[Review] Boris Bikes

[crangelsmith]

Story Review: #172

Story Name: Boris Bikes

Submitting Author: @mhauru @fedenanni @crangelsmith @lannelin

Pull Request: #221

Reviewers: @rchan26 @llewelld

Reviewer instructions & questions

Dear @rchan26 @llewelld, please carry out your review in this issue by updating the checklist below, and writing new comments in case you have any questions. If you cannot edit the checklist please:

• Make sure you're logged in to your GitHub account
• Be sure to accept the invite at this URL: https://github.com/alan-turing-institute/TuringDataStories/settings/access

Any questions, concerns or suggestions regarding the review process please let @crangelsmith, @DavidBeavan or @samvanstroud know.

✨ Please start on your review when you are able, and be sure to complete your review in the next six weeks, at the very latest ✨

Review Checklist

Code of conduct

• [ ] I confirm that I read and will adhere to the Turing Data Stories code of conduct.

General checks

• [ ] Notebook: Is the source code for this data story available as a notebook in the linked pull request?
• [ ] Contribution and authorship: Are the authors clearly listed? Does the author list seem appropriate and complete?
• [ ] Scope and eligibility: Does the submission contain an original and complete analysis of open data? Is the story aligned with the Turing Data Stories vision statement?

Reproducibility

• [ ] Does the notebook run in a local environment?
• [ ] Does the notebook build and run in binder?
• [ ] Are all data sources openly accessible and properly cited with a link?
• [ ] Are the data open, and do they have an explicit licence, provenance and attribution?

Pedagogy

• [ ] Does the story demonstrate some specific data analysis or visualisation techniques?
• [ ] Are these techniques well motivated?
• [ ] Are these techniques well implemented?
• [ ] Is the notebook well documented, using both markdown cells and comments in code cells?
• [ ] Does the notebook has a introduction section motivating the story?
• [ ] Does the notebook has a conclusion section discussing the main insight from the stories?
• [ ] Is the paper well written (it does not require editing for structure, language, or writing quality)?

Context

• [ ] Does the story give an insight into some societal issue?
• [ ] Is the context around this issue well referenced (newspaper articles, scientific papers, etc.)?

Ethical

• [ ] Is any linkage of datasets in the story unlikely to lead to an increased risk of the personal identification of individuals?
• [ ] Is the Story truthful and clear about any limitations of the analysis (and potential biases in data)?
• [ ] Is the Story unlikely to lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice?

AOB

You can find the story rendered in here.

[llewelld]

Review Checklist for @llewelld

(To help me keep track of my progress).

Code of conduct

• [x] I confirm that I read and will adhere to the Turing Data Stories code of conduct.

General checks

• [x] Notebook: Is the source code for this data story available as a notebook in the linked pull request?
• [x] Contribution and authorship: Are the authors clearly listed? Does the author list seem appropriate and complete?
• [x] Scope and eligibility: Does the submission contain an original and complete analysis of open data? Is the story aligned with the Turing Data Stories vision statement?

Reproducibility

• [x] Does the notebook run in a local environment?
• [ ] Does the notebook build and run in binder? I didn't test this due to the file size limit flagged elsewhere.
• [x] Are all data sources openly accessible and properly cited with a link?
• [x] Are the data open, and do they have an explicit licence, provenance and attribution?

Pedagogy

• [x] Does the story demonstrate some specific data analysis or visualisation techniques?
• [x] Are these techniques well motivated?
• [x] Are these techniques well implemented?
• [x] Is the notebook well documented, using both markdown cells and comments in code cells?
• [x] Does the notebook has a introduction section motivating the story?
• [x] Does the notebook has a conclusion section discussing the main insight from the stories?
• [x] Is the paper well written (it does not require editing for structure, language, or writing quality)?

Context

• [x] Does the story give an insight into some societal issue?
• [x] Is the context around this issue well referenced (newspaper articles, scientific papers, etc.)?

Ethical

• [x] Is any linkage of datasets in the story unlikely to lead to an increased risk of the personal identification of individuals?
• [x] Is the Story truthful and clear about any limitations of the analysis (and potential biases in data)?
• [x] Is the Story unlikely to lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice?

[llewelld]

Be sure to accept the invite at this URL: https://github.com/alan-turing-institute/TuringDataStories/settings/access

This link takes me to a "404 not found" error. Am I missing anything important?

[crangelsmith]

Be sure to accept the invite at this URL: https://github.com/alan-turing-institute/TuringDataStories/settings/access

This link takes me to a "404 not found" error. Am I missing anything important?

I think the 404 error happens when you already are part of the collaborators of the project (which you are), so you don't need to accept access anymore :)

[llewelld]

Perfect, thanks for clarifying @crangelsmith!

[rchan26]

Review Checklist for @rchan26

(To help me keep track of my progress).

Code of conduct

• [x] I confirm that I read and will adhere to the Turing Data Stories code of conduct.

General checks

• [x] Notebook: Is the source code for this data story available as a notebook in the linked pull request?
• [x] Contribution and authorship: Are the authors clearly listed? Does the author list seem appropriate and complete?
• [x] Scope and eligibility: Does the submission contain an original and complete analysis of open data? Is the story aligned with the Turing Data Stories vision statement?

Reproducibility

• [x] Does the notebook run in a local environment?
• [ ] Does the notebook build and run in binder?
• [x] Are all data sources openly accessible and properly cited with a link?
• [x] Are the data open, and do they have an explicit licence, provenance and attribution?

Pedagogy

• [x] Does the story demonstrate some specific data analysis or visualisation techniques?
• [x] Are these techniques well motivated?
• [x] Are these techniques well implemented?
• [x] Is the notebook well documented, using both markdown cells and comments in code cells?
• [x] Does the notebook has a introduction section motivating the story?
• [x] Does the notebook has a conclusion section discussing the main insight from the stories?
• [x] Is the paper well written (it does not require editing for structure, language, or writing quality)?

Context

• [x] Does the story give an insight into some societal issue?
• [x] Is the context around this issue well referenced (newspaper articles, scientific papers, etc.)?

Ethical

• [x] Is any linkage of datasets in the story unlikely to lead to an increased risk of the personal identification of individuals?
• [x] Is the Story truthful and clear about any limitations of the analysis (and potential biases in data)?
• [x] Is the Story unlikely to lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice?

[llewelld]

I tried running on Binder but got the following error:

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
/tmp/ipykernel_60/1748382341.py in <module>
      1 import datetime
      2 
----> 3 import h3 # uber geo package
      4 import pandas as pd
      5 import seaborn as sns

ModuleNotFoundError: No module named 'h3'