This blog post is the third in a series of pieces around data visualisation that will be shared on the SAGE Campus blog throughout May. They have been created by Andy Kirk, a UK-based data visualisation specialist, design consultant, training provider, lecturer, author, speaker, researcher, editor of an award-winning website, and course instructor on Introduction to Data Visualisation.
Every week in May we will be sharing Andy’s observations from his 'little’ of visualisation design blog series. This week’s post covers the use of annotations. Stay tuned throughout May for further posts on axis, and photo-imagery.
This is part of a series of posts about the 'little of visualisation design', respecting the small decisions that make a big difference towards the good and bad of this discipline. In each post I'm going to focus on just one small matter - a singular good or bad design choice - as demonstrated by a sample project. Each project may have many effective and ineffective aspects, but I'm just commenting on one.
In the well-known work 'Gun Crimes' by Periscopic, some of the main findings of analysis are provided in captions located beneath the main chart (this exists in both years of analysis, I'm focusing here on the 2010 view).
When you click on the respective caption, it provides a shortcut for the user by automatically applying the necessary criteria in the main chart above to formulate the associated view of the data that supports the finding described.
In this series of small multiple area charts we see y-axis interval labels 0% >> 30%, x-axis interval labels 1 >> 8 and the x-axis title 'Season' repeated across every one of the 20 charts. This unquestionably aids the direct readability of each panel but much of it could arguably be described as redundant.
SAGE Campus Bytsize courses are a series of short courses that teach core data science skills to people who are eager to learn, but short on time.
In Bytesize: Data Visualization you will learn effective presentation methods for various data types and variables, and how to create your own visualizations in Jupyter notebooks in Python or R. Find out more.