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Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations

Author

Listed:
  • Samuel H Langton

    (5289Manchester Metropolitan University, UK)

  • Reka Solymosi

Abstract

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.

Suggested Citation

  • Samuel H Langton & Reka Solymosi, 2021. "Cartograms, hexograms and regular grids: Minimising misrepresentation in spatial data visualisations," Environment and Planning B, , vol. 48(2), pages 348-357, February.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:2:p:348-357
    DOI: 10.1177/2399808319873923
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    References listed on IDEAS

    as
    1. Richard Harris & Martin Charlton & Chris Brunsdon, 2018. "Mapping the changing residential geography of White British secondary school children in England using visually balanced cartograms and hexograms," Journal of Maps, Taylor & Francis Journals, vol. 14(1), pages 65-72, January.
    2. Kelvyn Jones & David Manley & Ron Johnston & Dewi Owen, 2018. "Modelling residential segregation as unevenness and clustering: A multilevel modelling approach incorporating spatial dependence and tackling the MAUP," Environment and Planning B, , vol. 45(6), pages 1122-1141, November.
    3. Ligia Vizeu Barrozo & Reinaldo Paul Pérez-Machado & Christopher Small & William Cabral-Miranda, 2016. "Changing spatial perception: dasymetric mapping to improve analysis of health outcomes in a megacity," Journal of Maps, Taylor & Francis Journals, vol. 12(5), pages 1242-1247, October.
    4. Nicola Pontarollo & Silvia Ronchi & Carolina Serpieri, 2018. "European Union regional discomfort before and after the crisis," Environment and Planning A, , vol. 50(7), pages 1375-1380, October.
    Full references (including those not matched with items on IDEAS)

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