R/massey.R

In ndi: Neighborhood Deprivation Indices

#' Absolute Clustering based on Massey & Denton (1988)
#' 
#' Compute the aspatial Absolute Clustering (Massey & Denton) of a selected racial or ethnic subgroup(s) and U.S. geographies.
#'
#' @param geo_large Character string specifying the larger geographical unit of the data. The default is counties \code{geo_large = 'county'}.
#' @param geo_small Character string specifying the smaller geographical unit of the data. The default is census tracts \code{geo_small = 'tract'}.
#' @param year Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.
#' @param subgroup Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.
#' @param crs Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America \code{crs = 'ESRI:102008'}.
#' @param omit_NAs Logical. If FALSE, will compute index for a larger geographical unit only if all of its smaller geographical units have values. The default is TRUE.
#' @param quiet Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.
#' @param ... Arguments passed to \code{\link[tidycensus]{get_acs}} to select state, county, and other arguments for census characteristics
#'
#' @details This function will compute the aspatial Absolute Clustering (\emph{ACL}) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Massey & Denton (1988) \doi{10.1093/sf/67.2.281}. This function provides the computation of \emph{ACL} for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).
#' 
#' The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey characteristics used for the computation. The yearly estimates are available for 2009 onward when ACS-5 data are available (2010 onward for \code{geo_large = 'cbsa'} and 2011 onward for \code{geo_large = 'place'}, \code{geo_large = 'csa'}, or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial or ethnic subgroups (U.S. Census Bureau definitions) are:
#' \itemize{
#'  \item \strong{B03002_002}: not Hispanic or Latino \code{'NHoL'}
#'  \item \strong{B03002_003}: not Hispanic or Latino, white alone \code{'NHoLW'}
#'  \item \strong{B03002_004}: not Hispanic or Latino, Black or African American alone \code{'NHoLB'}
#'  \item \strong{B03002_005}: not Hispanic or Latino, American Indian and Alaska Native alone \code{'NHoLAIAN'}
#'  \item \strong{B03002_006}: not Hispanic or Latino, Asian alone \code{'NHoLA'}
#'  \item \strong{B03002_007}: not Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone \code{'NHoLNHOPI'}
#'  \item \strong{B03002_008}: not Hispanic or Latino, Some other race alone \code{'NHoLSOR'}
#'  \item \strong{B03002_009}: not Hispanic or Latino, Two or more races \code{'NHoLTOMR'}
#'  \item \strong{B03002_010}: not Hispanic or Latino, Two races including Some other race \code{'NHoLTRiSOR'}
#'  \item \strong{B03002_011}: not Hispanic or Latino, Two races excluding Some other race, and three or more races \code{'NHoLTReSOR'}
#'  \item \strong{B03002_012}: Hispanic or Latino \code{'HoL'}
#'  \item \strong{B03002_013}: Hispanic or Latino, white alone \code{'HoLW'}
#'  \item \strong{B03002_014}: Hispanic or Latino, Black or African American alone \code{'HoLB'}
#'  \item \strong{B03002_015}: Hispanic or Latino, American Indian and Alaska Native alone \code{'HoLAIAN'}
#'  \item \strong{B03002_016}: Hispanic or Latino, Asian alone \code{'HoLA'}
#'  \item \strong{B03002_017}: Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone \code{'HoLNHOPI'}
#'  \item \strong{B03002_018}: Hispanic or Latino, Some other race alone \code{'HoLSOR'}
#'  \item \strong{B03002_019}: Hispanic or Latino, Two or more races \code{'HoLTOMR'}
#'  \item \strong{B03002_020}: Hispanic or Latino, Two races including Some other race \code{'HoLTRiSOR'}
#'  \item \strong{B03002_021}: Hispanic or Latino, Two races excluding Some other race, and three or more races \code{'HoLTReSOR'}
#' }
#'
#' Use the internal \code{state} and \code{county} arguments within the \code{\link[tidycensus]{get_acs}} function to specify geographic extent of the data output.
#'
