From 0b655f1891cb8ddcff1fe43363be97fa59dd1f4e Mon Sep 17 00:00:00 2001 From: Ian D Buller Date: Fri, 23 Aug 2024 22:13:47 -0400 Subject: [PATCH] :sparkles: Initial commit for branch "dev_gini" (ndi v0.1.6.9004) * The `gini()` function now computes the aspatial racial or ethnic Gini Index (*G*) based on [Gini (1921)](https://doi.org/10.2307/2223319) as the main outcome. Arguments `geo_large`, `geo_small`, `subgroup`, and `omit_NAs` were added and argument `geo` was deprecated. The `gini()` function still retrieves the original output of the aspatial income Gini Index (*G*) at each smaller geography and is moved from the `g` output to `g_data` output. * Renamed 'race/ethnicity' or 'racial/ethnic' to 'race or ethnicity' or 'racial or ethnic' throughout documentation to use more modern, inclusive, and appropriate language --- DESCRIPTION | 27 +- NEWS.md | 18 +- R/anthopolos.R | 10 +- R/atkinson.R | 15 +- R/bell.R | 17 +- R/bemanian_beyer.R | 19 +- R/duncan.R | 27 +- R/gini.R | 412 +++++++++-- R/globals.R | 4 +- R/hoover.R | 13 +- R/krieger.R | 6 +- R/lieberson.R | 13 +- R/ndi-package.R | 20 +- R/sudano.R | 13 +- R/utils.R | 22 + R/white.R | 13 +- R/white_blau.R | 27 +- README.md | 157 ++-- cran-comments.md | 10 +- man/anthopolos.Rd | 8 +- man/atkinson.Rd | 10 +- man/bell.Rd | 10 +- man/bemanian_beyer.Rd | 12 +- man/duncan.Rd | 12 +- man/figures/g.png | Bin 370462 -> 0 bytes man/figures/g_inc.png | Bin 0 -> 367736 bytes man/figures/g_re.png | Bin 0 -> 376098 bytes man/gini.Rd | 67 +- man/hoover.Rd | 8 +- man/krieger.Rd | 6 +- man/lieberson.Rd | 8 +- man/ndi-package.Rd | 20 +- man/sudano.Rd | 8 +- man/white.Rd | 8 +- man/white_blau.Rd | 12 +- tests/testthat/test-gini.R | 44 +- vignettes/vignette.Rmd | 184 +++-- vignettes/vignette.html | 1431 ++++++++++++++++++++---------------- 38 files changed, 1692 insertions(+), 999 deletions(-) delete mode 100644 man/figures/g.png create mode 100644 man/figures/g_inc.png create mode 100644 man/figures/g_re.png diff --git a/DESCRIPTION b/DESCRIPTION index dfc5ed3..ba145f8 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: ndi Title: Neighborhood Deprivation Indices -Version: 0.1.6.9003 +Version: 0.1.6.9004 Date: 2024-08-23 Authors@R: c(person(given = "Ian D.", @@ -30,25 +30,26 @@ Description: Computes various metrics of socio-economic deprivation and disparit (2021) , (3) compute the aspatial Index of Concentration at the Extremes (ICE) based on Feldman et al. (2015) and Krieger et al. (2016) - , (4) compute the aspatial racial/ethnic + , (4) compute the aspatial racial or ethnic Dissimilarity Index (D) based on Duncan & Duncan (1955) , (5) - compute the aspatial income or racial/ethnic Atkinson Index (A) based on Atkinson - (1970) , (6) aspatial racial/ethnic Interaction + compute the aspatial income or racial or ethnic Atkinson Index (A) based on Atkinson + (1970) , (6) aspatial racial or ethnic Interaction Index (xPy*) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell - (1954) , (7) aspatial racial/ethnic Correlation Ratio (V) + (1954) , (7) aspatial racial or ethnic Correlation Ratio (V) based on Bell (1954) and White (1986) , - (8) aspatial racial/ethnic Location Quotient (LQ) based on Merton (1939) + (8) aspatial racial or ethnic Location Quotient (LQ) based on Merton (1939) and Sudano et al. (2013) - , (9) aspatial racial/ethnic Local + , (9) aspatial racial or ethnic Local Exposure and Isolation (LEx/Is) metric based on Bemanian & Beyer (2017) - , (10) aspatial racial/ethnic Delta (DEL) + , (10) aspatial racial or ethnic Delta (DEL) based on Hoover (1941) and Duncan et al. (1961; LC:60007089), (11) an index of spatial proximity (SP) based on White (1986) - and Blau (1977; ISBN-13:978-0-029-03660-0), and (12) the - aspatial racial/ethnic Isolatoin Index (xPx*) based on Lieberson (1981; - ISBN-13:978-1-032-53884-6) and Bell (1954) . Also using data - from the ACS-5 (2005-2009 onward), the package can retrieve the aspatial Gini - Index (G) based Gini (1921) . + and Blau (1977; ISBN-13:978-0-029-03660-0), (12) the + aspatial racial or ethnic Isolatoin Index (xPx*) based on Lieberson (1981; + ISBN-13:978-1-032-53884-6) and Bell (1954) , and (13) the + aspatial racial or ethnic Gini Index (G) based Gini (1921) . + Also using data from the ACS-5 (2005-2009 onward), the package can retrieve the + aspatial income Gini Index (G) based on Gini (1921) . License: Apache License (>= 2.0) Encoding: UTF-8 LazyData: true diff --git a/NEWS.md b/NEWS.md index d470816..af31465 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,14 +1,15 @@ # ndi (development version) -## ndi v0.1.6.9003 +## ndi v0.1.6.9004 ### New Features -* Added `hoover()` function to compute the aspatial racial/ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089) +* Added `hoover()` function to compute the aspatial racial or ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089) * Added `white_blau()` function to compute an index of spatial proximity (*SP*) based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0) -* Added `lieberson()` function to compute he aspatial racial/ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and and [Bell (1954)](https://doi.org/10.2307/2574118) +* Added `lieberson()` function to compute the aspatial racial or ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and and [Bell (1954)](https://doi.org/10.2307/2574118) * Added `geo_large = 'cbsa'` for Core Based Statistical Areas, `geo_large = 'csa'` for Combined Statistical Areas, and `geo_large = 'metro'` for Metropolitan Divisions as the larger geographical unit in `atkinson()`, `bell()`, `bemanian_beyer()`, `duncan()`, `hoover()`, `lieberson()`, `sudano()`, and `white()`, `white_blau()` functions. * Thank you for the feature suggestions, [Symielle Gaston](https://orcid.org/0000-0001-9495-1592) * Added `holder` argument to `atkinson()` function to toggle the computation with or without the Hölder mean. The function can now compute *A* without the Hölder mean. The default is `holder = FALSE`. +* The `gini()` function now computes the aspatial racial or ethnic Gini Index (*G*) based on [Gini (1921)](https://doi.org/10.2307/2223319) as the main outcome. Arguments `geo_large`, `geo_small`, `subgroup`, and `omit_NAs` were added and argument `geo` was deprecated. The `gini()` function still retrieves the original output of the aspatial income Gini Index (*G*) at each smaller geography and is moved from the `g` output to `g_data` output. ### Updates * `bell()` function computes the Interaction Index (Bell) not the Isolation Index as previously documented. Updated documentation throughout @@ -17,6 +18,7 @@ * `tigris` and `units` are now Imports * 'package.R' deprecated. Replaced with 'ndi-package.R' * Re-formatted code and documentation throughout for consistent readability +* Renamed 'race/ethnicity' or 'racial/ethnic' to 'race or ethnicity' or 'racial or ethnic' throughout documentation to use more modern, inclusive, and appropriate language * Updated documentation about value range of *V* (White) from `{0 to 1}` to `{-Inf to Inf}` * Add examples for `hoover()` and `white_blau()` functions in vignette and README * Add example for `holder` argument in `atkinson()` function in README @@ -39,11 +41,11 @@ ## ndi v0.1.4 ### New Features -* Added `atkinson()` function to compute the aspatial income or racial/ethnic Atkinson Index (*A*) based on [Atkinson (1970)](https://doi.org/10.1016/0022-0531(70)90039-6) for specified counties/tracts 2009 onward -* Added `bell()` function to compute the aspatial racial/ethnic Interaction Index (_xPy\*_) based on Shevky & Williams (1949; ISBN-13:978-0837156378) and [Bell (1954)](https://doi.org/10.2307/2574118) -* Added `white()` function to compute the aspatial racial/ethnic Correlation Ratio (*V*) based on [Bell (1954)](https://doi.org/10.2307/2574118) and [White (1986)](https://doi.org/10.2307/3644339) -* Added `sudano()` function to compute the aspatial racial/ethnic Location Quotient (*LQ*) based on [Merton (1939)](https://doi.org/10.2307/2084686) and [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015) -* Added `bemanian_beyer()` function to compute the aspatial racial/ethnic Local Exposure and Isolation (*LEx/Is*) metric based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926) +* Added `atkinson()` function to compute the aspatial income or racial or ethnic Atkinson Index (*A*) based on [Atkinson (1970)](https://doi.org/10.1016/0022-0531(70)90039-6) for specified counties/tracts 2009 onward +* Added `bell()` function to compute the aspatial racial or ethnic Interaction Index (_xPy\*_) based on Shevky & Williams (1949; ISBN-13:978-0837156378) and [Bell (1954)](https://doi.org/10.2307/2574118) +* Added `white()` function to compute the aspatial racial or ethnic Correlation Ratio (*V*) based on [Bell (1954)](https://doi.org/10.2307/2574118) and [White (1986)](https://doi.org/10.2307/3644339) +* Added `sudano()` function to compute the aspatial racial or ethnic Location Quotient (*LQ*) based on [Merton (1939)](https://doi.org/10.2307/2084686) and [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015) +* Added `bemanian_beyer()` function to compute the aspatial racial or ethnic Local Exposure and Isolation (*LEx/Is*) metric based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926) ### Updates * `car` is now Imports diff --git a/R/anthopolos.R b/R/anthopolos.R index 1d54da1..6ddbc91 100644 --- a/R/anthopolos.R +++ b/R/anthopolos.R @@ -4,13 +4,13 @@ #' #' @param geo Character string specifying the geography of the data either census tracts \code{geo = 'tract'} (the default) or counties \code{geo = 'county'}. #' @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/ethnic subgroup(s). See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s). See Details for available choices. #' @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 spatial Racial Isolation Index (\emph{RI}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002} who originally designed the metric for the racial isolation of non-Hispanic Black individuals. This function provides the computation of \emph{RI} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the spatial Racial Isolation Index (\emph{RI}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002} who originally designed the metric for the racial isolation of non-Hispanic Black individuals. This function provides the computation of \emph{RI} 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 geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are 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'} @@ -36,7 +36,7 @@ #' #' 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. NOTE: Current version does not correct for edge effects (e.g., census geographies along the specified spatial extent border, coastline, or U.S.-Mexico / U.S.-Canada border) may have few neighboring census geographies, and \emph{RI} values in these census geographies may be unstable. A stop-gap solution for the former source of edge effect is to compute the \emph{RI} for neighboring census geographies (i.e., the states bordering a study area of interest) and then use the estimates of the study area of interest. #' -#' A census geography (and its neighbors) that has nearly all of its population who identify with the specified race/ethnicity subgroup(s) (e.g., non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 1. In contrast, a census geography (and its neighbors) that has nearly none of its population who identify with the specified race/ethnicity subgroup(s) (e.g., not non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 0. +#' A census geography (and its neighbors) that has nearly all of its population who identify with the specified race or ethnicity subgroup(s) (e.g., non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 1. In contrast, a census geography (and its neighbors) that has nearly none of its population who identify with the specified race or ethnicity subgroup(s) (e.g., not non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 0. #' #' @return An object of class 'list'. This is a named list with the following components: #' @@ -167,7 +167,7 @@ anthopolos <- function(geo = 'tract', ## RI_{im} = (Sigma_{j∈∂_{i}} w_{ij} * T_{jm}) / (Sigma_{j∈∂_{i}} w_{ij} * T_{j}) ## Where: ## ∂_{i} denotes the set of index units i and its neighbors - ## Given M mutually exclusive racial/ethnic subgroups, m indexes the subgroups of M + ## Given M mutually exclusive racial or ethnic subgroups, m indexes the subgroups of M ## T_{i} denotes the total population in region i (TotalPop) ## T_{im} denotes the population of the selected subgroup(s) (subgroup1, ...) ## w_{ij} denotes a nXn first-order adjacency matrix, where n is the number of census geometries in the study area diff --git a/R/atkinson.R b/R/atkinson.R index 0758198..a72a1c4 100644 --- a/R/atkinson.R +++ b/R/atkinson.R @@ -1,20 +1,20 @@ #' Atkinson Index based on Atkinson (1970) #' -#' Compute the aspatial Atkinson Index of income or selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute the aspatial Atkinson Index of income or 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_large = '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 income or racial/ethnic subgroup(s) as the comparison population. See Details for available choices. +#' @param subgroup Character string specifying the income or racial or ethnic subgroup(s) as the comparison population. See Details for available choices. #' @param epsilon Numerical. Shape parameter that denotes the aversion to inequality. Value must be between 0 and 1.0 (the default is 0.5). #' @param holder Logical. If TRUE, will compute index using the Hölder mean. If FALSE, will not compute with the Hölder mean. The default is FALSE. #' @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 Atkinson Index (\emph{A}) of income or selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. This function provides the computation of \emph{A} for median household income and any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Atkinson Index (\emph{A}) of income or 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 Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. This function provides the computation of \emph{A} for median household income and 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. When \code{subgroup = 'MedHHInc'}, the metric will be computed for median household income ('B19013_001'). The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. When \code{subgroup = 'MedHHInc'}, the metric will be computed for median household income ('B19013_001'). 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'} @@ -40,7 +40,7 @@ #' #' 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{A} is a measure of the evenness of residential inequality (e.g., racial/ethnic segregation) when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{A} can range in value from 0 to 1 with smaller values indicating lower levels of inequality (e.g., less segregation). +#' \emph{A} is a measure of the evenness of residential inequality (e.g., racial or ethnic segregation) when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{A} can range in value from 0 to 1 with smaller values indicating lower levels of inequality (e.g., less segregation). #' #' The \code{epsilon} argument that determines how to weight the increments to inequality contributed by different proportions of the Lorenz curve. A user must explicitly decide how heavily to weight smaller geographical units at different points on the Lorenz curve (i.e., whether the index should take greater account of differences among areas of over- or under-representation). The \code{epsilon} argument must have values between 0 and 1.0. For \code{0 <= epsilon < 0.5} or less 'inequality-averse,' smaller geographical units with a subgroup proportion smaller than the subgroup proportion of the larger geographical unit contribute more to inequality ('over-representation'). For \code{0.5 < epsilon <= 1.0} or more 'inequality-averse,' smaller geographical units with a subgroup proportion larger than the subgroup proportion of the larger geographical unit contribute more to inequality ('under-representation'). If \code{epsilon = 0.5} (the default), units of over- and under-representation contribute equally to the index. See Section 2.3 of Saint-Jacques et al. (2020) \doi{10.48550/arXiv.2002.05819} for one method to select \code{epsilon}. #' @@ -57,6 +57,7 @@ #' @import dplyr #' @importFrom sf st_drop_geometry st_within #' @importFrom stats na.omit +#' @importFrom stringr str_trim #' @importFrom tidycensus get_acs #' @importFrom tidyr pivot_longer separate #' @importFrom tigris combined_statistical_areas core_based_statistical_areas metro_divisions @@ -270,8 +271,8 @@ atkinson <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic subgroup population + # Count of racial or ethnic subgroup populations + ## Count of racial or ethnic subgroup population if (length(in_subgroup) == 1) { out_dat <- out_dat %>% dplyr::mutate(subgroup = .[, in_subgroup]) diff --git a/R/bell.R b/R/bell.R index 5abef97..f309303 100644 --- a/R/bell.R +++ b/R/bell.R @@ -1,19 +1,19 @@ #' Interaction Index based on Shevky & Williams (1949) and Bell (1954) #' -#' Compute the aspatial Interaction Index (Bell) of a selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute the aspatial Interaction Index (Bell) 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_large = '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/ethnic subgroup(s). See Details for available choices. -#' @param subgroup_ixn Character string specifying the racial/ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s). See Details for available choices. +#' @param subgroup_ixn Character string specifying the racial or ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices. #' @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 Interaction Index (_xPy\*_) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of _xPy\*_ for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Interaction Index (_xPy\*_) 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 Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of _xPy\*_ 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = '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'} @@ -54,6 +54,7 @@ #' @import dplyr #' @importFrom sf st_drop_geometry 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 @@ -292,8 +293,8 @@ bell <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # 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 = .[, in_subgroup]) @@ -301,7 +302,7 @@ bell <- function(geo_large = 'county', out_dat <- out_dat %>% dplyr::mutate(subgroup = rowSums(.[, in_subgroup])) } - ## Count of racial/ethnic interaction subgroup population + ## Count of racial or ethnic interaction subgroup population if (length(in_subgroup_ixn) == 1) { out_dat <- out_dat %>% dplyr::mutate(subgroup_ixn = .[, in_subgroup_ixn]) diff --git a/R/bemanian_beyer.R b/R/bemanian_beyer.R index 646bf4e..1fdf986 100644 --- a/R/bemanian_beyer.R +++ b/R/bemanian_beyer.R @@ -1,19 +1,19 @@ #' Local Exposure and Isolation metric based on Bemanian & Beyer (2017) #' -#' Compute the aspatial Local Exposure and Isolation (Bemanian & Beyer) metric of a selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute the aspatial Local Exposure and Isolation (Bemanian & Beyer) metric 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_large = '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/ethnic subgroup(s). See Details for available choices. -#' @param subgroup_ixn Character string specifying the racial/ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s). See Details for available choices. +#' @param subgroup_ixn Character string specifying the racial or ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices. #' @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 Local Exposure and Isolation (\emph{LEx/Is}) metric of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. This function provides the computation of \emph{LEx/Is} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Local Exposure and Isolation (\emph{LEx/Is}) metric 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 Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. This function provides the computation of \emph{LEx/Is} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = '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'} @@ -39,7 +39,7 @@ #' #' 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{LEx/Is} is a measure of the probability that two individuals living within a specific smaller geography (e.g., census tract) of either different (i.e., exposure) or the same (i.e., isolation) racial/ethnic subgroup(s) will interact, assuming that individuals within a smaller geography are randomly mixed. \emph{LEx/Is} is standardized with a logit transformation and centered against an expected case that all races/ethnicities are evenly distributed across a larger geography. (Note: will adjust data by 0.025 if probabilities are zero, one, or undefined. The output will include a warning if adjusted. See \code{\link[car]{logit}} for additional details.) +#' \emph{LEx/Is} is a measure of the probability that two individuals living within a specific smaller geography (e.g., census tract) of either different (i.e., exposure) or the same (i.e., isolation) racial or ethnic subgroup(s) will interact, assuming that individuals within a smaller geography are randomly mixed. \emph{LEx/Is} is standardized with a logit transformation and centered against an expected case that all races or ethnicities are evenly distributed across a larger geography. (Note: will adjust data by 0.025 if probabilities are zero, one, or undefined. The output will include a warning if adjusted. See \code{\link[car]{logit}} for additional details.) #' #' \emph{LEx/Is} can range from negative infinity to infinity. If \emph{LEx/Is} is zero then the estimated probability of the interaction between two people of the given subgroup(s) within a smaller geography is equal to the expected probability if the subgroup(s) were perfectly mixed in the larger geography. If \emph{LEx/Is} is greater than zero then the interaction is more likely to occur within the smaller geography than in the larger geography, and if \emph{LEx/Is} is less than zero then the interaction is less likely to occur within the smaller geography than in the larger geography. Note: the exponentiation of each \emph{LEx/Is} metric results in the odds ratio of the specific exposure or isolation of interest in a smaller geography relative to the larger geography. #' @@ -57,6 +57,7 @@ #' @importFrom car logit #' @importFrom sf st_drop_geometry 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 @@ -295,8 +296,8 @@ bemanian_beyer <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # 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 = .[, in_subgroup]) @@ -304,7 +305,7 @@ bemanian_beyer <- function(geo_large = 'county', out_dat <- out_dat %>% dplyr::mutate(subgroup = rowSums(.[, in_subgroup])) } - ## Count of racial/ethnic interaction subgroup population + ## Count of racial or ethnic interaction subgroup population if (length(in_subgroup_ixn) == 1) { out_dat <- out_dat %>% dplyr::mutate(subgroup_ixn = .[, in_subgroup_ixn]) diff --git a/R/duncan.R b/R/duncan.R index 7f7a563..8532ed5 100644 --- a/R/duncan.R +++ b/R/duncan.R @@ -1,19 +1,19 @@ #' Dissimilarity Index based on Duncan & Duncan (1955) #' -#' Compute the aspatial Dissimilarity Index (Duncan & Duncan) of selected racial/ethnic subgroup(s) and U.S. geographies +#' Compute the aspatial Dissimilarity Index (Duncan & Duncan) of 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_large = '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/ethnic subgroup(s) as the comparison population. See Details for available choices. -#' @param subgroup_ref Character string specifying the racial/ethnic subgroup(s) as the reference population. See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices. +#' @param subgroup_ref Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices. #' @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 Dissimilarity Index (\emph{D}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Duncan & Duncan (1955) \doi{10.2307/2088328}. This function provides the computation of \emph{D} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Dissimilarity Index (\emph{D}) 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 Duncan & Duncan (1955) \doi{10.2307/2088328}. This function provides the computation of \emph{D} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = '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'} @@ -39,7 +39,7 @@ #' #' 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{D} is a measure of the evenness of racial/ethnic residential segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{D} can range in value from 0 to 1 and represents the proportion of racial/ethnic subgroup members that would have to change their area of residence to achieve an even distribution within the larger geographical area under conditions of maximum segregation. +#' \emph{D} is a measure of the evenness of racial or ethnic residential segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{D} can range in value from 0 to 1 and represents the proportion of racial or ethnic subgroup members that would have to change their area of residence to achieve an even distribution within the larger geographical area under conditions of maximum segregation. #' #' Larger geographies available include state \code{geo_large = 'state'}, county \code{geo_large = 'county'}, census tract \code{geo_large = 'tract'}, Core Based Statistical Area \code{geo_large = 'cbsa'}, Combined Statistical Area \code{geo_large = 'csa'}, and Metropolitan Division \code{geo_large = 'metro'} levels. Smaller geographies available include, county \code{geo_small = 'county'}, census tract \code{geo_small = 'tract'}, and census block group \code{geo_small = 'block group'} levels. If a larger geographical area is comprised of only one smaller geographical area (e.g., a U.S county contains only one census tract), then the \emph{D} value returned is NA. If the larger geographical unit is Combined Based Statistical Areas \code{geo_large = 'csa'} or Core Based Statistical Areas \code{geo_large = 'cbsa'}, only the smaller geographical units completely within a larger geographical unit are considered in the \emph{D} 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{D} computation. #' @@ -54,6 +54,7 @@ #' @import dplyr #' @importFrom sf st_drop_geometry 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 @@ -291,8 +292,8 @@ duncan <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # 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 = .[, in_subgroup]) @@ -300,7 +301,7 @@ duncan <- function(geo_large = 'county', out_dat <- out_dat %>% dplyr::mutate(subgroup = rowSums(.[, in_subgroup])) } - ## Count of racial/ethnic reference subgroup population + ## Count of racial or ethnic reference subgroup population if (length(in_subgroup_ref) == 1) { out_dat <- out_dat %>% dplyr::mutate(subgroup_ref = .[, in_subgroup_ref]) @@ -314,10 +315,10 @@ duncan <- function(geo_large = 'county', ## D_{jt} = 1/2 \sum_{i=1}^{k} | \frac{x_{ijt}}{X_{jt}}-\frac{y_{ijt}}{Y_{jt}}| ## Where for k smaller geographies: ## D_{jt} denotes the D of larger geography j at time t - ## x_{ijt} denotes the racial/ethnic subgroup population of smaller geography i within larger geography j at time t - ## X_{jt} denotes the racial/ethnic subgroup population of larger geography j at time t - ## y_{ijt} denotes the racial/ethnic referent subgroup population of smaller geography i within larger geography j at time t - ## Y_{jt} denotes the racial/ethnic referent subgroup population of larger geography j at time t + ## x_{ijt} denotes the racial or ethnic subgroup population of smaller geography i within larger geography j at time t + ## X_{jt} denotes the racial or ethnic subgroup population of larger geography j at time t + ## y_{ijt} denotes the racial or ethnic referent subgroup population of smaller geography i within larger geography j at time t + ## Y_{jt} denotes the racial or ethnic referent subgroup population of larger geography j at time t ## Compute out_tmp <- out_dat %>% diff --git a/R/gini.R b/R/gini.R index 299c54f..6bc0cf8 100644 --- a/R/gini.R +++ b/R/gini.R @@ -1,31 +1,63 @@ #' Gini Index based on Gini (1921) #' -#' Retrieve the aspatial Gini Index of income inequality. +#' Compute the aspatial racial or ethnic Gini Index and retrieve the aspatial income Gini Index #' -#' @param geo Character string specifying the geography of the data either census tracts \code{geo = 'tract'} (the default) or counties \code{geo = 'county'}. +#' @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_large = '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). See Details for available choices. +#' @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 #' @param ... Arguments passed to \code{\link[tidycensus]{get_acs}} to select state, county, and other arguments for census characteristics #' -#' @details This function will retrieve the aspatial Gini Index (\emph{G}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Gini (1921) \doi{10.2307/2223319}. -#' -#' The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey estimates of \emph{G} for income inequality (ACS: B19083). The estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. +#' @details This function will retrieve the aspatial Gini Index (\emph{G}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Gini (1921) \doi{10.2307/2223319} for income inequality (at smaller geographical units) and race or ethnicity inequality (at larger geographical units). #' +#' The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey estimates of \emph{G} for the geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. The function will retrieve the provided income inequality metric (\strong{B19083}) and 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. #' -#' According to the U.S. Census Bureau \url{https://www.census.gov/topics/income-poverty/income-inequality/about/metrics/gini-index.html}: 'The Gini Index is a summary measure of income inequality. The Gini coefficient incorporates the detailed shares data into a single statistic, which summarizes the dispersion of income across the entire income distribution. The Gini coefficient ranges from 0, indicating perfect equality (where everyone receives an equal share), to 1, perfect inequality (where only one recipient or group of recipients receives all the income). The Gini is based on the difference between the Lorenz curve (the observed cumulative income distribution) and the notion of a perfectly equal income distribution.' +#' According to the U.S. Census Bureau \url{https://www.census.gov/topics/income-poverty/income-inequality/about/metrics/gini-index.html}: 'The Gini Index is a summary measure of income inequality. The Gini coefficient incorporates the detailed shares data into a single statistic, which summarizes the dispersion of income across the entire income distribution. The Gini coefficient ranges from 0, indicating perfect equality (where everyone receives an equal share), to 1, perfect inequality (where only one recipient or group of recipients receives all the income). The Gini Index is based on the difference between the Lorenz curve (the observed cumulative income distribution) and the notion of a perfectly equal income distribution.' For racial or ethnic inequality, *G* is a summary measure of racial or ethnic unevenness or the mean absolute difference between a selected subgroup proportions weighted across all pairs of geographic units, expressed as a proportion of the maximum weighted difference. +#' +#' Larger geographies available include state \code{geo_large = 'state'}, county \code{geo_large = 'county'}, census tract \code{geo_large = 'tract'}, Core Based Statistical Area \code{geo_large = 'cbsa'}, Combined Statistical Area \code{geo_large = 'csa'}, and Metropolitan Division \code{geo_large = 'metro'} levels. Smaller geographies available include, county \code{geo_small = 'county'}, census tract \code{geo_small = 'tract'}, and census block group \code{geo_small = 'block group'} levels. If a larger geographical area is comprised of only one smaller geographical area (e.g., a U.S county contains only one census tract), then the \emph{V} value returned is NA. If the larger geographical unit is Combined Based Statistical Areas \code{geo_large = 'csa'} or Core Based Statistical Areas \code{geo_large = 'cbsa'}, 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{V} computation. #' #' @return An object of class 'list'. This is a named list with the following components: #' #' \describe{ -#' \item{\code{g}}{An object of class 'tbl' for the GEOID, name, and \emph{G} of specified census geographies.