Adding ‘all networks’ whenever we mention the using the test channel
without a restriction to a single network should help reinforce that
this sends real alerts.
We have been asked to support the government channel so that:
- it can be tested
- the option to use it is available for the most severe of emergencies,
where the public’s choice to opt-out is outweighed by the widespread
risk to life
The current_service.allowed_broadcast_provider is now always "all" or
one of the four providers, which means we can simply the code by not
checking if it is None.
Until all the data is updated to always be "all", we have to handle the
case of provider_restriction being set to None or "all" (which mean the
same thing).
The code can be tidied up once the broadcast provider_restriction is never None.
Do not allow platform admins to:
- create broadcasts
- approve broadcasts
- reject broadcasts
that is, unless they have a send_messages permission
for a given service.
This is so platform admins have the minimum permissions necessary
to cancel a broadcast that might have been sent out accidentally.
We want to know how many phones are in a user-supplied polygon, so we
can show the impact of a broadcast, in the same way that we do when
users pick areas from our library.
We already know how many phones are in each electoral ward. But there
are challenges with an arbitrary polygon:
- where it does overlap a ward, the overlap could be partial
- it could overlap more than one ward
- finding out which wards it overlaps by brute force (looping through
all the wards and seeing which ones intersect with our polygon) would
be way to slow to do in real time
Instead we can use a data structure called an R-tree[1] to build an
index which provides a much, much faster way of looking up which
polygons overlap another. We can build this tree in advance and save it
somewhere, which means there’s a lot of computation we don’t need to do
in real time.
The R-tree returns a set of objects (ward IDs) which we can go and look
up in our library of electoral wards. These wards will be the ones that
might have some overlap with our custom polygon.
Once we have this small set of wards which might overlap our ward, we
can look at the size of the area of overlap (relative to the size of the
whole ward) and multiply that by the known count of phones in that ward
to get an approximation of the count of phones in the overlap area.
Summing these approximations give an estimate for the whole area of the
custom polygon.
1. https://en.wikipedia.org/wiki/R-tree
Now we’ve split the old alerts onto two pages the verbs (‘Broadcast’ and
‘Rejected’) will always be the same for each alert – so they’re not
adding any differentiation.
The specifics of what the datetime means is available on the page for
each alert.
Removing the verbs makes the page a bit less cluttered and makes it
easier to scan down the right hand column.
The code for this page was making assumptions about properties which
aren’t present on rejected broadcasts.
This commit accounts for those properties and presents the relevant
elements on the page.
Two reasons to not hide rejected broadcasts:
- if a broadcast was rejected by mistake then it’s useful to have an
audit of who did that
- it means you can still see old broadcasts without having to leave
in pending-approval, which is dangerous because they might
accidentally be approved
This prevents us from giving unrealistically large or small bleed
estimates in case we have areas which are more dense or less dense than
the most/least dense areas we currently have.
Also means we don’t have to treat City of London as a special case.
There are basically two kinds of 4G masts:
Frequency | Range | Bandwidth
----------|-------------|----------------------------------
800MHz | Long (500m) | Low (can handle a bit of traffic)
1800Mhz | Short (5km) | High (can handle lots of traffic)
The 1800Mhz masts are better in terms of how much traffic they can
handle and how fast a connection they provide. But because they have
quite short range, it’s only economical to install them in very built up
areas†.
In more rural areas the 800MHz masts are better because they cover a
wider area, and have enough bandwidth for the lower population density.
The net effect of this is that cell broadcasts in rural areas are likely
to bleed further, because the masts they are being broadcast from are
less precise.
We can use population density as a proxy for how likely it is to be
covered by 1800Mhz masts, and therefore how much bleed we should expect.
So this commit varies the amount of bleed shown based on the population
density.
I came up with the formula based on 3 fixed points:
- The most remote areas (for example the Scottish Highlands) should have
the highest average bleed, estimated at 5km
- An town, like Crewe, should have about the same bleed as we were
estimating before (1.5km) – Pete D thinks this is about right based on
his knowledge of the area around his office in Crewe
- The most built up areas, like London boroughs, could have as little as
500m of bleed
Based on these three figures I came up with the following formula, which
roughly gives the right bleed distance (`b`) for each of their population
densities (`d`):
```
b = 5900 - (log10(d) × 1_250)
```
Plotted on a curve it looks like this:
This is based on averages – remember that the UI shows where is _likely_
to receive the alert, based on bleed, not where it’s _possible_ to
receive the alert.
