The origins of data visualisations

These days, everyone is a bit of a data analyst. At primary school, you learn to read and fill in tables. At secondary school, drawing line charts is often one of the first things you learn in maths. But you also encounter data visualisation every day outside school or work: bar and line charts feature in news reports almost daily, and many adverts use them to reinforce their message.Good visualisation helps communicate information efficiently. A line chart, for example, shows you at a glance the current state of coronavirus infections in the Netherlands, without you having to compare figures or do any calculations yourself. If the line rises, infections are increasing; if it falls, the infection rate is declining. This lets you draw a quick conclusion from a large amount of information using intuition and insight.Without any prior knowledge, almost anyone can read different types of visualisation quickly and accurately. But this has not always been the case. In fact, data visualisations as we know them today only began to emerge a few decades ago. So where did they originate? And where are they heading?

Early visualisations

The earliest data visualisation is a wall painting in a Turkish cave. Thought to date from 6200 BC, it is a map of the Neolithic village of Çatalhöyük. Alongside the village buildings, it depicts a nearby volcano that appears to be on the verge of erupting. A map may not be the first thing you think of when you hear ‘data visualisation’, but it is essentially a chart with an x-axis and a y-axis. In this chart, every building, viewed from above, has a set of coordinates indicating exactly where it is.

The cave drawing at Çatalhöyük is considered the oldest map in the world. Since then, maps have become increasingly precise and standardised. The most obvious reason is that measuring equipment has become much more accurate, but maps today also largely follow the same layout. Almost all maps have a scale, a compass rose showing where north is, and a grid with corresponding coordinates or markings.

Figure 1. The cave drawing at Çatalhöyük1

William Playfair – the first line chart

Alongside maps, line charts were among the first forms of charts as we know them today. The earliest line charts date from the late Middle Ages and the 16th century, when they were mainly used to record the positions of stars. Yet William Playfair, an 18th-century Scottish engineer and political economist, is often regarded as the founder of statistical graphics. In 1786, he designed the line chart as we know it. He also invented the bar chart and the area chart, and later the pie chart.

Playfair’s line chart plots two variables, English imports from and exports to Denmark and Norway, against time. The chart has all the elements of a modern line chart, including labelled axes, a title and a brief explanation of what the axes represent. But some stylistic choices make it less clear and would probably be handled differently today. For instance, the meaning of each line is written directly on the line itself, rather unclearly. A legend could help here. The conclusion you can draw from the chart is also shown in the chart itself, using coloured areas between the two lines. Today, a conclusion is often included in the accompanying text or the figure caption.

In the following century, John Snow showed that a conclusion need not always be stated directly in the visualisation: you can draw it from the visualisation yourself.

Figure 2. Playfair’s trade-balance time-series chart2

John Snow – cholera

In the 1850s, London’s Soho district was hit by cholera. At the time, little was known about bacteria and germs, and the disease’s sudden emergence and cause were a mystery. John Snow, a British scientist who later became a founder of epidemiology, decided to map every case of cholera. He suspected that water contaminated by waste might be responsible.

He marked every case of cholera with a line on a map of the district. A pattern emerged: most cases occurred on Broad Street, where you could see a clear cluster. A water pump stood at that very spot, and many local residents drew their water from it. This supported Snow’s suspicion, and he traced the cholera outbreak to the pump on Broad Street. Snow took and examined a water sample, but could not find sufficient evidence that the contaminated water was indeed the cause. Even so, the map was convincing enough for the local authorities to act and take the pump out of service. This marked the end of the cholera outbreak in Soho.

This example often comes up in accounts of the history of data visualisation because it is one of the first known instances in which a conclusion drawn purely from a visualisation led to action. Ultimately, that is what data visualisation is about: presenting data in a way that creates new insights or helps people understand what is happening. In John Snow’s case, plotting every case on a map helped pinpoint the source of the infection.

Figure 3. John Snow’s London Cholera Map3

The rise and future of data visualisation

Data visualisation has developed rapidly since the 1950s. The introduction of computers, in particular, has made many new forms of visualisation possible. Suddenly, larger volumes of data and predictions from computer models could be processed, leading to new forms of visualisation such as word clouds. As mentioned earlier, ‘old-fashioned’ visualisations such as line charts and bar charts are still used daily in the news and in newspapers, partly because almost everyone can read them without much difficulty.

Today, data visualisation goes beyond simply visualising a large amount of data. Infographics, often a unique combination of several data visualisations, are one way to do this. By combining images, data visualisations and just a small amount of text, you can often explain a complex problem or topic. Another interesting development is that these infographics are often designed by visual designers, rather than solely by scientists as they were in the past.

When creating data visualisations, you should bear in mind that readers see a visualisation as it has been designed. To some extent, you therefore rely on the designer’s interpretation of the data, whether that designer is a data scientist or a visual designer. The more complex the data, or the more data brought together in a visualisation, the more easily misunderstandings can arise for both its creator and its readers. That is why it is important to check for yourself whether the correlation shown really indicates causation, especially as a good visualisation is not necessarily based on a good analysis.

Personally, I think there is still much to gain in the future from bringing scientists and visual designers together, so that scientific findings can be communicated more effectively. The first steps have already been taken: many companies are setting up data teams in which data scientists work closely with data analysts. The next step is to involve creative professionals too.

References

1. http://www.sci-news.com/archaeology/science-catalhoyuk-map-mural-volcanic-eruption-01681.html
2. http://scihi.org/william-playfair-and-the-beginnings-of-infographics/
3. https://www.nationalgeographic.org/activity/mapping-london-epidemic/