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BallotMaps: Detecting Name Bias in Alphabetically Ordered Ballot Papers
Dec. 2011 (vol. 17 no. 12)
pp. 2384-2391
Jo Wood, giCentre, City University London
Donia Badawood, giCentre, City University London
Jason Dykes, giCentre, City University London
Aidan Slingsby, giCentre, City University London
The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections.

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Index Terms:
Voting, election, bias, democracy, governance, treemaps, geovisualization, hierarchy, governance.
Jo Wood, Donia Badawood, Jason Dykes, Aidan Slingsby, "BallotMaps: Detecting Name Bias in Alphabetically Ordered Ballot Papers," IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 12, pp. 2384-2391, Dec. 2011, doi:10.1109/TVCG.2011.174
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