About me

Running statistics from Santorini experience

2 mins read time ❤️ Views

Last year I participated to the trial race Santorini Experience. It started at Oia and depending on the number of kilometres that you wanted to run, it may have 5, 10 or 15 kilometres. I ran 15 km of the magical path that connects Oia to Fira, overlooking the Caldera. During the race, I used the open source tracker for android RunnerUp, which is a simple but very complete tracker without all the bloatware that have some other popular trackers. This post shows some graphics of the data that I captured during the race.

RunnerUp records the coordinates, time and altitude which I exported using a .tcx format. The tcx format is a xml format from Garmin, I converted to a csv file using a script from Corey Siegel. Then, I used pandas to pre-process the csv file. The details can be found in the python notebook. The coordinates latitude and longitude can be used to plot the trajectory of the race using folium library with a back end of Leaflet.

The race trajectory was symmetrical: 7.5 kilometres forwards to Fira and 7.5 kilometres backwards to Oia. It has 189 meters from the lowest point to the highest one. During the 15 km I have climbed 1486 meters.

I used ChartJS to plot my speed performance. My maximal speed was around 15 kilometres per hour and there are some parts of the race where I almost walked because the relief was too steep.

The first three kilometres were easy to overcome with a mean speed over 8 kilometres per hour. After the 4 kilometre it was difficult to keep a good average, then I have a average speed of 6.6 kilometres per hour. The speed oscillated with the relief and at the end I end the race within 1 hour and 40 minutes, having a average speed of 9 kilometres per hour.

In relation with 🏷️ run, python, chartjs, leaflet:

Marathon training program analysis using python and chartjs

A marathon needs to be prepared for at least 3 months. This post shows my preparation training and a prediction of my time for the Metz Marathon 2017

Two years data from Withings smart watch

The withings smart watch helps me find insights my health. How much I move and sleep.

Intelligent Video Analytics using SSD mobilenet on NVIDIA's Jetson Nano

This project implements a deep learning model on a Jetson Nano to count and track people passing in front of a video camera.

Analysis of Paris bike systems Velib

This post shows how to query data from Paris Open Data base, make some geographical plots using folium and also some analysis about the bike utilisation depending on the date

Subscribe to my newsletter 📰

and share it with your friends: