Having been trashed for the last forty years or so, Governments have unexpectedly taken back center stage thanks to the Covid19 pandemic. The virus does not need a passport to travel around the world, nor any tough immigration legislation has managed to prevent it from freely crossing national borders. No country will be spared seems to be its harsh mandate, in a world where technology and globalization permeate most human interactions. Highly contagious, the only way known today to decelerate its spread is by minimizing direct human contact. In the absence of a global governance mechanism, only national governments can take effective action.
When China first opted to completely shut down Wuhan earlier in the year, the usual suspects immediately criticized the action as “authoritarian” and
I joined Twitter in early 2008, 18 months after it was officially launched, but only started tweeting regularly after 2010. I have however never attempted to do any data mining on my tweets. I should probably say text mining instead, as Twitter is essentially a platform that captures words in sentences limited to 140 characters,, including web links.
One easy way to do a simple analysis is to generate a word cloud of tweets. A word could presents in graphical format the words used in tweets, ranked by frequency of use. Words most used
Apps and data
Almost five years ago, while working together with former UN colleagues, we decided to create a mobile app that could show data on the progress towards the achievement of the UN Millennium Development Goals (MDGs). The key purpose of the project was to raise awareness of people in general on how the MDG targets (almost 50 of them) were moving in time at the national level. The app also allowed to compare targets across countries as well as aggregate data by regions.
Developing the app, which we did initially for Android mobiles, was the easy part. Getting the actual data however proved to be an almost insurmountable challenge. While a few countries did have available data, getting official data for most of them was complicated as in some case the data did not exist in digital