A quick port of Joël Gombin’s spReapportion package, without the dependencies to the maptools and rgeos packages, which were both retired in 2023, and with additional support for sf objects.

The sfReapportion function is intended as a drop-in replacement for Joël’s spReapportion function: it takes exactly the same arguments, and outputs exactly the same kind of results, also as a data frame.

Use the following options with caution:

• use of weights with mode = "proportion" has only been lightly tested
• use of weight_matrix and weight_matrix_var has only been lightly tested

The package was ported in order to be used in this project.

Installation

If the package is not available from CRAN, install from GitHub instead:

install.packages("remotes")
remotes::install_github("briatte/sfReapportion")

Example

library(sfReapportion)

# Joël's example
data(ParisPollingStations2012)
data(ParisIris)
data(RP_2011_CS8_Paris)

CS_ParisPollingStations <- sfReapportion(ParisIris, 
                                         ParisPollingStations2012, 
                                         RP_2011_CS8_Paris, 
                                         "DCOMIRIS", 
                                         "ID", 
                                         "IRIS")

# our results
> head(CS_ParisPollingStations)[, 1:4]
         ID C11_POP15P C11_POP15P_CS1 C11_POP15P_CS2
1 750010001   1385.539    0.007534526       88.64129
2 750010002   1389.989    1.158961646       65.49323
3 750010003   1921.008    3.410431283       85.39031
4 750010004   1577.544    2.504085472       62.79095
5 750010005   1802.787    1.803202193       68.64467
6 750010006   1619.144    5.790202710       83.05042

# compare to Joël's test file
> head(readRDS("tests/testthat/CS_ParisPollingStations.rds"))[, 1:4]
         ID C11_POP15P C11_POP15P_CS1 C11_POP15P_CS2
1 750010001   1385.539    0.007534526       88.64129
2 750010002   1389.989    1.158961646       65.49323
3 750010003   1921.008    3.410431283       85.39031
4 750010004   1577.544    2.504085472       62.79095
5 750010005   1802.787    1.803202193       68.64467
6 750010006   1619.144    5.790202710       83.05042

The results match those of the areal package:

library(areal)
library(dplyr)
library(sf)

ParisPollingStations2012_sf <- sf::st_as_sf(ParisPollingStations2012)
ParisIris_sf <- sf::st_as_sf(ParisIris) %>%
  left_join(RP_2011_CS8_Paris, by = c("DCOMIRIS" = "IRIS"))

areal_equiv <- areal::aw_interpolate(ParisPollingStations2012_sf, tid = ID,
                                     source = ParisIris_sf, sid = DCOMIRIS,
                                     weight = "total", output = "sf",
                                     extensive = c("C11_POP15P",
                                                   "C11_POP15P_CS1",
                                                   "C11_POP15P_CS2"))

# match display of previous results
> select(areal_equiv, ID, C11_POP15P, C11_POP15P_CS1, C11_POP15P_CS2) %>%
+   arrange(ID) %>%
+   sf::st_drop_geometry() %>%
+   head()
         ID C11_POP15P C11_POP15P_CS1 C11_POP15P_CS2
1 750010001   1385.539    0.007534526       88.64129
2 750010002   1389.989    1.158961646       65.49323
3 750010003   1921.008    3.410431283       85.39031
4 750010004   1577.544    2.504085472       62.79095
5 750010005   1802.787    1.803202193       68.64467
6 750010006   1619.144    5.790202710       83.05042

The package contains further tests against the st_interpolate_aw function of the sf package.

Using a weights matrix

The package contains an example weights matrix that contains voter addresses located in the 20th arrondissement of Paris. The weighting variable, nb_adresses, only approximately counts voters at a given location (the number of voters at each address is unknown).

