I’ve just come back from a splendid week in Bologna, where I helped run csv,conf,v9 for the first time.

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A couple of weeks ago I saw this interesting map of commute times in Germany made by Datawrapper’s co-CEO David Kokkelink, noting how commutes into major cities are the longest in Germany.

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The Open Data Delusion

Open Data is about the “Data” as much as it is about the “Open.” Some stories from my experience as an Open Data activist and adviser illustrate it.

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Another little QGIS step-by-step note to my future self.

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I’ve been wanting to try using gpx data for a while. You can record your runs with a variety of apps, and even edit those with a text editor, as gpx is a format that is relatively human-readable. What I realised, though, is that I’d been using Strava quite a lot when we were in lockdown in 2020, my hour of daily air being, often, a jog. Personal best after personal best (which didn’t take much, as I’d never really been a runner before) I got to know areas close to where I lived that were suitable for running, and also venturing for longer runs, including some over 10k.

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First published on LinkedIn

Quite a few interesting discussions at Think Data yesterday, both on and off stage.

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TL;DR Here is the Colab notebook.

A few years back, the the UK Parliament released photo portraits of each Member of Parliament. So, I thought, it would be cool to do something data-driven with that image set. I had worked before with algorithms that allowed to find reference points on a face, using Terence Eden’s code that found the most similar painting to a face.

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I saw this map on Facebook. It asks what capitals are closest to each Italian town, creating what looks like a continuous map to display the result. I like this kind of thing, so I set out to replicate it using QGIS, and making the process as replicable as possible.

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