[Turing Data Story] Air Quality and Schools in London

[billfinnegan]

Story description

Please provide a high level description of the Turing Data Story Some of the main drivers of climate change result in poor air quality in urban areas. While climate change is abstract and global, air quality is local and personal:

• a few years back, poor air quality was reported outside thousands of schools in the UK
• a recent legal ruling determined that air pollution was the cause of death for an 8-year-old London girl
• the Mayor of London has plans to reduce air pollution at schools

This story could possibly build on Turing's work on London air quality while also pulling in information about schools (state v independent, % free school meals, pupil distance from school) and environmental initiatives (street trees, school streets scheme). The story could reveal which communities are most impacted (similar to the COVID story) while also exploring the risk of walking to school by different routes, and possibly looking over time at locations where school street schemes have been implemented.

Which datasets will you be using in this Turing Data Story? In addition to whatever might be available from the Turing London air quality project:

• Imperial London Air portal: https://www.londonair.org.uk/LondonAir/Default.aspx
• New initiative to install air quality sensors at hospitals and schools: https://www.breathelondon.org/
• London Schools Atlas (shows info on schools, including a heatmap of where the pupils live): https://maps.london.gov.uk/schools/
• Annual school statistics (includes free school meals): https://explore-education-statistics.service.gov.uk/find-statistics/school-pupils-and-their-characteristics
• GLA database of London's trees: https://data.london.gov.uk/dataset/local-authority-maintained-trees
• TFL list of school streets schemes: https://consultations.tfl.gov.uk/general/f23c0f13/

Additional context As Turing is elevating climate this year (in advance of the UN meeting in Glasgow in November), something connected to sustainability could be a good story. Also, Clean Air Day is 17 June, which could be a way to reach a wider audience.

I'm afraid I'm just an idea person on this, as I'm not able to run with the data analysis, but I'd be happy to help however I can.

Ethical guideline

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

• [ ] 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

• [ ] 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

[billfinnegan]

Update (after REG tech talk): London Air Quality project is ongoing, but should be public soon, and this story could build on that release. That project involved something similar to the question about routes to school in terms of low pollution jogging routes - see https://arxiv.org/abs/1906.07754 and https://www.wired.co.uk/article/walking-directions-air-pollution-google-maps.

[ChristinaLast]

• [ ] Christina to rank schools based on concentration of pollutants
  • within peak hrs? (e.g. 8-9 and 5-6?)
  • 24hr average?
• [ ] Christina to correlate deprivation and exposure

[ChristinaLast]

• [ ] @ChristinaLast to present the story ideas so far on Wednesday's (23rd Feb) meeting
• [ ] @ChristinaLast and @billfinnegan to meet on 4th March to hack above school location tasks.