469: quantum of sollazzo

#469: quantum of sollazzo – 24 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 Ornaldo Gjergji, a Data and Policy Analyst with the Osservatorio Balcani Caucaso Transeuropa, and member of the European Data Journalism Network.

This is a slightly reduced issue, as I’m travelling through Italy to attend a friend’s wedding. And, in the process, getting slightly angry at the traffic. In fact, I’m wondering about how transport data compares with other EU countries: the astonishing number of single-occupancy cars that have driven past me during peak hours in one of the largest urban areas in the country makes me think that there must be a better way. And yes, I was myself in a car, which is part of the problem (as well as being partly aware of what the problem is).

‘till next week,
Giuseppe @puntofisso

Six questions to...

Ornaldo Gjergji

Ornaldo is a Data and Policy Analyst at OBC Transeuropa – EDJNet.

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

I work for OBC Transeuropa within the European Data Journalism Network. Most of our work revolves around European affairs, or national and regional comparisons at the European level. So a lot of the data I use comes from Eurostat or other EU’s open data portals, together with data from national public administrations.
I use mostly R and RStudio for data analysis. Given that I have been doing a lot of geocomputation lately, I am starting to integrate QGIS and Mapbox as well in my workflow.

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

The piece of work I am most proud of is probably the one on Erasmus+ and its inequalities, which has  been the result of a transnational investigation with El Confidencial within the European Data Journalism Network framework. Working across newsrooms in different countries has been stimulating and very rewarding, and I really like how we managed to blend the quantitative data at the European level together with the personal experiences of individuals.

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

There are many. I would like to mention the data journalism project that made me want to start this profession: Life in the camps, published by Reuters. I read this article some 5 years ago when I was about to graduate, and it was probably my first encounter with data journalism. I just loved how information was conveyed in the article and my first thought was “I want to do things like these”. One day I will, eventually :).

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

If we agree (big if) that “data” and “information” can freely be used as synonyms, I would say that a dataset is any given way to systematize information. I think of tabular datasets because those are the ones I use the most. But a dataset can come in many different shapes, even a pdf can be a dataset.

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

Instead of thinking about how to bring life to a large dataset, try to understand how data can serve a story by enriching its narrative, or giving context to personal stories or qualitative elements more broadly. In my experience this makes the workflow within an organization smoother, contributes to the realization of better data stories, and builds trust between the data unit and the rest of the newsroom.

Data is or data are...

Probably ‘Data are’ is more correct, but I am not a native, so I unapologetically say and write ‘Data is’ as it sounds more intuitive to me.

Topical

Dollar street

A fascinating interactive website by Gapminder. “We visited 264 families in 50 countries and collected 30,000 photos. We sorted the homes by income, from left to right.“

Going back in time on abortion law

Which country are making abortion easier, and which are making it more restricted? Datawrapper’s Ivan Lokhov takes a look.

The distance hurdle

“How distance to the nearest abortion clinic changes for millions of women without Roe v. Wade“

Tools & Tutorials

Supervised Clustering: How to Use SHAP Values for Better Cluster Analysis

“Cluster analysis is a popular method for identifying subgroups within a population, but the results are often challenging to interpret and action. Supervised clustering leverages SHAP values to identify better-separated clusters using a more structured representation of the data”, data scientist Aidan Cooper explains in this very clear tutorial.

You Should Use This to Visualize SQL Joins Instead of Venn Diagrams

Interesting idea for a new way to visualize joins.

JSON and virtual columns in SQLite

“Generated columns have another great use case.“
I always forget about virtual columns, but they can be very useful. Here we see them used to parse JSOn fields on-the-fly.

Keepa

Keepa is a website tracking historic Amazon prices. Not fully free, but it could be useful.

Tools That Solve Your Files Problem

A website with dozens of easy-to-use tool for simple file-based tasks: image resizes, PDF merges, video compression, etc…

The FWL Theorem, Or How To Make Regressions Intuitive

“An introduction to the Frisch-Waugh-Lowell theorem and its applications.“
It’s a theorem that tells us “that there are multiple ways to estimate a single regression coefficient” and can help visualize conditional relationships.

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