The NOS and their visualizations

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.


Social media gebruik 2012

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.

Peiling zetelverdeling


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.

P2000 meldingen

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.

Bezuinigingen brandweer

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.

Hullman, J., & Diakopoulos, N. (2011). Visualization rhetoric: Framing effects in narrative visualization. Visualization and Computer Graphics, IEEE Transactions on, 17(12), 2231-2240.

Keim, D., Qu, H., & Ma, K. L. (2013). Big-data visualization. Computer Graphics and Applications, IEEE, 33(4), 20-21.

Reilly, K. M. (2014). 12 Open Data, Knowledge Management, and Development: New Challenges to Cognitive Justice. Open Development: Networked Innovations in International Development, 297.

Simply Statistics. (2012). The statisticians at Fox news use classic and novel graphical techniques to lead with data.


10 thoughts on “The NOS and their visualizations

  1. Nice story! I like the examples you gave of the NOS. Cairo described three different categories how people can be mislead. It surprised my that even the NOS shows visualizations which display so much data to obscure reality. You would think before NOS publish a graph it is checked by several people. If I would have worked at NOS and a colleague would have shown me the poll with the distribution of seats for political parties in parliament without doubts I would have told that the chart is horrible! I do agree with you that journalists should follow on a regular basis some extra trainings in order to keep up to date with all the new technologies. I can imagine if you are the age of our parents and working as a journalists you do not know a lot about all the graphic design programs such as illustrators. Also for them it is advisable to keep up to date with all the technological developments related to their work.


  2. Interesting article, although I don’t agree with you on some points.

    The first graph is not as misleading as you state it. I think it is pretty clear that it is about percentages of internet usage. When clicking on the CBS link ( you see that this graph was about internet usage. Furhermore, the original article is not attached to the graph, so maybe the article itself clearified the rest.

    The second graph doesn’t contain too much information in my opinion. The above part is really clear and I see your point on the part below, but the graph is interactive and when you hold your cursor on for example the VVD, the graph below shows only the line for the VVD.

    About the fourt graph you state that it is obvious that there are more alarms on new years eve than on christmas, but you can’t state that without any proof and that’s why this graph was made, just for proof. Furthermore, I think that they made a mistake with 1 December instead of 1 January and that this mistake was accidentally.

    The original articles are missing from all the graphs, so I don’t think you can look at it so straightforward as you look at it now.

    Of course I agree with you that it is more important to tell a good story than a beautiful story. I hope that journalists agree with us too.


  3. Nice article, I agree with your statement, it is important that the correct information and thereby a newsworthy story is told with graphics, than just using graphics because ‘it looks nice’. Furthermore, I think that the critical opinion of the reader is very important too. He/She can determine for his/herself if the data visualization is correct and what to do with it. Lastly, it depends on the context of the article. The amount of hamburgers sold compared to 5 years ago is less important than for instance graphs about political spendings.


  4. Interesting to see that even in our country a widely followed news organization uses misleading visualizations. It makes me think if they don’t just do it for the sake of getting attention? Or perhaps they think that because other countries (such as the US) do it, it is permitted to do so.

    Also, I agree with you when it comes to the value of a visualization in a news story. Good writing!


  5. Nice piece! Good job on narrowing it down to the NOS. It is the first blog in where I read that some misleading visualizations are actual mistakes. However, this doesn’t mean that they are not responsible for what they state and publish. And also there is the amount of journalists, who probably deceive intentionally. All in all, we as media consumers have to be critical and take some responsibility for ourselves with regard to what we believe or don’t believe.




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