tests/app/broadcast_areas/custom_polygons.py

BRISTOL = [
    [51.4371, -2.6216],
    [51.4371, -2.5750],
    [51.4668, -2.5750],
    [51.4668, -2.6216],
    [51.4371, -2.6216],
]

SKYE = [
    [57.1004, -6.8280],
    [57.1004, -5.7733],
    [57.7334, -5.7733],
    [57.7334, -6.8280],
    [57.1004, -6.8280],
]

SANTA_A = [
    [25.8890, 66.5500],
    [25.8890, 66.551],
    [25.8910, 66.551],
    [25.8910, 66.5500],
    [25.889, 66.55000],
]

BURFORD = [
    [51.8925, -1.7495],
    [51.7372, -1.7825],
    [51.7159, -1.5257],
    [51.8866, -1.5037],
    [51.8925, -1.7495],
]

CHELTENHAM = [
    [51.9324, -2.1265],
    [51.8633, -2.1306],
    [51.8637, -2.0242],
    [51.9328, -2.0221],
    [51.9324, -2.1265],
]

CHELTENHAM_AND_GLOUCESTER = [
    [51.8820, -2.2920],
    [51.8234, -2.2570],
    [51.8883, -2.0262],
    [51.9425, -2.0771],
    [51.8820, -2.2920],
]

SEVERN_ESTUARY = [
    [51.5719, -2.9388],
    [51.4180, -2.7259],
    [51.8493, -2.1958],
    [51.8883, -2.3016],
    [51.5719, -2.9388],
]
Estimate number of phones in an arbitrary polygon 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 2021-03-18 23:02:32 +00:00			`BRISTOL = [`
			`[51.4371, -2.6216],`
			`[51.4371, -2.5750],`
			`[51.4668, -2.5750],`
			`[51.4668, -2.6216],`
			`[51.4371, -2.6216],`
			`]`

			`SKYE = [`
			`[57.1004, -6.8280],`
			`[57.1004, -5.7733],`
			`[57.7334, -5.7733],`
			`[57.7334, -6.8280],`
			`[57.1004, -6.8280],`
			`]`
Add method to get wards for custom polygon This will be used as part of the area aggregation in the following commits. 2021-08-24 14:36:14 +01:00
			`SANTA_A = [`
			`[25.8890, 66.5500],`
			`[25.8890, 66.551],`
			`[25.8910, 66.551],`
			`[25.8910, 66.5500],`
			`[25.889, 66.55000],`
			`]`
Add function method to aggregate areas This is standalone for now - we'll plumb it in later. 2021-08-24 15:01:53 +01:00
			`BURFORD = [`
			`[51.8925, -1.7495],`
			`[51.7372, -1.7825],`
			`[51.7159, -1.5257],`
			`[51.8866, -1.5037],`
			`[51.8925, -1.7495],`
			`]`

			`CHELTENHAM = [`
			`[51.9324, -2.1265],`
			`[51.8633, -2.1306],`
			`[51.8637, -2.0242],`
			`[51.9328, -2.0221],`
			`[51.9324, -2.1265],`
			`]`
Support aggregating local authority clusters This applies some heuristics to try and keep the overall list of areas short when many are selected in the same wider area. Currently we only have relationship information between upper and lower tier local authorities, so we can't / won't aggregate up to Greater London (it's own special thing) or whole countries. 2021-08-24 15:49:46 +01:00
			`CHELTENHAM_AND_GLOUCESTER = [`
			`[51.8820, -2.2920],`
			`[51.8234, -2.2570],`
			`[51.8883, -2.0262],`
			`[51.9425, -2.0771],`
			`[51.8820, -2.2920],`
			`]`

			`SEVERN_ESTUARY = [`
			`[51.5719, -2.9388],`
			`[51.4180, -2.7259],`
			`[51.8493, -2.1958],`
			`[51.8883, -2.3016],`
			`[51.5719, -2.9388],`
			`]`