I am visiting family every month or so going from Berlin to Southern Germany, and, after I had been using hire cars, or the occasional flight, by now I changed to trains. Far more relaxing, no security checks, and the ability to strech my legs, get up, while travelling at 200 km/h. Plus, much better on my CO2 fottprint, and, actually cheaper than the fuel it would take (if you get yourself a BahnCard and go for a fixed train ticket).
During pandemic times, while I am used to and fine with wearing a face mask, I am interested in the pandemic situation across the country. What I would need to do, is flip between the train schedule and the incidence rates plots, and determine which state a particular train stop is in, so I also need a map or wikipedia.
It feels a bit silly to manually combine data that one reads off a computer screen to perform some data fusion in one’s head, so I came up with a few concepts on how to do this. It is only a jupyter notebook at the moment, I continually think its too late to launch a google maps overlay or even an app development.
The notebook connects to the Deutsche Bahn API, queries train connections (at the moment, I refrain from connecting trains as I am lucky to have a direct connection at my avail), then takes the RKI COVID dataset, and the geolocation of the trainstops to look up the state the geo coordinate of the train stop is in, to display incidence rates at my train stops.
The first visualisation shows bubbles in red or green indicating growing/reducing numbers, and the incidence rates in the region (Landkreis or kreisfreie Stadt).
The second flavour plot the indicence rates also as d-y plots. I have also added my home region rate as a reference line to the plot, this reflects my “I can feel safer/may want to be cautious” interest. The drawback, though, is that the top infoviz could easily be shown vertically next to the train stop itinerary as it consumes a lot less screen space.