With propop::propop() you can perform population
projections either for one or several regions. The function applys the
Cohort Component Method and is tailored to the context of Switzerland.
That is, the package was built to run with information provided by the
Federal Statistical Office (FSO). To run the function, you need to
provide:
a data frame with the starting population, that
is, the most up-to-date number of people for each demographic group
before the first projection year; to illustrate, the example population
data in propop are from 31. December 2018 and the first
projection year is 2019.
a data frame containing model parameters, that is, information about how key demographic variables such as mortality are expected to develop in the future;
global arguments which do not change over time or across demographic groups.
Importantly, the two data frames’ structure (number, names, type of columns) must correspond exactly to the specifications shown in this vignette. Among other things, it is mandatory to provide two levels for sex and nationality. The function is more flexible with respect to age groups. Although the examples use 1-year age groups ranging from 0 to 100 (incl. those who are older), the model should also run with aggregated groups (e.g., 0-19, 20-64, 65+ year olds) – provided that the information in the population and parameter data frames are compatible (e.g., by aggregating the parameters for the same age groups).
The package propop includes the population data from the
canton of Aargau from 2018 and the FSO parameters from the population
development scenarios 2020. Using these resources, we can project
the population for the canton as a whole for 1-year age groups for the
period 2019-2030.
The start and end of women’s fertile period, the proportion of babies
born as female, and the share of babies born by mothers who are not
Swiss are stable parameters that are passed to
propop::propop() as arguments.
projection_canton_2030 <- propop(
parameters = fso_parameters,
year_first = 2019,
year_last = 2030,
population = fso_population,
subregional = FALSE,
binational = TRUE
)
#> Running projection for: Aargau
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#>
#> ── Settings used for the projection ────────────────────────────────────────────
#> Year of starting population: 2018
#> Size of starting population: 678207
#> Projection period: 2019-2030
#> Projected population size (2030): 781634
#> Nationality-specific projection: "yes"
#> Subregional migration: "no"
#> ────────────────────────────────────────────────────────────────────────────────
projection_canton_2030 |>
DT::datatable(filter = "top")
To project the population development for subregions within a superordinate entity (e.g., districts or municipalities within a canton), we need input files with multiple regions. Since these are not yet available, we create them:
# fso parameters for fictitious subregions
fso_parameters_sub <- fso_parameters |>
# duplicating rows 5 times
tidyr::uncount(5) |>
# create 5 subregions
dplyr::mutate(spatial_unit = rep(1:5, times = nrow(fso_parameters))) |>
# divide the size of parameters with numbers by the number of regions (= 5);
# otherwise the multiplication of lines will inflate the population size.
dplyr::mutate(spatial_unit = as.character(spatial_unit))
# fso population for fictitious subregions
fso_population_sub <- fso_population |>
dplyr::rename(n_tot = n) |>
# duplicating rows 5 times
tidyr::uncount(5) |>
# create 5 subregions
dplyr::mutate(spatial_unit = rep(1:5, times = nrow(fso_population))) |>
dplyr::mutate(
# Create fictitious n for each subregion
n = dplyr::case_match(
spatial_unit,
1 ~ round(n_tot * 0.3),
2 ~ round(n_tot * 0.25),
3 ~ round(n_tot * 0.2),
4 ~ round(n_tot * 0.15),
5 ~ round(n_tot * 0.1),
.default = NA
),
.keep = "all"
) |>
dplyr::mutate(spatial_unit = as.character(spatial_unit)) |>
dplyr::select(-n_tot)We can then run the projection for the subregions and show the results for a selected group:
projection_subregions_2030 <- propop(
parameters = fso_parameters_sub,
year_first = 2019,
year_last = 2030,
population = fso_population_sub,
subregional = FALSE,
binational = TRUE
)
#> Running projection for: 1
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#> Running projection for: 2
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#> Running projection for: 3
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#> Running projection for: 4
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#> Running projection for: 5
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#>
#> ── Settings used for the projection ────────────────────────────────────────────
#> Year of starting population: 2018
#> Size of starting population: 678194
#> Projection period: 2019-2030
#> Projected population size (2030): 1366163
#> Nationality-specific projection: "yes"
#> Subregional migration: "no"
#> ────────────────────────────────────────────────────────────────────────────────
projection_subregions_2030 |>
dplyr::filter(sex == "m" & nat == "int" & age == 14) |>
DT::datatable(filter = "top")
When information about migration patterns within the superordinate
entity are available (e.g., moving between municipalities),
subregional can be set to TRUE to adjust the
population size in each subregion accordingly. This requires
imm_can as an additional parameter in the parameter data
frame.
It’s possible to run projections without distinguishing between Swiss
and non-Swiss nationals. The simplest way to achieve this is to provide
population data and parameters without the nationality-specific columns
(remove nat, acq,
births_int_ch).
Let’s adapt the input files accordingly. To keep things simple, we
run the projection for only one of the two nationalities. It goes
without saying that using propop::propop() like this in
real settings requires more preparation (e.g., determining a single
value when parameters differ between Swiss and non-Swiss people).
fso_parameters_int <- fso_parameters |>
# drop Swiss people
dplyr::filter(nat == "int") |>
# remove `nat`, `acq` and `births_int_ch` from `parameters`
dplyr::select(-c(nat, acq, births_int_ch))
fso_population_int <- fso_population |>
# drop Swiss people
dplyr::filter(nat == "int") |>
# remove `nat` from `population`
dplyr::select(-nat)When calling propop::propop(), you need to set
binational = FALSE.
projection_int <- propop(
parameters = fso_parameters_int,
year_first = 2019,
year_last = 2030,
population = fso_population_int,
subregional = FALSE,
binational = FALSE
)
#> Running projection for: Aargau
#> ✔ Year: 2019
#> ✔ Year: 2020
#> ✔ Year: 2021
#> ✔ Year: 2022
#> ✔ Year: 2023
#> ✔ Year: 2024
#> ✔ Year: 2025
#> ✔ Year: 2026
#> ✔ Year: 2027
#> ✔ Year: 2028
#> ✔ Year: 2029
#> ✔ Year: 2030
#>
#> ── Settings used for the projection ────────────────────────────────────────────
#> Year of starting population: 2018
#> Size of starting population: 170424
#> Projection period: 2019-2030
#> Projected population size (2030): 266529
#> Nationality-specific projection: "no"
#> Subregional migration: "no"
#> ────────────────────────────────────────────────────────────────────────────────
projection_int |>
DT::datatable(filter = "top")
The output file includes the number of people (n) per
demographic group for the base year and the projected years.