When looking at public data visualizations, benchmarks and targets play a key role in helping audiences understand what’s good, bad, or average. A single number doesn’t mean much until it’s compared against something meaningful like a national average, a target goal, or a previous year’s performance. Good visualizations use benchmarks to give context, helping viewers quickly see whether a result meets expectations or falls short.
The visualization I chose comes from The New York Times’ interactive page, “Tracking Coronavirus Vaccinations Across the U.S.” The chart compares vaccination rates by county and uses a vertical benchmark line labeled “Avg.” to indicate the national average vaccination rate. Each circle represents a county, sized by population and colored by region. The benchmark line helps viewers instantly see which counties are performing above or below the national average.
This visualization makes the benchmark comparison clear and effective. The vertical “Avg.” line stands out visually, and the distribution of circles around it tells a story that less vulnerable counties generally have higher vaccination rates than more vulnerable ones. Without the benchmark line, viewers would only see scattered data points and might miss the broader trend. The use of color, size, and benchmark placement all work together to make the message both meaningful and persuasive.
References:
The New York Times. “Tracking Coronavirus Vaccinations Across the U.S.” https://www.nytimes.com/interactive/2020/us/covid-19-vaccine-doses.html
I like your clear explanation on what a benchmark is and its role on graphs it makes it really easy for me to understand. I also like how you choose a graph on covid and the bubbles looks like covid in a way lol
ReplyDeleteI really like how you explained the purpose of the vertical benchmark line labeled avg. I got a clear sense of what you were talking about, and you presented it in a way that was easy to understand and think about. The way you discussed the use of color and circle size really helped me see the differences between the regions. Overall, your explanations did a great job showing how the visual design highlights the data and makes it more engaging.
ReplyDeleteI enjoyed how you explained how benchmarks are important in data visualizations. I think you make a great point in saying how good visualizations use benchmarks to give context, which in turn helps viewers see whether a result meets expectations or falls short. This chart gives us a clear understanding that the benchmark is the average vaccination rate among most to least vulnerable people. I think your explanation gives us a clear understanding of why benchmarks are important and helps us further understand the chart you presented.
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ReplyDeleteI really like how this benchmark chart looks. I will however, just looking at the chart with no context did leave a bit of confusion on my end. But, your explanation does help quite a lot. I got to understand a lot more on why benchmarks can make a big influence in perceiving your data representation.
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