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一分鐘完成驗證Analysis and visualization of Citi Bike data
翻譯準備中
取自儲存庫 README
正在載入完整 README。 以下為已儲存的摘錄。
• ctbk/ contains a Python library and CLI (ctbk) that derives various datasets from Citi Bike's public data at [s3://tripdata]
• [s3://ctbk] contains cleaned, public data output by [ctbk]
• www/ contains the static web app served at [ctbk.dev].
• GitHub Actions in .github/workflows:
• poll for new Citi Bike data at the start of each month
• compute new derived data when found, and
• build and publish the [ctbk.dev] website
Screenshots
[ctbk.dev]:
Station/Ridership Map
[ctbk.dev/stations]:
JC & Hoboken Only
ctbk.dev/?r=jh:檢視完整 README ↗ctbk) that derives various datasets from Citi Bike's public data at s3://tripdatactbkctbk.dev?d=1406-2102&g=mf&pct&s=g&y=m:
(Gender labels stopped appearing in the data in February 2021)
ctbk.dev?y=m&s=b&rt=ce&d=2002-:
This is a work-in-progress; red = newer, yellow = older:
ctbk.dev/?y=m&s=u&rt=e&d=2002-:
You can get some interesting upper bounds on e-bike fee revenue from this:
That doesn't count various reasons that e-bike minutes end up being free (positive bike angel points for the ride, no classic bikes available at station, etc.). With the discussion around recent price increases, you can do some envelope math like:
This isn't live anywhere yet, but:
The peak of the distribution (rounded down to the nearest 10s) is 4m20s. See notebook here.
Many great analyses of Citi Bike data have been done over the years!
My hope is that this dashboard will:
Feel free to file an issue here with any comments, bug reports, or feedback!