Data Vis Roundup

I’m supervising a capstone project right now where students are providing data analysis and visualization support for a local organization, and the following set of links have been queueing up in my feed as to-read items for me related to that project (and, hopefully, to-read items for them):

eagereyes has a nice summary of ISOTYPE (International System of Typographic Picture Education) which in the roughest strokes is those charts where the number of an item is represented not by a bar but as a collection of images or icons representing the thing being counted. But it’s a lot more complicated than that, including guidelines about the design of the icons.

The same site also has a nice, short illustration of how visualizing something makes it real in a way that just seeing/reading the source information or data does not. To me, this highlights the importance of being thoughtful about what you are presenting and the accuracy of the analysis behind your visualization.

In a similar vein, I enjoy Junk Charts dissections of poor data presentation; this critique of bubble charts via a self-sufficiency test analysis is a nice example and serves as a good model for a simple way to assess your own visualizations.

If you’re thinking about data visualization to monitor something (which my students are), you probably need to think about if a “dashboard” would be helpful. juice analytics has put together a collection of innovative dashboard designs, and also have a helpful link to a white paper they wrote on dashboard design at the top of the article.

We’re also probably working with some maps in our project, so this list of common problems in maps from cartonerd could be useful, as could the linked collection of UK maps which is recommended as a tool for looking for ideas of what not to do. The key focus here is on persuasiveness of the maps; the overall message is ultimately narrowed down to the question “is the map as it stands capable of properly supporting policy-making”?

On the tool front, the announcement that students can now download and use the full desktop version of Tableau for free got a fair bit of fanfare recently.

My colleagues and I teach a number of courses where we hope students will go out and find their own data sets to work with – Quandl looks like it will be a great source to share with them. It’s a searchable collection of free and open datasets, normalized into a standardized format which can then be output in a range of useful formats (right now, Excel, CSV, JSON, XML or R). I love that you can browse the data online and even see some basic graphs of it without downloading, though. This is a site that some will use to look at data and get answers directly, not just a repository to download from.

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