The Effect of Smoothing on the Interpretation of Time Series Data: A COVID-19 Case Study

TL;DR

This study investigates how different visualization methods, especially smoothing techniques like a 7-day average, affect people's interpretation and prediction of COVID-19 data trends, highlighting the importance of visualization choices.

Contribution

It provides empirical evidence on how smoothing impacts data perception and suggests strategies for better visualization to reduce misinterpretation of time series data.

Findings

7-day smoothing reduces distraction from periodic patterns

Smoothed lines help form consensus on future data trends

Varying smoothing and plot window sizes influences perceived patterns

Abstract

We conduct a controlled crowd-sourced experiment of COVID-19 case data visualization to study if and how different plotting methods, time windows, and the nature of the data influence people's interpretation of real-world COVID-19 data and people's prediction of how the data will evolve in the future. We find that a 7-day backward average smoothed line successfully reduces the distraction of periodic data patterns compared to just unsmoothed bar data. Additionally, we find that the presence of a smoothed line helps readers form a consensus on how the data will evolve in the future. We also find that the fixed 7-day smoothing window size leads to different amounts of perceived recurring patterns in the data depending on the time period plotted -- this suggests that varying the smoothing window size together with the plot window size might be a promising strategy to influence the perception of spurious patterns in the plot.