3 Questions You Should Ask Yourself When Creating Data Visualizations
By: Marie Lefevre
Tags: Data Science, Graphic Design, Charts, Learning, Data Visualization

3 Questions You Should Ask Yourself When Creating Data Visualizations

As an aspiring Data Analyst, I have been developing a passion for creating data visualizations of all kinds. While my first trials were very basic (and quite ugly if I want to be fully honest), I found myself developing a simple method before even getting into the graphical execution of any data visualization. During the conceptual phase of a graph creation, I asked myself 3 main questions. Answering them while conceiving, building and sharing a graph would help me keep in sight what I have been building this graph for. I hope that these tricks will help you create insightful and nice graphs!

Let me take you through these 3 tricks using the following graph as an example. If you wish, you can find the code behind this graph on my GitHub page.

© Marie Lefevre

1. Does your graph answer an interesting question?

Before you start creating the graph of your dreams, ask yourself this simple question: will my graph raise someone’s attention? To answer this, you will need to take a first crucial step: determine who this graph is addressed to. Once you have identified your target audience (it is no wonder if I am using a marketing term here), you should put yourself in your audience’s shoes and formulate a question that would raise their interest — some question that would get them look at your graph more than half a second. In order to convince your reader that they should dedicate some of their time to your graph, you need to create what I would call the “Mhmh” effect — this “mhmh” being a loud reflection noise.

In my case, I have been interested in the possible gap between a population’s perceived recycling behavior and the actual recycling behavior of this same population. Some surveys or opinion barometers often display the self-reported recycling rate of a country’s population. However these figures do not necessarily match with the reality of recycling rates published by local authorities. Hence the question: how close to reality are self-reported recycling rates in the 28 countries of the European Union? The graph I built tells something more than simply displaying the results of municipalities’ recycling rates, or the results of a survey. By combining two data sources, it shows the reader which countries have significant discrepancies (and in which direction) between the actual recycling rates and the ones reported by surveys.

Created using Imgflip

2. Is your graph truthful?

Once you have identified the interesting question you want to answer, you must ensure that you will answer it in a truthful way. This mainly lies in the way you will gather data and manipulate it.

Apple and oranges: compare what is comparable

First, check the reliability of your data sets. If you are using internal data from your business or data sets that you built yourself, chances are that the data you will be using is consistent. Even if this may sound stupid, check that it is indeed the case before going one step further. If you are looking for open-source data sets, the initial step of ensuring the reliability and comparability of your data sources is highly relevant.

For me, the main challenge was to find comparable data sets from open-source platforms focused on Europe. Moreover, I had to find some data about the actual recycling rates of European countries but also some figures about the recycling habits of their population (be they from an institutional report or a population survey for example). Finally, both data sets had to cover the same countries and the same time frame. After some research, I found consistent data on two different websites — yay! One comes from the European Environment Agency and covers 34 countries for the years 2014 and 2017, while the other comes from the EU Open Data Portal and covers 28 countries for the year 2017. So, combining both data sets enabled me to work on a consistent data base.

Less is more: be aware of the impact your graphical choices may have

The second thing you want to look carefully at concerns the graphical choices you make. Building a graph results from dozens of decisions and design choices. Make sure that all your graphical choices serve the truthfulness of the data you are visualizing. In particular, I would like to draw your attention to the following points, which should prevent biases in graphical representations and ensure that your graph genuinely and honestly represent the selected data:

Numerical units and scales : will you use percentage or absolute values? how to represent values having different scales on the same graph? where to display the units and scales used in your graph?

: will you use percentage or absolute values? how to represent values having different scales on the same graph? where to display the units and scales used in your graph? Colors : how will the chosen colors be perceived by the reader? should you rather use a continuous or a discrete color scale? which misleading interpretations could my graph lead to?

: how will the chosen colors be perceived by the reader? should you rather use a continuous or a discrete color scale? which misleading interpretations could my graph lead to? Projections : for a map, which Earth projection will you be using? do visual effects like 3D forms or shadows affect your graph’s interpretation?what role will play design elements like line thickness in your graph’s readability?

: for a map, which Earth projection will you be using? do visual effects like 3D forms or shadows affect your graph’s interpretation?what role will play design elements like line thickness in your graph’s readability? Ancillary elements: how will you arrange frames and text elements around the graph? which text font and style do you use? how to find the right mix between to much and too few information on and around the graph?

As for my graph, I chose a commonly used Earth projection and a scale focused on Europe, in order to comply with the geographical reality of the displayed countries, and not to mislead the reader by showing irrelevant countries on the map for example. I chose three colors that I find harmonious and that help emphasizing my key message: I chose orange for countries where the actual recycling rate is below the self-reported recycling rate, purple as a neutral color, and green for countries where the actual recycling rate is higher than the perceived recycling rate. Finally, I intentionally left additional frames or visual effects out, as they would have hindered a smooth reading of the graph.

Created using Imgflip

3. Does your graph teach something new in a clear way?

Teach something new…

This resonates with the first question “Does your graph answer an interesting question?”. After you caught your reader’s attention, you must keep his attention and eventually lead to a certain “Aha!” effect — this kind of reaction when you realize what the graph truly says, and what consequences this may have.

The message I want to convey through my chart is that the majority of countries report a higher recycling rate than what they actually do as recycling, and that this tendency is higher in some countries than in others. To avoid any judgmental view on this topic, I graphically displayed the gap between the actual and the self-reported recycling rates of various countries in Europe. I used colors to convey my messages and completed the graph itself with useful information in the title, legend and side notes, to help the reader quickly grasp what my graph demonstrates.

… in a clear way

If the question raised by your graph actually is interesting, chances are that the answer is multifaceted. But you should rather choose one clear message than try to convey too many ideas on one graph. If you want to communicate many ideas related to each other, you would benefit from splitting a complex message into two or three graphs with one effective key message each. This is indeed the secret of great data visualizations.

In my case, I wanted to display the value of the actual recycling rate per country, as well as the gap between this actual recycling rate and the self-reported recycling rate. But the graph’s geographical dimension combined with one color scale constitutes the maximum quantity of information that I could convey with one graph — adding the value of the actual recycling rate per country would have overloaded the graph, and the key message would get lost. Therefore, I came back to my initial question “how close to reality are self-reported recycling rates in the 28 countries of the European Union?”. The graphical decision that would give the clearest answer to this was to display one color scale on the map, highlighting the gap between actual and self-reported recycling rate with appropriately chosen colors.