A quite thought provoking piece from James Cheshire at UCL on Fast Thinking and Slow Thinking Visualisation.
strikingly random thoughts and 'maximum data existentialisation'
Archives for category: Visualization
Insightful, substantive and a must read for anyone working with data visualisation as consumer or designer. Julie Steele andNoah Iliisky’s new volume — Designing Data Visualisations — from O’Reilly succeeds in applying a long overdue and well crafted taxonomic process to the art of Data Vis. Building on their previous volume — Beautiful Visualisations — the authors take to the underpinnings of the pretty pictures and case studies presented in their edited volume. This shorter work would form a superb basis for an introduction to Data Vis course. Read the rest of this entry »
The R Cookbook by Paul Teetor is a solid addition to the well respected series. Teetor provides a rich collection of useful examples written in the proven method and covering everything from installing, configuring and running R to carrying out sophisticated statistical analysis tasks that demonstrate the power of R. The book is targeted at a wide audience from R novice eager to just start playing in R to more experienced practitioners looking to hone and round out their R repertoire.
The Data Source Handbook by Pete Warden provides a concise and handy guide to some of the main sources of public data accessible on the web today. It’s a very short book of 40 pages. This in itself does not stand against the book. These sources are rapidly changing and compiling and committing an exhaustive survey to a printed volume would damn it to almost instant obsolescence. It would also prevent any treatment of individual datasources in any useful detail.
Data Analysis with Open Source Tools by Philipp K Janert is a simply superb, solid and exhaustive synthesis of instruction, workshops and hands-on exercises designed for those serious about conducting professional data analysis. This is not a lightweight undertaking. This is a serious get-down-to-it and do-it-right kind of manual. The author (as has been mentioned elsewhere) is passionate about his subject and it shows. He knows how to convey the most complex concepts in an approachable and effective way.
Beautiful Data is a collection of essays on exploring the organisation, manipulation and display of data in ‘beautiful way’. The editors, Toby Segaran and Jeff Hammerbacher, have attempted to loosely organise the papers into logical process of: collection –> storage –> organisation –> retrieval –> visualisation –> analysis and in theory this works. The challenge as with any collection of papers from such a diverse set of authors (39 in this case) is finding that common thread that flows through the works. In this the editors achieve a passing grade, but frankly, this is sort of the book that offers the reader something they will find useful, but only due to the breadth of articles included. The downside is that there will certainly be articles that a reader will not. The authors seem to realise this and use the term ‘loose’ with some frequency. But I can’t criticise this and would want to. This is a strength of the book. It covers much ground and will appeal to many.
Great R:Level 1 by Michael Milton is a 2 hr video course which leads you from the basics of installing the R environment on your system to conducting basic analysis and presentation of your data. The course itself is delivered in a clear, concise manner and the author is very thorough in his approach. The pacing is very good and as a student you are kept engaged and don’t tend to fall behind, nor feel that you wish that things could hurry along. This video course is a superb complement to written tutorials and reference manuals on the R environment.
The Map Your Moves Challenge fascinates me. New York’s Public Radio station WNYC has devised a data visualisation challenge for their listeners. Curious about what makes people move from and to their community they polled stories from their listeners and collected them into a structured dataset and have released it into the wild. Now this is very cool…they want to take real stories and understand how these stories interact and how they can learn about their own community from them. Absolutely brilliant!
Data visualisation has become very vogue in the digital humanities community. Although there have been a scattering of brave practitioners over the past few years, only very recently has this interdisciplinary area started to feature prominently at DH conferences as a mainstream practise worthy of consideration.
For the last few months I have been looking for an opportunity (i.e. a bit of time) to delve into R and Processing, specifically with an eye towards taking some existing visualisations I am working on to a new level. 
The first book of interest is R in a Nutshell by James Adler recently published by O’Reilly.
R is a language and an environment to support data analytics and visualisation. Its approachable, extensible and open source. One of the advantages of R over other comers is the number of rather polished interpreters available for it and some of the great examples floating about that have been constructed in R. Hence my interest. I come to this interest from a digital humanities background and wondered whether the language could be of use for working with my own data coming from farm diaries exploring the cycle of seasonal farm activities.
Eric Fischer has posted a new series of visualisations ‘Locals and Tourists’ depicting the location of photos taken in urban areas around the world. In this 
series he attempts to distinguish between those taken by tourists (people who seem to be a local of a different city and who took pictures in this city for less than a month) and those by locals (people who have taken pictures in this city dated over a range of a month or more). Intriguing.
What immediately struck me was his ingenious re-use of the existing data to create new information. By exploring individuals posted pictures over time he was able to hypothesise as to whether they were visiting or residing in a particular area. This allowed for a means to compare the gaze of the two groups.
I immediately started to explore his map of Dublin to see if any patterns emerged and then to try and suggest explanations for them. There is a healthy and regular mix of photos by both groups in the central core, but immediately to the east is a large blue box of photos taken by locals. It appears to surround the new Aviva Lansdowne Stadium in Ballsbridge. Additionally on the northside the National Botanical Gardens have a heavy concentration of photographs by locals.
The most practical application of Locals versus tourists is to consider how a visitor might use these visualisations to find the hidden city known only to its inhabitants — to find those secret spots worthy of capture by locals, but seemingly missed in the tourist guides.
This set builds on his earlier work ‘The Geotaggers’ World Atlas’ looking at from where the pictures were taken, whether from car, bicyle or when walking.