We’re excited to announce that throughout May we will be sharing a series of posts created by data visualisation guru, Andy Kirk!
Andy is 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 on the 'little’ of visualisation design: the small decisions that make a big difference towards the good and bad of visualisation. Each of Andy’s pieces focus on just one small matter - a singular good or bad design choice - as demonstrated in a sample visualisation project.
You’ll discover how some common themes can make or break a visualisation, including use of:
The first post in the series, which posted on May 1st, considers the use of colour. Andy discusses visualisations that take a clever approach to colour - such as 'Rethinking Detroit' by the National Geographic, included below - as well as looking at ways that apporaches to colour can make a subject matter unnecessarily confusing!
To find out what makes 'Rethinking Detroit' a clever visualisation, read the full blog post here.
The second post in the series explores the positioning of categorical labels. Read the full post.
Want to know more about annotation design? Read the full post.
The projects that are the focus of this post demonstrate clever approaches to axis, and come from the Washington Post, the New York Times and Sports TV coverage. Read more!
Stay tuned to the SAGE Campus blog! Bookmark this page, and return on Tuesday May 29th, for the next in the series of the good and bad of data visualisation: photo-imagery.
If you work with any form of data, big or small, and want to increase the impact of your research, then Introduction to Data Visualisation could be the perfect course for you. Andy Kirk imparts the craft of data visualisation, helping you understand what to think, when to think, and how to resolve all the analytical and design decisions involved in any data-driven challenge.
Find out more and read the syllabus