I emailed the Geography team at the ONS:
> Hi geography team,
>
> I work on GOV.UK Notify, which is a service run by Government Digital Service (part of the Cabinet Office). I was given your email address by [redacted] who’s been helping answer some of my questions on the cross-government Slack.
>
> We’re using some of the boundary datasets from the Open Geography Portal, and mostly they’ve been excellent.
>
> In the abstract, the problem we’re trying to solve is, given a point outside an area, what is the minimum distance to a point within that area. So, for example, if a crow was somewhere in Cardiff, what’s the shortest distance it would have to fly to reach somewhere in the Bristol local authority district?
>
> We’ve noticed some problems with the data that means our calculations would be wrong. We’ve noticed this around Torquay, Norwich and Bristol. Here are some screenshots of Bristol, from the generalised and full resolution boundaries:
>
> The artefacts I’ve highlighted are closer to Cardiff than any actual part of the land area of Bristol. They are either:
> - in the sea
> - land that’s part of North Somerset
>
> I suspect that this is being caused by the process of clipping the actual region of Bristol (which, unusually, extends into the water) to the mean high water line.
>
> I’ve worked around this by filtering out any polygons that are smaller than ~7,500m². It’s a bit hacky because parts of the Scilly Isles start disappearing. That’s not a problem for what I’m working on, but it would be nice to not need the hack.
>
> So my questions would be:
>
> - Is there a better way to remove these artefacts than filtering by area?
> - Is there a plan to remove these artefacts from the data in future releases?
>
> Thanks in advance,
> Chris
They emailed back to say:
> Hi Chris
>
> Thank you for your enquiry.
>
> We have completed the amendments to the LAD MAY 2020 BFC and BGC boundaries as mentioned so you should be able to download them from the portal now.
>
> Hope this helps.
>
> Kind regards
> [redacted]
This commit brings in the files they’ve updated. We still have to do
some filtering (but now at a higher resolution) because they haven’t
fixed Norwich yet. I’ll email them separately about that.
We need to give people a better feel for the consequences of
broadcasting an alert. We’ve seen in research that some users will
assume it is subscription based, or opt-in, rather than going to every
phone in the area.
I reckon that the most effective way to communicate this is to put some
numbers next to the areas, to give people an idea of how many people
will get alerted.
We can estimate how many phones are in an area by:
- taking the population of all electoral wards in that area
- multiplying it by the percentage of people who own an internet
connected phone[1]
The Office for National Statistics publish both these datasets.
The number of people who own an intenet connected phone varies a lot by
age. Since the population data for each ward is broken down by age we
can factor this in. Simplified, the calculation looks like this:
- take the _Abbey_ ward of _Barking and Dagenham_
- in this ward there are 26 people aged 80
- 40% of people over 65 have an internet-connected phone
- therefore 10 of these 80-year-olds would be likely to receive a
broadcast
- (repeat for all other ages)
These numbers won’t be exact, but should be enough to give people a feel
for the severity of what they’re about to do. We can see if they acheive
this aim in user research.
1. This is a proxy for the number of people who are likely to have a 4G
capable phone, because only 4G capable phones will be receiving
broadcasts to begin with
We filter out very small polygons from the original data to remove
glitches. These glitches are caused by trying to subtract the water from
a polygon that includes some land and some water, but using two
different definitions or resolutions of mean high water line.
If we don’t do this then we end up with a bunch of very small polygons
which lie far outside the understood area of a place, causing large
overspill.
We need to increase the threshold for this process because we’re still
seeing this problem around Bristol and Norwich.
This does mean we lose a few very small polygons in places like Shetland
and the Scilly Isles, but not in such a way that we would avoid
broadcasting to them (because they’d still be caught by the
simplification and overspill).
This commit does two things:
- uses our new polygon-simplifying library to process the polygons
before storing them, rather than processing them in real time
- stores only the polygons in the database, rather than the whole
GeoJSON feature, because we don’t need any of the other information
about the feature
It’s been superceded by the ‘Local’ library (formerly ‘Electoral wards
in the United Kingdom’).
The latter is better because:
- it’s covers all 4 nations, not just England and Wales
- it has electoral wards as well as local authorities which group them,
so there’s more flexibility when choosing an area to broadcast to
We’ve observed people using ‘national’ and ‘local’ during user research.
It has less tongue-twisting ambiguity than county vs country.
But we think that maybe just getting rid of ‘counties’ is enough to
disambiguate them. So this commit just takes the ‘local’ concept.
This commit also gives the libraries and areas new IDs, which means if
we want to rename them in the future it won’t be a breaking change.
It made for a good early demo to show how we could have different
libraries, but we’d don’t think there’s a strong user need for being
able to broadcast to a region of England.
Regions also have the problem that:
- they are ambiguous – both England and Scotland have a region called
‘South east’
- Northern Ireland doesn’t have formal regions
This commit removes the regions library.
I think that even with good indexes, querying the area names from one
table is always going to be slow because there’s so much GeoJSON to scan
past.
This commit splits the data into two tables, one for the names and
grouping IDs and one for the blobs of GeoJSON. So for most pages the app
will never even be looking at the table where the GeoJSON is held.
I don’t know if this is a proper, normalised way of structuring the
data, but it does go brrr.