} -#' \item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for \emph{G}.} +#' \item{\code{g}}{An object of class 'tbl' for the GEOID, name, and \emph{G_re} metrics of specified census geographies.} +#' \item{\code{g_data}}{An object of class 'tbl' for the raw census values at specified smaller census geographies including \emph{G_inc}.} +#' \item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for \emph{G_inc} and each census variable used to compute \emph{G_re}.} #' } #' #' @import dplyr +#' @importFrom sf st_drop_geometry 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 +#' @importFrom utils stack #' @export #' #' @seealso \code{\link[tidycensus]{get_acs}} for additional arguments for geographic extent selection (i.e., \code{state} and \code{county}). @@ -34,86 +66,324 @@ #' \dontrun{ #' # Wrapped in \dontrun{} because these examples require a Census API key. #' -#' # Gini Index of income inequality +#' # Gini Index (a metric of unevenness) of Black populations #' ## of census tracts within Georgia, U.S.A., counties (2020) -#' gini(geo = 'tract', state = 'GA', year = 2020) -#' +#' gini( +#' geo_large = 'county', +#' geo_small = 'tract', +#' state = 'GA', +#' year = 2020, +#' subgroup = c('NHoLB', 'HoLB') +#' ) +#' #' } #' -gini <- function(geo = 'tract', +gini <- function(geo_large = 'county', + geo_small = 'tract', year = 2020, + subgroup, + omit_NAs = TRUE, quiet = FALSE, ...) { # Check arguments - match.arg(geo, choices = c('county', 'tract')) - stopifnot(is.numeric(year), year >= 2009) # the gini variable is available before and after 2009 but constrained for consistency with out indices (for now) - - # Select census variable - vars <- c(gini = 'B19083_001') - - # Acquire Gini Index - tmp_dat <- suppressMessages(suppressWarnings( - tidycensus::get_acs( - geography = geo, - year = year, - output = 'wide', - variables = vars, - ... + match.arg(geo_large, choices = c('state', 'county', 'tract', 'cbsa', 'csa', 'metro')) + match.arg(geo_small, choices = c('county', 'tract', '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' + ) ) - )) - - if (geo == 'tract') { - tmp_dat <- tmp_dat %>% - tidyr::separate(NAME, into = c('tract', 'county', 'state'), sep = ',') %>% - dplyr::mutate(tract = gsub('[^0-9\\.]', '', tract)) - } else { - tmp_dat <- tmp_dat %>% - tidyr::separate(NAME, into = c('county', 'state'), sep = ',') - } + + # Select census variable + 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', + G_inc = 'B19083_001' + ) - tmp_dat <- tmp_dat %>% - dplyr::mutate(G = giniE) + selected_vars <- vars[c('G_inc', 'TotalPop', subgroup)] + out_names <- names(selected_vars) # save for output + in_subgroup <- paste0(subgroup, 'E') + + # Acquire Gvariables 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, + ... + ) + )) - # Warning for missingness of census characteristics - missingYN <- tmp_dat %>% - dplyr::select(G) %>% - 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), ' %') - ) + # 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 == 'block group') { + out_dat <- out_dat %>% + tidyr::separate(NAME.y, into = c('block.group', 'tract', 'county', 'state'), sep = ',') %>% + dplyr::mutate( + tract = gsub('[^0-9\\.]', '', tract), block.group = gsub('[^0-9\\.]', '', block.group) + ) + } - if (quiet == FALSE) { - # Warning for missing census data - if (sum(missingYN$n_missing) > 0) { - message('Warning: Missing census data') - } + # Grouping IDs for R computation + if (geo_large == 'state') { + out_dat <- out_dat %>% + dplyr::mutate(oid = STATEFP, state = stringr::str_trim(state)) %>% + sf::st_drop_geometry() + } + 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) + ) %>% + sf::st_drop_geometry() + } + if (geo_large == 'county') { + out_dat <- out_dat %>% + dplyr::mutate( + oid = paste0(STATEFP, COUNTYFP), + state = stringr::str_trim(state), + county = stringr::str_trim(county) + ) %>% + sf::st_drop_geometry() + } + 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() + ) %>% + sf::st_drop_geometry() + } + 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() + ) %>% + sf::st_drop_geometry() + } + if (geo_large == 'metro') { + stopifnot(is.numeric(year), year >= 2011) # Metro 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() + ) %>% + sf::st_drop_geometry() } - if (geo == 'tract') { - out <- tmp_dat %>% - dplyr::select(GEOID, state, county, tract, G) + # 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 = .[, in_subgroup]) } else { - out <- tmp_dat %>% - dplyr::select(GEOID, state, county, G) + out_dat <- out_dat %>% + dplyr::mutate(subgroup = rowSums(.[, in_subgroup])) } + + # Compute G for race or ethnicity inequality + ## From Gini (1921) https://doi.org/10.2307/2223319 + ## G = \sum_{n}^{i=1}\sum_{n}^{j=1}\left [ t_{i}t_{j}\left| p_{i}-p_{j}\right| /2T^{2}P(1-P)\right ] + ## Where: + ## t_{i} is the total population of area i + ## t_{j} is the total population of area j + ## p_{i} is the proportion of the subgroup population of area i + ## p_{j} is the proportion of the subgroup population of area j + ## T is the total population of all areas + ## P is the proportion of the subgroup population of all areas + + ## Compute + out_tmp <- out_dat %>% + split(., f = list(out_dat$oid)) %>% + lapply(., FUN = g_fun, omit_NAs = omit_NAs) %>% + utils::stack(.) %>% + dplyr::mutate(G_re = values, oid = ind) %>% + dplyr::select(G_re, oid) - out <- out %>% - dplyr::mutate( - state = stringr::str_trim(state), - county = stringr::str_trim(county) - ) %>% - dplyr::arrange(GEOID) %>% - dplyr::as_tibble() - - out <- list(g = out, missing = missingYN) + # Warning for missingness of census characteristics + missingYN <- out_dat[, c('G_incE', 'TotalPopE', in_subgroup)] + 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$n_missing) > 0) { + message('Warning: Missing census data') + } + } + + # Format output + if (geo_large == 'state') { + out <- out_dat %>% + dplyr::left_join(out_tmp, by = dplyr::join_by(oid)) %>% + dplyr::select(oid, state, G_re) %>% + unique(.) %>% + dplyr::mutate(GEOID = oid) %>% + dplyr::select(GEOID, state, G_re) %>% + .[.$GEOID != 'NANA',] + } + if (geo_large == 'county') { + out <- out_dat %>% + dplyr::left_join(out_tmp, by = dplyr::join_by(oid)) %>% + dplyr::select(oid, state, county, G_re) %>% + unique(.) %>% + dplyr::mutate(GEOID = oid) %>% + dplyr::select(GEOID, state, county, G_re) %>% + .[.$GEOID != 'NANA',] + } + if (geo_large == 'tract') { + out <- out_dat %>% + dplyr::left_join(out_tmp, by = dplyr::join_by(oid)) %>% + dplyr::select(oid, state, county, tract, G_re) %>% + unique(.) %>% + dplyr::mutate(GEOID = oid) %>% + dplyr::select(GEOID, state, county, tract,G_re) %>% + .[.$GEOID != 'NANA',] + } + if (geo_large == 'cbsa') { + out <- out_dat %>% + dplyr::left_join(out_tmp, by = dplyr::join_by(oid)) %>% + dplyr::select(oid, cbsa, G_re) %>% + unique(.) %>% + dplyr::mutate(GEOID = oid) %>% + dplyr::select(GEOID, cbsa, G_re) %>% + .[.$GEOID != 'NANA', ] %>% + dplyr::distinct(GEOID, .keep_all = TRUE) %>% + dplyr::filter(stats::complete.cases(.)) + } + if (geo_large == 'csa') { + out <- out_dat %>% + dplyr::left_join(out_tmp, by = dplyr::join_by(oid)) %>% + dplyr::select(oid, csa, G_re) %>% + unique(.) %>% + dplyr::mutate(GEOID = oid) %>% + dplyr::select(GEOID, csa, G_re) %>% + .[.$GEOID != 'NANA', ] %>% + dplyr::distinct(GEOID, .keep_all = TRUE) %>% + dplyr::filter(stats::complete.cases(.)) + } + if (geo_large == 'metro') { + out <- out_dat %>% + dplyr::left_join(out_tmp, by = dplyr::join_by(oid)) %>% + dplyr::select(oid, metro, G_re) %>% + unique(.) %>% + dplyr::mutate(GEOID = oid) %>% + dplyr::select(GEOID, metro, G_re) %>% + .[.$GEOID != 'NANA', ] %>% + dplyr::distinct(GEOID, .keep_all = TRUE) %>% + dplyr::filter(stats::complete.cases(.)) + } + + out <- out %>% + dplyr::arrange(GEOID) %>% + dplyr::as_tibble() + + out_dat <- out_dat %>% + dplyr::rename(G_inc = G_incE) %>% + dplyr::arrange(GEOID) %>% + dplyr::as_tibble() + + out <- list(g = out, g_data = out_dat, missing = missingYN) return(out) } diff --git a/R/globals.R b/R/globals.R index 2c0cd46..cbd4fb5 100644 --- a/R/globals.R +++ b/R/globals.R @@ -256,7 +256,9 @@ globalVariables( 'km', 'A', 'D', - 'G', + 'G_inc', + 'G_incE', + 'G_re', 'xPx_star', 'xPy_star' ) diff --git a/R/hoover.R b/R/hoover.R index 2146a3c..a36b6ec 100644 --- a/R/hoover.R +++ b/R/hoover.R @@ -1,18 +1,18 @@ #' Delta based on Hoover (1941) and Duncan et al. (1961) #' -#' Compute the aspatial Delta (Hoover) of a selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute the aspatial Delta (Hoover) 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_large = '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/ethnic subgroup(s). See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s). See Details for available choices. #' @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 Delta (\emph{DEL}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan, Cuzzort, and Duncan (1961; LC:60007089). This function provides the computation of \emph{DEL} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Delta (\emph{DEL}) 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 Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan, Cuzzort, and Duncan (1961; LC:60007089). This function provides the computation of \emph{DEL} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = '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'} @@ -53,6 +53,7 @@ #' @import dplyr #' @importFrom sf st_drop_geometry 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 @@ -261,8 +262,8 @@ hoover <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # 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 = .[ , in_subgroup]) diff --git a/R/krieger.R b/R/krieger.R index 4dfc1de..a768b20 100644 --- a/R/krieger.R +++ b/R/krieger.R @@ -12,9 +12,9 @@ #' \itemize{ #' \item \strong{Income}: 80th income percentile vs. 20th income percentile #' \item \strong{Education}: less than high school vs. four-year college degree or more -#' \item \strong{Race/Ethnicity}: white non-Hispanic vs. black non-Hispanic -#' \item \strong{Income and race/ethnicity combined}: white non-Hispanic in 80th income percentile vs. black alone (including Hispanic) in 20th income percentile -#' \item \strong{Income and race/ethnicity combined}: white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile +#' \item \strong{Race or Ethnicity}: white non-Hispanic vs. black non-Hispanic +#' \item \strong{Income and race or ethnicity combined}: white non-Hispanic in 80th income percentile vs. black alone (including Hispanic) in 20th income percentile +#' \item \strong{Income and race or ethnicity combined}: white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile #' } #' #' 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 geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. The ACS-5 groups used in the computation of the five \emph{ICE} metrics are: diff --git a/R/lieberson.R b/R/lieberson.R index 07c13ec..ea23e19 100644 --- a/R/lieberson.R +++ b/R/lieberson.R @@ -1,18 +1,18 @@ #' Isolation Index based on Lieberson (1981) and Bell (1954) #' -#' Compute the aspatial Isolation Index (Lieberson) of a selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute the aspatial Isolation Index (Lieberson) 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_large = '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/ethnic subgroup(s). See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s). See Details for available choices. #' @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 Isolation Index (_xPx\*_) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of _xPx\*_ for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Isolation Index (_xPx\*_) 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 Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of _xPx\*_ 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = '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'} @@ -53,6 +53,7 @@ #' @import dplyr #' @importFrom sf st_drop_geometry 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 @@ -262,8 +263,8 @@ lieberson <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # 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 = .[, in_subgroup]) diff --git a/R/ndi-package.R b/R/ndi-package.R index 29cb891..be2e328 100644 --- a/R/ndi-package.R +++ b/R/ndi-package.R @@ -2,7 +2,7 @@ #' #' Computes various metrics of socio-economic deprivation and disparity in the United States based on information available from the U.S. Census Bureau. #' -#' @details The 'ndi' package computes various metrics of socio-economic deprivation and disparity in the United States. Some metrics are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other metrics are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (\emph{NDI}) are available: (1) based on Messer et al. (2006) \doi{10.1007/s11524-006-9094-x} and (2) based on Andrews et al. (2020) \doi{10.1080/17445647.2020.1750066} and Slotman et al. (2022) \doi{10.1016/j.dib.2022.108002} who use variables chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x}. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute the (1) spatial Racial Isolation Index (\emph{RI}) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002}, (2) spatial Educational Isolation Index (\emph{EI}) based on Bravo et al. (2021) \doi{10.3390/ijerph18179384}, (3) aspatial Index of Concentration at the Extremes (\emph{ICE}) based on Feldman et al. (2015) \doi{10.1136/jech-2015-205728} and Krieger et al. (2016) \doi{10.2105/AJPH.2015.302955}, (4) aspatial racial/ethnic Dissimilarity Index (\emph{DI}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}, (5) aspatial income or racial/ethnic Atkinson Index (\emph{AI}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}, (6) aspatial racial/ethnic Isolation Index (\emph{II}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}, (7) aspatial racial/ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}, (8) aspatial racial/ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}, (9) aspatial racial/ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}, (10) aspatial racial/ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089), and (11) an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). Also using data from the ACS-5 (2005-2009 onward), the package can retrieve the aspatial Gini Index (\emph{G}) based on Gini (1921) \doi{10.2307/2223319}. +#' @details The 'ndi' package computes various metrics of socio-economic deprivation and disparity in the United States. Some metrics are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other metrics are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (\emph{NDI}) are available: (1) based on Messer et al. (2006) \doi{10.1007/s11524-006-9094-x} and (2) based on Andrews et al. (2020) \doi{10.1080/17445647.2020.1750066} and Slotman et al. (2022) \doi{10.1016/j.dib.2022.108002} who use variables chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x}. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute the (1) spatial Racial Isolation Index (\emph{RI}) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002}, (2) spatial Educational Isolation Index (\emph{EI}) based on Bravo et al. (2021) \doi{10.3390/ijerph18179384}, (3) aspatial Index of Concentration at the Extremes (\emph{ICE}) based on Feldman et al. (2015) \doi{10.1136/jech-2015-205728} and Krieger et al. (2016) \doi{10.2105/AJPH.2015.302955}, (4) aspatial racial or ethnic Dissimilarity Index (\emph{DI}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}, (5) aspatial income or racial or ethnic Atkinson Index (\emph{AI}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}, (6) aspatial racial or ethnic Isolation Index (\emph{II}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}, (7) aspatial racial or ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}, (8) aspatial racial or ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}, (9) aspatial racial or ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}, (10) aspatial racial or ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089), and (11) an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). Also using data from the ACS-5 (2005-2009 onward), the package can retrieve the aspatial Gini Index (\emph{G}) based on Gini (1921) \doi{10.2307/2223319}. #' #' Key content of the 'ndi' package include:\cr #' @@ -10,31 +10,31 @@ #' #' \code{\link{anthopolos}} Computes the spatial Racial Isolation Index (\emph{RI}) based on Anthopolos (2011) \doi{10.1016/j.sste.2011.06.002}. #' -#' \code{\link{atkinson}} Computes the aspatial income or racial/ethnic Atkinson Index (\emph{A}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. +#' \code{\link{atkinson}} Computes the aspatial income or racial or ethnic Atkinson Index (\emph{A}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. #' -#' \code{\link{bell}} Computes the aspatial racial/ethnic Interaction Index (\emph{xPy\*}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. +#' \code{\link{bell}} Computes the aspatial racial or ethnic Interaction Index (\emph{xPy\*}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. #' -#' \code{\link{bemanian_beyer}} Computes the aspatial racial/ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. +#' \code{\link{bemanian_beyer}} Computes the aspatial racial or ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. #' #' \code{\link{bravo}} Computes the spatial Educational Isolation Index (\emph{EI}) based on Bravo (2021) \doi{10.3390/ijerph18179384}. #' -#' \code{\link{duncan}} Computes the aspatial racial/ethnic Dissimilarity Index (\emph{D}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}. +#' \code{\link{duncan}} Computes the aspatial racial or ethnic Dissimilarity Index (\emph{D}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}. #' -#' \code{\link{gini}} Retrieves the aspatial Gini Index (\emph{G}) based on Gini (1921) \doi{10.2307/2223319}. +#' \code{\link{gini}} Computes the aspatial Gini Index (\emph{G}) of racial or ethnic inequality and retrieves the aspatial Gini Index (\emph{G}) of income inequality based on Gini (1921) \doi{10.2307/2223319}. #' -#' \code{\link{hoover}} Computes the aspatial racial/ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{doi:10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089). +#' \code{\link{hoover}} Computes the aspatial racial or ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{doi:10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089). #' #' \code{\link{krieger}} Computes the aspatial Index of Concentration at the Extremes based on Feldman et al. (2015) \doi{10.1136/jech-2015-205728} and Krieger et al. (2016) \doi{10.2105/AJPH.2015.302955}. #' -#' \code{\link{lieberson}} Computes the aspatial racial/ethnic Isolation Index (\emph{xPx\*}) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. +#' \code{\link{lieberson}} Computes the aspatial racial or ethnic Isolation Index (\emph{xPx\*}) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. #' #' \code{\link{messer}} Computes the aspatial Neighborhood Deprivation Index (\emph{NDI}) based on Messer et al. (2006) \doi{10.1007/s11524-006-9094-x}. #' #' \code{\link{powell_wiley}} Computes the aspatial Neighborhood Deprivation Index (\emph{NDI}) based on Andrews et al. (2020) \doi{10.1080/17445647.2020.1750066} and Slotman et al. (2022) \doi{10.1016/j.dib.2022.108002} who use variables chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x}. #' -#' \code{\link{sudano}} Computes the aspatial racial/ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. +#' \code{\link{sudano}} Computes the aspatial racial or ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. #' -#' \code{\link{white}} Computes the aspatial racial/ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. +#' \code{\link{white}} Computes the aspatial racial or ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. #' #' \code{\link{white_blau}} Computes an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). #' diff --git a/R/sudano.R b/R/sudano.R index 317d3b4..d73d66f 100644 --- a/R/sudano.R +++ b/R/sudano.R @@ -1,18 +1,18 @@ #' Location Quotient based on Merton (1938) and Sudano et al. (2013) #' -#' Compute the aspatial Location Quotient (Sudano) of a selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute the aspatial Location Quotient (Sudano) 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_large = '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/ethnic subgroup(s). See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s). See Details for available choices. #' @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 Location Quotient (\emph{LQ}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. This function provides the computation of \emph{LQ} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Location Quotient (\emph{LQ}) 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 Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. This function provides the computation of \emph{LQ} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = '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'} @@ -53,6 +53,7 @@ #' @import dplyr #' @importFrom sf st_drop_geometry 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 @@ -262,8 +263,8 @@ sudano <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # 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 = .[, in_subgroup]) diff --git a/R/utils.R b/R/utils.R index c6f9a41..8711a4f 100644 --- a/R/utils.R +++ b/R/utils.R @@ -183,3 +183,25 @@ sp_fun <- function(x, omit_NAs) { return(SP) } } + +# Internal function for the Gini Index (Gini 1921) +## Returns NA value if only one smaller geography in a larger geography +g_fun <- function(x, omit_NAs) { + xx <- x[ , c('TotalPopE', 'subgroup')] + if (omit_NAs == TRUE) { xx <- xx[stats::complete.cases(xx), ] } + if (nrow(x) < 2 || any(xx < 0) || any(is.na(xx))) { + NA + } else { + x_i <- xx$subgroup + X <- sum(xx$subgroup, na.rm = TRUE) + t_i <- xx$TotalPopE + N <- sum(xx$TotalPopE, na.rm = TRUE) + p_i <- x_i / t_i + P <- X / N + titj <- apply(expand.grid(t_i, t_i), MARGIN = 1, FUN = prod) + pipj <- apply(expand.grid(p_i, p_i), MARGIN = 1, FUN = diff) + G <- sum(titj * abs(pipj), na.rm = TRUE) + G <- G / (2 * N ^ 2 * P * (1 - P)) + return(G) + } +} diff --git a/R/white.R b/R/white.R index fdd32f0..deaac8a 100644 --- a/R/white.R +++ b/R/white.R @@ -1,18 +1,18 @@ #' Correlation Ratio based on Bell (1954) and White (1986) #' -#' Compute the aspatial Correlation Ratio (White) of a selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute the aspatial Correlation Ratio (White) of a selected racial or ethnnic 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_large = '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/ethnic subgroup(s). See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnnic subgroup(s). See Details for available choices. #' @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 Correlation Ratio (\emph{V} or \eqn{Eta^{2}}{Eta^2}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. This function provides the computation of \emph{V} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute the aspatial Correlation Ratio (\emph{V} or \eqn{Eta^{2}}{Eta^2}) of selected racial or ethnnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. This function provides the computation of \emph{V} for any of the U.S. Census Bureau race or ethnnicity 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial or ethnnic 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'} @@ -53,6 +53,7 @@ #' @import dplyr #' @importFrom sf st_drop_geometry 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 @@ -258,8 +259,8 @@ white <- function(geo_large = 'county', sf::st_drop_geometry() } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # Count of racial or ethnnic subgroup populations + ## Count of racial or ethnnic comparison subgroup population if (length(in_subgroup) == 1) { out_dat <- out_dat %>% dplyr::mutate(subgroup = .[, in_subgroup]) diff --git a/R/white_blau.R b/R/white_blau.R index b13e134..872321e 100644 --- a/R/white_blau.R +++ b/R/white_blau.R @@ -1,19 +1,19 @@ #' An index of spatial proximity based on White (1986) and Blau (1977) #' -#' Compute an index of spatial proximity (White) of a selected racial/ethnic subgroup(s) and U.S. geographies. +#' Compute an index of spatial proximity (White) 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_large = '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/ethnic subgroup(s) as the comparison population. See Details for available choices. -#' @param subgroup_ref Character string specifying the racial/ethnic subgroup(s) as the reference population. See Details for available choices. +#' @param subgroup Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices. +#' @param subgroup_ref Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices. #' @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 an index of spatial proximity (\emph{SP}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). This function provides the computation of \emph{SP} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +#' @details This function will compute an index of spatial proximity (\emph{SP}) 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 White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). This function provides the computation of \emph{SP} 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +#' 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 = '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'} @@ -39,7 +39,7 @@ #' #' 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{SP} is a measure of clustering of racial/ethnic populations within smaller geographical areas that are located within larger geographical areas. \emph{SP} 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 between subgroup and referent group members. A value greater than 1 indicates subgroup members live nearer to one another than to referent subgroup members. A value less than 1 indicates subgroup live nearer to and referent subgroup members than to their own subgroup members. +#' \emph{SP} is a measure of clustering of racial or ethnic populations within smaller geographical areas that are located within larger geographical areas. \emph{SP} 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 between subgroup and referent group members. A value greater than 1 indicates subgroup members live nearer to one another than to referent subgroup members. A value less than 1 indicates subgroup live nearer to and referent subgroup members than to their own subgroup members. #' #' 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}. #' @@ -56,6 +56,7 @@ #' @import dplyr #' @importFrom sf st_centroid st_distance st_drop_geometry 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 @@ -289,8 +290,8 @@ white_blau <- function(geo_large = 'county', ) } - # Count of racial/ethnic subgroup populations - ## Count of racial/ethnic comparison subgroup population + # 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]) @@ -298,7 +299,7 @@ white_blau <- function(geo_large = 'county', out_dat <- out_dat %>% dplyr::mutate(subgroup = rowSums(as.data.frame(.)[, in_subgroup])) } - ## Count of racial/ethnic reference subgroup population + ## Count of racial or ethnic reference subgroup population if (length(in_subgroup_ref) == 1) { out_dat <- out_dat %>% dplyr::mutate(subgroup_ref = as.data.frame(.)