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Use CustomBroadcastArea to estimate phones in bleed area
Our current assumption is that the bleed area has the same population density as the broadcast area. This is particularly naïve when: - the bleed area overlaps the sea – no-one lives in the sea - the broadcast area is a village and the bleed area is the surrounding countryside - the broadcast area is adjacent to a densely populated area like a city We can be smarter about this now that we have a way of determining the number of phones in an arbitrary area, based on the known areas that we have population data about. Calculating the population in an overlap is a slightly more intensive calculation. So we only doing it for areas which are smaller enough that it doesn’t slow things down too much. For larger areas we still use the more naïve algorithm.
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@@ -144,6 +144,10 @@ class CustomBroadcastArea(BaseBroadcastArea):
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self.name = name
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self._polygons = polygons or []
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@classmethod
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def from_polygon_objects(cls, polygon_objects):
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return cls(name=None, polygons=polygon_objects.as_coordinate_pairs_lat_long)
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@property
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def polygons(self):
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return Polygons(
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@@ -7,6 +7,7 @@ from orderedset import OrderedSet
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from werkzeug.utils import cached_property
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from app.broadcast_areas.models import (
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CustomBroadcastArea,
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CustomBroadcastAreas,
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broadcast_area_libraries,
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)
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@@ -17,6 +18,10 @@ from app.notify_client.broadcast_message_api_client import (
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broadcast_message_api_client,
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)
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ESTIMATED_AREA_OF_LARGEST_UK_COUNTY = broadcast_area_libraries.get_areas(
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'ctyua19-E10000023' # North Yorkshire
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)[0].polygons.estimated_area
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class BroadcastMessage(JSONModel):
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@@ -176,12 +181,18 @@ class BroadcastMessage(JSONModel):
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@property
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def count_of_phones_likely(self):
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area_estimate = self.simple_polygons.estimated_area
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bleed_area_estimate = self.simple_polygons_with_bleed.estimated_area - area_estimate
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return round_to_significant_figures(
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self.count_of_phones + (self.count_of_phones * bleed_area_estimate / area_estimate),
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1
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)
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estimated_area = self.simple_polygons.estimated_area
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if estimated_area > ESTIMATED_AREA_OF_LARGEST_UK_COUNTY:
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count = self.count_of_phones * (
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self.simple_polygons_with_bleed.estimated_area / estimated_area
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)
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else:
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count = CustomBroadcastArea.from_polygon_objects(
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self.simple_polygons_with_bleed
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).count_of_phones
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return round_to_significant_figures(count, 1)
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def get_areas(self, areas):
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return broadcast_area_libraries.get_areas(
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@@ -830,7 +830,7 @@ def test_broadcast_page(
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], [
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'An area of 6.3 square miles Will get the alert',
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'An extra area of 22.6 square miles is Likely to get the alert',
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'9,000 to 40,000 phones',
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'9,000 to 10,000 phones',
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]),
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([
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'lad20-E09000019',
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@@ -839,7 +839,26 @@ def test_broadcast_page(
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], [
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'An area of 9.7 square miles Will get the alert',
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'An extra area of 4.7 square miles is Likely to get the alert',
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'200,000 to 300,000 phones',
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'200,000 to 500,000 phones',
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]),
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([
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'ctyua19-E10000019',
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], [
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'Lincolnshire remove',
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], [
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'An area of 3,986.6 square miles Will get the alert',
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'An extra area of 599.4 square miles is Likely to get the alert',
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'500,000 to 600,000 phones',
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]),
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([
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'ctyua19-E10000019',
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'ctyua19-E10000023'
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], [
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'Lincolnshire remove', 'North Yorkshire remove',
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], [
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'An area of 9,776.2 square miles Will get the alert',
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'An extra area of 1,654.6 square miles is Likely to get the alert',
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'1,000,000 phones estimated',
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]),
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))
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def test_preview_broadcast_areas_page(
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@@ -907,7 +926,7 @@ def test_preview_broadcast_areas_page(
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[
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'An area of 3,205.0 square miles Will get the alert',
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'An extra area of 763.4 square miles is Likely to get the alert',
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'4,000 to 5,000 phones',
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'4,000 phones estimated',
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]
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),
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))
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