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6cb326f153
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.
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
import math
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SMARTPHONE_OWNERSHIP_BY_AGE_RANGE = {
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# If no children have a phone when they’re born but 100% of
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# children have a phone by age 16 then 50% is a rough
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# approximation of how many children have phones
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(0, 15): 0.50,
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# https://www.finder.com/uk/mobile-internet-statistics
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(16, 24): 1.00,
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(25, 34): 0.97,
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(35, 44): 0.91,
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(45, 54): 0.88,
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(55, 64): 0.73,
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(65, math.inf): 0.40,
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}
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MEDIAN_AGE_UK = 40
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for min, max in SMARTPHONE_OWNERSHIP_BY_AGE_RANGE.keys():
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if min <= MEDIAN_AGE_UK <= max:
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MEDIAN_AGE_RANGE_UK = (min, max)
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class CITY_OF_LONDON:
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WARDS = (
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'E05009288', 'E05009289', 'E05009290', 'E05009291', 'E05009292',
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'E05009293', 'E05009294', 'E05009295', 'E05009296', 'E05009297',
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'E05009298', 'E05009299', 'E05009300', 'E05009301', 'E05009302',
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'E05009303', 'E05009304', 'E05009305', 'E05009306', 'E05009307',
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'E05009308', 'E05009309', 'E05009310', 'E05009311', 'E05009312',
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)
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# https://data.london.gov.uk/blog/daytime-population-of-london-2014/
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DAYTIME_POPULATION = 553_000
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# Exact area of the polygons we’re storing, which matches the 2.9km²
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# given by https://en.wikipedia.org/wiki/City_of_London
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AREA_SQUARE_METRES = 2_885_598
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class BRYHER:
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WD20_CODE = 'E05011090'
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POPULATION = 84
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def estimate_number_of_smartphones_for_population(population):
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smartphone_ownership_for_area_by_age_range = {}
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for range, ownership in SMARTPHONE_OWNERSHIP_BY_AGE_RANGE.items():
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min, max = range
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smartphone_ownership_for_area_by_age_range[range] = sum(
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people
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for age, people in population
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if min <= age <= max
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) * ownership
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total_population = sum(dict(population).values())
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total_phones = sum(smartphone_ownership_for_area_by_age_range.values())
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print( # noqa: T001
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f' Population:{total_population: 11,.0f}'
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f' Phones:{total_phones: 11,.0f}'
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)
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return total_phones
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