This repository contains the code and data for the paper Anchoring and Alignment: Data Factors in Part-to-Whole Visualization. See also the preprint, the supplementary document and the preregistration.
Fig. 1: Ⓐ A pair of example stimuli from each combination of pie and stacked bar charts, aligned (segment starts/ends at a marker) and unaligned, and anchor (segment has a recognizable size) and non-anchor values. Ⓑ Predicted mean absolute error and response time to 95% CI. Anchor values and alignment positions show significant performance improvements in estimating values for both chart types. Results between conditions are significant (p < 0.05) except where noted.
We explore the effects of data and design considerations through the example case of part-to-whole data relationships. Standard part-to-whole representations like pie charts and stacked bar charts make the relationships of parts to the whole explicit. Value estimation in these charts benefits from two perceptual mechanisms: anchoring, where the value is close to a reference value with an easily recognized shape, and alignment where the beginning or end of the shape is aligned with a marker. In an online study, we explore how data and design factors such as value, position, and encoding together impact these effects in making estimations in part-to-whole charts. The results show how salient values and alignment to positions on a scale affect task performance. This demonstrates the need for informed visualization design based around how data properties and design factors affect perceptual mechanisms.
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modelResults.ipynb
: Modelling the experiment results with GLMMsgenerateStimuli.ipynb
: Stratified random sampling generation of stimulicreateChart.js
: Generate the svg charts for each stimulus using D3.js
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results.csv
: Participant response results of the experimentstimuli.csv
: Corresponding stimuli used for the experiment