Visualization

I have a small hobby project over at kollektivkart.arktekk.no that is for visualizing changes in public transit in Norway. For some time I’ve been wanting to do some visualizations on public transit lines. For example, plot the mean delay at each stop used by a line over time. When trying to do some concept work on this, I discovered a puzzle in the data! Many lines go in two opposite directions. Here in Trondheim, Line 3 goes from Loholt to Hallset, but also from Hallset to Loholt. The way I can tell these apart is to look up the direction in the data. Within a line, there can be variations in each direction. Some services might skip some stops, or depending on how you look at it, others visit extra stops. But these are variations on a theme, and it probably makes sense to group them together to preserve our sanity and not get 12 different plots for each line—2 should be plenty! ...

For this blog post, I’m trying something different. This is a jupyter notebook that I’m using to study some data, and just dumping my brain out in text. If I can easily export this to a format that works with hugo, this might become a common occurrence. For this one, I’m leaving the code in. There isn’t that much of it, but I think it’s fun to show how much visualization per line of code you can get with seaborn and pandas. ...