InfoVis of the week – 3


My InfoVis of this week is this representation of “38 ways to make a perfect coffee”. I think it is ordered in an accessible way. Even without the label reading ‘coffee’ you can tell that that is what it is about. The stylised drawings make it easier to compare the different cups to one another.

I also like the way the ingredients are represented. In my opinion, it is easier to see the amount of an ingredient you have to add as a part of the cup, than as an amount in centiliters – it is harder to imagine how much 8 cl is, then when you say ‘a quarter of a cup’ if you don’t have a measuring cup.

Another aspect of this visualisation is that you can immediately see the type of cup you are supposed to drink a certain coffee from for optimal results. It is much more difficult to describe this in words than with a simple image.

One more thing is that they ordered the types of coffee according to their similarity, which is also nice if you want to change your usual coffee just a little bit, you just look at the image left or right from it.




LinkedIn Network

This week I look into a Social Network visualisation.

InMaps is an interactive visual representation of your professional network. The tool is sadly enough not active anymore. The tool creates an interactive visual representation of your professional universe which visualizes the relationships between you and your LinkedIn connections. With it you can better leverage your professional network to help pass along job opportunities, seek professional advice, gather insights, and more.

The map is color-coded to represent different affiliations or groups from your professional career, such as your previous employer, college classmates, or industries you’ve worked in. In this person’s InMap, his LinkedIn colleagues are blue, while former colleagues at Yahoo Analytics are pink and other at Yahoo are green and his Carnegie Mellon classmates are orange and tangerine.

Bigger names represent people who are the most connected within that specific cluster or group. When you click on a contact within a circle you’ll see their profile pop up on the right, as well as lines highlighting how they’re connected to your connections.

Exploration of the map is possible to measure your own impact or influence, or create opportunities for someone else.

One can get a general insight in his network with a first glance by recognizing certain clusters or groups and how these are interconnected. From thereforward, it is possible to explore further. Look into how certain clusters are connected and through who, how certain possible future connections can be made and who are key figures within your network.

Other possible visualization tools for Social Networks and more specifically LinkedIn are Socilab, Iko System, Socioboard,…



This week, a visualisation on how long drugs stay in your body using bar charts caught my eye.

It’s is generally considered that bar charts make good visualisations, because we can compare lengths much better than areas or angles [1] (at least when it comes to quantitative data). This is demonstrated above. It’s nice and intuitive to look at it and see the differences. And instead of making it a weird 3D bar chart that combine the blood, urine and hair results into one, they made 3 seperate graphs, each showing their data in a good way.

But there is something wrong with the visualisation as a whole. First, they sorted the data within each graph from small to large, this makes comparison between graphs much more difficult since position is the best way to represent something [1]. So when you want to compare LSD with heroin on all 3 aspects it’s rather hard to do. This can be solved partially by making it interactive or by ordering the drugs alphabetically, so they all have the same position in each graph. Secondly, the scale used on the X-axis is not the same for all 3 graphs. In the first they use hours, probably because the data is more precise and in the last 2 they use days instead, probably an indicator of less accurate data. So to compare the first to the second or third graph, a conversion of the first to days or the latter to hours must be made. This leads to a visual misinterpretation of the data: the first bars are larger than the other but actually represent a shorter time period.

Lastly, there are some things wrong with the individual graphs.
In the first there is one drug that has a lot more hours which would lead to a graph similar to the second graph. But instead of doing that they made the scale non-lineair in the last part, which makes the visualisation less representational.
The last graph on itself doesn’t say much. It just says that each drug can stay in your hair up to 90 days, except for LSD which only stays in your hair up to 3 days. First, the data is very inaccurate which makes a good comparison with the other 2 graphs harder. Secondly, the graph is only useful in comparison to the others due to the lack of variation in the data itself for the third graph.

[1] J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics,  5(2):100-141, 1986.




Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s