Graphics Lies, Misleading Visuals
During past decades we have witnessed the power of visual information. As our life becoming more busy and more intense, we have less time to process and understand information. Many times people just relying on visual information without understanding how much misleading it could deliver in order to create different opinion about given information.
Famous writer and visual journalist Alberto Cairo recently published “The Truthful Art” where he emphasize the Five Qualities of Great Visualization which he encourage to keep in mind for everyone who deal with data visualization.
~ Is it truthful? Means based on through and honest research.
~ Is it functional? Does it constitutes an accurate depiction of the data, and it is build in a way that lets people do meaningful operations based on it.
~ Is it beautiful? In the sense to be attractive, intriguing, and even aesthetically pleasing for its intended audience - scientists, in the first place, but the generally public, too.
~ Is it insightful? Does it reveals evidence that we would have a hard time seeing otherwise.
~ Is it enlightening? Choosing topic ethically and wisely - casting light over relevant issues.
The first quality of a good visualization is that it’s truthful. Truth could be considered subjective and it’s certainly on a continuum with some things being more truthful than others. And then it might be impossible to establish an absolute truth.
We as data scientists have two obligations when it comes to protecting the truth. First, it is we have to be honest with ourself when clean and summarize data. Is there activities we are engaging in likely to obscure a message because of the limitations that we’ve applied? We should consider explicitly each modification that we’re making to the data. And make sure we are practicing what Cairo calls self deception.
The second obligation is to our audience. That is there are techniques and data science and information visualization, which we can use to shine light on to specific pieces of data. Indeed this is generally the point of the fields.But if we omitted the ability of the reader to explore the phenomena more fully this will generate doubt and cast distrust.
In his second book “How Charts Lie” he geared toward helping people make better sense of charts, maps and other data visualizations that flood our social media feeds and nightly newscasts.
In the current political environment, misinformation is everywhere. A slick chart can add a veneer of authority to shoddy or misleading data. Cairo made it his goal to help readers avoid the common interpretive pitfalls that took him years to figure out for himself.
Cairo gives a grate example of this by chart, provided by the the national cable and telecommunications association. Chart which suggested that, after regulations were relaxed, cable companies invested four times as much in the industry.
And we can see here, the title of the chart alone, was intended to draw the reader to the conclusion, less regulation means more industry investment. But there’s some immediate problems with this graphic. First, it’s unclear whether the monies are adjusted for inflation, which is a big issue when providing charts of financial data. Second, the time spans for the two bars are different. One covers a span of three years, while the other covers a span of four years.The bars have the same width, but don’t represent the same period of time. there seems to be a bunch of missing data from years 1997 and 1998. We can see that after regulation, there were in fact years of sustained industry investment. We see a slight drop after deregulation, then a massive spike in spending. And then a significant fall from the period post 2002, much of which is excluded from the original chart. It’s unclear whether the dollar values are in fact corrected for inflation, seems pretty unreasonable to suggest from this data alone that regulations stems cable company investment and infrastructure.
After my own researches in the world of misleading visualized data I found very interesting public information which contain true information but based on the way it presented will definitely mislead the audience.
In just 15days the total number of # COVID19 cases in state Georgia is up 46% but we wouldn’t know it from looking at the state data visualization map cases. Map on a left posted by Georgia department of health on July 2nd Map on a right side from July 17th. The specific component of the visual that is misleading is red Color of counties in the state. Health department keeps changing the number of the map’s color legend to keep counties from getting darker blue to red. 2,961 cases was Red on July 2nd. Right pict. shows only over 3,769 in red but whatever was 2 000 became blue.This information was deleted after criticized in newspapers and news.
Conclusion
Misleading people isn’t just morally unacceptable, it also undermines credibility. Being truthful data scientist involves two different strategies: avoid self-deception and be honest with your audience.