Anchoring and Alignment: Data Factors in Part-to-Whole Visualization

Code and Data Repository

This repository contains the code and data for the paper Anchoring and Alignment: Data Factors in Part-to-Whole Visualization. It contains all the code and data needed to replicate the experiment along with the preprint, supplementary document and the preregistration.

Abstract

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.

Contents

• Notebooks

  • modelResults.ipynb: Modelling the experiment results with GLMMs
  • generateStimuli.ipynb: Stratified random sampling generation of stimuli
  • createChart.js: Generate the svg charts for each stimulus using D3.js

• Data

  • results.csv: Participant response results of the experiment
  • stimuli.csv: Corresponding stimuli used for the experiment