#scholarly visualisation

So one way to re-situate data visualization is to actually destabilize it by making dissent possible. How can we devise ways to talk back to the data? To question the facts? To present alternative views and realities? To contest and undermine even the basic tenets of the data's existence and collection? A visualization is often delivered from on high. An expert designer or team with specialized knowledge finds some data, does some wizardry and presents their artifact to the world with some highly prescribed ways to view it. Can we imagine an alternate way to include more voices in the conversation? Could we effect visualization collectively, inclusively, with dissent and contestation, at scale?” 

Networks Demystified 8: When Networks are Inappropriate | the scottbot irregular

'What happens when you visualise the interaction between organic human behaviour and a rigid mechanical storage system? What narratives and patterns are formed or imagined? What behaviours and insights can be deduced? The outcomes are constantly in flux, determined by the users of the LRS and the items contained within it.'

This student work was picked up by popsci. A comment thread on twitter picks up on some issues it raises: https://twitter.com/katecrawford/status/629362219072008193

“Corpus linguists have developed a number of ways to identify locutions that are really overrepresented in one sample of writing relative to others. One of the most widely used is Dunning’s log-likelihood: Ben Schmidt has explained why it works, and it’s easily accessible online through Voyant or downloaded in the AntConc application already mentioned. So if you have a sample of one author’s writing (say Wordsworth), and a reference corpus against which to contrast it (say, a collection of other poetry), it’s really pretty straightforward to identify terms that typify Wordsworth relative to the other sample. (There are also other ways to measure overrepresentation; Adam Kilgarrif recommends a Mann-Whitney test.)” 

I’ve picked on a single dataset, the list of price paid for property from The Land Registry, and every day over the next month I’ll use it to make 30 small things to talk about open data and visualisation.