[, in_subgroup_ref]) @@ -312,10 +313,10 @@ white_blau <- function(geo_large = 'county', ## D_{jt} = 1/2 \sum_{i=1}^{k} | \frac{x_{ijt}}{X_{jt}}-\frac{y_{ijt}}{Y_{jt}}| ## Where for k smaller geographies: ## D_{jt} denotes the DI of larger geography j at time t - ## x_{ijt} denotes the racial/ethnic subgroup population of smaller geography i within larger geography j at time t - ## X_{jt} denotes the racial/ethnic subgroup population of larger geography j at time t - ## y_{ijt} denotes the racial/ethnic referent subgroup population of smaller geography i within larger geography j at time t - ## Y_{jt} denotes the racial/ethnic referent subgroup population of larger geography j at time t + ## x_{ijt} denotes the racial or ethnic subgroup population of smaller geography i within larger geography j at time t + ## X_{jt} denotes the racial or ethnic subgroup population of larger geography j at time t + ## y_{ijt} denotes the racial or ethnic referent subgroup population of smaller geography i within larger geography j at time t + ## Y_{jt} denotes the racial or ethnic referent subgroup population of larger geography j at time t ## Compute out_tmp <- out_dat %>% diff --git a/README.md b/README.md index ab2e413..2f930d4 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ ### Overview -The *ndi* package is a suite of [**R**](https://cran.r-project.org/) functions to compute various metrics of socio-economic deprivation and disparity in the United States. Some metrics are considered 'spatial' because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other metrics are 'aspatial' because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation index (*NDI*) are available: (1) based on [Messer et al. (2006)](https://doi.org/10.1007/s11524-006-9094-x) and (2) based on [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) and [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) who use variables chosen by [Roux and Mair (2010)](https://doi.org/10.1111/j.1749-6632.2009.05333.x). Both are a decomposition of various demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward) pulled by the [tidycensus](https://CRAN.R-project.org/package=tidycensus) package. Using data from the ACS-5 (2005-2009 onward), the *ndi* package can also compute the (1) spatial Racial Isolation Index (*RI*) based on [Anthopolos et al. (2011)](https://doi.org/10.1016/j.sste.2011.06.002), (2) spatial Educational Isolation Index (*EI*) based on [Bravo et al. (2021)](https://doi.org/10.3390/ijerph18179384), (3) aspatial Index of Concentration at the Extremes (*ICE*) based on [Feldman et al. (2015)](https://doi.org/10.1136/jech-2015-205728) and [Krieger et al. (2016)](https://doi.org/10.2105/AJPH.2015.302955), (4) aspatial racial/ethnic Dissimilarity Index (*D*) based on [Duncan & Duncan (1955)](https://doi.org/10.2307/2088328), (5) aspatial income or racial/ethnic Atkinson Index (*A*) based on [Atkinson (1970)](https://doi.org/10.1016/0022-0531(70)90039-6), (6) aspatial racial/ethnic Interaction Index (_xPy\*_) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and [Bell (1954)](https://doi.org/10.2307/2574118), (7) aspatial racial/ethnic Correlation Ratio (*V*) based on [Bell (1954)](https://doi.org/10.2307/2574118) and [White (1986)](https://doi.org/10.2307/3644339), (8) aspatial racial/ethnic Location Quotient (*LQ*) based on [Merton (1939)](https://doi.org/10.2307/2084686) and [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015), (9) aspatial racial/ethnic Local Exposure and Isolation (*LEx/Is*) metric based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926), (10) aspatial racial/ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089), (11) an index of spatial proximity (*SP*) based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0), and (12) the aspatial racial/ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and [Bell (1954)](https://doi.org/10.2307/2574118). Also using data from the ACS-5 (2005-2009 onward), the *ndi* package can retrieve the aspatial Gini Index (*G*) based on [Gini (1921)](https://doi.org/10.2307/2223319). +The *ndi* package is a suite of [**R**](https://cran.r-project.org/) functions to compute various metrics of socio-economic deprivation and disparity in the United States. Some metrics are considered 'spatial' because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other metrics are 'aspatial' because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation index (*NDI*) are available: (1) based on [Messer et al. (2006)](https://doi.org/10.1007/s11524-006-9094-x) and (2) based on [Andrews et al. (2020)](https://doi.org/10.1080/17445647.2020.1750066) and [Slotman et al. (2022)](https://doi.org/10.1016/j.dib.2022.108002) who use variables chosen by [Roux and Mair (2010)](https://doi.org/10.1111/j.1749-6632.2009.05333.x). Both are a decomposition of various demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward) pulled by the [tidycensus](https://CRAN.R-project.org/package=tidycensus) package. Using data from the ACS-5 (2005-2009 onward), the *ndi* package can also compute the (1) spatial Racial Isolation Index (*RI*) based on [Anthopolos et al. (2011)](https://doi.org/10.1016/j.sste.2011.06.002), (2) spatial Educational Isolation Index (*EI*) based on [Bravo et al. (2021)](https://doi.org/10.3390/ijerph18179384), (3) aspatial Index of Concentration at the Extremes (*ICE*) based on [Feldman et al. (2015)](https://doi.org/10.1136/jech-2015-205728) and [Krieger et al. (2016)](https://doi.org/10.2105/AJPH.2015.302955), (4) aspatial racial or ethnic Dissimilarity Index (*D*) based on [Duncan & Duncan (1955)](https://doi.org/10.2307/2088328), (5) aspatial income or racial or ethnic Atkinson Index (*A*) based on [Atkinson (1970)](https://doi.org/10.1016/0022-0531(70)90039-6), (6) aspatial racial or ethnic Interaction Index (_xPy\*_) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and [Bell (1954)](https://doi.org/10.2307/2574118), (7) aspatial racial or ethnic Correlation Ratio (*V*) based on [Bell (1954)](https://doi.org/10.2307/2574118) and [White (1986)](https://doi.org/10.2307/3644339), (8) aspatial racial or ethnic Location Quotient (*LQ*) based on [Merton (1939)](https://doi.org/10.2307/2084686) and [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015), (9) aspatial racial or ethnic Local Exposure and Isolation (*LEx/Is*) metric based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926), (10) aspatial racial or ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089), (11) an index of spatial proximity (*SP*) based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0), and (12) the aspatial racial or ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and [Bell (1954)](https://doi.org/10.2307/2574118), and (13) the aspatial racial or ethnic Gini Index (*G*) based on [Gini (1921)](https://doi.org/10.2307/2223319). Also using data from the ACS-5 (2005-2009 onward), the *ndi* package can retrieve the aspatial income Gini Index (*G*) based on [Gini (1921)](https://doi.org/10.2307/2223319). ### Installation @@ -52,11 +52,11 @@ To install the development version from GitHub: bell -Compute the aspatial racial/ethnic Interaction Index (xPy*) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) +Compute the aspatial racial or ethnic Interaction Index (xPy*) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) bemanian_beyer -Compute the aspatial racial/ethnic Local Exposure and Isolation (LEx/Is) metric based on Bemanian & Beyer (2017) +Compute the aspatial racial or ethnic Local Exposure and Isolation (LEx/Is) metric based on Bemanian & Beyer (2017) bravo @@ -64,14 +64,14 @@ To install the development version from GitHub: duncan -Compute the aspatial racial/ethnic Dissimilarity Index (D) based on Duncan & Duncan (1955) +Compute the aspatial racial or ethnic Dissimilarity Index (D) based on Duncan & Duncan (1955) gini -Retrieve the aspatial Gini Index (G) based on Gini (1921) +Compute the aspatial racial or ethnic Gini Index (G) and retrieve the aspatial income Gini Index (G) based on Gini (1921) hoover -Compute the aspatial racial/ethnic Delta (DEL) based on Hoover (1941) and Duncan et al. (1961; LC:60007089) +Compute the aspatial racial or ethnic Delta (DEL) based on Hoover (1941) and Duncan et al. (1961; LC:60007089) krieger @@ -79,7 +79,7 @@ To install the development version from GitHub: lieberson -Compute the aspatial racial/ethnic Isolation Index (xPx*) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) +Compute the aspatial racial or ethnic Isolation Index (xPx*) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) messer @@ -91,11 +91,11 @@ To install the development version from GitHub: sudano -Compute the aspatial racial/ethnic Location Quotient (LQ) based on Merton (1938) and Sudano et al. (2013) +Compute the aspatial racial or ethnic Location Quotient (LQ) based on Merton (1938) and Sudano et al. (2013) white -Compute the aspatial racial/ethnic Correlation Ratio (V) based on Bell (1954) and White (1986) +Compute the aspatial racial or ethnic Correlation Ratio (V) based on Bell (1954) and White (1986) white_blau @@ -440,12 +440,21 @@ table(NDI_2020_DC$NDIQuart, NDI_2020_DC$NDIQuint) ``` ``` r -# ---------------------------- # -# Retrieve aspatial Gini Index # -# ---------------------------- # +# ------------------------------------------ # +# Compute aspatial race or ethnic Gini Index # +# ------------------------------------------ # -# Gini Index based on Gini (1921) from the ACS-5 -G_2020_DC <- gini(state = 'DC', year = 2020) +# Gini Index based on Gini (1921) +## Selected subgroup: Not Hispanic or Latino, Black or African American alone +## Selected large geography: census tract +## Selected small geography: census block group +G_2020_DC <- gini( + geo_large = 'tract', + geo_small = 'block group', + state = 'DC', + year = 2020, + subgroup = 'NHoLB' +) # Obtain the 2020 census tracts from the 'tigris' package tract_2020_DC <- tracts(state = 'DC', year = 2020, cb = TRUE) @@ -457,7 +466,52 @@ G_2020_DC <- tract_2020_DC %>% ggplot() + geom_sf( data = G_2020_DC, - aes(fill = G), + aes(fill = G_re), + color = 'white' + ) + + theme_bw() + + scale_fill_viridis_c(limits = c(0, 1)) + + labs( + fill = 'Index (Continuous)', + caption = 'Source: U.S. Census ACS 2016-2020 estimates' + ) + + ggtitle( + 'Gini Index\n + Washington, D.C. census block groups to tracts', + subtitle = 'Black non-Hispanic' + ) +``` + +![](man/figures/g_re.png) + +``` r +# ------------------------------------ # +# Retrieve aspatial income Gini Index # +# ------------------------------------ # + +# Gini Index based on Gini (1921) +## Selected subgroup: Not Hispanic or Latino, Black or African American alone +## Selected large geography: county (or district for DC) +## Selected small geography: census tract +G_2020_DC <- gini( + geo_large = 'county', + geo_small = 'tract', + state = 'DC', + year = 2020, + subgroup = 'NHoLB' +) + +# Obtain the 2020 census tracts from the 'tigris' package +tract_2020_DC <- tracts(state = 'DC', year = 2020, cb = TRUE) + +# Join the G (Gini) values found in `g_data` to the census tract geometry +G_2020_DC <- tract_2020_DC %>% + left_join(G_2020_DC$g_data , by = 'GEOID') + +ggplot() + + geom_sf( + data = G_2020_DC, + aes(fill = G_inc), color = 'white' ) + theme_bw() + @@ -467,12 +521,13 @@ ggplot() + caption = 'Source: U.S. Census ACS 2016-2020 estimates' ) + ggtitle( - 'Gini Index\nGrey color denotes no data', - subtitle = 'Washington, D.C. tracts' + 'Gini Index\n + Washington, D.C. census tracts', + subtitle = 'Income' ) ``` -![](man/figures/g.png) +![](man/figures/g_inc.png) ``` r # ---------------------------------------------------- # @@ -618,7 +673,7 @@ ggplot() + ![](man/figures/ice2.png) ```r -# Plot ICE for Race/Ethnicity +# Plot ICE for Race or Ethnicity ggplot() + geom_sf( data = ICE_2020_DC, @@ -637,7 +692,7 @@ ggplot() + caption = 'Source: U.S. Census ACS 2016-2020 estimates' ) + ggtitle( - 'Index of Concentration at the Extremes\nRace/Ethnicity (Krieger)', + 'Index of Concentration at the Extremes\nRace pr Ethnicity (Krieger)', subtitle = 'white non-Hispanic vs. black non-Hispanic' ) ``` @@ -645,7 +700,7 @@ ggplot() + ![](man/figures/ice3.png) ``` -# Plot ICE for Income and Race/Ethnicity Combined +# Plot ICE for Income and Race or Ethnicity Combined ## white non-Hispanic in 80th income percentile vs. ## black (including Hispanic) in 20th income percentile ggplot() + @@ -666,7 +721,7 @@ ggplot() + caption = 'Source: U.S. Census ACS 2016-2020 estimates' ) + ggtitle( - 'Index of Concentration at the Extremes\nIncome and race/ethnicity combined (Krieger)', + 'Index of Concentration at the Extremes\nIncome and race or ethnicity combined (Krieger)', subtitle = 'white non-Hispanic in 80th income percentile vs. black (incl. Hispanic) in 20th inc. percentile' ) @@ -675,7 +730,7 @@ ggplot() + ![](man/figures/ice4.png) ```r -# Plot ICE for Income and Race/Ethnicity Combined +# Plot ICE for Income and Race or Ethnicity Combined ## white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile ggplot() + geom_sf( @@ -695,7 +750,7 @@ ggplot() + caption = 'Source: U.S. Census ACS 2016-2020 estimates' ) + ggtitle( - 'Index of Concentration at the Extremes\nIncome and race/ethnicity combined (Krieger)', + 'Index of Concentration at the Extremes\nIncome and race or ethnicity combined (Krieger)', subtitle = 'white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile' ) @@ -704,9 +759,9 @@ ggplot() + ![](man/figures/ice5.png) ```r -# -------------------------------------------------------------------- # -# Compute aspatial racial/ethnic Dissimilarity Index (Duncan & Duncan) # -# -------------------------------------------------------------------- # +# ----------------------------------------------------------------------- # +# Compute aspatial racial or ethnic Dissimilarity Index (Duncan & Duncan) # +# ----------------------------------------------------------------------- # # Dissimilarity Index based on Duncan & Duncan (1955) ## Selected subgroup comparison: Not Hispanic or Latino, Black or African American alone @@ -751,9 +806,9 @@ ggplot() + ![](man/figures/d.png) ```r -# -------------------------------------------------------- # -# Compute aspatial racial/ethnic Atkinson Index (Atkinson) # -# -------------------------------------------------------- # +# ----------------------------------------------------------- # +# Compute aspatial racial or ethnic Atkinson Index (Atkinson) # +# ----------------------------------------------------------- # # Atkinson Index based on Atkinson (1970) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone @@ -797,9 +852,9 @@ ggplot() + ![](man/figures/a.png) ```r -# ----------------------------------------------------------------------------- # -# Compute aspatial racial/ethnic Atkinson Index (Atkinson) with the Hölder mean # -# ----------------------------------------------------------------------------- # +# -------------------------------------------------------------------------------- # +# Compute aspatial racial or ethnic Atkinson Index (Atkinson) with the Hölder mean # +# -------------------------------------------------------------------------------- # # Atkinson Index based on Atkinson (1970) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone @@ -845,9 +900,9 @@ ggplot() + ![](man/figures/a_holder.png) ```r -# ------------------------------------------------------- # -# Compute aspatial racial/ethnic Interaction Index (Bell) # -# ------------------------------------------------------- # +# ---------------------------------------------------------- # +# Compute aspatial racial or ethnic Interaction Index (Bell) # +# ---------------------------------------------------------- # # Interaction Index based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone @@ -892,9 +947,9 @@ ggplot() + ![](man/figures/xpy_star.png) ```r -# -------------------------------------------------------- # -# Compute aspatial racial/ethnic Correlation Ratio (White) # -# -------------------------------------------------------- # +# ----------------------------------------------------------- # +# Compute aspatial racial or ethnic Correlation Ratio (White) # +# ----------------------------------------------------------- # # Correlation Ratio based on Bell (1954) and White (1986) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone @@ -942,9 +997,9 @@ ggplot() + ![](man/figures/v.png) ```r -# --------------------------------------------------------- # -# Compute aspatial racial/ethnic Location Quotient (Sudano) # -# --------------------------------------------------------- # +# ------------------------------------------------------------ # +# Compute aspatial racial or ethnic Location Quotient (Sudano) # +# ------------------------------------------------------------ # # Location Quotient based on Merton (1938) and Sudano (2013) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone @@ -987,9 +1042,9 @@ ggplot() + ![](man/figures/lq.png) ```r -# ------------------------------------------------------------------------------------- # -# Compute aspatial racial/ethnic Local Exposure and Isolation (Bemanian & Beyer) metric # -# ------------------------------------------------------------------------------------- # +# ---------------------------------------------------------------------------------------- # +# Compute aspatial racial or ethnic Local Exposure and Isolation (Bemanian & Beyer) metric # +# ---------------------------------------------------------------------------------------- # # Local Exposure and Isolation metric based on Bemanian & Beyer (2017) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone @@ -1039,9 +1094,9 @@ ggplot() + ![](man/figures/lexis.png) ```r -# --------------------------------------------- # -# Compute aspatial racial/ethnic Delta (Hoover) # -# --------------------------------------------- # +# ------------------------------------------------ # +# Compute aspatial racial or ethnic Delta (Hoover) # +# ------------------------------------------------ # # Delta based on Hoover (1941) and Duncan et al. (1961) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone @@ -1135,9 +1190,9 @@ ggplot() + ![](man/figures/sp.png) ```r -# ---------------------------------------------------------- # -# Compute aspatial racial/ethnic Isolation Index (Lieberson) # -# ---------------------------------------------------------- # +# ------------------------------------------------------------- # +# Compute aspatial racial or ethnic Isolation Index (Lieberson) # +# ------------------------------------------------------------- # # Interaction Index based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) ## Selected subgroup: Not Hispanic or Latino, Black or African American alone diff --git a/cran-comments.md b/cran-comments.md index 697a822..b5b09b6 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,17 +1,19 @@ ## This is the seventh resubmission * Actions taken since previous submission: - * Added `hoover()` function to compute the aspatial racial/ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089) + * Added `hoover()` function to compute the aspatial racial or ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089) * Added `white_blau()` function to compute an index of spatial proximity (*SP*) based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0) - * Added `lieberson()` function to compute he aspatial racial/ethnic Isolation Index (_xPx\*_) based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0) - * Added `geo_large = 'cbsa'` for Core Based Statistical Areas, `geo_large = 'csa'` for Combined Statistical Areas, and `geo_large = 'metro'` for Metropolitan Divisions as the larger geographical unit in `atkinson()`, `bell()`, `bemanian_beyer()`, `duncan()`, `hoover()`, `sudano()`, and `white()`, `white_blau()` functions. - * Added `holder` argument to `atkinson()` function to toggle the computation with or without the Hölder mean. The function can now compute *A* without the Hölder mean. The default is `holder = TRUE`. + * Added `lieberson()` function to compute the aspatial racial or ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and and [Bell (1954)](https://doi.org/10.2307/2574118) + * Added `geo_large = 'cbsa'` for Core Based Statistical Areas, `geo_large = 'csa'` for Combined Statistical Areas, and `geo_large = 'metro'` for Metropolitan Divisions as the larger geographical unit in `atkinson()`, `bell()`, `bemanian_beyer()`, `duncan()`, `hoover()`, `lieberson()`, `sudano()`, and `white()`, `white_blau()` functions. + * Added `holder` argument to `atkinson()` function to toggle the computation with or without the Hölder mean. The function can now compute *A* without the Hölder mean. The default is `holder = FALSE`. + * The `gini()` function now computes the aspatial racial or ethnic Gini Index (*G*) based on [Gini (1921)](https://doi.org/10.2307/2223319) as the main outcome. Arguments `geo_large`, `geo_small`, `subgroup`, and `omit_NAs` were added and argument `geo` was deprecated. The `gini()` function still retrieves the original output of the aspatial income Gini Index (*G*) at each smaller geography and is moved from the `g` output to `g_data` output. * `bell()` function computes the Interaction Index (Bell) not the Isolation Index as previously documented. Updated documentation throughout * Fixed bug in `bell()`, `bemanian_beyer()`, `duncan()`, `sudano()`, and `white()` functions when a smaller geography contains n=0 total population, will assign a value of zero (0) in the internal calculation instead of NA * Renamed *AI* as *A*, *DI* as *D*, *Gini* as *G*, and *II* as _xPy\*_ to align with the definitions from [Massey & Denton (1988)](https://doi.org/10.1093/sf/67.2.281). The output for `atkinson()` now produces `a` instead of `ai`. The output for `duncan()` now produces `d` instead of `ai`. The output for `gini()` now produces `g` instead of `gini`. The output for `bell()` now produces `xPy_star` instead of `II`. The internal functions `ai_fun()`, `di_fun()` and `ii_fun()` were renamed `a_fun()`, `d_fun()` and `xpy_star_fun()`, respectively. * `tigris` and `units` are now Imports * 'package.R' deprecated. Replaced with 'ndi-package.R' * Re-formatted code and documentation throughout for consistent readability + * Renamed 'race/ethnicity' or 'racial/ethnic' to 'race or ethnicity' or 'racial or ethnic' throughout documentation to use more modern, inclusive, and appropriate language * Updated documentation about value range of *V* (White) from `{0 to 1}` to `{-Inf to Inf}` * Add examples for `hoover()` and `white_blau()` functions in vignette and README * Add example for `holder` argument in `atkinson()` function in README diff --git a/man/anthopolos.Rd b/man/anthopolos.Rd index 8d4039b..2895b6b 100644 --- a/man/anthopolos.Rd +++ b/man/anthopolos.Rd @@ -11,7 +11,7 @@ anthopolos(geo = "tract", year = 2020, subgroup, quiet = FALSE, ...) \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s). See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s). See Details for available choices.} \item{quiet}{Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.} @@ -29,9 +29,9 @@ An object of class 'list'. This is a named list with the following components: Compute the spatial Racial Isolation Index (Anthopolos) of selected subgroup(s). } \details{ -This function will compute the spatial Racial Isolation Index (\emph{RI}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002} who originally designed the metric for the racial isolation of non-Hispanic Black individuals. This function provides the computation of \emph{RI} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the spatial Racial Isolation Index (\emph{RI}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002} who originally designed the metric for the racial isolation of non-Hispanic Black individuals. This function provides the computation of \emph{RI} 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 geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are 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'} @@ -57,7 +57,7 @@ The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. 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. NOTE: Current version does not correct for edge effects (e.g., census geographies along the specified spatial extent border, coastline, or U.S.-Mexico / U.S.-Canada border) may have few neighboring census geographies, and \emph{RI} values in these census geographies may be unstable. A stop-gap solution for the former source of edge effect is to compute the \emph{RI} for neighboring census geographies (i.e., the states bordering a study area of interest) and then use the estimates of the study area of interest. -A census geography (and its neighbors) that has nearly all of its population who identify with the specified race/ethnicity subgroup(s) (e.g., non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 1. In contrast, a census geography (and its neighbors) that has nearly none of its population who identify with the specified race/ethnicity subgroup(s) (e.g., not non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 0. +A census geography (and its neighbors) that has nearly all of its population who identify with the specified race or ethnicity subgroup(s) (e.g., non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 1. In contrast, a census geography (and its neighbors) that has nearly none of its population who identify with the specified race or ethnicity subgroup(s) (e.g., not non-Hispanic or Latino, Black or African American alone) will have an \emph{RI} value close to 0. } \examples{ \dontrun{ diff --git a/man/atkinson.Rd b/man/atkinson.Rd index b9c2dcd..8af7fe6 100644 --- a/man/atkinson.Rd +++ b/man/atkinson.Rd @@ -23,7 +23,7 @@ atkinson( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the income or racial/ethnic subgroup(s) as the comparison population. See Details for available choices.} +\item{subgroup}{Character string specifying the income or racial or ethnic subgroup(s) as the comparison population. See Details for available choices.} \item{epsilon}{Numerical. Shape parameter that denotes the aversion to inequality. Value must be between 0 and 1.0 (the default is 0.5).} @@ -45,12 +45,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Atkinson Index of income or selected racial/ethnic subgroup(s) and U.S. geographies. +Compute the aspatial Atkinson Index of income or selected racial or ethnic subgroup(s) and U.S. geographies. } \details{ -This function will compute the aspatial Atkinson Index (\emph{A}) of income or selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. This function provides the computation of \emph{A} for median household income and any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Atkinson Index (\emph{A}) of income or 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 Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. This function provides the computation of \emph{A} for median household income and 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. When \code{subgroup = 'MedHHInc'}, the metric will be computed for median household income ('B19013_001'). The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. When \code{subgroup = 'MedHHInc'}, the metric will be computed for median household income ('B19013_001'). 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'} @@ -76,7 +76,7 @@ The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. 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{A} is a measure of the evenness of residential inequality (e.g., racial/ethnic segregation) when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{A} can range in value from 0 to 1 with smaller values indicating lower levels of inequality (e.g., less segregation). +\emph{A} is a measure of the evenness of residential inequality (e.g., racial or ethnic segregation) when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{A} can range in value from 0 to 1 with smaller values indicating lower levels of inequality (e.g., less segregation). The \code{epsilon} argument that determines how to weight the increments to inequality contributed by different proportions of the Lorenz curve. A user must explicitly decide how heavily to weight smaller geographical units at different points on the Lorenz curve (i.e., whether the index should take greater account of differences among areas of over- or under-representation). The \code{epsilon} argument must have values between 0 and 1.0. For \code{0 <= epsilon < 0.5} or less 'inequality-averse,' smaller geographical units with a subgroup proportion smaller than the subgroup proportion of the larger geographical unit contribute more to inequality ('over-representation'). For \code{0.5 < epsilon <= 1.0} or more 'inequality-averse,' smaller geographical units with a subgroup proportion larger than the subgroup proportion of the larger geographical unit contribute more to inequality ('under-representation'). If \code{epsilon = 0.5} (the default), units of over- and under-representation contribute equally to the index. See Section 2.3 of Saint-Jacques et al. (2020) \doi{10.48550/arXiv.2002.05819} for one method to select \code{epsilon}. diff --git a/man/bell.Rd b/man/bell.Rd index 02fc8ba..b1ddb1e 100644 --- a/man/bell.Rd +++ b/man/bell.Rd @@ -22,9 +22,9 @@ bell( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s). See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s). See Details for available choices.} -\item{subgroup_ixn}{Character string specifying the racial/ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices.} +\item{subgroup_ixn}{Character string specifying the racial or ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices.} \item{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.} @@ -42,12 +42,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Interaction Index (Bell) of a selected racial/ethnic subgroup(s) and U.S. geographies. +Compute the aspatial Interaction Index (Bell) of a selected racial or ethnic subgroup(s) and U.S. geographies. } \details{ -This function will compute the aspatial Interaction Index (\emph{xPy\*}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of \emph{xPy\*} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Interaction Index (\emph{xPy\*}) 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 Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of \emph{xPy\*} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = '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'} diff --git a/man/bemanian_beyer.Rd b/man/bemanian_beyer.Rd index 1fabd38..0697688 100644 --- a/man/bemanian_beyer.Rd +++ b/man/bemanian_beyer.Rd @@ -22,9 +22,9 @@ bemanian_beyer( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s). See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s). See Details for available choices.} -\item{subgroup_ixn}{Character string specifying the racial/ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices.} +\item{subgroup_ixn}{Character string specifying the racial or ethnic subgroup(s) as the interaction population. If the same as \code{subgroup}, will compute the simple isolation of the group. See Details for available choices.} \item{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.} @@ -42,12 +42,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Local Exposure and Isolation (Bemanian & Beyer) metric of a selected racial/ethnic subgroup(s) and U.S. geographies. +Compute the aspatial Local Exposure and Isolation (Bemanian & Beyer) metric of a selected racial or ethnic subgroup(s) and U.S. geographies. } \details{ -This function will compute the aspatial Local Exposure and Isolation (\emph{LEx/Is}) metric of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. This function provides the computation of \emph{LEx/Is} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Local Exposure and Isolation (\emph{LEx/Is}) metric 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 Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. This function provides the computation of \emph{LEx/Is} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = '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'} @@ -73,7 +73,7 @@ The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. 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{LEx/Is} is a measure of the probability that two individuals living within a specific smaller geography (e.g., census tract) of either different (i.e., exposure) or the same (i.e., isolation) racial/ethnic subgroup(s) will interact, assuming that individuals within a smaller geography are randomly mixed. \emph{LEx/Is} is standardized with a logit transformation and centered against an expected case that all races/ethnicities are evenly distributed across a larger geography. (Note: will adjust data by 0.025 if probabilities are zero, one, or undefined. The output will include a warning if adjusted. See \code{\link[car]{logit}} for additional details.) +\emph{LEx/Is} is a measure of the probability that two individuals living within a specific smaller geography (e.g., census tract) of either different (i.e., exposure) or the same (i.e., isolation) racial or ethnic subgroup(s) will interact, assuming that individuals within a smaller geography are randomly mixed. \emph{LEx/Is} is standardized with a logit transformation and centered against an expected case that all races or ethnicities are evenly distributed across a larger geography. (Note: will adjust data by 0.025 if probabilities are zero, one, or undefined. The output will include a warning if adjusted. See \code{\link[car]{logit}} for additional details.) \emph{LEx/Is} can range from negative infinity to infinity. If \emph{LEx/Is} is zero then the estimated probability of the interaction between two people of the given subgroup(s) within a smaller geography is equal to the expected probability if the subgroup(s) were perfectly mixed in the larger geography. If \emph{LEx/Is} is greater than zero then the interaction is more likely to occur within the smaller geography than in the larger geography, and if \emph{LEx/Is} is less than zero then the interaction is less likely to occur within the smaller geography than in the larger geography. Note: the exponentiation of each \emph{LEx/Is} metric results in the odds ratio of the specific exposure or isolation of interest in a smaller geography relative to the larger geography. diff --git a/man/duncan.Rd b/man/duncan.Rd index a55a080..09918de 100644 --- a/man/duncan.Rd +++ b/man/duncan.Rd @@ -22,9 +22,9 @@ duncan( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s) as the comparison population. See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.