Behind the scenes of “Right place, right time”

Jan Ustohal
Mar 6, 2018 · 10 min read

The Data

Data pre-processing

Data cleanup 🛁

The famous A100 by the Tegel airport (© OpenStreetMap contributors)

Making sense out of it

Analysis 1: Average availability

No technical post would be complete without a screenshot of the IDE. Yes, I use VSCode.

Analysis 2: Average car density per zip code

One of the initial attempts at geoclustering using seaborn

Getting Berlin’s geographical data 🗺

Empty SVG without any data and with a timestamp placeholder which served as the foundation for the generated choropleth map (Based on data from https://www.suche-postleitzahl.org/ under Open Database License)

Calculating the absolute density of each area over time 💎

Generating the images 🖼

Same image as above, this time using real data to generate the choropleth

Making it all fast ⚡️

~/D/c/python >>> time python generate_images.py 1
Starting processing
Reading data from DB
python generate_images.py 1 533.95s user 21.20s system 93% cpu 9:54.11 total
~/D/c/python >>> time python generate_images.py 4
Starting processing
Reading data from DB
python generate_images.py 4 792.82s user 22.94s system 325% cpu 4:10.76 total
~/D/c/python >>> time python generate_images.py 8
Starting processing
Reading data from DB
python generate_images.py 8 827.38s user 30.15s system 255% cpu 5:35.11 total

Making it move 📹

All the 1020 frames of the resulting video, stitched together with ImageMagick

Was it all worth it?

Jan Ustohal

Written by

Building products 🚀 and trying to make the perfect cup of coffee ☕️. Also 🚴‍. Now working on an early-stage stealth product 🤫. Formerly Marley Spoon, Fyber.

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