The data newsletter by @puntofisso.
Hello folks, quite a bit happening in my work life, including 3 fantastic new starters this week and, hopefully, our first team pic-nic. Really excited about the all-star team I’m building, including data techies, scientists, and community builders. Quite a bit of work ahead!
I found some time to again play with cyanotyping maps extracted from OpenStreetMap. Still not quite there with the results, but the hope is, at some point, to make a few prints that are good enough to go on my good old maps Etsy shop (there are still 3 road colouring maps available, btw…).
This week’s data hero interview is with FiveThirtyEight’s Anna Wiederkehr. I’ve been following her steps since she worked at the Neue Zürcher Zeitung, and she’s been thoroughly impressive over the years, so I’m really pleased she agreed to be interviewed. I hope you enjoy it!
Speaking of interviews: which people working in or with data would you like me to interview? Just hit reply and let me know. Thanks!
And last, here’s the latest edition of my ‘slow weeknotes’, if you’re curious to know what I’ve been up to in May.
‘till next week,
This piece by FlowingData references this San Francisco Chronicle article, which, of course, finds that the stats are slightly skewed towards software developers marrying developers… you know, it’s the Bay Area.
Yeah, Italy won, stop reminding me of that. You know, I’m a big fan of the previous Italian winner (mainly because a lifetime ago it got me a 2-page spread on a printed British newspaper).
But Eurovision inspired, as usual, quite a bit of dataviz. Among others, Reuters Graphics published this wholesome explainer (extra points if you recognise the pretty obvious outlier in the picture), while this page by EurovisionWorld.com is a mine of data about the competition.
(via Ian Chaplin)
Isabella Chua at Kontinentalist, a storytelling outlet based in Singapore, takes a pretty peculiar approach to analysing climate change: she shows how different generations in Asia have witnessed or are witnessing changing average temperatures.
“To put things into perspective, we plot how average temperatures will change in each generation’s lifetime. The charts begin with the earliest birth year of each generation: 1946, 1965, 1981, and 1997, for Boomers, Gen X, Millennials, and Gen Z, respectively.“
“Zero deaths in some cities. Thousands in others. The pandemic’s fault lines continue to widen as vaccines flow toward rich countries.“
The New York times reports on vaccine inequality (while also showing how cities in South East Asia and Australasia remained relatively unscathed with both low vaccination rates and low infection numbers, presumably because of early, strict lockdown measures).
Researcher Constance Crozier writes about a topic that received a lot of attention and debate during the early stages of the pandemic: how to predict the trend in a sigmoid curve – functions that “start with exponential growth, then increase linearly, and finally level off “ – as more data becomes available. TL;DR: it’s hard. Some code is available, too.
“An evergreen CSS course and reference to level up your web styling expertise.”
It benefits from its interactivity, and could be useful to some of you web dataviz folks.
“Flat explores how to make it easy to work with data in git and GitHub. It builds on the “git scraping” approach pioneered by Simon Willison to offer a simple pattern for bringing working datasets into your repositories and versioning them, because developing against local datasets is faster and easier than working with data over the wire.“
Another sign that GitHub is slowly moving way beyond its code repository beginnings. An effect of the Microsoft acquisition?
“A dot plot visualizes a univariate (1D) distribution by showing each value as a dot and stacking dots that overlap.” Interactive code on Observable (and a comparison with Vega).
“Click to drop a raindrop anywhere in the contiguous United States and watch where it ends up”.
This is a truly mind-boggling piece of data visualization. The data sources used are all linked.
(via Guy Lipman)
Gregor Aisch, Datawrapper’s CTO, shows his attempts at a different take on creating a weather app: “Instead of a single weather icon, I want to use two columns, one for the sunshine duration and the other for rain! Who cares about a little bit of rain when there are six hours of sunshine during the day?”. Data sources are linked.
“ThanAverage is a small unscientific investigation into how we value and compare ourselves to each other.“
Ok, not quite about data, but I see it applying to data quite a bit!
“The Einstellung effect is a psychological phenomenon that changes the way we all come to solutions and impedes innovation.“
“I’m back from a week at my mom’s house and now I’m getting ads for her toothpaste brand, the brand I’ve been putting in my mouth for a week. We never talked about this brand or googled it or anything like that.
As a privacy tech worker, let me explain why this is happening.“
“Despite the clear superiority of engines, there ARE positions which chess engines don’t (and possibly can’t) understand that are quite comprehensible for human players. Typically these positions showcase the human ability to think creatively and formulate plans and understand long-term factors in the position.“
I know just enough about chess to understand what this article is saying, and much less about automatic chess engines, but… doesn’t this issue strictly depend on the type of AI algorithm used – something that the article only acknowledges without further exploring it? I’d understand if the automatic player is using a traditional Minimax with Alpha/Beta pruning, but I’m not entirely sure why this would affect, generally speaking, a neural network algorithm.)
quantum of sollazzo is also supported by ProofRed’s excellent proofreading service. If you need high-quality copy editing or proofreading, head to http://proofred.co.uk. Oh, they also make really good explainer videos.