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Calculate shares for distributing immigration among subregions

Source: R/calculate_shares.R
calculate_shares.Rd

Calculates historical immigration shares across spatial units within a region. These shares are used to allocate emigrants moving from one subregion to another subregion (which can be done with calculate_rates).

Usage

calculate_shares(
  past_migration,
  imm_n,
  year_range = NULL,
  age_group = 1,
  binational = TRUE,
  two_sex = TRUE
)

Arguments

past_migration

data frame, historical records (e.g., immigration from other cantons, countries or subregions). Required columns are: year, spatial_unit, age and a column that contains aggregated historical migration records. The columns nat and sex are optional.

imm_n

character, name of the column which contains the data for aggregated historical migration records.

year_range

(optional) vector, years taken into consideration to calculate historical shares. Default uses all years present in the data.

age_group

(optional) integer, divides continuous age values into intervals for calculating shares. If the argument is not specified, the default uses 1-year age groups.

binational

(optional) boolean, TRUE indicates that the calculation discriminates between two groups of nationalities. FALSE indicates that the calculation does not distinguish between nationalities.

two_sex

(optional) boolean, TRUE indicates that the calculation discriminates between two sexes. FALSE indicates that the calculation does not distinguish between sexes.

Value

A data frame that includes the average share per demographic group and spatial unit. imm_share can be used as imm_sub parameter when propop::propop() uses subregional = "rate".

See also

propop() for details on how to account for subregional migration using the rate method, calculate_rates() for calculating the associated emigration rate emi_sub.

Examples

# Calculate shares to distribute subregional immigration among spatial units
calculate_shares(
  past_migration = ag_migration_subregional,
  imm_n = "imm_n",
  year_range = c(2022:2024),
  age_group = 10,
  binational = TRUE,
  two_sex = TRUE
)
#> # A tibble: 6,060 × 10
#>     year spatial_unit   age age_group nat   sex   imm_n sum_imm_n imm_share
#>    <int> <chr>        <dbl> <chr>     <chr> <chr> <int>     <int>     <dbl>
#>  1  2022 1                0 age_0_9   ch    m         8       435    0.0184
#>  2  2022 1                1 age_0_9   ch    m        37       435    0.0851
#>  3  2022 1                2 age_0_9   ch    m        29       435    0.0667
#>  4  2022 1                3 age_0_9   ch    m        20       435    0.0460
#>  5  2022 1                4 age_0_9   ch    m        18       435    0.0414
#>  6  2022 1                5 age_0_9   ch    m        11       435    0.0253
#>  7  2022 1                6 age_0_9   ch    m         8       435    0.0184
#>  8  2022 1                7 age_0_9   ch    m        11       435    0.0253
#>  9  2022 1                8 age_0_9   ch    m         7       435    0.0161
#> 10  2022 1                9 age_0_9   ch    m         8       435    0.0184
#> # ℹ 6,050 more rows
#> # ℹ 1 more variable: method <chr>