What I learnt from creating The Data Visualisation Catalogue


Before I started working on The Data Visualisation Catalogue, my chart toolbox was limited. I knew of only simple charts like a Bar Graph, Pie Chart, Pictogram Chart, Proportional Area Chart, et cetera. Y’know, the type of charts that fit well onto an infographic. However, this felt insufficient and I had strong urge to expand my knowledge of data visualisation.


Image: The Data Visualisation Catalogue.

Previously, I had studied information design, lightly touching on data visualisation, and I had already worked on a few infographic projects; but since I had only recently graduated from my Bachelors a couple of years ago, my knowledge was still at a “beginners” level when it came to data visualisation.  I also wanted to expand my options when working with data and easily identify the best way to showcase my data.

In the past, the only guides I knew for selecting charts were Ralph Lengler’s and Martin J.Eppler’s A Periodic Table of Visualization Methods and Christian Behrens’ InfoDesignPatterns project (no longer online). However, I needed a reference tool of my own that I could adapt to suit my own needs.

So initially, I began by collecting a list of different chart types into a spreadsheet. This only included simple bits of data, such as photo of the chart, its common name and links to webpages with more information on it.

Eventually, once I had enough time to spare, I went about designing and developing a website based on the research I had. 

But it was not enough to effectively describe how each chart works or why it’s useful.

I wanted to have some kind of system to help me select a chart, but my knowledge of what distinguished each chart from one another was limited.  So I thought, for now I don’t have any overarching system to organise these charts into, but I do know that they all serve some kind of function. So for the time-being, I organised charts based on the functions they perform. It wasn’t a perfect system, but I thought that as I learnt more about data visualisation and each chart type individually, I would eventually develop a better system.

To build a solid understanding of each chart, how they’re constructed and what they’re useful for communicating, I would need to dig deeper.

I started to research further into each chart type I had recorded, using a number of online and print sources, to keep it as objective as possible. Some sources provided a good description of how the chart is drawn, while others might provide insight on how the chart is useful.

Once I felt I had sufficiently researched a chart, I would read through my notes, and then distill and write my own description of the chart. This enabled me to solidify my understanding of the chart.


Image: Studying many different types of charts has helped expand my data visualisation toolkit.

Over the past couple of years, a number of people have offered to help me, but I’ve had to refuse because the process of researching and compiling the chart reference pages is integral to the project’s original goal: expanding my knowledge of data visualisation. If someone else had done this work for me, I wouldn’t have learnt about each chart as intimately.

Today, I’ve compiled 57 reference pages of different chart types, and there’s more to go. This has allowed me to become more familiar with the many different ways to visualise data and in turn, to better identify charts and how to read them. For example, some financial charts, such as Candlestick Charts and Point & Figure Chart, are particularly complicated to read. But from dedicating the time to reading into them, I can now distill meaning that usually only traders can properly make sense of.


Image: Point & Figure Charts display the relationship between supply and demand of a particular asset through a series of columns made up of X's and O’s.

My research has also provided some insight into the design and creation of charts. Many graphs represent data by encoding it into visual forms, generated by some kind of axis-based plotting system. For example, Bar Graphs use the length of their bars on a fixed axis to compare values. This works well because the human visual system better distinguishes the length or positing of objects that are in view. The other ways data can be visually encoded into a graph is through curvature, position, angle, area, and shading.  So now, when I need to develop my own custom visualisations, I know the visual language to use.

Although by now I’ve researched into a large number of charts, I still don’t feel I’ve developed a better system of selecting them. There’s so many more charts left for to look into, but I don’t think researching them will help in this regard. Deciding which chart to use for your data is dependant on many factors (data type, audience, output medium) and therefore the choice is complicated.

I will still continue to research into different chart types but, to gain a fuller understanding of data visualisation, I think it is also important to expand my research beyond charts.