} -\item{subgroup_ref}{Character string specifying the racial/ethnic subgroup(s) as the reference population. See Details for available choices.} +\item{subgroup_ref}{Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices.} \item{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.} @@ -42,12 +42,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Dissimilarity Index (Duncan & Duncan) of selected racial/ethnic subgroup(s) and U.S. geographies +Compute the aspatial Dissimilarity Index (Duncan & Duncan) of selected racial or ethnic subgroup(s) and U.S. geographies } \details{ -This function will compute the aspatial Dissimilarity Index (\emph{D}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Duncan & Duncan (1955) \doi{10.2307/2088328}. This function provides the computation of \emph{D} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Dissimilarity Index (\emph{D}) 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 Duncan & Duncan (1955) \doi{10.2307/2088328}. This function provides the computation of \emph{D} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = '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'} @@ -73,7 +73,7 @@ The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. 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{D} is a measure of the evenness of racial/ethnic residential segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{D} can range in value from 0 to 1 and represents the proportion of racial/ethnic subgroup members that would have to change their area of residence to achieve an even distribution within the larger geographical area under conditions of maximum segregation. +\emph{D} is a measure of the evenness of racial or ethnic residential segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. \emph{D} can range in value from 0 to 1 and represents the proportion of racial or ethnic subgroup members that would have to change their area of residence to achieve an even distribution within the larger geographical area under conditions of maximum segregation. Larger geographies available include state \code{geo_large = 'state'}, county \code{geo_large = 'county'}, census tract \code{geo_large = 'tract'}, Core Based Statistical Area \code{geo_large = 'cbsa'}, Combined Statistical Area \code{geo_large = 'csa'}, and Metropolitan Division \code{geo_large = 'metro'} levels. Smaller geographies available include, county \code{geo_small = 'county'}, census tract \code{geo_small = 'tract'}, and census block group \code{geo_small = 'block group'} levels. If a larger geographical area is comprised of only one smaller geographical area (e.g., a U.S county contains only one census tract), then the \emph{D} value returned is NA. If the larger geographical unit is Combined Based Statistical Areas \code{geo_large = 'csa'} or Core Based Statistical Areas \code{geo_large = 'cbsa'}, only the smaller geographical units completely within a larger geographical unit are considered in the \emph{D} 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{D} computation. } diff --git a/man/figures/g.png b/man/figures/g.png deleted file mode 100644 index 6a2301ef66888fc605111394db989abf0e98c743..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 370462 zcmdqJby$_{_BHBjgRi2Zpdg`0mr4l;C<@X7(k&^7Ac%mZg+ZyLD2N~(BHf@!N_T^# zG}6*%EMtHB`<-+CJ=b@gwfDO>zOvSOp8KBnoMVnL=JQNm?($w@3gRtWw(OOZxTLsc 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z)B>LT(=XO39*Ll2WHkAeDMki8F;y{N!9P@fTh#Jbxz#1XDLja@F_V!<(hXE>43hIRFaT8Dqg`F1m? zF1${w#-tCXV{=R3HgOo?wCdWTcl48Idudcl>DjW4PY?&glZDLe z2r3Qo@vL`onN{H3yLgvo6-(|t5BeVauX*&5czh!M!+CP_h8U=z+ zNQW#(x0o#~X?!pYDEnAp`QX&b#1MbmnB2*hJ{MCu3zgYZxo5(I)mi$@mFDugcmCUo zll;XTGp+C|h5yWX`>7_8hqQ`%S{%aKJdDyKXScRI)a`p!ty0?(bm= zVZ&b0w#h9Qm=+#~<3+R{)(^ocbvbX4928oWWRbow{3fu?IhjlC?V9DS*9N^-J9>Em zEAWX@v2?^M=vF1tn&jA8^B8dSj_}tw7(>Ziq|I^Q4 n?>22hb;0N8o&Q5Wq}5q!4cGmqFzFEDt81z29cLW1`s;rH`#g#? literal 0 HcmV?d00001 diff --git a/man/gini.Rd b/man/gini.Rd index ff99e1c..63e4e34 100644 --- a/man/gini.Rd +++ b/man/gini.Rd @@ -4,13 +4,27 @@ \alias{gini} \title{Gini Index based on Gini (1921)} \usage{ -gini(geo = "tract", year = 2020, quiet = FALSE, ...) +gini( + geo_large = "county", + geo_small = "tract", + year = 2020, + subgroup, + omit_NAs = TRUE, + quiet = FALSE, + ... +) } \arguments{ -\item{geo}{Character string specifying the geography of the data either census tracts \code{geo = 'tract'} (the default) or counties \code{geo = 'county'}.} +\item{geo_large}{Character string specifying the larger geographical unit of the data. The default is counties \code{geo_large = 'county'}.} + +\item{geo_small}{Character string specifying the smaller geographical unit of the data. The default is census tracts \code{geo_large = 'tract'}.} \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s). See Details for available choices.} + +\item{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.} + \item{quiet}{Logical. If TRUE, will display messages about potential missing census information} \item{...}{Arguments passed to \code{\link[tidycensus]{get_acs}} to select state, county, and other arguments for census characteristics} @@ -19,30 +33,61 @@ gini(geo = "tract", year = 2020, quiet = FALSE, ...) An object of class 'list'. This is a named list with the following components: \describe{ -\item{\code{g}}{An object of class 'tbl' for the GEOID, name, and \emph{G} of specified census geographies.} -\item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for \emph{G}.} +\item{\code{g}}{An object of class 'tbl' for the GEOID, name, and \emph{G_re} metrics of specified census geographies.} +\item{\code{g_data}}{An object of class 'tbl' for the raw census values at specified smaller census geographies including \emph{G_inc}.} +\item{\code{missing}}{An object of class 'tbl' of the count and proportion of missingness for \emph{G_inc} and each census variable used to compute \emph{G_re}.} } } \description{ -Retrieve the aspatial Gini Index of income inequality. +Compute the aspatial racial or ethnic Gini Index and retrieve the aspatial income Gini Index } \details{ -This function will retrieve the aspatial Gini Index (\emph{G}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Gini (1921) \doi{10.2307/2223319}. +This function will retrieve the aspatial Gini Index (\emph{G}) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Gini (1921) \doi{10.2307/2223319} for income inequality (at smaller geographical units) and race or ethnicity inequality (at larger geographical units). -The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey estimates of \emph{G} for income inequality (ACS: B19083). The estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. +The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. Census Bureau 5-year American Community Survey estimates of \emph{G} for the geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. The function will retrieve the provided income inequality metric (\strong{B19083}) and 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. -According to the U.S. Census Bureau \url{https://www.census.gov/topics/income-poverty/income-inequality/about/metrics/gini-index.html}: 'The Gini Index is a summary measure of income inequality. The Gini coefficient incorporates the detailed shares data into a single statistic, which summarizes the dispersion of income across the entire income distribution. The Gini coefficient ranges from 0, indicating perfect equality (where everyone receives an equal share), to 1, perfect inequality (where only one recipient or group of recipients receives all the income). The Gini is based on the difference between the Lorenz curve (the observed cumulative income distribution) and the notion of a perfectly equal income distribution.' +According to the U.S. Census Bureau \url{https://www.census.gov/topics/income-poverty/income-inequality/about/metrics/gini-index.html}: 'The Gini Index is a summary measure of income inequality. The Gini coefficient incorporates the detailed shares data into a single statistic, which summarizes the dispersion of income across the entire income distribution. The Gini coefficient ranges from 0, indicating perfect equality (where everyone receives an equal share), to 1, perfect inequality (where only one recipient or group of recipients receives all the income). The Gini Index is based on the difference between the Lorenz curve (the observed cumulative income distribution) and the notion of a perfectly equal income distribution.' For racial or ethnic inequality, \emph{G} is a summary measure of racial or ethnic unevenness or the mean absolute difference between a selected subgroup proportions weighted across all pairs of geographic units, expressed as a proportion of the maximum weighted difference. + +Larger geographies available include state \code{geo_large = 'state'}, county \code{geo_large = 'county'}, census tract \code{geo_large = 'tract'}, Core Based Statistical Area \code{geo_large = 'cbsa'}, Combined Statistical Area \code{geo_large = 'csa'}, and Metropolitan Division \code{geo_large = 'metro'} levels. Smaller geographies available include, county \code{geo_small = 'county'}, census tract \code{geo_small = 'tract'}, and census block group \code{geo_small = 'block group'} levels. If a larger geographical area is comprised of only one smaller geographical area (e.g., a U.S county contains only one census tract), then the \emph{V} value returned is NA. If the larger geographical unit is Combined Based Statistical Areas \code{geo_large = 'csa'} or Core Based Statistical Areas \code{geo_large = 'cbsa'}, 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{V} computation. } \examples{ \dontrun{ # Wrapped in \dontrun{} because these examples require a Census API key. - # Gini Index of income inequality + # Gini Index (a metric of unevenness) of Black populations ## of census tracts within Georgia, U.S.A., counties (2020) - gini(geo = 'tract', state = 'GA', year = 2020) - + gini( + geo_large = 'county', + geo_small = 'tract', + state = 'GA', + year = 2020, + subgroup = c('NHoLB', 'HoLB') + ) + } } diff --git a/man/hoover.Rd b/man/hoover.Rd index 8c6d6e1..a732fc5 100644 --- a/man/hoover.Rd +++ b/man/hoover.Rd @@ -21,7 +21,7 @@ hoover( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s). See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s). See Details for available choices.} \item{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.} @@ -39,12 +39,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Delta (Hoover) of a selected racial/ethnic subgroup(s) and U.S. geographies. +Compute the aspatial Delta (Hoover) of a selected racial or ethnic subgroup(s) and U.S. geographies. } \details{ -This function will compute the aspatial Delta (\emph{DEL}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan, Cuzzort, and Duncan (1961; LC:60007089). This function provides the computation of \emph{DEL} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Delta (\emph{DEL}) 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 Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan, Cuzzort, and Duncan (1961; LC:60007089). This function provides the computation of \emph{DEL} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = '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'} diff --git a/man/krieger.Rd b/man/krieger.Rd index f9cb60e..9625765 100644 --- a/man/krieger.Rd +++ b/man/krieger.Rd @@ -32,9 +32,9 @@ This function will compute three aspatial Index of Concentration at the Extremes \itemize{ \item \strong{Income}: 80th income percentile vs. 20th income percentile \item \strong{Education}: less than high school vs. four-year college degree or more -\item \strong{Race/Ethnicity}: white non-Hispanic vs. black non-Hispanic -\item \strong{Income and race/ethnicity combined}: white non-Hispanic in 80th income percentile vs. black alone (including Hispanic) in 20th income percentile -\item \strong{Income and race/ethnicity combined}: white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile +\item \strong{Race or Ethnicity}: white non-Hispanic vs. black non-Hispanic +\item \strong{Income and race or ethnicity combined}: white non-Hispanic in 80th income percentile vs. black alone (including Hispanic) in 20th income percentile +\item \strong{Income and race or ethnicity combined}: white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile } 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 geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available but are available from other U.S. Census Bureau surveys. The ACS-5 groups used in the computation of the five \emph{ICE} metrics are: diff --git a/man/lieberson.Rd b/man/lieberson.Rd index 99aa856..424cecd 100644 --- a/man/lieberson.Rd +++ b/man/lieberson.Rd @@ -21,7 +21,7 @@ lieberson( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s). See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s). See Details for available choices.} \item{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.} @@ -39,12 +39,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Isolation Index (Lieberson) of a selected racial/ethnic subgroup(s) and U.S. geographies. +Compute the aspatial Isolation Index (Lieberson) of a selected racial or ethnic subgroup(s) and U.S. geographies. } \details{ -This function will compute the aspatial Isolation Index (\emph{xPx\*}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of \emph{xPx\*} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Isolation Index (\emph{xPx\*}) 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 Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. This function provides the computation of \emph{xPx\*} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = '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'} diff --git a/man/ndi-package.Rd b/man/ndi-package.Rd index 0ee32e6..518d9ff 100644 --- a/man/ndi-package.Rd +++ b/man/ndi-package.Rd @@ -9,7 +9,7 @@ Computes various metrics of socio-economic deprivation and disparity in the United States based on information available from the U.S. Census Bureau. } \details{ -The 'ndi' package computes various metrics of socio-economic deprivation and disparity in the United States. Some metrics are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other metrics are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (\emph{NDI}) are available: (1) based on Messer et al. (2006) \doi{10.1007/s11524-006-9094-x} and (2) based on Andrews et al. (2020) \doi{10.1080/17445647.2020.1750066} and Slotman et al. (2022) \doi{10.1016/j.dib.2022.108002} who use variables chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x}. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute the (1) spatial Racial Isolation Index (\emph{RI}) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002}, (2) spatial Educational Isolation Index (\emph{EI}) based on Bravo et al. (2021) \doi{10.3390/ijerph18179384}, (3) aspatial Index of Concentration at the Extremes (\emph{ICE}) based on Feldman et al. (2015) \doi{10.1136/jech-2015-205728} and Krieger et al. (2016) \doi{10.2105/AJPH.2015.302955}, (4) aspatial racial/ethnic Dissimilarity Index (\emph{DI}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}, (5) aspatial income or racial/ethnic Atkinson Index (\emph{AI}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}, (6) aspatial racial/ethnic Isolation Index (\emph{II}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}, (7) aspatial racial/ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}, (8) aspatial racial/ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}, (9) aspatial racial/ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}, (10) aspatial racial/ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089), and (11) an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). Also using data from the ACS-5 (2005-2009 onward), the package can retrieve the aspatial Gini Index (\emph{G}) based on Gini (1921) \doi{10.2307/2223319}. +The 'ndi' package computes various metrics of socio-economic deprivation and disparity in the United States. Some metrics are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other metrics are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (\emph{NDI}) are available: (1) based on Messer et al. (2006) \doi{10.1007/s11524-006-9094-x} and (2) based on Andrews et al. (2020) \doi{10.1080/17445647.2020.1750066} and Slotman et al. (2022) \doi{10.1016/j.dib.2022.108002} who use variables chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x}. Both are a decomposition of multiple demographic characteristics from the U.S. Census Bureau American Community Survey 5-year estimates (ACS-5; 2006-2010 onward). Using data from the ACS-5 (2005-2009 onward), the package can also compute the (1) spatial Racial Isolation Index (\emph{RI}) based on Anthopolos et al. (2011) \doi{10.1016/j.sste.2011.06.002}, (2) spatial Educational Isolation Index (\emph{EI}) based on Bravo et al. (2021) \doi{10.3390/ijerph18179384}, (3) aspatial Index of Concentration at the Extremes (\emph{ICE}) based on Feldman et al. (2015) \doi{10.1136/jech-2015-205728} and Krieger et al. (2016) \doi{10.2105/AJPH.2015.302955}, (4) aspatial racial or ethnic Dissimilarity Index (\emph{DI}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}, (5) aspatial income or racial or ethnic Atkinson Index (\emph{AI}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}, (6) aspatial racial or ethnic Isolation Index (\emph{II}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}, (7) aspatial racial or ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}, (8) aspatial racial or ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}, (9) aspatial racial or ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}, (10) aspatial racial or ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089), and (11) an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). Also using data from the ACS-5 (2005-2009 onward), the package can retrieve the aspatial Gini Index (\emph{G}) based on Gini (1921) \doi{10.2307/2223319}. Key content of the 'ndi' package include:\cr @@ -17,31 +17,31 @@ Key content of the 'ndi' package include:\cr \code{\link{anthopolos}} Computes the spatial Racial Isolation Index (\emph{RI}) based on Anthopolos (2011) \doi{10.1016/j.sste.2011.06.002}. -\code{\link{atkinson}} Computes the aspatial income or racial/ethnic Atkinson Index (\emph{A}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. +\code{\link{atkinson}} Computes the aspatial income or racial or ethnic Atkinson Index (\emph{A}) based on Atkinson (1970) \doi{10.1016/0022-0531(70)90039-6}. -\code{\link{bell}} Computes the aspatial racial/ethnic Interaction Index (\emph{xPy\*}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. +\code{\link{bell}} Computes the aspatial racial or ethnic Interaction Index (\emph{xPy\*}) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) \doi{10.2307/2574118}. -\code{\link{bemanian_beyer}} Computes the aspatial racial/ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. +\code{\link{bemanian_beyer}} Computes the aspatial racial or ethnic Local Exposure and Isolation (\emph{LEx/Is}) metric based on Bemanian & Beyer (2017) \doi{10.1158/1055-9965.EPI-16-0926}. \code{\link{bravo}} Computes the spatial Educational Isolation Index (\emph{EI}) based on Bravo (2021) \doi{10.3390/ijerph18179384}. -\code{\link{duncan}} Computes the aspatial racial/ethnic Dissimilarity Index (\emph{D}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}. +\code{\link{duncan}} Computes the aspatial racial or ethnic Dissimilarity Index (\emph{D}) based on Duncan & Duncan (1955) \doi{10.2307/2088328}. -\code{\link{gini}} Retrieves the aspatial Gini Index (\emph{G}) based on Gini (1921) \doi{10.2307/2223319}. +\code{\link{gini}} Computes the aspatial Gini Index (\emph{G}) of racial or ethnic inequality and retrieves the aspatial Gini Index (\emph{G}) of income inequality based on Gini (1921) \doi{10.2307/2223319}. -\code{\link{hoover}} Computes the aspatial racial/ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{doi:10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089). +\code{\link{hoover}} Computes the aspatial racial or ethnic Delta (\emph{DEL}) based on Hoover (1941) \doi{doi:10.1017/S0022050700052980} and Duncan et al. (1961; LC:60007089). \code{\link{krieger}} Computes the aspatial Index of Concentration at the Extremes based on Feldman et al. (2015) \doi{10.1136/jech-2015-205728} and Krieger et al. (2016) \doi{10.2105/AJPH.2015.302955}. -\code{\link{lieberson}} Computes the aspatial racial/ethnic Isolation Index (\emph{xPx\*}) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. +\code{\link{lieberson}} Computes the aspatial racial or ethnic Isolation Index (\emph{xPx\*}) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) \doi{10.2307/2574118}. \code{\link{messer}} Computes the aspatial Neighborhood Deprivation Index (\emph{NDI}) based on Messer et al. (2006) \doi{10.1007/s11524-006-9094-x}. \code{\link{powell_wiley}} Computes the aspatial Neighborhood Deprivation Index (\emph{NDI}) based on Andrews et al. (2020) \doi{10.1080/17445647.2020.1750066} and Slotman et al. (2022) \doi{10.1016/j.dib.2022.108002} who use variables chosen by Roux and Mair (2010) \doi{10.1111/j.1749-6632.2009.05333.x}. -\code{\link{sudano}} Computes the aspatial racial/ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. +\code{\link{sudano}} Computes the aspatial racial or ethnic Location Quotient (\emph{LQ}) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. -\code{\link{white}} Computes the aspatial racial/ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. +\code{\link{white}} Computes the aspatial racial or ethnic Correlation Ratio (\emph{V}) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. \code{\link{white_blau}} Computes an index of spatial proximity (\emph{SP}) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). diff --git a/man/sudano.Rd b/man/sudano.Rd index 4f0b1c3..977a278 100644 --- a/man/sudano.Rd +++ b/man/sudano.Rd @@ -21,7 +21,7 @@ sudano( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s). See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s). See Details for available choices.} \item{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.} @@ -39,12 +39,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Location Quotient (Sudano) of a selected racial/ethnic subgroup(s) and U.S. geographies. +Compute the aspatial Location Quotient (Sudano) of a selected racial or ethnic subgroup(s) and U.S. geographies. } \details{ -This function will compute the aspatial Location Quotient (\emph{LQ}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. This function provides the computation of \emph{LQ} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Location Quotient (\emph{LQ}) 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 Merton (1939) \doi{10.2307/2084686} and Sudano et al. (2013) \doi{10.1016/j.healthplace.2012.09.015}. This function provides the computation of \emph{LQ} 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = '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'} diff --git a/man/white.Rd b/man/white.Rd index d17d21e..5a57532 100644 --- a/man/white.Rd +++ b/man/white.Rd @@ -21,7 +21,7 @@ white( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s). See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnnic subgroup(s). See Details for available choices.} \item{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.} @@ -39,12 +39,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute the aspatial Correlation Ratio (White) of a selected racial/ethnic subgroup(s) and U.S. geographies. +Compute the aspatial Correlation Ratio (White) of a selected racial or ethnnic subgroup(s) and U.S. geographies. } \details{ -This function will compute the aspatial Correlation Ratio (\emph{V} or \eqn{Eta^{2}}{Eta^2}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. This function provides the computation of \emph{V} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute the aspatial Correlation Ratio (\emph{V} or \eqn{Eta^{2}}{Eta^2}) of selected racial or ethnnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bell (1954) \doi{10.2307/2574118} and White (1986) \doi{10.2307/3644339}. This function provides the computation of \emph{V} for any of the U.S. Census Bureau race or ethnnicity 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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 aspatial 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial or ethnnic 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'} diff --git a/man/white_blau.Rd b/man/white_blau.Rd index 6fd55c9..d01cb8c 100644 --- a/man/white_blau.Rd +++ b/man/white_blau.Rd @@ -22,9 +22,9 @@ white_blau( \item{year}{Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.} -\item{subgroup}{Character string specifying the racial/ethnic subgroup(s) as the comparison population. See Details for available choices.} +\item{subgroup}{Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.} -\item{subgroup_ref}{Character string specifying the racial/ethnic subgroup(s) as the reference population. See Details for available choices.} +\item{subgroup_ref}{Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices.} \item{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.} @@ -42,12 +42,12 @@ An object of class 'list'. This is a named list with the following components: } } \description{ -Compute an index of spatial proximity (White) of a selected racial/ethnic subgroup(s) and U.S. geographies. +Compute an index of spatial proximity (White) of a selected racial or ethnic subgroup(s) and U.S. geographies. } \details{ -This function will compute an index of spatial proximity (\emph{SP}) of selected racial/ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). This function provides the computation of \emph{SP} for any of the U.S. Census Bureau race/ethnicity subgroups (including Hispanic and non-Hispanic individuals). +This function will compute an index of spatial proximity (\emph{SP}) 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 White (1986) \doi{10.2307/3644339} and Blau (1977; ISBN-13:978-0-029-03660-0). This function provides the computation of \emph{SP} 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 = 'csa'} or \code{geo_large = 'metro'}) but may be available from other U.S. Census Bureau surveys. The twenty racial/ethnic subgroups (U.S. Census Bureau definitions) are: +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 = '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'} @@ -73,7 +73,7 @@ The function uses the \code{\link[tidycensus]{get_acs}} function to obtain U.S. 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{SP} is a measure of clustering of racial/ethnic populations within smaller geographical areas that are located within larger geographical areas. \emph{SP} 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 between subgroup and referent group members. A value greater than 1 indicates subgroup members live nearer to one another than to referent subgroup members. A value less than 1 indicates subgroup live nearer to and referent subgroup members than to their own subgroup members. +\emph{SP} is a measure of clustering of racial or ethnic populations within smaller geographical areas that are located within larger geographical areas. \emph{SP} 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 between subgroup and referent group members. A value greater than 1 indicates subgroup members live nearer to one another than to referent subgroup members. A value less than 1 indicates subgroup live nearer to and referent subgroup members than to their own subgroup members. 