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.…

First published on LinkedIn Quite a few interesting discussions at Think Data yesterday, both on and off stage. Three main thoughts from me: the “future of data” is… overrated. There is a lot of work needed to get the basics right in terms of data quality, seamless data pipelines, understanding of the goods and bads of data. We need to move out of that mentality of blind faith in data as “the solution”.…

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. My first thought was: I could calculate the similarity of each face, and pick the face with the median similarity as the “average face” of the dataset.…

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. The TL;DR is as follows I first got the data and boundaries I found a formula to calculate the closest capital by distance I found a way to apply a parametric colouring to the map.…