# Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations

CHI, pp. 1303-1312, 2009.

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Abstract:

We investigate techniques for visualizing time series data and evaluate their effect in value comparison tasks. We compare line charts with horizon graphs - a space-efficient time series visualization technique - across a range of chart sizes, measuring the speed and accuracy of subjects' estimates of value differences between charts. We ...More

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Introduction

- Time series — sets of values changing over time — are one of the most common forms of recorded data.
- Tufte [27] advises designers to maximize data density and researchers regularly promote visualization techniques (e.g., [12, 22, 25]) for their “space-filling” properties.
- Such approaches excel at increasing the amount of information that can be encoded within a display.
- Increased data density does not necessarily imply improved graphical perception for visualization viewers

Highlights

- Time series — sets of values changing over time — are one of the most common forms of recorded data
- Effective presentation of multiple time series is an instance of a larger problem in visualization research: increasing the amount of data with which human analysts can effectively work
- We found no significant difference in either estimation time or accuracy between chart types and reject our hypothesis that offset graphs would provide better performance than mirror graphs
- The results confirm our hypothesis regarding the effects of band count on performance: both estimation time and error increased with more bands
- Though estimation time was slower with 3 bands than with 2, accuracy did not suffer
- One reason we focused on value comparison is that graphical perception of rates of change has been studied previously [1, 5] and techniques for determining aspect ratios optimized to aid trend perception already exist [6, 8]

Methods

- Subjects viewed two charts, each with a position marked either T or B (Figure 3).
- Subjects first performed the discrimination task in which they reported whether position T or position B represented the greater value.
- Subjects performed the estimation task in which they reported the absolute difference between the values at positions T and B.
- The authors labeled the y-axis of each chart with the ranges for the first band (e.g., 0-50 or 0-33, see Figure 4)

Results

- For all conditions discrimination accuracy averaged 99% or higher, so the authors focus on the results of the estimation task.
- The RM-MANOVA found a significant main effect for band count (F(4,68) = 11.01, p < 0.001), but did not find an effect for chart type (F(2,16) = 0.367, p = 0.699) nor any interaction (F(4,68) = 0.211, p = 0.163).
- The authors performed univariate analysis of time and error for band counts.
- Univariate analysis of the estimation error found a significant main effect for band count (F(2,34) = 58.27, p = 0.013).

Conclusion

- The authors found no significant difference in either estimation time or accuracy between chart types and reject the hypothesis that offset graphs would provide better performance than mirror graphs.
- The results confirm the hypothesis regarding the effects of band count on performance: both estimation time and error increased with more bands.
- Multiple subjects verbally reported that as the band count rose they experienced increased difficulty identifying and remembering which band contained a value and that performing mental math became fatiguing.
- Though estimation time was slower with 3 bands than with 2, accuracy did not suffer

Summary

## Introduction:

Time series — sets of values changing over time — are one of the most common forms of recorded data.- Tufte [27] advises designers to maximize data density and researchers regularly promote visualization techniques (e.g., [12, 22, 25]) for their “space-filling” properties.
- Such approaches excel at increasing the amount of information that can be encoded within a display.
- Increased data density does not necessarily imply improved graphical perception for visualization viewers
## Objectives:

The authors' objective was to quantify the effects of chart sizing and layering on the speed and accuracy of graphical perception.## Methods:

Subjects viewed two charts, each with a position marked either T or B (Figure 3).- Subjects first performed the discrimination task in which they reported whether position T or position B represented the greater value.
- Subjects performed the estimation task in which they reported the absolute difference between the values at positions T and B.
- The authors labeled the y-axis of each chart with the ranges for the first band (e.g., 0-50 or 0-33, see Figure 4)
## Results:

For all conditions discrimination accuracy averaged 99% or higher, so the authors focus on the results of the estimation task.- The RM-MANOVA found a significant main effect for band count (F(4,68) = 11.01, p < 0.001), but did not find an effect for chart type (F(2,16) = 0.367, p = 0.699) nor any interaction (F(4,68) = 0.211, p = 0.163).
- The authors performed univariate analysis of time and error for band counts.
- Univariate analysis of the estimation error found a significant main effect for band count (F(2,34) = 58.27, p = 0.013).
## Conclusion:

The authors found no significant difference in either estimation time or accuracy between chart types and reject the hypothesis that offset graphs would provide better performance than mirror graphs.- The results confirm the hypothesis regarding the effects of band count on performance: both estimation time and error increased with more bands.
- Multiple subjects verbally reported that as the band count rose they experienced increased difficulty identifying and remembering which band contained a value and that performing mental math became fatiguing.
- Though estimation time was slower with 3 bands than with 2, accuracy did not suffer

Funding

- The second author was funded by an NSERC Postgraduate Scholarship
- This research was supported by NSF grant CCF-0643552

Reference

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Best Paper of CHI, 2009

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