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}. diff --git a/tests/testthat/test-gini.R b/tests/testthat/test-gini.R index e4b7b23..d580d55 100644 --- a/tests/testthat/test-gini.R +++ b/tests/testthat/test-gini.R @@ -7,29 +7,45 @@ context('gini') test_that('gini throws error with invalid arguments', { # Unavailable geography expect_error(gini( - geo = 'zcta', + geo_small = 'zcta', state = 'DC', year = 2020, + subgroup = 'NHoLB', quiet = TRUE )) + expect_error( + gini( + geo_large = 'block group', + state = 'DC', + year = 2020, + subgroup = 'NHoLB', + quiet = TRUE + ) + ) # Unavailable year expect_error(gini( state = 'DC', year = 2005, + subgroup = 'NHoLB', + quiet = TRUE + )) + + # Unavailable subgroup + expect_error(gini( + state = 'DC', + year = 2020, + subgroup = 'terran', quiet = TRUE )) skip_if(Sys.getenv('CENSUS_API_KEY') == '') # Incorrect state - expect_error(gini(state = 'AB', year = 2020)) - - # Unavailable geography for DC (only 1 'county' in DC so, alone, NDI cannot be computed) expect_error(gini( - geo = 'county', - state = 'DC', - year = 2009, + state = 'AB', + year = 2020, + subgroup = 'NHoLB', quiet = TRUE )) @@ -38,11 +54,23 @@ test_that('gini throws error with invalid arguments', { test_that('gini works', { skip_if(Sys.getenv('CENSUS_API_KEY') == '') - expect_message(gini(state = 'DC', year = 2020)) + expect_message(gini( + state = 'DC', + year = 2020, + subgroup = c('NHoLB', 'HoLB') + )) + + expect_silent(gini( + state = 'DC', + year = 2020, + subgroup = 'NHoLB', + quiet = TRUE + )) expect_silent(gini( state = 'DC', year = 2020, + subgroup = c('NHoLB', 'HoLB'), quiet = TRUE )) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 463adf0..aa584fc 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -537,23 +537,23 @@ Since version v0.1.1, the [*ndi*](https://CRAN.R-project.org/package=ndi) packag 1. `anthopolos()` function that computes the Racial Isolation Index (*RI*) based on [Anthopolos et al. (2011)](https://doi.org/10.1016/j.sste.2011.06.002) 2. `bravo()` function that computes the Educational Isolation Index (*EI*) based on [Bravo et al. (2021)](https://doi.org/10.3390/ijerph18179384) -3. `gini()` function that retrieves the Gini Index (*G*) based on [Gini (1921)](https://doi.org/10.2307/2223319) +3. `gini()` function that computes the Gini Index (*G*) of racial or ethnic inequality and retrieves the Gini Index (*G*) of income inequality based on [Gini (1921)](https://doi.org/10.2307/2223319) 4. `krieger()` function that computes the Index of Concentration at the Extremes (*ICE*) based on based on [Feldman et al. (2015)](https://doi.org/10.1136/jech-2015-205728) and [Krieger et al. (2016)](https://doi.org/10.2105/AJPH.2015.302955) 5. `duncan()` function that computes the Dissimilarity Index (*D*) based on [Duncan & Duncan (1955)](https://doi.org/10.2307/2088328) 6. `atkinson()` function that computes the Atkinson Index (*A*) based on [Atkinson (1970)](https://doi.org/10.1016/0022-0531(70)90039-6) -7. `bell()` function that computes the aspatial racial/ethnic Interaction Index (_xPy\*_) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and [Bell (1954)](https://doi.org/10.2307/2574118) -8. `white()` function that computes the aspatial racial/ethnic Correlation Ratio (*V*) based on [Bell (1954)](https://doi.org/10.2307/2574118) and [White (1986)](https://doi.org/10.2307/3644339) -9. `sudano()` function that computes the aspatial racial/ethnic Location Quotient (*LQ*) based on [Merton (1939)](https://doi.org/10.2307/2084686) and [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015) -10. `bemanian_beyer()` function that computes the aspatial racial/ethnic Local Exposure and Isolation (*LEx/Is*) metric based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926) -11. `hoover()` function that computes the aspatial racial/ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089) +7. `bell()` function that computes the aspatial racial or ethnic Interaction Index (_xPy\*_) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and [Bell (1954)](https://doi.org/10.2307/2574118) +8. `white()` function that computes the aspatial racial or ethnic Correlation Ratio (*V*) based on [Bell (1954)](https://doi.org/10.2307/2574118) and [White (1986)](https://doi.org/10.2307/3644339) +9. `sudano()` function that computes the aspatial racial or ethnic Location Quotient (*LQ*) based on [Merton (1939)](https://doi.org/10.2307/2084686) and [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015) +10. `bemanian_beyer()` function that computes the aspatial racial or ethnic Local Exposure and Isolation (*LEx/Is*) metric based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926) +11. `hoover()` function that computes the aspatial racial or ethnic Delta (*DEL*) based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089) 12. `white_blau()` function that computes an index of spatial proximity (*SP*) based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0) -13. `lieberson()` function that computes the aspatial racial/ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and [Bell (1954)](https://doi.org/10.2307/2574118) +13. `lieberson()` function that computes the aspatial racial or ethnic Isolation Index (_xPx\*_) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and [Bell (1954)](https://doi.org/10.2307/2574118) #### Compute Racial Isolation Index (*RI*) -Compute the spatial *RI* values (2006-2010 5-year ACS) for North Carolina, U.S.A., census tracts. This metric is based on [Anthopolos et al. (2011)](https://doi.org/10.1016/j.sste.2011.06.002) that assessed the racial isolation of the population that identifies as non-Hispanic or Latino, Black or African American alone. Multiple racial/ethnic subgroups are available in the `anthopolos()` function, including: +Compute the spatial *RI* values (2006-2010 5-year ACS) for North Carolina, U.S.A., census tracts. This metric is based on [Anthopolos et al. (2011)](https://doi.org/10.1016/j.sste.2011.06.002) that assessed the racial isolation of the population that identifies as non-Hispanic or Latino, Black or African American alone. Multiple racial or ethnic subgroups are available in the `anthopolos()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -576,7 +576,7 @@ Compute the spatial *RI* values (2006-2010 5-year ACS) for North Carolina, U.S. | B03002_020 | Hispanic or Latino, two races including some other race | HoLTRiSOR | | B03002_021 | Hispanic or Latino, two races excluding some other race, and three or more races | HoLTReSOR | -A census geography (and its neighbors) that has nearly all of its population who identify with the specified race/ethnicity subgroup(s) (e.g., Not Hispanic or Latino, Black or African American alone) will have an *RI* value close to 1. In contrast, a census geography (and its neighbors) that is nearly none of its population who identify with the specified race/ethnicity subgroup(s) (e.g., not Not Hispanic or Latino, Black or African American alone) will have an *RI* value close to 0. +A census geography (and its neighbors) that has nearly all of its population who identify with the specified race or ethnicity subgroup(s) (e.g., Not Hispanic or Latino, Black or African American alone) will have an *RI* value close to 1. In contrast, a census geography (and its neighbors) that is nearly none of its population who identify with the specified race or ethnicity subgroup(s) (e.g., not Not Hispanic or Latino, Black or African American alone) will have an *RI* value close to 0. ```{r anthopolos_prep, results = 'hide'} anthopolos2010NC <- anthopolos(state = 'NC', year = 2010, subgroup = 'NHoLB') @@ -726,14 +726,91 @@ ggplot() + Can correct one source of edge effect in the same manner as shown for the *RI* metric. -#### Retrieve the Gini Index (*G*) +#### The racial or ethnic Gini Index (*G*) -Retrieve the aspatial Gini Index (*G*) values (2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts. This metric is based on [Gini (1921)](https://doi.org/10.2307/2223319), and the `gini()` function retrieves the estimate from the ACS-5. +Compute the aspatial racial or ethnic Gini Index (*G*) values (2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts within counties. This metric is based on [Gini (1921)](https://doi.org/10.2307/2223319). Multiple racial or ethnic subgroups are available in the `gini()` function, including: -According to the [U.S. Census Bureau](https://census.gov/topics/income-poverty/income-inequality/about/metrics/gini-index.html): 'The Gini Index is a summary measure of income inequality. The Gini coefficient incorporates the detailed shares data into a single statistic, which summarizes the dispersion of income across the entire income distribution. The Gini coefficient ranges from 0, indicating perfect equality (where everyone receives an equal share), to 1, perfect inequality (where only one recipient or group of recipients receives all the income). *G* is based on the difference between the Lorenz curve (the observed cumulative income distribution) and the notion of a perfectly equal income distribution.' +| ACS table source | racial or ethnic subgroup | character for `subgroup` arguments | +| -------------- | ------------- | ---------------- | +| B03002_002 | not Hispanic or Latino | NHoL | +| B03002_003 | not Hispanic or Latino, white alone | NHoLW | +| B03002_004 | not Hispanic or Latino, Black or African American alone | NHoLB | +| B03002_005 | not Hispanic or Latino, American Indian and Alaska Native alone | NHoLAIAN | +| B03002_006 | not Hispanic or Latino, Asian alone | NHoLA | +| B03002_007 | not Hispanic or Latino, Native Hawaiian and Other Pacific Islander alone | NHoLNHOPI | +| B03002_008 | not Hispanic or Latino, some other race alone | NHoLSOR | +| B03002_009 | not Hispanic or Latino, two or more races | NHoLTOMR | +| B03002_010 | not Hispanic or Latino, two races including some other race | NHoLTRiSOR | +| B03002_011 | not Hispanic or Latino, two races excluding some other race, and three or more races | NHoLTReSOR | +| B03002_012 | Hispanic or Latino | HoL | +| B03002_013 | Hispanic or Latino, white alone | HoLW | +| B03002_014 | Hispanic or Latino, Black or African American alone | HoLB | +| B03002_015 | Hispanic or Latino, American Indian and Alaska Native alone | HoLAIAN | +| B03002_016 | Hispanic or Latino, Asian alone | HoLA | +| B03002_017 | Hispanic or Latino, Native Hawaiian and other Pacific Islander alone | HoLNHOPI | +| B03002_018 | Hispanic or Latino, some other race alone | HoLSOR | +| B03002_019 | Hispanic or Latino, two or more races | HoLTOMR | +| B03002_020 | Hispanic or Latino, two races including some other race | HoLTRiSOR | +| B03002_021 | Hispanic or Latino, two races excluding some other race, and three or more races | HoLTReSOR | + +*G* is a measure of the evenness of racial or ethnic residential populations when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. *G* can range in value from 0 to 1 and the mean absolute difference between a selected subgroup proportions weighted across all pairs of geographic units, expressed as a proportion of the maximum weighted difference. + +```{r g_re_prep, results = 'hide'} +gini2010MA <- gini( + geo_large = 'county', + geo_small = 'tract', + state = 'MA', + year = 2010, + subgroup = c('NHoLB', 'HoLB') +) + +# Obtain the 2010 census tracts from the 'tigris' package +county2010MA <- counties(state = 'MA', year = 2010, cb = TRUE) +# Remove first 9 characters from GEOID for compatibility with tigris information +county2010MA$GEOID <- substring(county2010MA$GEO_ID, 10) + +# Join the G (Gini) values to the census tract geometry +MA2010gini <- county2010MA %>% + left_join(gini2010MA$g, by = 'GEOID') +``` + +```{r g_re_plot, fig.height = 4, fig.width = 7} +# Visualize the G (Gini) values (2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts +ggplot() + + geom_sf( + data = MA2010gini, + aes(fill = G_re), + size = 0.05, + color = 'transparent' + ) + + geom_sf( + data = county2010MA, + fill = 'transparent', + color = 'white', + size = 0.2 + ) + + theme_minimal() + + scale_fill_viridis_c(limits = c(0, 1)) + + labs( + fill = 'Index (Continuous)', + caption = 'Source: U.S. Census ACS 2006-2010 estimates') + + ggtitle('Gini Index', subtitle = 'Black population of MA counties') +``` + +#### The income Gini Index (*G*) + +Retrieve the aspatial income Gini Index (*G*) values (2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts. This metric is based on [Gini (1921)](https://doi.org/10.2307/2223319), and the `gini()` function retrieves the estimate from the ACS-5 when calculating the Gini Index (*G*) for racial or ethnic inequality. + +According to the [U.S. Census Bureau](https://census.gov/topics/income-poverty/income-inequality/about/metrics/gini-index.html): 'The Gini Index is a summary measure of income inequality. The Gini coefficient incorporates the detailed shares data into a single statistic, which summarizes the dispersion of income across the entire income distribution. The Gini coefficient ranges from 0, indicating perfect equality (where everyone receives an equal share), to 1, perfect inequality (where only one recipient or group of recipients receives all the income). The Gini Index is based on the difference between the Lorenz curve (the observed cumulative income distribution) and the notion of a perfectly equal income distribution.' ```{r gini_prep, results = 'hide'} -gini2010MA <- gini(state = 'MA', year = 2010) +gini2010MA <- gini( + geo_large = 'county', + geo_small = 'tract', + state = 'MA', + year = 2010, + subgroup = c('NHoLB', 'HoLB') +) # Obtain the 2010 census tracts from the 'tigris' package tract2010MA <- tracts(state = 'MA', year = 2010, cb = TRUE) @@ -745,7 +822,7 @@ county2010MA <- counties(state = 'MA', year = 2010, cb = TRUE) # Join the G (Gini) values to the census tract geometry MA2010gini <- tract2010MA %>% - left_join(gini2010MA$g, by = 'GEOID') + left_join(gini2010MA$g_data, by = 'GEOID') ``` ```{r gini_plot, fig.height = 4, fig.width = 7} @@ -753,7 +830,7 @@ MA2010gini <- tract2010MA %>% ggplot() + geom_sf( data = MA2010gini, - aes(fill = G), + aes(fill = G_inc), size = 0.05, color = 'transparent' ) + @@ -764,11 +841,12 @@ ggplot() + size = 0.2 ) + theme_minimal() + - scale_fill_viridis_c() + + scale_fill_viridis_c(limits = c(0, 1)) + labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') + - ggtitle('Gini Index', subtitle = 'MA census tracts') + ggtitle('Gini Index', subtitle = 'Income of MA census tracts') ``` + ### Index of Concentration at the Extremes (*ICE*) Compute the aspatial Index of Concentration at the Extremes values (2006-2010 5-year ACS) for Wayne County, Michigan, U.S.A., census tracts. Wayne County is the home of Detroit, Michigan, a highly segregated city in the U.S. This metric is based on [Feldman et al. (2015)](https://doi.org/10.1136/jech-2015-205728) and [Krieger et al. (2016)](https://doi.org/10.2105/AJPH.2015.302955) who expanded the metric designed by Massey in a chapter of [Booth & Crouter (2001)](https://doi.org/10.4324/9781410600141) initially designed for residential segregation. The `krieger()` function computes five *ICE* metrics using the following ACS-5 groups: @@ -777,9 +855,9 @@ Compute the aspatial Index of Concentration at the Extremes values (2006-2010 5- | -------------- | ------------- | ---------------- | | B19001 | Income, 'ICE_inc'| 80th income percentile vs. 20th income percentile | | B15002 | Education, 'ICE_edu'| less than high school vs. four-year college degree or more | -| B03002 | Race/Ethnicity, 'ICE_rewb'| 80th income percentile vs. 20th income percentile | -| B19001 & B19001B & B19001H | Income and race/ethnicity combined, 'ICE_wbinc' | white non-Hispanic in 80th income percentile vs. black alone (including Hispanic) in 20th income percentile | -| B19001 & B19001H | Income and race/ethnicity combined, 'ICE_wpcinc'| white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile | +| B03002 | Race or Ethnicity, 'ICE_rewb'| 80th income percentile vs. 20th income percentile | +| B19001 & B19001B & B19001H | Income and race or ethnicity combined, 'ICE_wbinc' | white non-Hispanic in 80th income percentile vs. black alone (including Hispanic) in 20th income percentile | +| B19001 & B19001H | Income and race or ethnicity combined, 'ICE_wpcinc'| white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile | *ICE* metrics can range in value from −1 (most deprived) to 1 (most privileged). A value of 0 can thus represent two possibilities: (1) none of the residents are in the most privileged or most deprived categories, or (2) an equal number of persons are in the most privileged and most deprived categories, and in both cases indicates that the area is not dominated by extreme concentrations of either of the two groups. @@ -839,7 +917,7 @@ ggplot() + subtitle = 'less than high school vs. four-year college degree or more' ) -# Plot ICE for Race/Ethnicity +# Plot ICE for Race or Ethnicity ggplot() + geom_sf( data = ice2020WC, @@ -856,11 +934,11 @@ ggplot() + ) + labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') + ggtitle( - 'Index of Concentration at the Extremes\nRace/Ethnicity (Krieger)', + 'Index of Concentration at the Extremes\nRace or Ethnicity (Krieger)', subtitle = 'white non-Hispanic vs. black non-Hispanic' ) -# Plot ICE for Income and Race/Ethnicity Combined +# Plot ICE for Income and Race or Ethnicity Combined ## white non-Hispanic in 80th income percentile vs. ## black (including Hispanic) in 20th income percentile ggplot() + @@ -879,11 +957,11 @@ ggplot() + ) + labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') + ggtitle( - 'Index of Concentration at the Extremes\nIncome & race/ethnicity combined (Krieger)', + 'Index of Concentration at the Extremes\nIncome & race or ethnicity combined (Krieger)', subtitle = 'white non-Hispanic in 80th inc ptcl vs. black alone in 20th inc pctl' ) -# Plot ICE for Income and Race/Ethnicity Combined +# Plot ICE for Income and Race or Ethnicity Combined ## white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile ggplot() + geom_sf( @@ -901,16 +979,16 @@ ggplot() + ) + labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') + ggtitle( - 'Index of Concentration at the Extremes\nIncome & race/ethnicity combined (Krieger)', + 'Index of Concentration at the Extremes\nIncome & race or ethnicity combined (Krieger)', subtitle = 'white non-Hispanic (WNH) in 80th inc pctl vs. WNH in 20th inc pctl' ) ``` -#### Compute racial/ethnic Dissimilarity Index (*D*) +#### Compute racial or ethnic Dissimilarity Index (*D*) -Compute the aspatial racial/ethnic *D* values (2006-2010 5-year ACS) for Pennsylvania, U.S.A., counties from census tracts. This metric is based on [Duncan & Duncan (1955)](https://doi.org/10.2307/2088328) that assessed the racial/ethnic isolation of students that identify as non-Hispanic or Latino, Black or African American alone compared to students that identify as non-Hispanic or Latino, white alone between schools and school districts. Multiple racial/ethnic subgroups are available in the `duncan()` function, including: +Compute the aspatial racial or ethnic *D* values (2006-2010 5-year ACS) for Pennsylvania, U.S.A., counties from census tracts. This metric is based on [Duncan & Duncan (1955)](https://doi.org/10.2307/2088328) that assessed the racial or ethnic isolation of students that identify as non-Hispanic or Latino, Black or African American alone compared to students that identify as non-Hispanic or Latino, white alone between schools and school districts. Multiple racial or ethnic subgroups are available in the `duncan()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` or `subgroup_ref` arguments | +| ACS table source | racial or ethnic subgroup | character for `subgroup` or `subgroup_ref` arguments | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -933,7 +1011,7 @@ Compute the aspatial racial/ethnic *D* values (2006-2010 5-year ACS) for Pennsyl | B03002_020 | Hispanic or Latino, two races including some other race | HoLTRiSOR | | B03002_021 | Hispanic or Latino, two races excluding some other race, and three or more races | HoLTReSOR | -*D* is a measure of the evenness of racial/ethnic residential segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. *D* can range in value from 0 to 1 and represents the proportion of racial/ethnic subgroup members that would have to change their area of residence to achieve an even distribution within the larger geographical area under conditions of maximum segregation. +*D* is a measure of the evenness of racial or ethnic residential segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. *D* can range in value from 0 to 1 and represents the proportion of racial or ethnic subgroup members that would have to change their area of residence to achieve an even distribution within the larger geographical area under conditions of maximum segregation. ```{r duncan_prep, results = 'hide'} duncan2010PA <- duncan( @@ -979,11 +1057,11 @@ ggplot() + ) ``` -#### Compute aspatial income or racial/ethnic Atkinson Index (*A*) +#### Compute aspatial income or racial or ethnic Atkinson Index (*A*) -Compute the aspatial income or racial/ethnic *A* values (2017-2021 5-year ACS) for Kentucky, U.S.A., counties from census block groups. This metric is based on [Atkinson (1970)](https://doi.org/10.2307/2088328) that assessed the distribution of income within 12 counties but has since been adapted to study racial/ethnic segregation (see [James & Taeuber 1985](https://doi.org/10.2307/270845)). To compare median household income, specify `subgroup = 'MedHHInc'` which will use the ACS-5 variable 'B19013_001' in the computation. Multiple racial/ethnic subgroups are available in the `atkinson()` function, including: +Compute the aspatial income or racial or ethnic *A* values (2017-2021 5-year ACS) for Kentucky, U.S.A., counties from census block groups. This metric is based on [Atkinson (1970)](https://doi.org/10.2307/2088328) that assessed the distribution of income within 12 counties but has since been adapted to study racial or ethnic segregation (see [James & Taeuber 1985](https://doi.org/10.2307/270845)). To compare median household income, specify `subgroup = 'MedHHInc'` which will use the ACS-5 variable 'B19013_001' in the computation. Multiple racial or ethnic subgroups are available in the `atkinson()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -1006,7 +1084,7 @@ Compute the aspatial income or racial/ethnic *A* values (2017-2021 5-year ACS) f | B03002_020 | Hispanic or Latino, two races including some other race | HoLTRiSOR | | B03002_021 | Hispanic or Latino, two races excluding some other race, and three or more races | HoLTReSOR | -*A* is a measure of the inequality and, in the context of residential race/ethnicity, segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. *A* can range in value from 0 to 1 and smaller values of the index indicate lower levels of inequality (e.g., less segregation). +*A* is a measure of the inequality and, in the context of residential race or ethnicity, segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. *A* can range in value from 0 to 1 and smaller values of the index indicate lower levels of inequality (e.g., less segregation). *A* is sensitive to the choice of `epsilon` argument or the shape parameter that determines how to weight the increments to inequality (segregation) contributed by different proportions of the Lorenz curve. A user must explicitly decide how heavily to weight smaller geographical units at different points on the Lorenz curve (i.e., whether the index should take greater account of differences among areas of over- or under-representation). The `epsilon` argument must have values between 0 and 1.0. For `0 <= epsilon < 0.5` or less 'inequality-averse,' smaller geographical units with a subgroup proportion smaller than the subgroup proportion of the larger geographical unit contribute more to inequality ('over-representation'). For `0.5 < epsilon <= 1.0` or more 'inequality-averse,' smaller geographical units with a subgroup proportion larger than the subgroup proportion of the larger geographical unit contribute more to inequality ('under-representation'). If `epsilon = 0.5` (the default), units of over- and under-representation contribute equally to the index. See Section 2.3 of [Saint-Jacques et al. (2020)](https://doi.org/10.48550/arXiv.2002.05819) for one method to select `epsilon`. We choose `epsilon = 0.67` in the example below: @@ -1052,11 +1130,11 @@ ggplot() + ) ``` -#### Compute racial/ethnic Interaction Index (_xPy\*_) +#### Compute racial or ethnic Interaction Index (_xPy\*_) -Compute the aspatial racial/ethnic _xPy\*_ values (2017-2021 5-year ACS) for Ohio, U.S.A., counties from census tracts. This metric is based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and adapted by [Bell (1954)](https://doi.org/10.2307/2574118). Multiple racial/ethnic subgroups are available in the `bell()` function, including: +Compute the aspatial racial or ethnic _xPy\*_ values (2017-2021 5-year ACS) for Ohio, U.S.A., counties from census tracts. This metric is based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and adapted by [Bell (1954)](https://doi.org/10.2307/2574118). Multiple racial or ethnic subgroups are available in the `bell()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` or `subgroup_ixn` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` or `subgroup_ixn` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -1125,9 +1203,9 @@ ggplot() + #### Compute Correlation Ratio (*V*) -Compute the aspatial racial/ethnic *V* values (2017-2021 5-year ACS) for South Carolina, U.S.A., counties from census tracts. This metric is based on [Bell (1954)](https://doi.org/10.2307/2574118) and adapted by [White (1986)](https://doi.org/10.2307/3644339). Multiple racial/ethnic subgroups are available in the `white()` function, including: +Compute the aspatial racial or ethnic *V* values (2017-2021 5-year ACS) for South Carolina, U.S.A., counties from census tracts. This metric is based on [Bell (1954)](https://doi.org/10.2307/2574118) and adapted by [White (1986)](https://doi.org/10.2307/3644339). Multiple racial or ethnic subgroups are available in the `white()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -1200,9 +1278,9 @@ ggplot() + #### Compute Location Quotient (*LQ*) -Compute the aspatial racial/ethnic *LQ* values (2017-2021 5-year ACS) for Tennessee, U.S.A., counties vs. the state. This metric is based on [Merton (1939)](https://doi.org/10.2307/2084686) and adapted by [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015). Multiple racial/ethnic subgroups are available in the `sudano()` function, including: +Compute the aspatial racial or ethnic *LQ* values (2017-2021 5-year ACS) for Tennessee, U.S.A., counties vs. the state. This metric is based on [Merton (1939)](https://doi.org/10.2307/2084686) and adapted by [Sudano et al. (2013)](https://doi.org/10.1016/j.healthplace.2012.09.015). Multiple racial or ethnic subgroups are available in the `sudano()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -1270,9 +1348,9 @@ ggplot() + #### Compute Local Exposure and Isolation (*LEx/Is*) -Compute the aspatial racial/ethnic Local Exposure and Isolation metric (2017-2021 5-year ACS) for Mississippi, U.S.A., counties vs. the state. This metric is based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926). Multiple racial/ethnic subgroups are available in the `bemanian_beyer()` function, including: +Compute the aspatial racial or ethnic Local Exposure and Isolation metric (2017-2021 5-year ACS) for Mississippi, U.S.A., counties vs. the state. This metric is based on [Bemanian & Beyer (2017)](https://doi.org/10.1158/1055-9965.EPI-16-0926). Multiple racial or ethnic subgroups are available in the `bemanian_beyer()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` or `subgroup_ixn` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` or `subgroup_ixn` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -1295,7 +1373,7 @@ Compute the aspatial racial/ethnic Local Exposure and Isolation metric (2017-202 | B03002_020 | Hispanic or Latino, two races including some other race | HoLTRiSOR | | B03002_021 | Hispanic or Latino, two races excluding some other race, and three or more races | HoLTReSOR | -*LEx/Is* is a measure of the probability that two individuals living within a specific smaller geography (e.g., census tract) of either different (i.e., exposure) or the same (i.e., isolation) racial/ethnic subgroup(s) will interact, assuming that individuals within a smaller geography are randomly mixed. *LEx/Is* is standardized with a logit transformation and centered against an expected case that all races/ethnicities are evenly distributed across a larger geography. *LEx/Is* can range from negative infinity to infinity. If *LEx/Is* is zero then the estimated probability of the interaction between two people of the given subgroup(s) within a smaller geography is equal to the expected probability if the subgroup(s) were perfectly mixed in the larger geography. If *LEx/Is* is greater than zero then the interaction is more likely to occur within the smaller geography than in the larger geography, and if *LEx/Is* is less than zero then the interaction is less likely to occur within the smaller geography than in the larger geography. Note: the exponentiation of each *LEx/Is* metric results in the odds ratio of the specific exposure or isolation of interest in a smaller geography relative to the larger geography. Similar to *LQ* (Sudano), *LEx/Is* computes values for each smaller geography relative to the larger geography. +*LEx/Is* is a measure of the probability that two individuals living within a specific smaller geography (e.g., census tract) of either different (i.e., exposure) or the same (i.e., isolation) racial or ethnic subgroup(s) will interact, assuming that individuals within a smaller geography are randomly mixed. *LEx/Is* is standardized with a logit transformation and centered against an expected case that all races or ethnicities are evenly distributed across a larger geography. *LEx/Is* can range from negative infinity to infinity. If *LEx/Is* is zero then the estimated probability of the interaction between two people of the given subgroup(s) within a smaller geography is equal to the expected probability if the subgroup(s) were perfectly mixed in the larger geography. If *LEx/Is* is greater than zero then the interaction is more likely to occur within the smaller geography than in the larger geography, and if *LEx/Is* is less than zero then the interaction is less likely to occur within the smaller geography than in the larger geography. Note: the exponentiation of each *LEx/Is* metric results in the odds ratio of the specific exposure or isolation of interest in a smaller geography relative to the larger geography. Similar to *LQ* (Sudano), *LEx/Is* computes values for each smaller geography relative to the larger geography. ```{r bemanian_beyer_prep, results = 'hide'} bemanian_beyer2021MS <- bemanian_beyer( @@ -1370,9 +1448,9 @@ ggplot() + #### Compute Delta (*DEL*) -Compute the aspatial racial/ethnic *DEL* values (2017-2021 5-year ACS) for Alabama, U.S.A., counties from census tracts. This metric is based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089). Multiple racial/ethnic subgroups are available in the `hoover()` function, including: +Compute the aspatial racial or ethnic *DEL* values (2017-2021 5-year ACS) for Alabama, U.S.A., counties from census tracts. This metric is based on [Hoover (1941)](https://doi.org/10.1017/S0022050700052980) and Duncan et al. (1961; LC:60007089). Multiple racial or ethnic subgroups are available in the `hoover()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -1440,9 +1518,9 @@ ggplot() + #### Compute an index of spatial proximity (*SP*) -Compute an index of spatial proximity (2010-2014 5-year ACS) for Atlanta, GA, metropolitan area from census tracts. This metric is based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0) that designed the metric to identify racial or ethnic enclaves. Multiple racial/ethnic subgroups are available in the `white_blau()` function, including: +Compute an index of spatial proximity (2010-2014 5-year ACS) for Atlanta, GA, metropolitan area from census tracts. This metric is based on [White (1986)](https://doi.org/10.2307/3644339) and Blau (1977; ISBN-13:978-0-029-03660-0) that designed the metric to identify racial or ethnic enclaves. Multiple racial or ethnic subgroups are available in the `white_blau()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` or `subgroup_ref` arguments | +| ACS table source | racial or ethnic subgroup | character for `subgroup` or `subgroup_ref` arguments | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | @@ -1465,7 +1543,7 @@ Compute an index of spatial proximity (2010-2014 5-year ACS) for Atlanta, GA, me | B03002_020 | Hispanic or Latino, two races including some other race | HoLTRiSOR | | B03002_021 | Hispanic or Latino, two races excluding some other race, and three or more races | HoLTReSOR | -*SP* is a measure of clustering of racial/ethnic populations within smaller geographical areas that are located within larger geographical areas. *SP* 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 between subgroup and referent group members. A value greater than 1 indicates subgroup members live nearer to one another than to referent subgroup members. A value less than 1 indicates subgroup live nearer to and referent subgroup members than to their own subgroup members. +*SP* is a measure of clustering of racial or ethnic populations within smaller geographical areas that are located within larger geographical areas. *SP* 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 between subgroup and referent group members. A value greater than 1 indicates subgroup members live nearer to one another than to referent subgroup members. A value less than 1 indicates subgroup live nearer to and referent subgroup members than to their own subgroup members. ```{r white_blau_prep, results = 'hide'} whiteblau2014GA <- white_blau( @@ -1520,11 +1598,11 @@ ggplot() + ) ``` -#### Compute racial/ethnic Isolation Index (_xPx\*_) +#### Compute racial or ethnic Isolation Index (_xPx\*_) -Compute the aspatial racial/ethnic _xPx\*_ values (2015-2019 5-year ACS) for Delaware, U.S.A., census tracts from census block groups. This metric is based on [Bell (1954)](https://doi.org/10.2307/2574118) and adapted by Lieberson (1981; ISBN-13:978-1-032-53884-6). Multiple racial/ethnic subgroups are available in the `lieberson()` function, including: +Compute the aspatial racial or ethnic _xPx\*_ values (2015-2019 5-year ACS) for Delaware, U.S.A., census tracts from census block groups. This metric is based on [Bell (1954)](https://doi.org/10.2307/2574118) and adapted by Lieberson (1981; ISBN-13:978-1-032-53884-6). Multiple racial or ethnic subgroups are available in the `lieberson()` function, including: -| ACS table source | racial/ethnic subgroup | character for `subgroup` argument | +| ACS table source | racial or ethnic subgroup | character for `subgroup` argument | | -------------- | ------------- | ---------------- | | B03002_002 | not Hispanic or Latino | NHoL | | B03002_003 | not Hispanic or Latino, white alone | NHoLW | diff --git a/vignettes/vignette.html b/vignettes/vignette.html index 96b983c..fdcfffd 100644 --- a/vignettes/vignette.html +++ b/vignettes/vignette.html @@ -12,7 +12,7 @@ - + ndi: Neighborhood Deprivation Indices @@ -340,7 +340,7 @@

