Summary indices of migration connectivity

Usage

index_connectivity(
  m = NULL,
  gini_orig_all = FALSE,
  gini_dest_all = FALSE,
  gini_corrected = TRUE,
  orig = "orig",
  dest = "dest",
  flow = "flow",
  long = TRUE
)

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

Rogers, A., & Raymer, J. (1998). The Spatial Focus of US Interstate Migration Flows. International Journal of Population Geography, 4(1), 63–80. https://doi.org/10.1002/(SICI)1099-1220(199803)4%3A1<63%3A%3AAID-IJPG87>3.0.CO%3B2-U

Rogers, A., & Sweeney, S. (1998). Measuring the Spatial Focus of Migration Patterns. Professional Geographer, 50(2), 232–242.

Plane, D., & Mulligan, G. F. (1997). Measuring spatial focusing in a migration system. Demography, 34(2), 251–262.

Arguments

m: A matrix or data frame of origin-destination flows. For matrix the first and second dimensions correspond to origin and destination respectively. For a data frame ensure the correct column names are passed to orig, dest and flow.
gini_orig_all: Logical to include gini index values for all origin regions. Default FALSE.
gini_dest_all: Logical to include gini index values for all destination regions. Default FALSE.
gini_corrected: Logical to use corrected denominator in Gini index of Bell (2002) or original of David A. Plane and Mulligan (1997)
orig: Character string of the origin column name (when m is a data frame rather than a matrix)
dest: Character string of the destination column name (when m is a data frame rather than a matrix)
flow: Character string of the flow column name (when m is a data frame rather than a matrix)
long: Logical to return a long data frame with index values all in one column

Value

A tibble with 12 summary measures:

connectivity: Migration connectivity index of Bell et. al. (2002) for the share of non-zero flows. A value of 0 means no connections (all zero flows) and 1 shows that all regions are connected by migrants.
inequality_equal: Migration inequality index of Bell et. al. (2002) based on a distributions of flows compared to equal distributions of expected flows . A value of 0 shows complete equality in flows and 1 shows maximum inequality.
inequality_sim: Migration inequality index of Bell et. al. (2002) based on a distributions of flows compared to distributions of expected flows from a Poisson regression independence fit flow ~ orig + dest. A value of 0 shows complete equality in flows and 1 shows maximum inequality.
gini_total: Overall concentration of migration from Bell (2002), corrected from Plane and Mulligan (1997). A value of 0 means no spatial focusing and 1 shows that all migrants are found in one single flow. Calculated using migration.indices::migration.gini.total()
gini_orig_standardized: Relative extent to which the origin selections of out-migrations are spatially focused. A value of 0 means no spatial focusing and 1 shows maximum focusing. Adapted from migration.indices::migration.gini.row.standardized().
gini_dest_standardized: Relative extent to which the destination selections of in-migrations are spatially focused. A value of 0 means no spatial focusing and 1 shows maximum focusing. Adapted from migration.indices::migration.gini.col.standardized().
mwg_orig: Origin spatial focusing, from Bell et. al. (2002). Calculated using migration.indices::migration.weighted.gini.out()
mwg_dest: Destination spatial focusing, from Bell et. al. (2002). Calculated using migration.indices::migration.weighted.gini.in()
mwg_mean: Mean spatial focusing, from Bell et. al. (2002). Average of the origin and destination migration weighted Gini indices (mwg_orig and mwg_dest). A value of 0 means no spatial focusing and 1 shows that all migrants are found in one region. Calculated using migration.indices::migration.weighted.gini.mean()
cv: Coefficient of variation from Rogers and Raymer (1998).
acv: Aggregated system-wide coefficient of variation from Rogers and Sweeney (1998), using migration.indices::migration.acv()

Examples

library(dplyr)
korea_gravity %>%
  filter(year == 2020) %>%
  select(orig, dest, flow) %>%
  index_connectivity()
#> # A tibble: 11 × 2
#>    measure                  value
#>    <chr>                    <dbl>
#>  1 connectivity            1     
#>  2 inequality_equal        0.541 
#>  3 inequality_sim          0.281 
#>  4 gini_total              0.709 
#>  5 gini_orig_standardized  0.0493
#>  6 gini_dest_standardized  0.0517
#>  7 mwg_orig                0.0370
#>  8 mwg_dest                0.0389
#>  9 mwg_mean                0.0379
#> 10 cv                     17.9   
#> 11 acv                     3.43