Brings in https://github.com/alphagov/notifications-utils/pull/889/files
At the moment, we are not doing any transformation of features before
applying geometric algorithms to them. This is, in effect, assuming that
the earth is flat.
This new version of utils implements the transformation of our polygons
to a Cartesian plane. In other words, it converts them from being
defined in spherical degrees to metres.
For the admin app this means we need to convert places where the code
expects things to be measured in degrees to work in metres instead.
The sentences in the key to the map have 2 forms:
1. the visual form, with an graphic pattern
alongside text saying what the pattern means
in the map
2. the semantic form, with text describing what
the pattern means and how big an area it covers
for this alert
This is achieved by making the graphic
non-semantic and having hidden words in the
sentence for each paragraph.
While the spaces between words are correct for
both forms of these sentences, we observed screen
readers speaking some groups of words as one when
they spoke the sentence. Most noticably, 'the
alert' was spoken as 'thealert'.
Swapping the class used to hide the visually
hidden words from ours to the GOVUK one seemed to
fix this. It's unclear why it does but the
`govuk-visually-hidden` class adds the following
styles to those in ours:
- width: 1px
- height: 1px
- white-space: nowrap
It also has `margin: 0` instead of `margin: -1px`.
At the moment, the icons in the key of the map
showing what each type of area drawn on the map
means change colour in high contrast mode. This
causes a mismatch between the areas on the map and
the key.
These changes remake the images in SVG to prevent
the colours changing in high contrast modes. They
also add a white background to the icons, to make
sure they match the areas they refer to in the
map.
I recently found that there is a problem with the
overuse of lists online for sequences of content
that don't need to be in a list. I think that's
what we're doing with our map key.
The problem is large enough that browsers include
heuristics to determine if lists should be
presented as such to the accessibility API (and so
to assistive tech' like screen readers). This
thread contains more details:
https://twitter.com/cookiecrook/status/1337226933822603270
Based on the metric described in the thread:
'If all of the styles that make it “list-like”
have been removed, it’s no longer relevant to
convey it as a list.'
...or
'if a sighted user doesn’t need to know it’s a
list, why would a screen reader user need to know
or want to know?'
Based on that, I think the items in our map key
should be paragraphs, not items in a list.
Also, they read really well as sentences when
announced by a screen reader, partly because of
the extra (hidden) info @quis added to them.
This also doesn't change their visual appearance.
We give estimates of the area for those who can’t see the map. These
estimates were needlessly precise, gave a false sense of accuracy and
were causing intermittent test failures between different environments.
This commit rounds them in the same way that we round the count of
phones.
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.
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.
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 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
Since the key relies on visual association between the shapes on the
maps and the styling of the key, it won’t work for non-visual users.
An alternative way of giving them the same information is by providing
the size of the area numerically.
