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Strategies for Detecting Difference in Map Line-Up Tasks

graphical inference
spatial autocorrelation
cognition

Johanna Haider Doppler, Margit Pohl, Roger Beecham and Jason Dykes (2021) “Strategies for Detecting Difference in Map Line-Up Tasks”, Lecture Notes in Computer Science, doi: 10.1007/978-3-030-85613-7_36

Authors
Affiliations

Johanna Haider Doppler

Human Centered Visualisation, Vienna University of Technology

Margit Pohl

Human Centered Visualisation, Vienna University of Technology

Roger Beecham

School of Geography, University of Leeds

Jason Dykes

Department of Computer Science, City University of London

Published

August 2021

Doi

10.1007/978-3-030-85613-7_36

Abstract

The line-up task hides a plot of real data amongst a line-up of decoys built around some plausible null hypothesis. It has been proposed as a mechanism for lending greater reliability and confidence to statistical inferences made from data graphics. The proposition is a seductive one, but whether or not line-ups guarantee consistent interpretation of statistical structure is an open question, especially when applied to representations of geo-spatial data. We build on empirical work around the extent to which statistical structure can be reliably judged in map line-ups, paying particular attention to the strategies employed when making line-up judgements. We conducted in-depth experiments with 19 graduate students equipped with a moderate background in geovisualization. The experiments consisted of a series of map line-up tasks with two map designs: choropleth maps and a centroid-dot alternative. We chose challenging tasks in the hope of exposing participants’ sensemaking activities. Through structured qualitative analysis of think-aloud protocols, we identify six sensemaking strategies and evaluate their effects in making judgements from map line-ups. We find five sensemaking strategies applicable to most visualization types, but one that seems particular to map line-up designs. We could not identify one single successful strategy, but users adopt a mix of different strategies, depending on the circumstances. We also found that choropleth maps were easier to use than centroid-dot maps.

Important figure

Figure 6: Participants favoured plot 6 over 2 in the left choropleth line-up because of the smaller number of clusters; In the centroid-dot map (center) most participants chose plot 5 because of the centered cluster instead of the smoother, higher autocorrelated plot 9 leading to the worst success rate. Most participants chose plot 8 in the right choropleth line-up because of a perceived figure although plot 7 shows a higher autocorrelation value.

BibTeX citation

@inproceedings{doppler_strategies_2021,
  author    = {Johanna Haider Doppler and
               Margit Pohl and
                             Beecham, R. and
                             Jason Dykes},
  editor    = {David Lamas and
               Fernando Loizides and
               Lennart E. Nacke and
               Helen Petrie and
               Marco Winckler and
               Panayiotis Zaphiris},
  title     = {Strategies for Detecting Difference in Map Line-Up Tasks},
  booktitle = {{INTERACT} 2021 - 18th
               International Conference on Human-Computer Interaction, Bari, Italy, August 30th-2nd September, 2021, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12934},
  pages     = {},
  publisher = {Springer},
  year      = {2021}
}