Summary indices of migration intensity
Source
Bell, M., Blake, M., Boyle, P., Duke-Williams, O., Rees, P. H., Stillwell, J., & Hugo, G. J. (2002). Cross-national comparison of internal migration: issues and measures. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(3), 435–464. https://doi.org/10.1111/1467-985X.00247
Courgeau, D. (1973). Migrants et migrations. Population, 28(1), 95–129. https://doi.org/10.2307/1530972
Bernard, A., Rowe, F., Bell, M., Ueffing, P., Charles-Edwards, E., & Zhu, Y. (2017). Comparing internal migration across the countries of Latin America: A multidimensional approach. Plos One, 12(3), e0173895. https://doi.org/10.1371/journal.pone.0173895
Arguments
- mig_total
Numeric value for the total number of migrations.
- pop_total
Numeric value for the total population.
- n
Numeric value for the number of regions used in the definition of migration for
mig_total.- long
Logical to return a long data frame with index values all in one column
Value
A tibble with 2 summary measures where
- cmp
Crude migration probability from Bell et. al. (2002), sometimes known as crude migration intensity, e.g. Bernard (2017)
- courgeau_k
Intensity measure of Courgeau (1973)
Examples
# single year
library(dplyr)
m <- korea_gravity %>%
filter(year == 2020,
orig != dest)
m
#> # A tibble: 272 × 20
#> orig dest year flow dist_cent dist_min dist_pw contig orig_pop dest_pop
#> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <lgl> <dbl> <dbl>
#> 1 Seoul Busan 2020 20990 319. 294. 324. FALSE 9.67 3.39
#> 2 Seoul Daegu 2020 15216 241. 209. 236. FALSE 9.67 2.42
#> 3 Seoul Inche… 2020 38409 44.1 0 25.0 TRUE 9.67 2.94
#> 4 Seoul Gwang… 2020 11232 266. 244. 265. FALSE 9.67 1.45
#> 5 Seoul Daeje… 2020 15116 139. 109. 138. FALSE 9.67 1.46
#> 6 Seoul Ulsan 2020 7062 300. 263. 307. FALSE 9.67 1.14
#> 7 Seoul Sejong 2020 5107 112. 79.6 117. FALSE 9.67 0.356
#> 8 Seoul Gyeon… 2020 266375 17.9 0 13.2 TRUE 9.67 13.4
#> 9 Seoul Gangw… 2020 20048 116. 34.3 113. FALSE 9.67 1.54
#> 10 Seoul Chung… 2020 14574 118. 57.4 108. FALSE 9.67 1.60
#> # ℹ 262 more rows
#> # ℹ 10 more variables: orig_area <units>, dest_area <units>, orig_gdp_pc <dbl>,
#> # orig_ginc_pc <dbl>, orig_iinc_pc <dbl>, orig_pconsum_pc <dbl>,
#> # dest_gdp_pc <dbl>, dest_ginc_pc <dbl>, dest_iinc_pc <dbl>,
#> # dest_pconsum_pc <dbl>
p <- korea_gravity %>%
filter(year == 2020) %>%
distinct(dest, dest_pop)
p
#> # A tibble: 17 × 2
#> dest dest_pop
#> <chr> <dbl>
#> 1 Seoul 9.67
#> 2 Busan 3.39
#> 3 Daegu 2.42
#> 4 Incheon 2.94
#> 5 Gwangju 1.45
#> 6 Daejeon 1.46
#> 7 Ulsan 1.14
#> 8 Sejong 0.356
#> 9 Gyeonggi-do 13.4
#> 10 Gangwon-do 1.54
#> 11 Chungcheongbuk-do 1.60
#> 12 Chungcheongnam-do 2.12
#> 13 Jeollabuk-do 1.80
#> 14 Jeollanam-do 1.85
#> 15 Gyeongsangbuk-do 2.64
#> 16 Gyeongsangnam-do 3.34
#> 17 Jeju 0.675
index_intensity(mig_total = sum(m$flow), pop_total = sum(p$dest_pop*1e6), n = nrow(p))
#> # A tibble: 2 × 2
#> measure value
#> <chr> <dbl>
#> 1 cmp 4.89
#> 2 courgeau_k 0.863
# multiple years
library(tidyr)
library(purrr)
mm <- korea_gravity %>%
filter(orig != dest) %>%
group_by(year) %>%
summarise(m = sum(flow))
mm
#> # A tibble: 9 × 2
#> year m
#> <int> <int>
#> 1 2012 2512740
#> 2 2013 2423429
#> 3 2014 2507796
#> 4 2015 2551424
#> 5 2016 2453342
#> 6 2017 2410930
#> 7 2018 2429184
#> 8 2019 2384948
#> 9 2020 2534114
pp <- korea_gravity %>%
group_by(year) %>%
distinct(dest, dest_pop) %>%
summarise(p = sum(dest_pop)*1e6,
n = n_distinct(dest))
pp
#> # A tibble: 9 × 3
#> year p n
#> <int> <dbl> <int>
#> 1 2012 50948272 17
#> 2 2013 51141463 17
#> 3 2014 51327916 17
#> 4 2015 51529338 17
#> 5 2016 51696216 17
#> 6 2017 51778544 17
#> 7 2018 51826059 17
#> 8 2019 51849861 17
#> 9 2020 51829023 17
library(purrr)
library(tidyr)
mm %>%
left_join(pp) %>%
mutate(i = pmap(
.l = list(m, p, n),
.f = ~index_intensity(mig_total = ..1, pop_total = ..2,n = ..3, long = FALSE)
)) %>%
unnest(cols = i)
#> Joining with `by = join_by(year)`
#> # A tibble: 9 × 6
#> year m p n cmp courgeau_k
#> <int> <int> <dbl> <int> <dbl> <dbl>
#> 1 2012 2512740 50948272 17 4.93 0.870
#> 2 2013 2423429 51141463 17 4.74 0.836
#> 3 2014 2507796 51327916 17 4.89 0.862
#> 4 2015 2551424 51529338 17 4.95 0.874
#> 5 2016 2453342 51696216 17 4.75 0.838
#> 6 2017 2410930 51778544 17 4.66 0.822
#> 7 2018 2429184 51826059 17 4.69 0.827
#> 8 2019 2384948 51849861 17 4.60 0.812
#> 9 2020 2534114 51829023 17 4.89 0.863