At Visual Arena we and our partners are launching our newly established focus areas. As part of this process we want to arouse interest in data visualisation and encourage involvement in it. We asked Kristina Knaving, senior research and interaction designer at RISE Research Institutes of Sweden, about her views on visualising data.
What use do you make of data visualisation in your everyday work?
Data visualisation is a way of presenting data so that people understand it immediately. Trends and comparisons that are impossible to identify in a data set consisting of thousands of rows and columns of figures can be understood directly in visual form. The fact that people comprehend visual data so quickly and so clearly is one of its most important features.
If I have some data that I need to understand, the best way of doing this is often to put it into visual form. If it includes spatial data, for example a city and how it is structured, visualisation is one method of understanding the city: where the roads go, how to reach different places, what the socioeconomic characteristics of the areas are or where the restaurants are located. The options are limited only by the data that you have (and, of course, how accurate that data is).
What do you believe are the main research challenges involved in the visualisation of data?
A good visualisation requires sound underlying data and must be in form that reflects this data in an accurate, comprehensible and insightful way. I think there are a number of research challenges:
Open, secure and verified data sources
Educating people in data literacy, which is the ability to comprehend and take a critical approach to data, statistics and the visualisation of statistics. To understand data you must also understand where it has come from, how it has been collected and what its limitations are.
More research is needed into the ways in which people comprehend and create a picture of data using different forms of data visualisation. Most of the insights we have are already very outdated.
How algorithms and AI systems that process data can communicate visually to give people the best possible opportunity both to evaluate the process and understand the results.
What do you believe is the potential of visualising data and becoming involved in this focus area?
We have a growing need to understand data. As a result of the digitalisation of society, we have to interact with large quantities of stored data. This consists of data that we have created intentionally, for example information we have put on social media, and the digital traces that individuals leave behind them when they take actions in the digital world. Visualisation makes it possible for people to take data on board and understand it which in turn enables them to make informed decisions and act accordingly.
The coronavirus crisis has highlighted the importance of data visualisation. Data in visual form has been freely shared. It has received both appreciative and critical responses and has helped to shape both the dialogue and the understanding of the pandemic among professionals and ordinary citizens.
By ensuring that we become better at visualising data in accurate, intelligible and insightful ways, we can add value to the data we have. This will lead to greater understanding and to well-informed decisions in both our professional and our private lives.
If you had to divide data visualisation into different categories, what would they be?
When I teach data visualisation to master’s students in journalism, I usually distinguish between explorative and communicative visualisation. The explorative approach involves searching for understanding and insights in the data. The key feature here is that you can manipulate the data yourself by filtering it, zooming in, including other data sets and quickly creating new visualisations. You use the communicative method when your main aim is to communicate an insight. Here it is important to design the visualisation carefully so that the intended audience understands it.
The main distinction between the visualisations themselves is whether they represent a virtual model of a real object where the shape and colour of the object are reflected in the visualisation or whether they present the data in abstract form. A 3D visualisation of a chair can be created on a computer, a city can have a virtual twin and a map on a smartphone can show the area you live in. Information visualisation displays data in an abstract way, for example using a bar chart or a scatter diagram. Many visualisations are often a combination of the two, for example when a map of an area is coloured to show how many people voted for a certain political party. Another category that is often mentioned is infographics, but here it is generally just a question of the font and layout used for information visualisation rather than a separate category.
Regardless of the category, the intention is often the same: to make use of what is known about visual perception, interaction design and reporting to give data a visual form that allows you yourself or others to understand it.