    ndi: Neighborhood Deprivation Indices

    Ian D. Buller (GitHub: @idblr)

    -

    2024-08-22

    +

    2024-08-23

    @@ -1135,8 +1135,8 @@

    Assign the referent (U.S.-Standardized Metric)

     )

    The process to compute a US-standardized NDI (Powell-Wiley) -took about 24.7 minutes to run on a machine with the features listed at -the end of the vignette.

    +took about 3 minutes to run on a machine with the features listed at the +end of the vignette.

    @@ -1152,9 +1152,9 @@

    Additional metrics socio-economic deprivation and disparity

  1. bravo() function that computes the Educational Isolation Index (EI) based on Bravo et al. (2021)
    2. -
  2. gini() function that retrieves the Gini Index -(G) based on Gini -(1921)
    3. +
  3. gini() function that computes the Gini Index +(G) of racial or ethnic inequality and retrieves the Gini Index +(G) of income inequality based on Gini (1921)
  4. krieger() function that computes the Index of Concentration at the Extremes (ICE) based on based on Feldman et al. (2015) and Krieger et @@ -1165,26 +1165,26 @@

    Additional metrics socio-economic deprivation and disparity

  5. atkinson() function that computes the Atkinson Index (A) based on Atkinson (1970)
    6. -
  6. bell() function that computes the aspatial -racial/ethnic Interaction Index (xPy*) based on Shevky & -Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954)
    7. -
  7. white() function that computes the aspatial -racial/ethnic Correlation Ratio (V) based on Bell (1954) and White (1986)
    8. -
  8. sudano() function that computes the aspatial -racial/ethnic Location Quotient (LQ) based on Merton (1939) and Sudano et +
  9. bell() function that computes the aspatial racial or +ethnic Interaction Index (xPy*) based on Shevky & Williams +(1949; ISBN-13:978-0-837-15637-8) and Bell (1954)
    10. +
  10. white() function that computes the aspatial racial or +ethnic Correlation Ratio (V) based on Bell (1954) and White (1986)
    11. +
  11. sudano() function that computes the aspatial racial or +ethnic Location Quotient (LQ) based on Merton (1939) and Sudano et al. (2013)
  12. bemanian_beyer() function that computes the aspatial -racial/ethnic Local Exposure and Isolation (LEx/Is) metric +racial or ethnic Local Exposure and Isolation (LEx/Is) metric based on Bemanian & Beyer (2017)
    13. -
  13. hoover() function that computes the aspatial -racial/ethnic Delta (DEL) based on Hoover (1941) and +
  14. hoover() function that computes the aspatial racial or +ethnic Delta (DEL) based on Hoover (1941) and Duncan et al. (1961; LC:60007089)
  15. white_blau() function that computes an index of spatial proximity (SP) based on White (1986) and Blau (1977; ISBN-13:978-0-029-03660-0)
    16. -
  16. lieberson() function that computes the aspatial -racial/ethnic Isolation Index (xPx*) based on Lieberson (1981; +
  17. lieberson() function that computes the aspatial racial +or ethnic Isolation Index (xPx*) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954)
@@ -1193,7 +1193,7 @@

Compute Racial Isolation Index (RI)

North Carolina, U.S.A., census tracts. This metric is based on Anthopolos et al. (2011) that assessed the racial isolation of the population that identifies as non-Hispanic or Latino, Black or African American alone. -Multiple racial/ethnic subgroups are available in the +Multiple racial or ethnic subgroups are available in the anthopolos() function, including:

@@ -1204,7 +1204,7 @@

Compute Racial Isolation Index (RI)

- + @@ -1316,13 +1316,13 @@

Compute Racial Isolation Index (RI)

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup argument

A census geography (and its neighbors) that has nearly all of its -population who identify with the specified race/ethnicity subgroup(s) +population who identify with the specified race or ethnicity subgroup(s) (e.g., Not Hispanic or Latino, Black or African American alone) will have an RI value close to 1. In contrast, a census geography (and its neighbors) that is nearly none of its population who identify -with the specified race/ethnicity subgroup(s) (e.g., not Not Hispanic or -Latino, Black or African American alone) will have an RI value -close to 0.

+with the specified race or ethnicity subgroup(s) (e.g., not Not Hispanic +or Latino, Black or African American alone) will have an RI +value close to 0.

anthopolos2010NC <- anthopolos(state = 'NC', year = 2010, subgroup = 'NHoLB')
 
 # Obtain the 2010 census tracts from the 'tigris' package
@@ -1506,42 +1506,161 @@ 
Compute Educational Isolation Index (EI)

 
Can correct one source of edge effect in the same manner as shown for
 the RI metric.
-
-

Retrieve the Gini Index (G)

-

Retrieve the aspatial Gini Index (G) values (2006-2010 -5-year ACS) for Massachusetts, U.S.A., census tracts. This metric is -based on Gini (1921), and -the gini() function retrieves the estimate from the -ACS-5.

-

According to the U.S. -Census Bureau: ‘The Gini Index is a summary measure of income -inequality. The Gini coefficient incorporates the detailed shares data -into a single statistic, which summarizes the dispersion of income -across the entire income distribution. The Gini coefficient ranges from -0, indicating perfect equality (where everyone receives an equal share), -to 1, perfect inequality (where only one recipient or group of -recipients receives all the income). G is based on the -difference between the Lorenz curve (the observed cumulative income -distribution) and the notion of a perfectly equal income -distribution.’