#' \emph{ACL} is a measure of clustering of racial or ethnic populations within smaller geographical units that are located within larger geographical units. \emph{ACL} can range in value from 0 to Inf and represents the degree to which an area is a racial or ethnic enclave. A value of 1 indicates there is no differential clustering of the racial or ethnic subgroup. A value greater than 1 indicates the racial or ethnic subgroup live nearer to one another. A value less than 1 indicates the racial or ethnic subgroup do not live near one another.
#'
#' The metric uses the exponential transform of a distance matrix (kilometers) between smaller geographical area centroids, with a diagonal defined as \code{(0.6*a_{i})^{0.5}} where \code{a_{i}} is the area (square kilometers) of smaller geographical unit \code{i} as defined by White (1983) \doi{10.1086/227768}.
#'
#' Larger geographical units available include states \code{geo_large = 'state'}, counties \code{geo_large = 'county'}, census tracts \code{geo_large = 'tract'}, census-designated places \code{geo_large = 'place'}, core-based statistical areas \code{geo_large = 'cbsa'}, combined statistical areas \code{geo_large = 'csa'}, and metropolitan divisions \code{geo_large = 'metro'}. Smaller geographical units available include, counties \code{geo_small = 'county'}, census tracts \code{geo_small = 'tract'}, and census block groups \code{geo_small = 'cbg'}. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the \emph{ACL} value returned is NA. If the larger geographical unit is census-designated places \code{geo_large = 'place'}, core-based statistical areas \code{geo_large = 'cbsa'}, combined statistical areas \code{geo_large = 'csa'}, or metropolitan divisions \code{geo_large = 'metro'}, only the smaller geographical units completely within a larger geographical unit are considered in the \emph{V} computation (see internal \code{\link[sf]{st_within}} function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal \code{state} argument to ensure all appropriate smaller geographical units are included in the \emph{ACL} computation.
#' 
#' @return An object of class 'list'. This is a named list with the following components:
#'
#' \describe{
#' \item{\code{acl}}{An object of class 'tbl' for the GEOID, name, and \emph{ACL} at specified larger census geographies.}
#' \item{\code{acl_data}}{An object of class 'tbl' for the raw census values at specified smaller census geographies.}
#' \item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute \emph{ACL}.}
#' }
#'
#' @seealso \code{\link[tidycensus]{get_acs}} for additional arguments for geographic extent selection (i.e., \code{state} and \code{county}).
#' @seealso Relative Clustering Index: \code{\link{denton}}
#' @seealso Proximity measures: \code{\link{morgan_denton}}, \code{\link{morgan_massey}}, \code{\link{white_blau}}
#' 
#' @references Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. \emph{Social Forces}, 67(1):281-315. \doi{10.1093/sf/67.2.281}
#' 
#' @import dplyr
#' @importFrom sf st_centroid st_distance st_drop_geometry st_transform st_within
#' @importFrom stats complete.cases
#' @importFrom stringr str_trim
#' @importFrom tidycensus get_acs
#' @importFrom tidyr pivot_longer separate
#' @importFrom tigris combined_statistical_areas core_based_statistical_areas metro_divisions places
#' @importFrom units drop_units set_units
#' @importFrom utils stack
#' @export
#'
#' @examples
#' \dontrun{
#' # Wrapped in \dontrun{} because these examples require a Census API key.
#'
#'   # Absolute Clustering (a measure of clustering)
#'   ## of Black populations
#'   ## in census tracts within counties of Georgia, U.S.A. (2020)
#'   massey(
#'     geo_large = 'county',
#'     geo_small = 'tract',
#'     state = 'GA',
#'     year = 2020,
#'     subgroup = c('NHoLB', 'HoLB')
#'    )
#'
#' }
#'
massey <- function(geo_large = 'county',
                   geo_small = 'tract',
                   year = 2020,
                   subgroup,
                   crs = 'ESRI:102008',
                   omit_NAs = TRUE,
                   quiet = FALSE,
                   ...) {
  