Here’s what it looks like on the map:
---
†There are some additional subtleties which make this not strictly true:
- The 800Mhz masts are also used in built up areas to fill in the gaps
between the areas covered by the 1800Mhz masts
- Switching between masts is inefficient, so if you’re moving fast
through a built up area (for example on a train) your phone will only
use the 800MHz masts so that you have to handoff from one mast to
another less often
Broadcasts created by the API are different in that:
- they aren’t created by any user, so don’t have a `created_by_id`
- they are created instantly, not in steps, so don’t have an
`updated_at` time
This commit alters the views to account for when these pieces of
information aren’t present.
At the moment the admin app expects all broadcasts to have a template,
and expects the content of the alert to come from the template.
This commit makes it so those pages can still get a `Template` instance,
but populated with content straight from the `content` field in the
database.
We think that in some cases alerts will be composed in the moment, and
therefore making people first create a template is:
- not a good use of their time
- adding some conceptual complexity which they don’t need
This commit makes it possible to type some words and have them go
straight into the `content` field in the database.
In the future we might want to progressively enhance the radio buttons
so they show on the same page (like we do with the grey buttons on the
templates page).
When the list of areas is restricted to half the width of the page it
starts to look pretty higgledy-piggledy when you have lots of areas or
areas with very long names.
To do this I’ve ripped out the table markup in favour of headings,
paragraphs and lists. Probably pros and cons for each, but it was really
hard to do the layout with the content in a table.
For emails and text messages we sort by the time the user (or API) sent
them.
This makes sense for broadcasts too, since most users will receive the
alert within seconds of it being broadcast.
For alerts that haven’t started yet we can sort by `updated_at`, which
is when the user preparing the broadcast submitted it for approval.
Now that pending alerts aren’t in their own section there’s nothing to
label them as pending. So this commit replaces the extra metadata we
show for a pending alert (the name of the person who created it, which
was only ever a reckon) with an explicit label that says it’s waiting
for approval.
Splitting the dashboard into multiple sections was confusing, and people
sometimes mistook the headings as labels, especially when a section was
empty. It just wasn’t clear what the hierarchy of the page was.
This commit combines the current and pending broadcasts into one list
on the dashboard. Previous broadcasts have already moved to their own
page.
If you refresh the page on a current broadcast while someone has
cancelled it you’ll see the wrong navigation item selected. This commit
adds redirects to take you to the correct endpoint in these edge cases.
Once a broadcast has been submitted for approval it either lives on the
‘Current alerts’ or ‘Previous alerts’ page, depending on where it is
in its lifecycle.
Therefore when clicking into a broadcast from one of those pages the
same navigation item should remain selected.
Because we select the navigation items based on the request endpoint,
this means we need an endpoint for each navigation page, even if the
content of the pages will be the same in both cases.
This commit adds the two new end points, removes the old, single
endpoint and updates links to point to the new endpoint.
The most important part of the broadcast is what content was sent where
(and when).
This commit reduces the priority of the ‘meta’ information, like who
prepared and approved the broadcast. I also think that the ‘end’ time is
a lot less important than the start time, since most people will receive
the alert at or near to the start time.
Our style for areas is pale blue background with black keylines or bold
black text.
This commit makes the display of area names on the dashboard consistent
with that visual style.
This also means that we’re not truncating the list of areas, which is
appropriate because no one area is more important than any of the
others.
The dashboard for normal services is quite general, because it tells
you a bit about channels, templates and spend.
What is now the dashboard for broadcast services is much more specific,
therefore less like a dashboard. We can reflect this by giving it a more
specific name. This should reduce the amount of navigation surfing
people need to do in order to find the thing they’re looking for.
Previous alerts are much less important than ones that are live or
waiting for approval.
Therefore we can make the dashboard more focused by moving previous
alerts to their own page.
This prefixes everything to do with areas/the map with `area-list`, so
from looking at one element you know which `.scss` file will contain the
relevant code.
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’ve had some feedback from user research that difference between
‘will get alert’ and ‘likely to get alert’ is not clear, and it’s hard
to tell if the latter is inclusive of the former. This leads people to
question the validity of these numbers, which is important, because an
the estimate should give you some idea of the impact of what you’re
about to do.
This commit reformats the number as a range, for example 1,000 to 2,000
phones.
If the range is small, eg 40,000,000 to 40,800,000 then this suggests
a false level of accuracy. So instead we just give one number and say
it’s an estimate, eg ‘40,000,000 phones estimated’
We want it to be very clear whether you’re in live or training mode
because:
- you may be switching back and forth between them
- doing something in live mode when you think you’re in training mode
would have… consequences
By adding a label next to the service name you’ll will have some
indication, on every page, which mode you are in.
Style of the label is based on the ‘Tag’ component from the Design
System:
https://design-system.service.gov.uk/components/tag/#showing-multiple-statuses
