466: quantum of sollazzo

#466: quantum of sollazzo – 3 May 2022

The data newsletter by @puntofisso.

Hello, regular readers and welcome new ones :) This is Quantum of Sollazzo, the newsletter about all things data. I am Giuseppe Sollazzo, or @puntofisso. I’ve been sending this newsletter since 2012 to be a summary of all the articles with or about data that captured my attention over the previous week. The newsletter is and will always (well, for as long as I can keep going!) be free, but you’re welcome to become a friend via the links below.

Every week I include a six-question interview with an inspiring data person. This week, I speak with Rosamund Pearce, Visual Data Journalist at The Economist. As readers of Quantum of Sollazzo, you’ve probably seen her contributions to The Economist’s Graphic Detail section linked here.

‘till next week,
Giuseppe @puntofisso

Six questions to...

Rosamund Pearce

Rosamund is a Visual Data Journalist at The Economist.

What is your daily data work like and what tools do you use?

I mainly work on our Graphic Detail section, which is a collaboration with another data journalist who performs the statistical analysis and writes the copy, while I create the graphics and lay out the page. The elements evolve in tandem: giving something greater prominence in the graphics influences what’s written, and mentions of a category or place in the copy affects what I show in the graphic.

I use R for data cleaning, analysis and visualisation, QGIS for maps, Adobe Illustrator for laying out and polishing up the design, and D3 for things I can’t do elsewhere (like force-directed graphs).

Tell me about a data project that you're proud of...

Unusual data visualisations can be eye-catching but also run the risk of being hard to understand, since readers are more familiar with tried-and-tested methods. So I’m always pleased when I create something that is both striking and intuitive, like our visualisation of social distancing in bees. The diagram was also a lot of fun to design.

I’m also proud of the newsletter that the data team launched last year. I enjoy writing about data visualisation and having a dedicated newsletter gives me a space to get into the nitty-gritty of designing charts without worrying that the topic is too niche.

...and a data project that someone else did and you're jealous of.

I am constantly in awe of other visual journalists’ work. Among the other newsrooms I admire are Reuters for their clear and clean diagrams, The Washington Post for their gorgeous maps, The New York Times for their innovative data visualisation and the South China Morning Post for their beautiful print layouts and use of illustration.

If I say "dataset", you think of...

My mind immediately jumps to practical considerations. Is it in a format I can use? Is the source reputable? What type of visualisations could work with this type of data? Does it have the right coverage (time and place)?

Give someone new to data a tip or lesson you wish you'd learned earlier.

Where to start! I wish I’d learned R sooner, as it’s far superior to spreadsheets for most tasks. The ggplot package is also great for exploring different visualisation methods so you can get a quick idea of what works, even if I sometimes end up producing the visualisation elsewhere (for example with an interactive I will redo it in D3).

There are loads of little tricks in Adobe Illustrator that I wish I’d learned sooner. “Convert to shape” allows you to resize scatters without the circles distorting. The blend tool is useful for creating colour scales for maps. And if there isn’t a built-in way of doing something it’s worth checking to see if someone’s written a script for it.

Data is or data are...

After nearly three years at The Economist, I’m slowly coming around to “Data are”.

Topical

Can you reach net zero by 2050?

“See if you can save the planet from the worst effects of climate change.“
This climate-related game by the Financial Times is doing the rounds in the data journalism circles. The choice of advisers is straight from “Don’t look up”.

How many Ukrainian refugees are there in the European Union – and where?

Gianluca De Feo for OBC Transeuropa/EDJNet takes a look at Ukrainian refugee flows.

The Extreme Bias Of Florida’s New Congressional Map

FiveThirtyEight explains gerrymandering redistricting in Florida. That this degree of meddling with elections is legal and socially acceptable in the US never ceases to surprise me.

Tools & Tutorials

NYC 311 Reports

Chris Whong has launched a web app that “that lets you draw an area of interest in NYC and get a report of recent 311 activity.“
311 is the number used to report non-emergency issues in many US areas.

K-Nearest Neighbor (KNN) Explained

“One Machine Learning algorithm that relies on the concepts of proximity and similarity is K-Nearest Neighbor (KNN). KNN is a supervised learning algorithm capable of performing both classification and regression tasks.“

Latlon2map

The European Data Journalism Network has launched this handy web app. “Latlon2map is a tool developed by OBC Transeuropa’s Giorgio Comai that you may find useful if you have a spreadsheet with longitude and latitude columns and would like to have a look at those data on a map, or associate them with administrative units across Europe.“
This Medium post explains how it works.

Data thinking

‘I think I discovered a military base in the middle of the ocean’

Or, “Null Island, the most real of fictional places”.
A serious article. Here’s the abstract: “This paper explores Null Island, a fictional place located at 0∘ latitude and 0∘ longitude in the WGS84 geographic coordinate system. Null Island is erroneously associated with large amounts of geographic data in a wide variety of location-based services, place databases, social media and web-based maps. While it was originally considered a joke within the geospatial community, this article will demonstrate implications of its existence, both technological and social in nature, promoting Null Island as a fundamental issue of geographic information that requires more widespread awareness. The article summarizes error sources that lead to data being associated with Null Island. We identify four evolutionary phases which help explain how this fictional place evolved and established itself as an entity reaching beyond the geospatial profession to the point of being discovered by the visual arts and the general population. After providing an accurate account of data that can be found at (0, 0), geospatial, technological and social implications of Null Island are discussed. Guidelines to avoid misplacing data to Null Island are provided. Since data will likely continue to appear at this location, our contribution is aimed at both GIScientists and the general population to promote awareness of this error source.“

Dataviz, Data Analysis, & Interactive

Social Media Usage by Age

“Social media apps are on a lot of phones these days, but some tend towards a younger audience and others an older. Some are common across the population. Here’s the breakdown by age for American adults in 2021, based on data from Pew Research Center.“
Great work by Flowingdata.

2022 London Studio Pay Analysis

Kudos to ustwo for publishing a highly visual report on their London gender pay gap.
(Note that publishing the data is a legal requirement in the UK for companies with more than 250 employees, and voluntary for all others.)

Safe Cycling in London

A Google map by Safe Cycle London, via the excellent Mobility Matters Daily.

AI

Machine-learning models vulnerable to undetectable backdoors: new claim

“Boffins from UC Berkeley, MIT, and the Institute for Advanced Study in the United States have devised techniques to implant undetectable backdoors in machine learning (ML) models.“

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