- The very clear statement of what SIMD measures and how it works, and can be used, on page two
- The case studies that demonstrate that living in a neighbourhood ranked as deprived by SIMD isn’t awful, and that it can help target resources for great things to happen
- The very clear “do not use SIMD for” list on page six
- The fact that nowhere does it say which is the “most” or “least” deprived neighbourhood.
The team that did SIMD were nominated for a UK Civil Service Award, which they won!
I was incredibly pleased on their behalf, because they really deserve it.Here’s @PermSecScot congratulating member of Scottish Index of Multiple Deprivations team-winners of #csawards Use of Evidence award. Her pride is plain to see! pic.twitter.com/1uYo8ULhiB— Suzannah Brecknell (@SuzannahCSW) November 23, 2017
And, they’ve only gone and done it again – not won an award, but produced a report that is staggeringly good.
It’s a report on a set of experimental statistics that have used new questions on material deprivation asked in the Scottish Household Survey to create a local poverty measure. Have a look at it here. It is soooooo good.
Again, they’ve combined qualitative data with the presentation of the statistics to make the reality of lived experience come to life, but in a non-stigmatising way, for example on page six “Mary” describes how:
“My kettle blew up, so I went and got a kettle off my catalogue. Cause I wouldn’t have been able to afford to just go and buy a kettle. And I didn’t want to say to anybody, ‘I can’t afford a kettle.’ Ken, people are coming in for a cup of tea and that, and ‘oh my kettle’s blew up, and I can’t afford another one’”
An absolutely brilliant way to describe what material poverty means.
And it gets better. There are bar charts throughout which show a percentage with confidence intervals. Rather than getting bogged down with complex descriptions of confidence intervals and statistical significance, the charts are simply labelled “The bars show measurement uncertainty. Where two bars overlap, there may not be a real difference between the two groups”. I mean!!!!!!
AND AND AND it gets even better. Not only do they have CI’s clearly labelled, they then go onto interpret them for you. Each chart has a very clear description of “What the data says” and “What the data doesn’t say” to ensure that people don’t misunderstand the charts.
And the data is really interesting. I just wish all official statistics documents were published this well, with this much thought put into their presentation.
With thanks to the Taylor Swift Open Data Institute.