The art of using data is ensuring that it moves people to action; these stories are no less fragile.
One of the clearer roots of the problem is the wretched state of American policing, which has been in dire need of reform for longer than I’ve been alive; a rallying call of the 2020 movement has been “Defund the Police”. I’ve not looked into police spending to know how much is too much, so I decided to do a comparative analysis of some American and international cities to see what their police budgets looked like.
I actually don’t spend that much time working with Tableau, so in the last year I’m a little out of practice. I went back to my training to restart my practice; do something you’re passionate about, and draw it out first.
On Wednesday my esteemed colleague @liluns and I went to the AWS Summit in London, which is several thousand people crammed into one end of the Excel Centre in the Docklands.
If you know me in real life, you probably didn’t know me for very long before realising that privacy and mass surveillance are some of my hot topics, which I could discuss for several hours without much of a break.
What is particularly interesting about this article and the debate it ignited (or soured, depending on how you look at it) is that it seems blind to the increasing interdisciplinary trend in data science – of which medicine is an obvious part – where fresh eyes find new ways to understand a problem.
Yesterday in the Hague, Radovan Karadzic was convicted for 40 years for his role in the Bosnian war. What some people may not know is that statistical analysis of migrational movements and killings was one of the ways external observers demonstrated that there was a systematic campaign of ethnic cleansing in Serbia.
Our societies use “threat levels” determined by domestic intelligence agencies for policing and security policy – but what do they mean?
Fragile Data 