How commuting is too much? It depends on where you live. https://t.co/Zjsf3vlqwf pic.twitter.com/eIjAhzzxsS
— Nathan Yau (@flowingdata) October 16, 2019
How commuting is too much? It depends on where you live. https://t.co/Zjsf3vlqwf pic.twitter.com/eIjAhzzxsS
— Nathan Yau (@flowingdata) October 16, 2019
I think this is an interesting topic but found this visualization hard to follow (no surprise if you've been reading my complaints about animated plots).
— Sean J. Taylor (@seanjtaylor) October 19, 2019
I have nothing to do tonight so i'm going to try to re-visualize this data. Starting a THREAD I'll keep updated as I go. https://t.co/aHl4S3L9iP
It's sadly not a CSV file, it's a "dat.gz" file and you need some XML schema file to read it in. In R you need the `ipumsr` package to actually load it (ughh). But about 10 mins later, I have a data frame! Game on. pic.twitter.com/Ip15Nxf6xV
— Sean J. Taylor (@seanjtaylor) October 19, 2019
Ok this is the one I'm going to end on.
— Sean J. Taylor (@seanjtaylor) October 19, 2019
- Cities are clearly ranked and listed in order.
- Divided it up into the important quantiles: 10th (shortest commutes), 25-75 (normal-ish range), and 90th (longest)
- Mean time is still visible (the dot), always higher than the median. pic.twitter.com/is7EZK9Ktp
I mapped when and where people start their commutes to go work in major cities. Here are the maps for the 3 busiest: Los Angeles, Chicago, and New York. https://t.co/JcDmCEJT47 pic.twitter.com/6WDgA3rTNK
— Nathan Yau (@flowingdata) October 22, 2019