The data were obtained by subsetting from the Répertoire électoral unique (REU):

library(arrow)
library(dplyr)
library(sf)

# subset polling stations
bv20 <- arrow::read_parquet("table-bv-reu.parquet") %>%
  dplyr::filter(code_commune %in% c("75120")) %>%
  dplyr::select(id_brut_reu, libelle_reu, nb_adresses)

# subset voter addresses (slow)
addr20 <- arrow::read_parquet("table-adresses-reu.parquet") %>%
    dplyr::filter(id_brut_bv_reu %in% unique(bv20$id_brut_reu))

# convert to spatial points
Paris20eAddresses <- addr20 %>% 
  dplyr::group_by(geo_adresse, longitude, latitude) %>%
  dplyr::summarise(nb_adresses = sum(nb_adresses)) %>%
  sf::st_as_sf(coords = c("longitude", "latitude")) %>%
  sf::st_set_crs(4326)

# save to .rda (LazyData: true)
save(Paris20eAddresses, file = "Paris20eAddresses.rda", compress = "xz")

The results can be tested by subsetting the rest of the test data included in the package to the 20th arrondissement of Paris:

library(dplyr)
library(ggplot2)
library(sf)
library(sfReapportion)

# spatial points of voter addresses in Paris 20th district
data(Paris20eAddresses)

data(ParisPollingStations2012)
data(ParisIris)
data(RP_2011_CS8_Paris)

# subset geometry of polling stations (new geom, 76 polling stations)
ParisPollingStations2012 <- sf::st_as_sf(ParisPollingStations2012) %>%
  dplyr::filter(arrondisse %in% c(20)) %>%
  dplyr::mutate(id_brut_bv_reu = paste("75020_", num_bv))

# subset geometry of census tracts (old geom, 356 polygons)
# will throw a warning about polygons expected to be spatially constant
ParisIris <- sf::st_as_sf(ParisIris) %>%
  sf::st_intersection(ParisPollingStations2012)

# this is what we're reapportioning
ggplot(ParisIris) +
  geom_sf() +
  geom_sf(data = Paris20eAddresses, aes(size = nb_adresses), alpha = 1/4) +
  scale_size_area(max_size = 10) +
  theme_void()

# subset census data to reapportion (93 distinct census tracts)
RP_2011_CS8_Paris <- dplyr::filter(RP_2011_CS8_Paris,
                                   IRIS %in% ParisIris$DCOMIRIS)

Comparing weighted and unweighted results is probably a good idea:

# unweighted
r1 <- sfReapportion(ParisIris, ParisPollingStations2012, RP_2011_CS8_Paris,
                    "DCOMIRIS", "ID", "IRIS")

# weighted
# will throw a warning about `weight_matrix` having only been lightly tested
r2 <- sfReapportion(ParisIris, ParisPollingStations2012,
                    RP_2011_CS8_Paris,
                    "DCOMIRIS", "ID", "IRIS",
                    weight_matrix = Paris20eAddresses,
                    weight_matrix_var = "nb_adresses")

The differences are non-trivial, at least when looking at adult population and socio-professional categories as we do in this example:

> # unweighted
> head(r1[, 1:4])
         ID C11_POP15P C11_POP15P_CS1 C11_POP15P_CS2
1 750200001   2026.819              0       56.46644
2 750200002   1465.669              0       34.65999
3 750200003   2308.119              0       60.81294
4 750200004   1934.005              0       53.48857
5 750200005   1886.533              0       45.53472
6 750200006   1873.657              0       44.32671
>
> # weighted
> head(r2[, 1:4])
         ID C11_POP15P C11_POP15P_CS1 C11_POP15P_CS2
1 750200001   2006.434              0       54.75713
2 750200002   1543.915              0       35.65698
3 750200003   1930.616              0       51.33953
4 750200004   2135.882              0       60.72449
5 750200005   1925.147              0       46.37813
6 750200006   1953.638              0       47.51444
>
> # correlations
> round(diag(cor(r1[,-1], r2[,-1])), 2)
    C11_POP15P C11_POP15P_CS1 C11_POP15P_CS2 C11_POP15P_CS3 C11_POP15P_CS4 
          0.30           0.75           0.58           0.46           0.30 
C11_POP15P_CS5 C11_POP15P_CS6 C11_POP15P_CS7 C11_POP15P_CS8 
          0.48           0.70           0.26           0.52

See also

• Areal Interpolation in R in the areal package
• Areal Weighted Interpolation in the areal package
• “Area-weighted interpolation” (in Spatial Data Science, 2023)
• Guidelines on areal interpolation methods
• Population Downscaling Using Areal Interpolation - A Comparative Analysis in the populR package
• R-spatial evolution: retirement of rgdal, rgeos and maptools (2022)
• r-spatial/sf#2563 for a technical discussion on merging polygons

I also wrote a very short blog post to illustrate what the package does.