Project description

An investigation into UK\’s settlement scheme: Plans by the UK Home Office to vet every single eligible EU national in just 27 months, appear flawed. With insufficient data, not enough staff at hand and a debatable operational track record, many – foremost vulnerable groups, often overlooked by the media – could fall through the cracks. Without a contingency plan by the Home Office, those will be much worse off in a post-Brexit Britain.

What makes this project innovative?

Strong investigative journalism married with scrollytelling, a newsgame and long-form news-writing.

What was the impact of your project? How did you measure it?

The project received widespread approval from national campaigning groups (and EU nationals), most notably from the the3million comparing that included the results of the analysis in their appeal to the government (resulting in new demands on how to avoid EU groups at risk to fall through the cracks, especially on a local level).

One data journalism student was so impressed by it, he chose the "Neglected last %" as his reviewal project for his master in data journalism course and received full marks on his review (he interviewed me on the techniques used for the project and how the analysis was conducted).

Feedback on the project from senior journalists:
Robert Schoeffel, Investigative editor at @BR_data: "Your project, looks great"
Michael Ovaska Graphics News Editor: "Really love seeing this Brexit piece! This is really impressive – great coding and great reporting"
Simon Scarr, Deputy Head of @reutersgraphics: "Love the colour palette of the scatter plots too."
(feel free to check with them, if it helps)

How did you measure it:
Data from ONS was crunched and analyzed on the potential correlation between wealth and living standards vs the 2016 Brexit vote, and then specifically compared with where EU migrants from various groups settled down. A large part of the analysis was conducted in R (drafts in ggplot2), some parts in python.
The geographic analysis was mainly performed in QGIS where I produced various versions of binned-dots polygon maps that illustrate the 'exact' (estimated) number of EU nationals in the form of dots for each local authority.
The visual recognition of the difference between certain EU national groups was then arranged in a manner that the reader was able to perceive divergence by overlaying the different maps programmatically.
Other analysis in the piece resulted from the statistical interpretation of material mentioned by the UK Home Secretary. The calculation on the number of people that could fall into a trap of ending up without an immigration status in the next years was performed with a measure of care (see the section on how trustworthy the data is according to confidence intervals) and thoroughly checked again by several experts (including Madeleine Sumption, Director of Migration observatory) on the estimates validity. If you have other questions, do please reach out.

This goes without saying, but the piece was independently produced.

Source and methodology

ONS, Migration Observatory, the3million campaign

Technologies Used

D3.js/Javascript/CSS, R, Python, QGIS, statistics

Project members

Ben Heubl

Link

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