Data Visualisation in Operational Research

Practice of OR – sharing experiences, building networks
20 February 2024

Dr Nicola Rennie

Welcome!

Why is data visualisation important?

Data visualisation has two main purposes:

Exploratory data analysis and identifying data issues
Communicating insights and results

Examples of sequential, diverging, and qualitative palettes

The role of data visualisation in OR

Improves decision-making through visual insights.
Facilitates exploration and discovery of trends and patterns.
Enhances communication of optimisation strategies and results.
Enables deeper exploration of complex operational models and scenarios.

Why is good data visualisation important?

A survey in 2021 asked Royal Statistical Society members their views on Significance magazine. Respondents were asked, “What aspects of content could be improved?”

“Better, more consistent charts… I’d like to see a house style like The Economist”
“The plots sometimes look amateurish…”
“The figures are often difficult to read…”

How do we improve data visualisations?

Data visualisations must serve a purpose.

Ask yourself:

What is the purpose?
Does the visualisation support the purpose?
Is it quick, accurate, and intuitive?

What is the purpose?

Choosing the right chart type: ft-interactive.github.io/visual-vocabulary

Does the visualisation support the purpose?

John Snow collected data on cholera deaths and created a visualisation where the number of deaths was represented by the height of a bar at the corresponding address in London.

This visualisation showed that the deaths clustered around Broad Street, which helped identify the cause of the cholera transmission, the Broad Street water pump.

Snow. 1854.

Does the visualisation support the purpose?

Does the visualisation support the purpose?

On the plot on the left, how tall is the bar?

Is it quick, accurate, and intuitive?

Source: Georgia Department of Public Health

Key points

Visualisation can be a very effective means of communication. However:

Charts should have a purpose.
Visualisations should be actively designed.
Default settings aren’t always the best choices.
There isn’t a perfect set of rules that works in every situation.

Roundtable discussion

How do you currently use data visualisation as part of your work? What is the purpose of those visualisations? If you don’t use them, why not?
What are some of the most common types of data visualisations you use, or see?
Do you use any guidance, style guides, or design processes when creating charts?
Are there any rules you disagree with?
How easy do stakeholders find it to interpret visualisations? What approaches might make this easier?

Feel free to add discussion points or comments in the Whiteboard.