[Turing Data Story] Park run analysis

[helendduncan]

Story description

Please provide a high level description of the Turing Data Story Shamelessly stolen this idea from @nbarlowATI originally posted here who has agreed for me to pitch it as a TDS The idea is to build a web scraper to get London-based (or whole UK) park run data and perform analysis on the data - course times, perhaps link with weather, demographics etc

Which datasets will you be using in this Turing Data Story? Park run publishes the results onto their websites. Nick had made a brief foray into this and when he tried to download the data the results webpage had a message asking to please not.

Additional context Nick did think about getting into contact with park run at the beginning of the pandemic and pitch the idea that they might want to allow a national institute for data science to do this. Anonymised data is already shared with the Advanced Wellbeing Research Centre (AWRC), a UK Government DHSC funded organisation according to their website so they may have a prior claim on all data/the analysis may already be done.

Ethical guideline

Ideally a Turing Data Story has these properties and follows the 5 safes framework.

There may be some ethical issues with accessing the data without the explicit permission of park run (see comment above) The data that is freely available on the website appears to contain some potentially sensitive information, including age and gender. However park run appear to have anonymised the data for the AWRC so we could (if they agree) ask for access to the anonymised data instead.

If the park run option is not viable - perhaps an option would be to use marathon data instead?

• [ ] The analysis you produce is openly available and reproducible.
• [ ] Any data used are open and have an explicit licence, provenance and attribution.
• [ ] Any data used are not personal data (i.e. the data is anonymous or anonymised).
• [ ] Any linkage of datasets in your data story does not lead to an increased risk of the personal identification of individuals.
• [ ] The Story must be truthful and clear about any limitations of analysis (and potential biases in data).
• [ ] The Story will not lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice.

Current status

• [x] Scoping of the story
• [ ] Write story outline
• [ ] Add material to the story notebook
• [ ] Combine materials into a readable story
• [ ] Proofread
• [ ] Request reviews
• [ ] Address reviews
• [ ] Merge to master branch.

Updates

Potential issues:

Plan B if this wouldn't work out - Marathon courses? There might be a similar limitation but I haven't looked into it yet

[kevinxufs]

Hi @helendduncan @nbarlowATI I think I'd be quite interested in this one, and seeing how it goes. I went on my first park run recently and am now (apparently) motivated! Do you know if there was any progress on this?

[helendduncan]

Congratulations on your first park run @kevinxufs I haven't made any progress yet, so I think my first step will be to draft an email to park run asking how they would feel about us using the data and get feedback from the TDS team before sending, how does that sound?

[kevinxufs]

thanks @helendduncan that sounds good - let me know if I can help at all, otherwise looking forward to hearing what they say.

[joseph-palmer]

Hi @helendduncan, is this still in the works? If so I'd be interested in contributing.

[helendduncan]

Hey - I haven't worked on this since last time I mentioned I hadn't worked on this but I am happy tor re-ignite if there is interest.