.
ColtekinALokkaIEBoérA (2015) August. The utilization of publicly available map types by non-experts – A choice experiment. In: Proceedings of the 27th international cartographic conference (ICC2015), Rio de Janeiro, Brazil, 23–28 August, pp.23–28.
10.
DorlingD (1996) Area cartograms: Their use and creation. Concepts and Techniques in Modern Geography (CATMOG) 59: 1–69.
11.
DorlingD (2012) The Visualization of Spatial Social Structure.
Chichester, UK:
John Wiley & Sons.
12.
DougenikJAChrismanNRNiemeyerDR (1985)
An algorithm to construct continuous area cartograms. The Professional Geographer37(1): 75–81.
13.
FlanneryJJ (1971)
The relative effectiveness of some common graduated point symbols in the presentation of quantitative data. Cartographica: The International Journal for Geographic Information and Geovisualization8: 96–109.
14.
GarnierS (2018) viridis: Default color maps from ‘matplotlib'. R package version 0.5.1. Available at: https://CRAN.R-project.org/package=viridis (accessed 28 August 2019).
15.
GastnerMTNewmanME (2004)
Diffusion-based method for producing density-equalizing maps. Proceedings of the National Academy of Sciences101(20): 7499–7504.
HarrisRCharltonMBrunsdonC, et al. (2017a). Tackling the curse of cartograms: Addressing misrepresentation due to invisibility and to distortion. In: Proceedings of the 25th GIS research UK conference, Manchester, 18–21 April 2017. Available at: http://huckg.is/gisruk2017/GISRUK_2017_paper_29.pdf (accessed 28 August 2019).
18.
HarrisRCharltonMBrunsdonC, et al. (2017b)
Balancing visibility and distortion: Remapping the results of the 2015 UK general election. Environment and Planning A49(9): 1945–1947.
19.
HarrisRCharltonMBrunsdonC (2018a)
Mapping the changing residential geography of white British secondary school children in England using visually balanced cartograms and hexograms. Journal of Maps14(1): 65–72.
JonesKManleyDJohnstonR, et al. (2018)
Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP. Environment and Planning B: Urban Analytics and City Science45(6): 1122–1141.
25.
LloydRESteinkeTR (1976)
The decisionmaking process for judging the similarity of choropleth maps. The American Cartographer3(2): 177–184.
26.
LloydRESteinkeTR (1977)
Visual and statistical comparison of choropleth maps. Annals of the Association of American Geographers67(3): 429–436.
27.
MarkDMFreksaCHirtleSC, et al. (1999)
Cognitive models of geographical space. International Journal of Geographical Information Science13(8): 747–774.
28.
McCartyHHSalisburyNE (1961) Visual Comparison of Isopleth Maps as a Means of Determining Correlations Between Spatially Distributed Phenomena (No. 3).
Iowa City:
Iowa State University.
29.
MullerJC (1980)
Visual comparison of continuously shaded maps. Cartographica: The International Journal for Geographic Information and Geovisualization17(1): 40–52.
30.
NakayaT (2010)
‘Geomorphology’ of population health in Japan: Looking through the cartogram lens. Environment and Planning A: Economy and Space42(12): 2807–2808.
31.
NusratSKobourovS (2016)
June. The state of the art in cartograms. Computer Graphics Forum35(3): 619–642.
PontarolloNRonchiSSerpieriC (2018)
European union regional discomfort before and after the crisis. Environment and Planning A: Economy and Space50(7): 1375–1380.
35.
RaeA (2011)
Flow-data analysis with geographical information systems: a visual approach. Environment and Planning B: Planning and Design38(5): 776–794.
36.
R Core Team (2018) R: A Language and Environment for Statistical Computing.
Vienna, Austria:
R Foundation for Statistical Computing.
37.
SkauDKosaraR (2016)
June. Arcs, angles, or areas: Individual data encodings in pie and donut charts. Computer Graphics Forum35(3): 121–130.
38.
StigmarHHarrieL (2011)
Evaluation of analytical measures of map legibility. The Cartographic Journal48(1): 41–53.
39.
ToblerW (2004)
Thirty five years of computer cartograms. Annals of the Association of American Geographers94(1): 58–73.
40.
WickhamH (2017) tidyverse: Easily Install and Load the ‘Tidyverse'. R package version 1.2.1. Available at: https://CRAN.R-project.org/package=tidyverse (accessed 28 August 2019).
41.
WuertzDSetzTChalabiY (2017) fMultivar: Rmetrics – Analysing and modeling multivariate financial return distributions. R package version 3042.80. Available at: https://CRAN.R-project.org/package=fMultivar (accessed 28 August 2019).