  # Check arguments
  match.arg(geo_large, choices = c('state', 'county', 'tract', 'place', 'cbsa', 'csa', 'metro'))
  match.arg(geo_small, choices = c('county', 'tract', 'cbg', 'block group'))
  stopifnot(is.numeric(year), year >= 2009) # all variables available 2009 onward
  match.arg(
    subgroup,
    several.ok = TRUE,
    choices = c(
      'NHoL',
      'NHoLW',
      'NHoLB',
      'NHoLAIAN',
      'NHoLA',
      'NHoLNHOPI',
      'NHoLSOR',
      'NHoLTOMR',
      'NHoLTRiSOR',
      'NHoLTReSOR',
      'HoL',
      'HoLW',
      'HoLB',
      'HoLAIAN',
      'HoLA',
      'HoLNHOPI',
      'HoLSOR',
      'HoLTOMR',
      'HoLTRiSOR',
      'HoLTReSOR'
    )
  )
  
  # Select census variables
  vars <- c(
    TotalPop = 'B03002_001',
    NHoL = 'B03002_002',
    NHoLW = 'B03002_003',
    NHoLB = 'B03002_004',
    NHoLAIAN = 'B03002_005',
    NHoLA = 'B03002_006',
    NHoLNHOPI = 'B03002_007',
    NHoLSOR = 'B03002_008',
    NHoLTOMR = 'B03002_009',
    NHoLTRiSOR = 'B03002_010',
    NHoLTReSOR = 'B03002_011',
    HoL = 'B03002_012',
    HoLW = 'B03002_013',
    HoLB = 'B03002_014',
    HoLAIAN = 'B03002_015',
    HoLA = 'B03002_016',
    HoLNHOPI = 'B03002_017',
    HoLSOR = 'B03002_018',
    HoLTOMR = 'B03002_019',
    HoLTRiSOR = 'B03002_020',
    HoLTReSOR = 'B03002_021'
  )
  
  selected_vars <- vars[c('TotalPop', subgroup)]
  out_names <- c(names(selected_vars), 'ALAND') # save for output
  in_subgroup <- paste0(subgroup, 'E')
  
  # Acquire ACL variables and sf geometries
  out_dat <- suppressMessages(suppressWarnings(
    tidycensus::get_acs(
      geography = geo_small,
      year = year,
      output = 'wide',
      variables = selected_vars,
      geometry = TRUE,
      keep_geo_vars = TRUE,
      ...
    )
  ))
  
  # Format output
  if (geo_small == 'county') {
    out_dat <- out_dat %>%
      tidyr::separate(NAME.y, into = c('county', 'state'), sep = ',')
  }
  if (geo_small == 'tract') {
    out_dat <- out_dat %>%
      tidyr::separate(NAME.y, into = c('tract', 'county', 'state'), sep = ',') %>%
      dplyr::mutate(tract = gsub('[^0-9\\.]', '', tract))
  }
  if (geo_small == 'cbg' | geo_small == 'block group') {
    out_dat <- out_dat %>%
      tidyr::separate(NAME.y, into = c('cbg', 'tract', 'county', 'state'), sep = ',') %>%
      dplyr::mutate(
        tract = gsub('[^0-9\\.]', '', tract),
        cbg = gsub('[^0-9\\.]', '', cbg)
      )
  }
  
  # Grouping IDs for ACL computation
  if (geo_large == 'state') {
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = STATEFP,
        state = stringr::str_trim(state)
      )
  }
  if (geo_large == 'tract') {
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = paste0(STATEFP, COUNTYFP, TRACTCE),
        state = stringr::str_trim(state),
        county = stringr::str_trim(county)
      )
  }
  if (geo_large == 'county') {
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = paste0(STATEFP, COUNTYFP),
        state = stringr::str_trim(state),
        county = stringr::str_trim(county)
      )
  }
  if (geo_large == 'place') {
    stopifnot(is.numeric(year), year >= 2011) # Places only available 2011 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::places(
      year = year, state = unique(out_dat$state))
    ))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 4] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        place = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 5] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      )
  }
  if (geo_large == 'cbsa') {
    stopifnot(is.numeric(year), year >= 2010) # CBSAs only available 2010 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::core_based_statistical_areas(year = year)))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 3] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        cbsa = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 4] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      )
  }
  if (geo_large == 'csa') {
    stopifnot(is.numeric(year), year >= 2011) # CSAs only available 2011 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::combined_statistical_areas(year = year)))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 2] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        csa = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 3] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      )
  }
  if (geo_large == 'metro') {
    stopifnot(is.numeric(year), year >= 2011) # Metropolitan Divisions only available 2011 onward
    lgeom <- suppressMessages(suppressWarnings(tigris::metro_divisions(year = year)))
    wlgeom <- sf::st_within(out_dat, lgeom)
    out_dat <- out_dat %>%
      dplyr::mutate(
        oid = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 4] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist(),
        metro = lapply(wlgeom, function(x) { 
          tmp <- lgeom[x, 5] %>% sf::st_drop_geometry()
          lapply(tmp, function(x) { if (length(x) == 0) NA else x })
        }) %>% 
          unlist()
      )
  }
  
  # Count of racial or ethnic subgroup populations
  ## Count of racial or ethnic comparison subgroup population
  if (length(in_subgroup) == 1) {
    out_dat <- out_dat %>%
      dplyr::mutate(subgroup = as.data.frame(.)[, in_subgroup])
  } else {
    out_dat <- out_dat %>%
      dplyr::mutate(subgroup = rowSums(as.data.frame(.)[, in_subgroup]))
  }
  