-
gini2010MA <- gini(state = 'MA', year = 2010)
-
-# Obtain the 2010 census tracts from the 'tigris' package
-tract2010MA <- tracts(state = 'MA', year = 2010, cb = TRUE)
-# Remove first 9 characters from GEOID for compatibility with tigris information
-tract2010MA$GEOID <- substring(tract2010MA$GEO_ID, 10) 
-
-# Obtain the 2010 counties from the 'tigris' package
-county2010MA <- counties(state = 'MA', year = 2010, cb = TRUE)
-
-# Join the G (Gini) values to the census tract geometry
-MA2010gini <- tract2010MA %>%
-  left_join(gini2010MA$g, by = 'GEOID')
+
+

The racial or ethnic Gini Index (G)

+

Compute the aspatial racial or ethnic Gini Index (G) values +(2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts within +counties. This metric is based on Gini (1921). Multiple racial +or ethnic subgroups are available in the gini() function, +including:

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ACS table sourceracial or ethnic subgroupcharacter for subgroup arguments
B03002_002not Hispanic or LatinoNHoL
B03002_003not Hispanic or Latino, white aloneNHoLW
B03002_004not Hispanic or Latino, Black or African American aloneNHoLB
B03002_005not Hispanic or Latino, American Indian and Alaska Native aloneNHoLAIAN
B03002_006not Hispanic or Latino, Asian aloneNHoLA
B03002_007not Hispanic or Latino, Native Hawaiian and Other Pacific Islander +aloneNHoLNHOPI
B03002_008not Hispanic or Latino, some other race aloneNHoLSOR
B03002_009not Hispanic or Latino, two or more racesNHoLTOMR
B03002_010not Hispanic or Latino, two races including some other raceNHoLTRiSOR
B03002_011not Hispanic or Latino, two races excluding some other race, and +three or more racesNHoLTReSOR
B03002_012Hispanic or LatinoHoL
B03002_013Hispanic or Latino, white aloneHoLW
B03002_014Hispanic or Latino, Black or African American aloneHoLB
B03002_015Hispanic or Latino, American Indian and Alaska Native aloneHoLAIAN
B03002_016Hispanic or Latino, Asian aloneHoLA
B03002_017Hispanic or Latino, Native Hawaiian and other Pacific Islander +aloneHoLNHOPI
B03002_018Hispanic or Latino, some other race aloneHoLSOR
B03002_019Hispanic or Latino, two or more racesHoLTOMR
B03002_020Hispanic or Latino, two races including some other raceHoLTRiSOR
B03002_021Hispanic or Latino, two races excluding some other race, and three +or more racesHoLTReSOR
+

G is a measure of the evenness of racial or ethnic +residential populations when comparing smaller geographical areas to +larger ones within which the smaller geographical areas are located. +G can range in value from 0 to 1 and the mean absolute +difference between a selected subgroup proportions weighted across all +pairs of geographic units, expressed as a proportion of the maximum +weighted difference.

+
gini2010MA <- gini(
+  geo_large = 'county',
+  geo_small = 'tract',
+  state = 'MA',
+  year = 2010,
+  subgroup = c('NHoLB', 'HoLB')
+)
+
+# Obtain the 2010 census tracts from the 'tigris' package
+county2010MA <- counties(state = 'MA', year = 2010, cb = TRUE)
+# Remove first 9 characters from GEOID for compatibility with tigris information
+county2010MA$GEOID <- substring(county2010MA$GEO_ID, 10) 
+
+# Join the G (Gini) values to the census tract geometry
+MA2010gini <- county2010MA %>%
+  left_join(gini2010MA$g, by = 'GEOID')
# Visualize the G (Gini) values (2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts 
 ggplot() +
   geom_sf(
     data = MA2010gini,
-    aes(fill = G),
+    aes(fill = G_re),
     size = 0.05,
     color = 'transparent'
   ) +
@@ -1552,10 +1671,70 @@ 
Retrieve the Gini Index (G)

     size = 0.2
   ) +
   theme_minimal() +
-  scale_fill_viridis_c() +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
-  ggtitle('Gini Index', subtitle = 'MA census tracts')
-

+ scale_fill_viridis_c(limits = c(0, 1)) + + labs( + fill = 'Index (Continuous)', + caption = 'Source: U.S. Census ACS 2006-2010 estimates') + + ggtitle('Gini Index', subtitle = 'Black population of MA counties')
+

+
+
+

The income Gini Index (G)

+

Retrieve the aspatial income Gini Index (G) values +(2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts. This +metric is based on Gini +(1921), and the gini() function retrieves the estimate +from the ACS-5 when calculating the Gini Index (G) for racial +or ethnic inequality.

+

According to the U.S. +Census Bureau: ‘The Gini Index is a summary measure of income +inequality. The Gini coefficient incorporates the detailed shares data +into a single statistic, which summarizes the dispersion of income +across the entire income distribution. The Gini coefficient ranges from +0, indicating perfect equality (where everyone receives an equal share), +to 1, perfect inequality (where only one recipient or group of +recipients receives all the income). The Gini Index is based on the +difference between the Lorenz curve (the observed cumulative income +distribution) and the notion of a perfectly equal income +distribution.’

+
gini2010MA <- gini(
+  geo_large = 'county',
+  geo_small = 'tract',
+  state = 'MA',
+  year = 2010,
+  subgroup = c('NHoLB', 'HoLB')
+)
+
+# Obtain the 2010 census tracts from the 'tigris' package
+tract2010MA <- tracts(state = 'MA', year = 2010, cb = TRUE)
+# Remove first 9 characters from GEOID for compatibility with tigris information
+tract2010MA$GEOID <- substring(tract2010MA$GEO_ID, 10) 
+
+# Obtain the 2010 counties from the 'tigris' package
+county2010MA <- counties(state = 'MA', year = 2010, cb = TRUE)
+
+# Join the G (Gini) values to the census tract geometry
+MA2010gini <- tract2010MA %>%
+  left_join(gini2010MA$g_data, by = 'GEOID')
+
# Visualize the G (Gini) values (2006-2010 5-year ACS) for Massachusetts, U.S.A., census tracts 
+ggplot() +
+  geom_sf(
+    data = MA2010gini,
+    aes(fill = G_inc),
+    size = 0.05,
+    color = 'transparent'
+  ) +
+  geom_sf(
+    data = county2010MA,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c(limits = c(0, 1)) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
+  ggtitle('Gini Index', subtitle = 'Income of MA census tracts')
+

@@ -1596,18 +1775,18 @@

Index of Concentration at the Extremes (ICE)

B03002 -Race/Ethnicity, ‘ICE_rewb’ +Race or Ethnicity, ‘ICE_rewb’ 80th income percentile vs. 20th income percentile B19001 & B19001B & B19001H -Income and race/ethnicity combined, ‘ICE_wbinc’ +Income and race or ethnicity combined, ‘ICE_wbinc’ white non-Hispanic in 80th income percentile vs. black alone (including Hispanic) in 20th income percentile B19001 & B19001H -Income and race/ethnicity combined, ‘ICE_wpcinc’ +Income and race or ethnicity combined, ‘ICE_wpcinc’ white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile @@ -1620,134 +1799,134 @@

Index of Concentration at the Extremes (ICE)

and most deprived categories, and in both cases indicates that the area is not dominated by extreme concentrations of either of the two groups.

-
ice2020WC <- krieger(state = 'MI', county = 'Wayne', year = 2010)
-
-# Obtain the 2010 census tracts from the 'tigris' package
-tract2010WC <- tracts(state = 'MI', county = 'Wayne', year = 2010, cb = TRUE)
-# Remove first 9 characters from GEOID for compatibility with tigris information
-tract2010WC$GEOID <- substring(tract2010WC$GEO_ID, 10) 
-
-# Join the ICE values to the census tract geometry
-ice2020WC <- tract2010WC %>%
-  left_join(ice2020WC$ice, by = 'GEOID')
-
# Plot ICE for Income
-ggplot() +
-  geom_sf(
-    data = ice2020WC,
-    aes(fill = ICE_inc),
-    color = 'white',
-    size = 0.05
-  ) +
-  theme_bw() +
-  scale_fill_gradient2(
-    low = '#998ec3',
-    mid = '#f7f7f7',
-    high = '#f1a340',
-    limits = c(-1, 1)
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
-  ggtitle(
-    'Index of Concentration at the Extremes\nIncome (Krieger)',
-    subtitle = '80th income percentile vs. 20th income percentile'
-  )
-
-# Plot ICE for Education
-ggplot() +
-  geom_sf(
-    data = ice2020WC,
-    aes(fill = ICE_edu),
-    color = 'white',
-    size = 0.05
-  ) +
-  theme_bw() +
-  scale_fill_gradient2(
-    low = '#998ec3',
-    mid = '#f7f7f7',
-    high = '#f1a340',
-    limits = c(-1, 1)
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
-  ggtitle(
-    'Index of Concentration at the Extremes\nEducation (Krieger)',
-    subtitle = 'less than high school vs. four-year college degree or more'
-  )
-
-# Plot ICE for Race/Ethnicity
-ggplot() +
-  geom_sf(
-    data = ice2020WC,
-    aes(fill = ICE_rewb),
-    color = 'white',
-    size = 0.05
-  ) +
-  theme_bw() +
-  scale_fill_gradient2(
-    low = '#998ec3',
-    mid = '#f7f7f7',
-    high = '#f1a340',
-    limits = c(-1, 1)
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
-  ggtitle(
-    'Index of Concentration at the Extremes\nRace/Ethnicity (Krieger)',
-    subtitle = 'white non-Hispanic vs. black non-Hispanic'
-  )
-
-# Plot ICE for Income and Race/Ethnicity Combined
-## white non-Hispanic in 80th income percentile vs. 
-## black (including Hispanic) in 20th income percentile
-ggplot() +
-  geom_sf(
-    data = ice2020WC,
-    aes(fill = ICE_wbinc),
-    color = 'white',
-    size = 0.05
-  ) +
-  theme_bw() +
-  scale_fill_gradient2(
-    low = '#998ec3',
-    mid = '#f7f7f7',
-    high = '#f1a340',
-    limits = c(-1, 1)
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
-  ggtitle(
-    'Index of Concentration at the Extremes\nIncome & race/ethnicity combined (Krieger)',
-    subtitle = 'white non-Hispanic in 80th inc ptcl vs. black alone in 20th inc pctl'
-  )
-
-# Plot ICE for Income and Race/Ethnicity Combined
-## white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile
-ggplot() +
-  geom_sf(
-    data = ice2020WC,
-    aes(fill = ICE_wpcinc),
-    color = 'white',
-    size = 0.05
-  ) +
-  theme_bw() +
-  scale_fill_gradient2(
-    low = '#998ec3',
-    mid = '#f7f7f7',
-    high = '#f1a340',
-    limits = c(-1, 1)
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
-  ggtitle(
-    'Index of Concentration at the Extremes\nIncome & race/ethnicity combined (Krieger)',
-    subtitle = 'white non-Hispanic (WNH) in 80th inc pctl vs. WNH in 20th inc pctl'
-  )
-

-
-

Compute racial/ethnic Dissimilarity Index (D)

-

Compute the aspatial racial/ethnic D values (2006-2010 +

ice2020WC <- krieger(state = 'MI', county = 'Wayne', year = 2010)
+
+# Obtain the 2010 census tracts from the 'tigris' package
+tract2010WC <- tracts(state = 'MI', county = 'Wayne', year = 2010, cb = TRUE)
+# Remove first 9 characters from GEOID for compatibility with tigris information
+tract2010WC$GEOID <- substring(tract2010WC$GEO_ID, 10) 
+
+# Join the ICE values to the census tract geometry
+ice2020WC <- tract2010WC %>%
+  left_join(ice2020WC$ice, by = 'GEOID')
+
# Plot ICE for Income
+ggplot() +
+  geom_sf(
+    data = ice2020WC,
+    aes(fill = ICE_inc),
+    color = 'white',
+    size = 0.05
+  ) +
+  theme_bw() +
+  scale_fill_gradient2(
+    low = '#998ec3',
+    mid = '#f7f7f7',
+    high = '#f1a340',
+    limits = c(-1, 1)
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
+  ggtitle(
+    'Index of Concentration at the Extremes\nIncome (Krieger)',
+    subtitle = '80th income percentile vs. 20th income percentile'
+  )
+
+# Plot ICE for Education
+ggplot() +
+  geom_sf(
+    data = ice2020WC,
+    aes(fill = ICE_edu),
+    color = 'white',
+    size = 0.05
+  ) +
+  theme_bw() +
+  scale_fill_gradient2(
+    low = '#998ec3',
+    mid = '#f7f7f7',
+    high = '#f1a340',
+    limits = c(-1, 1)
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
+  ggtitle(
+    'Index of Concentration at the Extremes\nEducation (Krieger)',
+    subtitle = 'less than high school vs. four-year college degree or more'
+  )
+
+# Plot ICE for Race or Ethnicity
+ggplot() +
+  geom_sf(
+    data = ice2020WC,
+    aes(fill = ICE_rewb),
+    color = 'white',
+    size = 0.05
+  ) +
+  theme_bw() +
+  scale_fill_gradient2(
+    low = '#998ec3',
+    mid = '#f7f7f7',
+    high = '#f1a340',
+    limits = c(-1, 1)
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
+  ggtitle(
+    'Index of Concentration at the Extremes\nRace or Ethnicity (Krieger)',
+    subtitle = 'white non-Hispanic vs. black non-Hispanic'
+  )
+
+# Plot ICE for Income and Race or Ethnicity Combined
+## white non-Hispanic in 80th income percentile vs. 
+## black (including Hispanic) in 20th income percentile
+ggplot() +
+  geom_sf(
+    data = ice2020WC,
+    aes(fill = ICE_wbinc),
+    color = 'white',
+    size = 0.05
+  ) +
+  theme_bw() +
+  scale_fill_gradient2(
+    low = '#998ec3',
+    mid = '#f7f7f7',
+    high = '#f1a340',
+    limits = c(-1, 1)
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
+  ggtitle(
+    'Index of Concentration at the Extremes\nIncome & race or ethnicity combined (Krieger)',
+    subtitle = 'white non-Hispanic in 80th inc ptcl vs. black alone in 20th inc pctl'
+  )
+
+# Plot ICE for Income and Race or Ethnicity Combined
+## white non-Hispanic in 80th income percentile vs. white non-Hispanic in 20th income percentile
+ggplot() +
+  geom_sf(
+    data = ice2020WC,
+    aes(fill = ICE_wpcinc),
+    color = 'white',
+    size = 0.05
+  ) +
+  theme_bw() +
+  scale_fill_gradient2(
+    low = '#998ec3',
+    mid = '#f7f7f7',
+    high = '#f1a340',
+    limits = c(-1, 1)
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
+  ggtitle(
+    'Index of Concentration at the Extremes\nIncome & race or ethnicity combined (Krieger)',
+    subtitle = 'white non-Hispanic (WNH) in 80th inc pctl vs. WNH in 20th inc pctl'
+  )
+

+
+

Compute racial or ethnic Dissimilarity Index (D)

+

Compute the aspatial racial or ethnic D values (2006-2010 5-year ACS) for Pennsylvania, U.S.A., counties from census tracts. This metric is based on Duncan -& Duncan (1955) that assessed the racial/ethnic isolation of +& Duncan (1955) that assessed the racial or ethnic isolation of students that identify as non-Hispanic or Latino, Black or African American alone compared to students that identify as non-Hispanic or Latino, white alone between schools and school districts. Multiple -racial/ethnic subgroups are available in the duncan() +racial or ethnic subgroups are available in the duncan() function, including:

@@ -1758,7 +1937,7 @@

Compute racial/ethnic Dissimilarity Index (D)

- + @@ -1870,64 +2049,64 @@

Compute racial/ethnic Dissimilarity Index (D)

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup or subgroup_ref arguments
-

D is a measure of the evenness of racial/ethnic residential -segregation when comparing smaller geographical areas to larger ones -within which the smaller geographical areas are located. D can -range in value from 0 to 1 and represents the proportion of -racial/ethnic subgroup members that would have to change their area of -residence to achieve an even distribution within the larger geographical -area under conditions of maximum segregation.

-
duncan2010PA <- duncan(
-  geo_large = 'county',
-  geo_small = 'tract',
-  state = 'PA',
-  year = 2010,
-  subgroup = 'NHoLB',
-  subgroup_ref = 'NHoLW'
-)
-
-# Obtain the 2010 census counties from the 'tigris' package
-county2010PA <- counties(state = 'PA', year = 2010, cb = TRUE)
-# Remove first 9 characters from GEOID for compatibility with tigris information
-county2010PA$GEOID <- substring(county2010PA$GEO_ID, 10) 
-
-# Join the D values to the county geometry
-PA2010duncan <- county2010PA %>%
-  left_join(duncan2010PA$d, by = 'GEOID')
-
# Visualize the D values (2006-2010 5-year ACS) for Pennsylvania, U.S.A., counties 
-ggplot() +
-  geom_sf(
-    data = PA2010duncan,
-    aes(fill = D),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2010PA,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_viridis_c(limits = c(0, 1)) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
-  ggtitle(
-    'Dissimilarity Index (Duncan & Duncan)\nPennsylvania census tracts to counties',
-    subtitle = 'Black non-Hispanic vs. white non-Hispanic'
-  )
+

D is a measure of the evenness of racial or ethnic +residential segregation when comparing smaller geographical areas to +larger ones within which the smaller geographical areas are located. +D can range in value from 0 to 1 and represents the proportion +of racial or ethnic subgroup members that would have to change their +area of residence to achieve an even distribution within the larger +geographical area under conditions of maximum segregation.

+
duncan2010PA <- duncan(
+  geo_large = 'county',
+  geo_small = 'tract',
+  state = 'PA',
+  year = 2010,
+  subgroup = 'NHoLB',
+  subgroup_ref = 'NHoLW'
+)
+
+# Obtain the 2010 census counties from the 'tigris' package
+county2010PA <- counties(state = 'PA', year = 2010, cb = TRUE)
+# Remove first 9 characters from GEOID for compatibility with tigris information
+county2010PA$GEOID <- substring(county2010PA$GEO_ID, 10) 
+
+# Join the D values to the county geometry
+PA2010duncan <- county2010PA %>%
+  left_join(duncan2010PA$d, by = 'GEOID')
+
# Visualize the D values (2006-2010 5-year ACS) for Pennsylvania, U.S.A., counties 
+ggplot() +
+  geom_sf(
+    data = PA2010duncan,
+    aes(fill = D),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2010PA,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c(limits = c(0, 1)) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
+  ggtitle(
+    'Dissimilarity Index (Duncan & Duncan)\nPennsylvania census tracts to counties',
+    subtitle = 'Black non-Hispanic vs. white non-Hispanic'
+  )

-
-

Compute aspatial income or racial/ethnic Atkinson Index +
+

Compute aspatial income or racial or ethnic Atkinson Index (A)

-

Compute the aspatial income or racial/ethnic A values +

Compute the aspatial income or racial or ethnic A values (2017-2021 5-year ACS) for Kentucky, U.S.A., counties from census block groups. This metric is based on Atkinson (1970) that assessed the distribution of income within 12 counties but has since been adapted -to study racial/ethnic segregation (see James & Taeuber 1985). To +to study racial or ethnic segregation (see James & Taeuber 1985). To compare median household income, specify subgroup = 'MedHHInc' which will use the ACS-5 variable -‘B19013_001’ in the computation. Multiple racial/ethnic subgroups are +‘B19013_001’ in the computation. Multiple racial or ethnic subgroups are available in the atkinson() function, including:

@@ -1938,7 +2117,7 @@

Compute aspatial income or racial/ethnic Atkinson Index

- + @@ -2050,7 +2229,7 @@

Compute aspatial income or racial/ethnic Atkinson Index

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup argument

A is a measure of the inequality and, in the context of -residential race/ethnicity, segregation when comparing smaller +residential race or ethnicity, segregation when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. A can range in value from 0 to 1 and smaller values of the index indicate lower levels of inequality (e.g., less @@ -2075,51 +2254,51 @@

Compute aspatial income or racial/ethnic Atkinson Index the index. See Section 2.3 of Saint-Jacques et al. (2020) for one method to select epsilon. We choose epsilon = 0.67 in the example below:

-
atkinson2021KY <- atkinson(
-  geo_large = 'county',
-  geo_small = 'block group',
-  state = 'KY',
-  year = 2021,
-  subgroup = 'NHoLB',
-  epsilon = 0.67
-)
-
-# Obtain the 2021 census counties from the 'tigris' package
-county2021KY <- counties(state = 'KY', year = 2021, cb = TRUE)
-
-# Join the A values to the county geometry
-KY2021atkinson <- county2021KY %>% 
-  left_join(atkinson2021KY$a, by = 'GEOID')
-
# Visualize the A values (2017-2021 5-year ACS) for Kentucky, U.S.A., counties
-ggplot() +
-  geom_sf(
-    data = KY2021atkinson,
-    aes(fill = A),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2021KY,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_viridis_c(limits = c(0, 1)) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
-  ggtitle(
-    'Atkinson Index (Atkinson)\nKentucky census block groups to counties',
-    subtitle = expression(paste('Black non-Hispanic (', epsilon, ' = 0.67)'))
-  )
-

+
atkinson2021KY <- atkinson(
+  geo_large = 'county',
+  geo_small = 'block group',
+  state = 'KY',
+  year = 2021,
+  subgroup = 'NHoLB',
+  epsilon = 0.67
+)
+
+# Obtain the 2021 census counties from the 'tigris' package
+county2021KY <- counties(state = 'KY', year = 2021, cb = TRUE)
+
+# Join the A values to the county geometry
+KY2021atkinson <- county2021KY %>% 
+  left_join(atkinson2021KY$a, by = 'GEOID')
+
# Visualize the A values (2017-2021 5-year ACS) for Kentucky, U.S.A., counties
+ggplot() +
+  geom_sf(
+    data = KY2021atkinson,
+    aes(fill = A),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2021KY,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c(limits = c(0, 1)) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
+  ggtitle(
+    'Atkinson Index (Atkinson)\nKentucky census block groups to counties',
+    subtitle = expression(paste('Black non-Hispanic (', epsilon, ' = 0.67)'))
+  )
+

-
-

Compute racial/ethnic Interaction Index (xPy*)

-

Compute the aspatial racial/ethnic xPy* values (2017-2021 +

+

Compute racial or ethnic Interaction Index (xPy*)

+

Compute the aspatial racial or ethnic xPy* values (2017-2021 5-year ACS) for Ohio, U.S.A., counties from census tracts. This metric is based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and adapted by Bell (1954). -Multiple racial/ethnic subgroups are available in the +Multiple racial or ethnic subgroups are available in the bell() function, including:

@@ -2130,7 +2309,7 @@

Compute racial/ethnic Interaction Index (xPy*)

- + @@ -2248,52 +2427,52 @@

Compute racial/ethnic Interaction Index (xPy*)

isolation) when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. xPy* can range in value from 0 to 1.

