accessibility (version 1.4.0)

gini_index: Gini Index

Description

Calculates the Gini Index of a given accessibility distribution.

Usage

gini_index(
  accessibility_data,
  sociodemographic_data,
  opportunity,
  population,
  group_by = character(0)
)

Value

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

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.

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.

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.

See Also

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

Examples

if (FALSE) { # identical(tolower(Sys.getenv("NOT_CRAN")), "true")
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"
)

gini <- gini_index(
  access,
  sociodemographic_data = land_use_data,
  opportunity = "jobs",
  population = "population"
)
gini
}