If you regularly read the news by means of a website or mobile app, you might have noticed that more and more news organisations have started to use fancy graphics to support their news stories. These data visualizations are used to deal with complex sets of data, and to make sense of the numbers that tell a story. Data visualization techniques provide alternative approaches to knowledge production as opposed to just reading a text or interpreting numbers to understand a story. (Reilly, 2014) However, this doesn’t mean that there are no risks at data visualization. It is also an easy way to mislead an audience. The American news organisation Fox News (2012) has some quite remarkable examples in which journalists use several data visualization techniques to mislead their audiences. In this article I’ll take a look into some Dutch examples of misleading visualizations, and try to explain why it is more important to provide the correct information than to create a fancy graphic.
In our own country
After a search on the internet, I found out that the dutch news organisation NOS has an application with an archive of several data visualizations which they have used to support news articles that they have published. Although most of these visualizations seems to be correct, I still found some examples of misleading data visualization. Some of these visualizations are considered misleading, because it was relevant information was hidden, too much information was displayed in the graph and therefore unreadable, or information was presented by inappropriate ways. According to Cairo (2015) this are examples of three strategies on which most misleading visualizations are made. The next paragraphs contains five examples of visualizations from the NOS in which these strategies were used.
Social media use in 2012
The first visualization I found misleading was a graph of the social media use in 2012. This graph showed the use of social media and social network sites (such as Facebook and Twitter) categorized according to age groups.
Now there are two things that (from my point of view) are misleading because of hidden relevant data. First, it is not clear what is exactly meant by the difference between “social media” and “social networks”. In fact it is not clear at all which social media are included in this graph, and whether there might be social media that were excluded. Furthermore, it is not clear if Facebook and Twitter were only categorized as social networks or as social media as well.
Secondly, it is not exactly clear what is meant by the numbers on the y-axis of the graph. Although it seems percentages of the total use, it also could have been total amount of hours spent on social media. The omission of such relevant information might be motivated by the assumption that the audience knows what is meant for each variable (Hullman & Diakopoulos, 2011). But because of the omission of this information, the visualizations rather become confusing than informative.
Political polls and purchasing power
Now having too much information to interpret is also not very desirable. For example, the NOS has published a poll with the distribution of seats for political parties in parliament, which contains a lot of information and therefore has become very confusing.
It is not very clear what is meant by the numbers in between the parentheses, and the graph that shows a development in several lines is too small to be able to make a distinction between them. The overload of information makes it difficult to easily interpret this data.
In another graph from the NOS on purchasing power, the visualization was clearly organized at first sight. There were only two different lines presented. However, the user has the possibility to add additional lines which made the graph still too crowded to be able to draw any conclusions from it.
Incident reports and cuts to the fire department.
The NOS has also published some visualizations in which the data was presented in an inappropriate way. At the beginning of 2015 the NOS made a graph about the amount of P2000-alerts (the number of times that emergency services were called). In this graph they compared the amount of alerts on new year’s night with the amount of alerts on other days.
Now the first thing that is a bit doubtful, is the fact that the NOS only compared new year’s night with the christmas days in 2014. It may seem a bit logical that there are more calls to emergency services on new year’s eve when people setting off fireworks, than on christmas when most people are at home with their families having christmas dinner. Secondly, on the right of the screen an overview is presented with some emergency calls around 00:00 at night. However, these are calls from the first of december (from year “unknown”) instead of the first of january in 2015. Now this seems a bit like introducing a certain level of ‘noice’ into the visualization, a technique that is called Obscuring (Hullman and Diakopoulos, 2011). Because of the unrelevant extra information it is unclear why these messages are posted there, and just confusing for those who are trying to understand this graph.
Another misleading visualization by inappropriate presentation from the NOS was made on austerity in budget for the fire departments across the Netherlands. In this graph a map with all regions for the fire departments is presented, accompanied by the total of budget for each department in 2015.
The NOS intended to inform their audience about the proposed austerity in budget for the fire departments until the year 2018. However, they distorted some information causing much confusion about the real budgets for the fire departments in 2018. The NOS visualized the budget by a grey line and a number, which represented the total budget for the year 2015. Directly below the grey line they created a red line and a number which represented an amount of euros. Now one could be misleaded because at first sight it looks like a major cut in budget will be made until 2018. However, the red line is not presented the total budget for 2018, it actually presents the total amount of the austerity which will be made up until 2018. By presenting this information in such a doubtful way, there is a risk people get wrong impressions of reality. In worst case people use the distorted reality in real life events, for example in social, economical or political issues.
Challenges in data visualization
With the emerge of big data and data journalism, visualization of data sets has proven to be very effective for presenting complex analysis (Keim, Qu & MA, 2013). But as demonstrated, it also allows journalists to manipulate a story and to mislead their audience. According to Cairo (2015) this is caused due to the fact that a lot of journalists and designers are not seriously trained in scientific methods, research techniques and data analysis. Because of this lack of certain knowledge, journalists and designers actually make mistakes that can be categorized as “lie” or “misleading information”. Now there might be journalists or news organisations that mislead on purpose, and the only way to unmask those is train ourselves in techniques for deceiving. However, for those who just don’t have the appropriate knowledge to avoid such mistakes, it is just a matter of getting trained in statistics and data analysis. Although it might seem more important to create a “fancy” good looking visualization, I believe it is more important that the correct information and thereby a newsworthy story is told with graphics.
Cairo, A. (2015). Graphics groin, misleading visuals: Reflections on the challenges and pitfalls of evidence-driven visual communication. In Bihanic D. (Ed.), New challenges for data design (pp. 103-116). Springer-Verlag, London.