Calculates the Palma Ratio of a given accessibility distribution. Originally defined as the income share of the richest 10% of a population divided by the income share of the poorest 40%, this measure has been adapted in transport planning as the average accessibility of the richest 10% divided by the average accessibility of the poorest 40%.

palma_ratio(
  accessibility_data,
  sociodemographic_data,
  opportunity,
  population,
  income,
  group_by = character(0)
)

Arguments

accessibility_data

A data frame. The accessibility levels whose inequality should be calculated. Must contain the columns id and any others specified in opportunity.

sociodemographic_data

A data frame. The distribution of sociodemographic characteristics of the population in the study area cells. Must contain the columns id and any others specified in population and income.

opportunity

A string. The name of the column in accessibility_data with the accessibility levels to be considerend when calculating inequality levels.

population

A string. The name of the column in sociodemographic_data with the number of people in each cell. Used to weigh accessibility levels when calculating inequality.

income

A string. The name of the column in sociodemographic_data with the income variable that should be used to classify the population in socioeconomic groups. Please note that this variable should describe income per capita (e.g. mean income per capita, household income per capita, etc), instead of the total amount of income in each cell.

group_by

A character vector. When not character(0) (the default), indicates the accessibility_data columns that should be used to group the inequality estimates by. For example, if accessibility_data includes a scenario column that identifies distinct scenarios that each accessibility estimates refer to (e.g. before and after a transport policy intervention), passing "scenario" to this parameter results in inequality estimates grouped by scenario.

Value

A data frame containing the inequality estimates for the study area.

See also

Other inequality: concentration_index(), gini_index(), theil_t()

Examples

data_dir <- system.file("extdata", package = "accessibility")
travel_matrix <- readRDS(file.path(data_dir, "travel_matrix.rds"))
land_use_data <- readRDS(file.path(data_dir, "land_use_data.rds"))

access <- cumulative_cutoff(
  travel_matrix,
  land_use_data,
  cutoff = 30,
  opportunity = "jobs",
  travel_cost = "travel_time"
)

palma <- palma_ratio(
  access,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population",
  income = "income_per_capita"
)
palma
#>    palma_ratio
#> 1:    3.800465