stress_scores_regions.xlsx

	A	B
1	Campaign
2	Client	Electric Tabacconist
3	Pod
4	Data Analyst	Andrei
5	Research Question	[AUGMENTED] Index: Regions where you're most likely to be stressed
6
7	Data Review
8	Reviewer 1	Daniel Smiech
9	Reviewer 2
10	Date	08/04/2025
11	Research Method
12	Electric Tabacconist conducted this research to understand which parts of the UK are under the most pressure or “stress” when it comes to factors like work, money, education, health, environment, and overall quality of life. We wanted to take a broad view that combines social and economic issues with environmental concerns, to highlight which regions might be struggling the most — and in what ways. We started by collecting data from over 35 official datasets provided mostly by the Office for National Statistics (ONS). These included tables about: Employment and economy (e.g. economic inactivity, unemployment, employment rates, income, and claimant counts), Business activity (e.g. active businesses, start-ups, and closures), Education (such as qualifications and school performance), Health and well-being (including obesity, anxiety, smoking, and happiness), and New housing and apprenticeships, broken down by UK region. This data spans multiple years, and wherever possible, we focused on the most recent five years available, to reduce short-term fluctuations and give a better sense of longer-term patterns. These values were then grouped by region. Because each dataset uses a different scale — for example, one might be in percentages, another in pounds, and another in counts — we converted all real-world values into a 0 to 100 percentile “stress score.” A score of 100 means the worst value relative to other regions, and 0 means the best. For instance, if the North East had the highest unemployment rate among all regions, it would receive a stress score of 100 for that metric. It's important to clarify that a score of 0 does not mean the actual value was zero. It simply means that, for that specific measure, the region had the lowest value compared to the rest of the UK. Similarly, a score of 100 doesn’t mean something is dangerously high — it just means it's the highest in the dataset. This allows us to fairly compare very different kinds of data across regions. To include environmental factors, we also analysed greenspace availability using OpenStreetMap (OSM) data. We filtered the full UK .pbf file using relevant tags like: ``` a/landuse=grass a/landuse=meadow a/landuse=forest a/leisure=park a/leisure=garden a/leisure=common a/natural=wood a/boundary=national_park ``` These tags helped us extract all mapped natural and recreational green areas. We then spatially joined these greenspace features with a UK regional shapefile to count how many were found in each region. The region with the fewest greenspaces received the highest stress score, and the region with the most received the lowest. Next, we turned to air pollution, using live station readings from the UK-AIR platform operated by DEFRA. Historic data for 2023 and earlier was limited or outdated, so we used the latest readings available as of April 2025, across all available monitoring stations. These were averaged per region for: Ozone (O₃) – harmful to lungs, especially for those with asthma. Nitrogen Dioxide (NO₂) – mostly from traffic; linked to respiratory problems. Sulphur Dioxide (SO₂) – less common today but still dangerous in short bursts. PM2.5 – fine particles that penetrate deep into the lungs and bloodstream. PM10 – larger particles that cause coughing and other respiratory issues. A single “Pollution Score” was calculated per region by averaging all five pollutant types and then applying the same percentile method (higher = worse air quality). Lastly, we created a final “Overall Stress Score” for each region. This was calculated by combining all the individual stress scores across every category — work, income, education, housing, health, greenspace, and air pollution — to give each region a single index value. This allowed us to clearly identify the most and least stressed regions in the UK, based on a wide and diverse range of real-world data.
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14	Sources
15	https://explore-local-statistics.beta.ons.gov.uk/indicators/economic-inactivity-rate#get-the-data
16	https://www.ons.gov.uk/employmentandlabourmarket/peoplenotinwork/unemployment/datasets/claimantcountbyunitaryandlocalauthorityexperimental
17	https://www.geofabrik.de/
18	https://uk-air.defra.gov.uk/
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