Birth Data¶

Original Data¶

To obtain the population data for each year, we used two tables from StatCan:

1. 1999 - 2021:

   For past years, we used Table 17-10-00005-01 from StatCan.

   The *.csv file can be downloaded from here: 17100005-eng.zip

   and is saved as: LEAP/leap/original_data/17100005.csv

2. 2021 - 2065:

   For future years, we used Table 17-10-0057-01 from StatCan.

   The *.csv file can be downloaded from here: 17100057-eng.zip.

   and is saved as: LEAP/leap/original_data/17100057.csv

Generating Processed Data¶

To run the data processing for the population data, with data points taken every year:

cd LEAP
python leap/data_generation/birth_data.py --time-delta P1Y

This will update the following data files:

1. leap/processed_data/{time_delta_tag}/birth/birth_estimate.csv

2. leap/processed_data/{time_delta_tag}/birth/initial_population.csv

The --time-delta argument must be in ISO 8601 format:

Processed Data¶

The output of the data generation for the Birth module is two .csv files:

birth_estimate.csv

initial_population.csv

leap.data_generation.birth_data module¶

leap.data_generation.birth_data.get_projection_scenario_id(projection_scenario: str) → str[source]¶

Convert the long form of the projection scenario to the 2-letter ID.

Parameters:¶

projection_scenario: str¶

The long form of the projection scenario, e.g. Projection scenario M1.

Returns:¶

The 2-letter ID of the projection scenario, e.g. M1.

leap.data_generation.birth_data.filter_age_group(age_group: str) → bool[source]¶

Filter out grouped categories such as “Median”, “Average”, “All”, “to”, “over”.

Parameters:¶

age_group: str¶

The age group string.

Returns:¶

True if the age group is not a grouped category, False otherwise.

leap.data_generation.birth_data.load_past_births_population_data(time_delta: leap.utils.TimeDelta, min_timepoint: datetime.datetime = datetime.datetime(2000, 1, 1, 0, 0), max_timepoint: datetime.datetime = datetime.datetime(2021, 1, 1, 0, 0)) → pandas.DataFrame[source]¶

Load the past birth data from the CSV file.

Parameters:¶

time_delta: leap.utils.TimeDelta¶

The duration of the time intervals to use for the data, e.g. 1 year, 5 years, etc.

min_timepoint: datetime.datetime = datetime.datetime(2000, 1, 1, 0, 0)¶

The minimum timepoint to include in the data.

max_timepoint: datetime.datetime = datetime.datetime(2021, 1, 1, 0, 0)¶

The maximum timepoint to include in the data.

Returns:¶

The past birth data. Columns:

• timepoint: The date / time of the data.

• province: The 2-letter province ID.

• N: The total number of births in that time interval.

• prop_male: The proportion of births in that time interval that are male.

• projection_scenario: The projection scenario; all values are "past".

leap.data_generation.birth_data.load_projected_births_population_data(time_delta: leap.utils.TimeDelta, min_timepoint: datetime.datetime, max_timepoint: datetime.datetime = datetime.datetime(2070, 1, 1, 0, 0)) → pandas.DataFrame[source]¶

Load the projected births data from the CSV file from StatCan.

Parameters:¶

time_delta: leap.utils.TimeDelta¶

The duration of the time intervals to use for the data, e.g. 1 year, 5 years, etc.

min_timepoint: datetime.datetime¶

The starting timepoint for the projected data.

max_timepoint: datetime.datetime = datetime.datetime(2070, 1, 1, 0, 0)¶

The ending timepoint for the projected data.

Returns:¶

The projected births data. Columns:

• timepoint: The starting date / time of the time interval.

• province: The 2-letter province ID.

• N: The total number of births predicted for that time interval.

• prop_male: The proportion of predicted births in that time interval that are male.

• projection_scenario: The projection scenario, one of:

  • LG: low-growth projection

  • HG: high-growth projection

  • M1: medium-growth 1 projection

  • M2: medium-growth 2 projection

  • M3: medium-growth 3 projection

  • M4: medium-growth 4 projection

  • M5: medium-growth 5 projection

  • M6: medium-growth 6 projection

  • FA: fast-aging projection

  • SA: slow-aging projection

leap.data_generation.birth_data.load_past_initial_population_data(time_delta: leap.utils.TimeDelta, min_timepoint: datetime.datetime = datetime.datetime(2000, 1, 1, 0, 0), max_timepoint: datetime.datetime = datetime.datetime(2021, 1, 1, 0, 0)) → pandas.DataFrame[source]¶

Load the past initial population data from the CSV file.

