Analyse bicycle network using network measures about paris-bikes HOT 7 CLOSED

@operte Not yet, sorry. I aim to have it up by end of October, and will ping here when it's done.

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@Liyubov I'll try not to do this too much, but ... network centrality measured using osmnx and networkx will be generic, and not particularly accurate. An accurate measure of centrality of the actual bicycle network based on accurate bicycle-specific routing algorithms can be obtained with the dodgr_centrality() function.

• Disclaimer#1: That's self-promotion of my own software 😉
• Disclaimer#2: It's R, and not python.
• Non-disclaimer: the algorithm is more efficient than the networkx version, and will easily scale to every street segment in Paris.

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I just put here the data from the article https://github.com/CorrelAid/paris-bikes/blob/main/data/odmatrix_p_distances_final.zip

article link is here https://www.sciencedirect.com/science/article/pii/S2590198222001099?via%3Dihub#appSB

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There are hourly count data at https://opendata.paris.fr/explore/dataset/comptage-velo-donnees-compteurs/ which can be used to calibrate cycling estimates throughout the network. The dataset is huge (just over 1GB), and contains counts from 98 stations, each one having an average of < 10,000 hourly counts. The data seem to be updated daily, with the version downloaded 17 Aug 22 finishing yesterday, and starting on 25-01-2021. That spans 537 days, making 12,888 hours, so not quite full hourly coverage from all stations, but close enough.

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I've finally managed to get an interactive map of cycling flows up at https://urbananalyst.github.io/paris-bikes/. It's just a proof-of-principle on an arbitrary scale from 0 to 5, but now that I've got that, I can easily start to include more realistic cyclilng behaviour and update as i go.

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Hey @mpadge this looks interesting. Can you tell us more about what we are seeing? You mentioned you are writing a blogpost about it. Is it ready yet? :)

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@mpadge Any news on that blog post? :)

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