  # Compute ACL
  ## From Massey & Denton (1988) https://doi.org/10.1093/sf/67.2.281
  ## ACL = \frac{\sum_{i=1}^{n}\frac{x_{i}}{X}\sum_{i=1}^{n}c_{ij}x_{j}-\frac{X}{n^{2}}\sum_{i=1}^{n}\sum_{ij=1}^{n}c_{ij}}
  ##            {\sum_{i=1}^{n}\frac{x_{i}}{X}\sum_{i=1}^{n}c_{ij}t_{j}-\frac{X}{n^{2}}\sum_{i=1}^{n}\sum_{ij=1}^{n}c_{ij}}
  ## Where for i & j smaller geographical units:
  ## x_{i} denotes the racial or ethnic subgroup population of smaller geographical unit i
  ## X denotes the racial or ethnic subgroup population of a larger geographical unit
  ## t_{i} denotes the total population of smaller geographical unit i
  ## c_{ij} denotes the distance function between smaller geographical units where
  ## c_{ij} = exp(-d_{ij})
  ## d_{ij} denotes the distance between smaller geographical unit centroids
  ## d_{ii} = 0.6(a_{i})^{0.05}
  ## a_{i} denotes the land area of smaller geographical unit i
  
  ## Compute
  out_tmp <- out_dat %>%
    .[.$oid != 'NANA', ] %>%
    split(., f = list(.$oid)) %>%
    lapply(., FUN = acl_fun, crs = crs, omit_NAs = omit_NAs) %>%
    utils::stack(.) %>%
    dplyr::mutate(
      ACL = values,
      oid = ind
    ) %>%
    dplyr::select(ACL, oid) %>%
    sf::st_drop_geometry()
  
  # Warning for missingness of census characteristics
  missingYN <- out_dat[, c('TotalPopE', in_subgroup, 'ALAND')] %>% 
    sf::st_drop_geometry()
  names(missingYN) <- out_names
  missingYN <- missingYN %>%
    tidyr::pivot_longer(
      cols = dplyr::everything(),
      names_to = 'variable',
      values_to = 'val'
    ) %>%
    dplyr::group_by(variable) %>%
    dplyr::summarise(
      total = dplyr::n(),
      n_missing = sum(is.na(val)),
      percent_missing = paste0(round(mean(is.na(val)) * 100, 2), ' %')
    )
  
  if (quiet == FALSE) {
    # Warning for missing census data
    if (sum(missingYN$n_missing) > 0) {
      message('Warning: Missing census data')
    }
  }
  
  # Format output
  out <- out_dat %>%
    sf::st_drop_geometry() %>%
    dplyr::left_join(out_tmp, by = dplyr::join_by(oid))
  if (geo_large == 'state') {
    out <- out %>%
      dplyr::select(oid, state, ACL) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, state, ACL)
  }
  if (geo_large == 'county') {
    out <- out %>%
      dplyr::select(oid, state, county, ACL) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, state, county, ACL)
  }
  if (geo_large == 'tract') {
    out <- out %>%
      dplyr::select(oid, state, county, tract, ACL) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, state, county, tract, ACL)
  }
  if (geo_large == 'place') {
    out <- out %>%
      dplyr::select(oid, place, ACL) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, place, ACL)
  }
  if (geo_large == 'cbsa') {
    out <- out %>%
      dplyr::select(oid, cbsa, ACL) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, cbsa, ACL)
  }
  if (geo_large == 'csa') {
    out <- out %>%
      dplyr::select(oid, csa, ACL) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, csa, ACL)
  }
  if (geo_large == 'metro') {
    out <- out %>%
      dplyr::select(oid, metro, ACL) %>%
      unique(.) %>%
      dplyr::mutate(GEOID = oid) %>%
      dplyr::select(GEOID, metro, ACL)
  }
  
  out <- out %>%
    .[.$GEOID != 'NANA', ] %>%
    dplyr::filter(!is.na(GEOID)) %>%
    dplyr::distinct(GEOID, .keep_all = TRUE) %>%
    dplyr::arrange(GEOID) %>%
    dplyr::as_tibble()
  
  out_dat <- out_dat %>%
    dplyr::arrange(GEOID) %>%
    dplyr::as_tibble()
  
  out <- list(acl = out, acl_data = out_dat, missing = missingYN)
  
  return(out)
}