-
bell2021OH <- bell(
-  geo_large = 'county',
-  geo_small = 'tract',
-  state = 'OH',
-  year = 2021,
-  subgroup = 'NHoLB',
-  subgroup_ixn = 'NHoLW'
-)
-
-# Obtain the 2021 census counties from the 'tigris' package
-county2021OH <- counties(state = 'OH', year = 2021, cb = TRUE)
-
-# Join the xPy* values to the county geometry
-OH2021bell <- county2021OH %>%
-  left_join(bell2021OH$xpy_star, by = 'GEOID')
-
# Visualize the xPy* values (2017-2021 5-year ACS) for Ohio, U.S.A., counties
-ggplot() +
-  geom_sf(
-    data = OH2021bell,
-    aes(fill = xPy_star),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2021OH,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_viridis_c(limits = c(0, 1)) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
-  ggtitle(
-    'Interaction Index (Bell)\nOhio census tracts to counties',
-    subtitle = 'Black non-Hispanic vs. white non-Hispanic'
-  )
+
bell2021OH <- bell(
+  geo_large = 'county',
+  geo_small = 'tract',
+  state = 'OH',
+  year = 2021,
+  subgroup = 'NHoLB',
+  subgroup_ixn = 'NHoLW'
+)
+
+# Obtain the 2021 census counties from the 'tigris' package
+county2021OH <- counties(state = 'OH', year = 2021, cb = TRUE)
+
+# Join the xPy* values to the county geometry
+OH2021bell <- county2021OH %>%
+  left_join(bell2021OH$xpy_star, by = 'GEOID')
+
# Visualize the xPy* values (2017-2021 5-year ACS) for Ohio, U.S.A., counties
+ggplot() +
+  geom_sf(
+    data = OH2021bell,
+    aes(fill = xPy_star),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2021OH,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c(limits = c(0, 1)) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
+  ggtitle(
+    'Interaction Index (Bell)\nOhio census tracts to counties',
+    subtitle = 'Black non-Hispanic vs. white non-Hispanic'
+  )

Compute Correlation Ratio (V)

-

Compute the aspatial racial/ethnic V values (2017-2021 +

Compute the aspatial racial or ethnic V values (2017-2021 5-year ACS) for South Carolina, U.S.A., counties from census tracts. This metric is based on Bell -(1954) and adapted by White (1986). Multiple -racial/ethnic subgroups are available in the white() -function, including:

+(1954) and adapted by White (1986). Multiple racial +or ethnic subgroups are available in the white() function, +including:

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup or subgroup_ixn argument
@@ -2303,7 +2482,7 @@

Compute Correlation Ratio (V)

- + @@ -2422,55 +2601,55 @@

Compute Correlation Ratio (V)

with a member of another subgroup(s) with higher values signifying higher probability of interaction (less isolation). V can range in value from 0 to Inf.

-
white2021SC <- white(
-  geo_large = 'county',
-  geo_small = 'tract',
-  state = 'SC',
-  year = 2021,
-  subgroup = 'NHoLB'
-)
-
-# Obtain the 2021 census counties from the 'tigris' package
-county2021SC <- counties(state = 'SC', year = 2021, cb = TRUE)
-
-# Join the V values to the county geometry
-SC2021white <- county2021SC %>%
-  left_join(white2021SC$v, by = 'GEOID')
-
# Visualize the V values (2017-2021 5-year ACS) for South Carolina, U.S.A., counties
-ggplot() +
-  geom_sf(
-    data = SC2021white,
-    aes(fill = V),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2021SC,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_gradient2(
-    low = '#998ec3', 
-    mid = '#f7f7f7', 
-    high = '#f1a340', 
-    midpoint = 1
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
-  ggtitle(
-    'Correlation Ratio (White)\nSouth Carolina census tracts to counties',
-    subtitle = 'Black non-Hispanic'
-  )
+
white2021SC <- white(
+  geo_large = 'county',
+  geo_small = 'tract',
+  state = 'SC',
+  year = 2021,
+  subgroup = 'NHoLB'
+)
+
+# Obtain the 2021 census counties from the 'tigris' package
+county2021SC <- counties(state = 'SC', year = 2021, cb = TRUE)
+
+# Join the V values to the county geometry
+SC2021white <- county2021SC %>%
+  left_join(white2021SC$v, by = 'GEOID')
+
# Visualize the V values (2017-2021 5-year ACS) for South Carolina, U.S.A., counties
+ggplot() +
+  geom_sf(
+    data = SC2021white,
+    aes(fill = V),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2021SC,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_gradient2(
+    low = '#998ec3', 
+    mid = '#f7f7f7', 
+    high = '#f1a340', 
+    midpoint = 1
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
+  ggtitle(
+    'Correlation Ratio (White)\nSouth Carolina census tracts to counties',
+    subtitle = 'Black non-Hispanic'
+  )

Compute Location Quotient (LQ)

-

Compute the aspatial racial/ethnic LQ values (2017-2021 +

Compute the aspatial racial or ethnic LQ values (2017-2021 5-year ACS) for Tennessee, U.S.A., counties vs. the state. This metric is based on Merton (1939) and adapted by Sudano et -al. (2013). Multiple racial/ethnic subgroups are available in the +al. (2013). Multiple racial or ethnic subgroups are available in the sudano() function, including:

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup argument
@@ -2481,7 +2660,7 @@

Compute Location Quotient (LQ)

- + @@ -2603,50 +2782,50 @@

Compute Location Quotient (LQ)

subgroup population in its larger geography. Unlike the previous metrics that aggregate to the larger geography, LQ computes values for each smaller geography relative to the larger geography.

-
sudano2021TN <- sudano(
-  geo_large = 'state',
-  geo_small = 'county',
-  state = 'TN',
-  year = 2021,
-  subgroup = 'NHoLB'
-)
-
-# Obtain the 2021 census counties from the 'tigris' package
-county2021TN <- counties(state = 'TN', year = 2021, cb = TRUE)
-
-# Join the LQ values to the county geometry
-TN2021sudano <- county2021TN %>% 
-                   left_join(sudano2021TN$lq, by = 'GEOID')
-
# Visualize the LQ values (2017-2021 5-year ACS) for Tennessee, U.S.A., counties
-ggplot() +
-  geom_sf(
-    data = TN2021sudano,
-    aes(fill = LQ),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2021TN,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_viridis_c() +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
-  ggtitle(
-    'Location Quotient (Sudano)\nTennessee counties vs. state',
-    subtitle = 'Black non-Hispanic'
-  )
+
sudano2021TN <- sudano(
+  geo_large = 'state',
+  geo_small = 'county',
+  state = 'TN',
+  year = 2021,
+  subgroup = 'NHoLB'
+)
+
+# Obtain the 2021 census counties from the 'tigris' package
+county2021TN <- counties(state = 'TN', year = 2021, cb = TRUE)
+
+# Join the LQ values to the county geometry
+TN2021sudano <- county2021TN %>% 
+                   left_join(sudano2021TN$lq, by = 'GEOID')
+
# Visualize the LQ values (2017-2021 5-year ACS) for Tennessee, U.S.A., counties
+ggplot() +
+  geom_sf(
+    data = TN2021sudano,
+    aes(fill = LQ),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2021TN,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c() +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
+  ggtitle(
+    'Location Quotient (Sudano)\nTennessee counties vs. state',
+    subtitle = 'Black non-Hispanic'
+  )

Compute Local Exposure and Isolation (LEx/Is)

-

Compute the aspatial racial/ethnic Local Exposure and Isolation +

Compute the aspatial racial or ethnic Local Exposure and Isolation metric (2017-2021 5-year ACS) for Mississippi, U.S.A., counties vs. the state. This metric is based on Bemanian & -Beyer (2017). Multiple racial/ethnic subgroups are available in the -bemanian_beyer() function, including:

+Beyer (2017). Multiple racial or ethnic subgroups are available in +the bemanian_beyer() function, including:

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup argument
@@ -2656,7 +2835,7 @@

Compute Local Exposure and Isolation (LEx/Is)

- + @@ -2770,98 +2949,98 @@

Compute Local Exposure and Isolation (LEx/Is)

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup or subgroup_ixn argument

LEx/Is is a measure of the probability that two individuals living within a specific smaller geography (e.g., census tract) of -either different (i.e., exposure) or the same (i.e., isolation) -racial/ethnic subgroup(s) will interact, assuming that individuals -within a smaller geography are randomly mixed. LEx/Is is -standardized with a logit transformation and centered against an -expected case that all races/ethnicities are evenly distributed across a -larger geography. LEx/Is can range from negative infinity to -infinity. If LEx/Is is zero then the estimated probability of -the interaction between two people of the given subgroup(s) within a -smaller geography is equal to the expected probability if the -subgroup(s) were perfectly mixed in the larger geography. If -LEx/Is is greater than zero then the interaction is more likely -to occur within the smaller geography than in the larger geography, and -if LEx/Is is less than zero then the interaction is less likely -to occur within the smaller geography than in the larger geography. -Note: the exponentiation of each LEx/Is metric results in the -odds ratio of the specific exposure or isolation of interest in a -smaller geography relative to the larger geography. Similar to -LQ (Sudano), LEx/Is computes values for each smaller -geography relative to the larger geography.

-
bemanian_beyer2021MS <- bemanian_beyer(
-  geo_large = 'state',
-  geo_small = 'county',
-  state = 'MS',
-  year = 2021,
-  subgroup = 'NHoLB',
-  subgroup_ixn = 'NHoLW'
-)
-
-# Obtain the 2021 census counties from the 'tigris' package
-county2021MS <- counties(state = 'MS', year = 2021, cb = TRUE)
-
-# Join the LEx/Is values to the county geometry
-MS2021bemanian_beyer <- county2021MS %>%
-  left_join(bemanian_beyer2021MS$lexis, by = 'GEOID')
-
# Visualize the LEx/Is values (2017-2021 5-year ACS) for Mississippi, U.S.A., counties
-ggplot() +
-  geom_sf(
-    data = MS2021bemanian_beyer,
-    aes(fill = LExIs),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2021MS,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_gradient2(
-    low = '#998ec3',
-    mid = '#f7f7f7',
-    high = '#f1a340'
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
-  ggtitle(
-    'Local Exposure and Isolation (Bemanian & Beyer)\nMississippi counties vs. state',
-    subtitle = 'Black non-Hispanic vs. White non-Hispanic'
-  )
+either different (i.e., exposure) or the same (i.e., isolation) racial +or ethnic subgroup(s) will interact, assuming that individuals within a +smaller geography are randomly mixed. LEx/Is is standardized +with a logit transformation and centered against an expected case that +all races or ethnicities are evenly distributed across a larger +geography. LEx/Is can range from negative infinity to infinity. +If LEx/Is is zero then the estimated probability of the +interaction between two people of the given subgroup(s) within a smaller +geography is equal to the expected probability if the subgroup(s) were +perfectly mixed in the larger geography. If LEx/Is is greater +than zero then the interaction is more likely to occur within the +smaller geography than in the larger geography, and if LEx/Is +is less than zero then the interaction is less likely to occur within +the smaller geography than in the larger geography. Note: the +exponentiation of each LEx/Is metric results in the odds ratio +of the specific exposure or isolation of interest in a smaller geography +relative to the larger geography. Similar to LQ (Sudano), +LEx/Is computes values for each smaller geography relative to +the larger geography.

+
bemanian_beyer2021MS <- bemanian_beyer(
+  geo_large = 'state',
+  geo_small = 'county',
+  state = 'MS',
+  year = 2021,
+  subgroup = 'NHoLB',
+  subgroup_ixn = 'NHoLW'
+)
+
+# Obtain the 2021 census counties from the 'tigris' package
+county2021MS <- counties(state = 'MS', year = 2021, cb = TRUE)
+
+# Join the LEx/Is values to the county geometry
+MS2021bemanian_beyer <- county2021MS %>%
+  left_join(bemanian_beyer2021MS$lexis, by = 'GEOID')
+
# Visualize the LEx/Is values (2017-2021 5-year ACS) for Mississippi, U.S.A., counties
+ggplot() +
+  geom_sf(
+    data = MS2021bemanian_beyer,
+    aes(fill = LExIs),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2021MS,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_gradient2(
+    low = '#998ec3',
+    mid = '#f7f7f7',
+    high = '#f1a340'
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
+  ggtitle(
+    'Local Exposure and Isolation (Bemanian & Beyer)\nMississippi counties vs. state',
+    subtitle = 'Black non-Hispanic vs. White non-Hispanic'
+  )

-
# Visualize the exponentiated LEx/Is values (2017-2021 5-year ACS) for 
-## Mississippi, U.S.A., counties
-ggplot() +
-  geom_sf(
-    data = MS2021bemanian_beyer,
-    aes(fill = exp(LExIs)),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2021MS,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_viridis_c() +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
-  ggtitle(
-    'Odds ratio of Local Exposure and Isolation (Bemanian & Beyer)\n
-    Mississippi counties vs. state',
-    subtitle = 'Black non-Hispanic vs. White non-Hispanic'
-  )
+
# Visualize the exponentiated LEx/Is values (2017-2021 5-year ACS) for 
+## Mississippi, U.S.A., counties
+ggplot() +
+  geom_sf(
+    data = MS2021bemanian_beyer,
+    aes(fill = exp(LExIs)),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2021MS,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c() +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
+  ggtitle(
+    'Odds ratio of Local Exposure and Isolation (Bemanian & Beyer)\n
+    Mississippi counties vs. state',
+    subtitle = 'Black non-Hispanic vs. White non-Hispanic'
+  )

Compute Delta (DEL)

-

Compute the aspatial racial/ethnic DEL values (2017-2021 +

Compute the aspatial racial or ethnic DEL values (2017-2021 5-year ACS) for Alabama, U.S.A., counties from census tracts. This metric is based on Hoover (1941) and -Duncan et al. (1961; LC:60007089). Multiple racial/ethnic subgroups are -available in the hoover() function, including:

+Duncan et al. (1961; LC:60007089). Multiple racial or ethnic subgroups +are available in the hoover() function, including:

@@ -2871,7 +3050,7 @@

Compute Delta (DEL)

- + @@ -2988,41 +3167,41 @@

Compute Delta (DEL)

subgroup population that would have to move across geographic units to achieve a uniform density. DEL can range in value from 0 to 1.

-
hoover2021AL <- hoover(
-  geo_large = 'county',
-  geo_small = 'tract',
-  state = 'AL',
-  year = 2021,
-  subgroup = 'NHoLB'
-)
-
-# Obtain the 2021 census counties from the 'tigris' package
-county2021AL <- counties(state = 'AL', year = 2021, cb = TRUE)
-
-# Join the DEL values to the county geometry
-AL2021hoover <- county2021AL %>%
-  left_join(hoover2021AL$del, by = 'GEOID')
-
# Visualize the DEL values (2017-2021 5-year ACS) for Alabama, U.S.A., counties
-ggplot() +
-  geom_sf(
-    data = AL2021hoover,
-    aes(fill = DEL),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = county2021AL,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_viridis_c(limits = c(0, 1)) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
-  ggtitle(
-    'Delta (Hoover)\nAlabama census tracts to counties',
-    subtitle = 'Black non-Hispanic'
-  )
+
hoover2021AL <- hoover(
+  geo_large = 'county',
+  geo_small = 'tract',
+  state = 'AL',
+  year = 2021,
+  subgroup = 'NHoLB'
+)
+
+# Obtain the 2021 census counties from the 'tigris' package
+county2021AL <- counties(state = 'AL', year = 2021, cb = TRUE)
+
+# Join the DEL values to the county geometry
+AL2021hoover <- county2021AL %>%
+  left_join(hoover2021AL$del, by = 'GEOID')
+
# Visualize the DEL values (2017-2021 5-year ACS) for Alabama, U.S.A., counties
+ggplot() +
+  geom_sf(
+    data = AL2021hoover,
+    aes(fill = DEL),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = county2021AL,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c(limits = c(0, 1)) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2017-2021 estimates') +
+  ggtitle(
+    'Delta (Hoover)\nAlabama census tracts to counties',
+    subtitle = 'Black non-Hispanic'
+  )

@@ -3031,7 +3210,7 @@

Compute an index of spatial proximity (SP)

Atlanta, GA, metropolitan area from census tracts. This metric is based on White (1986) and Blau (1977; ISBN-13:978-0-029-03660-0) that designed the metric to identify -racial or ethnic enclaves. Multiple racial/ethnic subgroups are +racial or ethnic enclaves. Multiple racial or ethnic subgroups are available in the white_blau() function, including:

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup argument
@@ -3042,7 +3221,7 @@

Compute an index of spatial proximity (SP)

- + @@ -3154,70 +3333,70 @@

Compute an index of spatial proximity (SP)

ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup or subgroup_ref arguments
-

SP is a measure of clustering of racial/ethnic populations -within smaller geographical areas that are located within larger -geographical areas. SP 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 between -subgroup and referent group members. A value greater than 1 indicates -subgroup members live nearer to one another than to referent subgroup -members. A value less than 1 indicates subgroup live nearer to and -referent subgroup members than to their own subgroup members.

-
whiteblau2014GA <- white_blau(
-  geo_large = 'csa',
-  geo_small = 'tract',
-  state = c('GA', 'AL', 'TN', 'FL'),
-  year = 2014,
-  subgroup = 'NHoLB',
-  subgroup_ref = 'NHoLW'
-)
-
-# Obtain the 2014 Combined Statistical Areas from the 'tigris' package
-csa2014 <- combined_statistical_areas(year = 2014, cb = TRUE)
-# Obtain the 2014 state from the 'tigris' package
-state2014 <- states(cb = TRUE)
-
-# Join the SP values to the CSA geometries and filter for Georgia
-GA2010whiteblau <- csa2014 %>%
-  left_join(whiteblau2014GA$sp, by = 'GEOID') %>%
-  filter(!st_is_empty(.)) %>%
-  filter(!is.na(SP)) %>%
-  st_filter(state2014 %>% filter(STUSPS == 'GA')) %>%
-  st_make_valid()
-
# Visualize the SP values (2010-2014 5-year ACS) for Georgia, U.S.A., CSAs 
-ggplot() +
-  geom_sf(
-    data = GA2010whiteblau,
-    aes(fill = SP),
-   # size = 0.05,
-   # color = 'white'
-  ) +
-  geom_sf(
-    data = state2014 %>% filter(STUSPS == 'GA'),
-    fill = 'transparent',
-    color = 'black',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_gradient2(
-    low = '#998ec3', 
-    mid = '#f7f7f7', 
-    high = '#f1a340', 
-    midpoint = 1
-  ) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2010-2014 estimates') +
-  ggtitle(
-    'An index of spatial proximity (White)\nCensus tracts to Combined Statistical Areas in Georgia',
-    subtitle = 'Black non-Hispanic vs. white non-Hispanic'
-  )
+

SP is a measure of clustering of racial or ethnic +populations within smaller geographical areas that are located within +larger geographical areas. SP 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 +between subgroup and referent group members. A value greater than 1 +indicates subgroup members live nearer to one another than to referent +subgroup members. A value less than 1 indicates subgroup live nearer to +and referent subgroup members than to their own subgroup members.

+
whiteblau2014GA <- white_blau(
+  geo_large = 'csa',
+  geo_small = 'tract',
+  state = c('GA', 'AL', 'TN', 'FL'),
+  year = 2014,
+  subgroup = 'NHoLB',
+  subgroup_ref = 'NHoLW'
+)
+
+# Obtain the 2014 Combined Statistical Areas from the 'tigris' package
+csa2014 <- combined_statistical_areas(year = 2014, cb = TRUE)
+# Obtain the 2014 state from the 'tigris' package
+state2014 <- states(cb = TRUE)
+
+# Join the SP values to the CSA geometries and filter for Georgia
+GA2010whiteblau <- csa2014 %>%
+  left_join(whiteblau2014GA$sp, by = 'GEOID') %>%
+  filter(!st_is_empty(.)) %>%
+  filter(!is.na(SP)) %>%
+  st_filter(state2014 %>% filter(STUSPS == 'GA')) %>%
+  st_make_valid()
+
# Visualize the SP values (2010-2014 5-year ACS) for Georgia, U.S.A., CSAs 
+ggplot() +
+  geom_sf(
+    data = GA2010whiteblau,
+    aes(fill = SP),
+   # size = 0.05,
+   # color = 'white'
+  ) +
+  geom_sf(
+    data = state2014 %>% filter(STUSPS == 'GA'),
+    fill = 'transparent',
+    color = 'black',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_gradient2(
+    low = '#998ec3', 
+    mid = '#f7f7f7', 
+    high = '#f1a340', 
+    midpoint = 1
+  ) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2010-2014 estimates') +
+  ggtitle(
+    'An index of spatial proximity (White)\nCensus tracts to Combined Statistical Areas in Georgia',
+    subtitle = 'Black non-Hispanic vs. white non-Hispanic'
+  )

-
-

Compute racial/ethnic Isolation Index (xPx*)

-

Compute the aspatial racial/ethnic xPx* values (2015-2019 +

+

Compute racial or ethnic Isolation Index (xPx*)

+

Compute the aspatial racial or ethnic xPx* values (2015-2019 5-year ACS) for Delaware, U.S.A., census tracts from census block groups. This metric is based on Bell (1954) and adapted by -Lieberson (1981; ISBN-13:978-1-032-53884-6). Multiple racial/ethnic +Lieberson (1981; ISBN-13:978-1-032-53884-6). Multiple racial or ethnic subgroups are available in the lieberson() function, including:

@@ -3229,7 +3408,7 @@

Compute racial/ethnic Isolation Index (xPx*)

- + @@ -3346,43 +3525,43 @@

Compute racial/ethnic Isolation Index (xPx*)

isolation) when comparing smaller geographical areas to larger ones within which the smaller geographical areas are located. xPx* can range in value from 0 to 1.

-
lieberson2021DE <- lieberson(
-  geo_large = 'tract',
-  geo_small = 'block group',
-  state = 'DE',
-  year = 2019,
-  subgroup = 'NHoLB'
-)
-
-# Obtain the 2021 census counties from the 'tigris' package
-tract2021DE <- tracts(state = 'DE', year = 2019, cb = TRUE)
-
-# Join the xPx* values to the county geometry
-DE2021lieberson <- tract2021DE %>%
-  left_join(lieberson2021DE$xpx_star, by = 'GEOID')
-
# Visualize the xPx* values (2015-2019 5-year ACS) for Delaware, U.S.A., census tracts
-ggplot() +
-  geom_sf(
-    data = DE2021lieberson,
-    aes(fill = xPx_star),
-    size = 0.05,
-    color = 'white'
-  ) +
-  geom_sf(
-    data = tract2021DE,
-    fill = 'transparent',
-    color = 'white',
-    size = 0.2
-  ) +
-  theme_minimal() +
-  scale_fill_viridis_c(limits = c(0, 1)) +
-  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2015-2019 estimates') +
-  ggtitle(
-    'Isolation Index (Lieberson)\nDelaware census block groups to census tracts',
-    subtitle = 'Black non-Hispanic'
-  )
+
lieberson2021DE <- lieberson(
+  geo_large = 'tract',
+  geo_small = 'block group',
+  state = 'DE',
+  year = 2019,
+  subgroup = 'NHoLB'
+)
+
+# Obtain the 2021 census counties from the 'tigris' package
+tract2021DE <- tracts(state = 'DE', year = 2019, cb = TRUE)
+
+# Join the xPx* values to the county geometry
+DE2021lieberson <- tract2021DE %>%
+  left_join(lieberson2021DE$xpx_star, by = 'GEOID')
+
# Visualize the xPx* values (2015-2019 5-year ACS) for Delaware, U.S.A., census tracts
+ggplot() +
+  geom_sf(
+    data = DE2021lieberson,
+    aes(fill = xPx_star),
+    size = 0.05,
+    color = 'white'
+  ) +
+  geom_sf(
+    data = tract2021DE,
+    fill = 'transparent',
+    color = 'white',
+    size = 0.2
+  ) +
+  theme_minimal() +
+  scale_fill_viridis_c(limits = c(0, 1)) +
+  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2015-2019 estimates') +
+  ggtitle(
+    'Isolation Index (Lieberson)\nDelaware census block groups to census tracts',
+    subtitle = 'Black non-Hispanic'
+  )

-
sessionInfo()
+
sessionInfo()
## R version 4.4.1 (2024-06-14 ucrt)
 ## Platform: x86_64-w64-mingw32/x64
 ## Running under: Windows 10 x64 (build 19045)
@@ -3404,7 +3583,7 @@ 
Compute racial/ethnic Isolation Index (xPx*)

 ## [1] stats     graphics  grDevices utils     datasets  methods   base     
 ## 
 ## other attached packages:
-## [1] tigris_2.1       tidycensus_1.6.5 sf_1.0-16        ndi_0.1.6.9002  
+## [1] tigris_2.1       tidycensus_1.6.5 sf_1.0-16        ndi_0.1.6.9003  
 ## [5] ggplot2_3.5.1    dplyr_1.1.4      knitr_1.48      
 ## 
 ## loaded via a namespace (and not attached):
ACS table sourceracial/ethnic subgroupracial or ethnic subgroup character for subgroup argument