Parameters:¶

time_delta: leap.utils.TimeDelta¶

The duration of the time intervals to use for the data, e.g. 1 year, 5 years, 1 month, etc.

min_timepoint: datetime.datetime = datetime.datetime(2000, 1, 1, 0, 0)¶

The starting timepoint for the past data; only timepoints >= this value will be included in the returned data.

max_timepoint: datetime.datetime = datetime.datetime(2021, 1, 1, 0, 0)¶

The ending timepoint for the past data; only timepoints <= this value will be included in the returned data.

Returns:¶

The past initial population data. Columns:

• timepoint: The date / time of the data.

• province: The 2-letter province ID, e.g. BC.

• age: The age of the population.

• prop_male: The proportion of the population in that age group that are male.

• n_age: The total number of people in that age group for the given time interval, province, and projection scenario.

• n_birth: The total number of births in the given time interval, province, and projection scenario.

• prop: The proportion of the total number of people in that age group to the total number of births in that time interval.

• projection_scenario: The projection scenario; all values are “past”.

leap.data_generation.birth_data.load_projected_initial_population_data(time_delta: leap.utils.TimeDelta, min_timepoint: datetime.datetime, max_timepoint: datetime.datetime = datetime.datetime(2070, 1, 1, 0, 0)) → pandas.DataFrame[source]¶

Load the projected initial population data from the CSV file.

Parameters:¶

time_delta: leap.utils.TimeDelta¶

The duration of the time intervals to use for the data, e.g. 1 year, 5 years, 1 month, etc.

min_timepoint: datetime.datetime¶

The starting timepoint for the projected data.

max_timepoint: datetime.datetime = datetime.datetime(2070, 1, 1, 0, 0)¶

The ending timepoint for the projected data.

Returns:¶

The projected initial population data. Columns:

• timepoint: The starting date / time of the time interval.

• province: The 2-letter province ID, e.g. BC.

• age: The age of the population.

• prop_male: The proportion of the population in that age group that are male.

• n_age: The total number of people in that age group for the given time interval, province, and projection scenario.

• n_birth: The total number of births in the given time interval, province, and projection scenario.

• prop: The proportion of the total number of people in that age group to the total number of births in that time interval.

• projection_scenario: The projection scenario, one of:

  • LG: low-growth projection

  • HG: high-growth projection

  • M1: medium-growth 1 projection

  • M2: medium-growth 2 projection

  • M3: medium-growth 3 projection

  • M4: medium-growth 4 projection

  • M5: medium-growth 5 projection

  • M6: medium-growth 6 projection

  • FA: fast-aging projection

  • SA: slow-aging projection

leap.data_generation.birth_data.generate_birth_estimate_data(time_delta: leap.utils.TimeDelta, draw_plot: bool = True)[source]¶

Create/update the birth_estimate.csv file.

Parameters:¶

time_delta: leap.utils.TimeDelta¶

The duration of the time intervals to use for the data, e.g. 1 year, 5 years, etc.

draw_plot: bool = True¶

If True, generate a plot for validation.

leap.data_generation.birth_data.generate_initial_population_data(time_delta: leap.utils.TimeDelta, draw_plot: bool = True)[source]¶

Create/update the initial_population.csv file.

Parameters:¶

time_delta: leap.utils.TimeDelta¶

The duration of the time intervals to use for the data, e.g. 1 year, 5 years, etc.

draw_plot: bool = True¶

If True, generate a plot for validation.

leap.data_generation.birth_data.plot(df: pandas.DataFrame, y: str, color: str, title: str = '', file_path: pathlib.Path | None = None, width: int = 2000, height: int = 1500)[source]¶

Plot the incidence or prevalence of asthma.

Parameters:¶

df: pandas.DataFrame¶

A dataframe containing either incidence or prevalence data. Must have columns:

• timepoint (dt.datetime): The given timepoint.

• province (str): The 2-letter province ID, e.g. BC.

• projection_scenario (str): The projection scenario, one of:

  • past: past data from StatCan, up to the most recent census date (2021-01-01)

  • LG: low-growth projection

  • HG: high-growth projection

  • M1: medium-growth 1 projection

  • M2: medium-growth 2 projection

  • M3: medium-growth 3 projection

  • M4: medium-growth 4 projection

  • M5: medium-growth 5 projection

  • M6: medium-growth 6 projection

  • FA: fast-aging projection

  • SA: slow-aging projection

y: str¶

The name of the column in the dataframe which will be plotted as the y data.

color: str¶

The name of the column in the dataframe which will be used to color the data.

title: str = ''¶

The title of the plot.

file_path: pathlib.Path | None = None¶

The path to save the plot to.

width: int = 2000¶

The width of the plot.

height: int = 1500¶

The height of the plot.