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Calculate segregation at multiple geographies

calculate_segregation_metrics.Rd

Calculate multi-group segregation metrics using the Mutual Information Index (M)

Usage

calculate_segregation_metrics(
  data,
  data_format,
  nesting_geography_geoid_length
)

Arguments

data

A dataframe containing a GEOID column and the required input measures, e.g., of race or income, at a single geography (e.g., tract). The GEOID column must be a character column, and each GEOID must be unique. If data are formatted wide, there must be at least two columns in addition to GEOID. If data are formatted long, there must be a single columd in addition to GEOID. inputted data cannot contain other measures.For example: tibble::tribble( ~GEOID, ~race_nonhispanic_white_alone, ~race_nonhispanic_black_alone, "37001020100", 2835, 1035, "37001020200", 1205, 1321)

data_format

Describe the structure of the inputted data. One of "wide" or "long". Data are returned in the same format in which they are passed to the function.

nesting_geography_geoid_length

The length of the GEOID that identifies nesting geographies. For example, if smaller_geography_data is defined at the tract level (with a GEOID of length 11), then nesting_geography_geoid_length = 5 would return segregation metrics for counties (which have a GEOID of length 5) and for tracts (relative to other tracts within the same county).

Value

A dataframe comprising segregation estimates and associated p-values at both geographic levels.

Details

Given data at a smaller geography (e.g., tract), calculate_segregation_metrics() returns Mutual Information Index (M) values and associated p-values for a perfectly nested larger geography (e.g., a county or state) as well as decomposed values for the smaller geography (e.g., tract). Note that all segregation calculations rely on segregation and users should refer to that package at https://github.com/elbersb/segregation for further implementation details.

See also

Functions used for underlying segregation calculations are from the segregation package.

Examples

if (FALSE) { # \dontrun{
variables = c(
  race_nonhispanic_white_alone_ = "B03002_003",
  race_nonhispanic_black_alone_ = "B03002_004",
  race_nonhispanic_native_alone_ = "B03002_005",
  race_nonhispanic_asian_alone_ = "B03002_006",
  race_nonhispanic_nhpi_alone_ = "B03002_007")
df_long = tidycensus::get_acs(
  geography = "tract",
  state = "SC",
  variables = variables,
  output = "tidy") %>%
  # can only include a GEOID column and segregation-related measures
  dplyr::select(-c(NAME, matches("_M$")))
calculate_segregation_metrics(
  data = df_long,
  data_format = "long",
  nesting_geography_geoid_length = 5)
} # }