Title: Neighborhood Deprivation Indices
Version: 0.2.1
Date: 2025-09-04
Maintainer: Ian D. Buller <ian.buller@alumni.emory.edu>
Description: Computes various geospatial indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (NDI) are available: including: (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 indices of racial or ethnic residential segregation, including but limited to those discussed in Massey & Denton (1988) <doi:10.1093/sf/67.2.281>, and additional indices of socioeconomic disparity.
License: Apache License (≥ 2.0)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Depends: R (≥ 3.5.0)
Imports: car, dplyr, Hmisc, MASS, Matrix, psych, sf, stats, stringr, tidycensus, tidyr, tigris, units, utils
Suggests: DescTools, ggplot2, testthat, R.rsp, spelling, usethis
VignetteBuilder: R.rsp
Language: en-US
URL: https://github.com/idblr/ndi
BugReports: https://github.com/idblr/ndi/issues
NeedsCompilation: no
Packaged: 2025-09-04 22:06:24 UTC; ian.buller
Author: Ian D. Buller ORCID iD [aut, cre, cph], NCI [cph, fnd]
Repository: CRAN
Date/Publication: 2025-09-05 16:40:10 UTC

The ndi Package: Neighborhood Deprivation Indices

Description

Computes various geospatial indices of socioeconomic deprivation and disparity in the United States based on information available from the U.S. Census Bureau.

Details

The 'ndi' package computes various indices of socioeconomic deprivation and disparity in the United States. Some indices are considered "spatial" because they consider the values of neighboring (i.e., adjacent) census geographies in their computation, while other indices are "aspatial" because they only consider the value within each census geography. Two types of aspatial neighborhood deprivation indices (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 indices of racial or ethnic residential segregation, including but limited to those discussed in Massey & Denton (1988) doi:10.1093/sf/67.2.281, and additional indices of socioeconomic disparity.

Key content of the 'ndi' package include:

Neighborhood Deprivation Indices

messer Computes the aspatial Neighborhood Deprivation Index (NDI) based on Messer et al. (2006) doi:10.1007/s11524-006-9094-x.

powell_wiley Computes the aspatial Neighborhood Deprivation Index (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.

Indices of Racial or Ethnic Residential Segregation

Indices of Racial or Ethnic Residential Evenness

atkinson Computes the aspatial Atkinson Index (A) based on Atkinson (1970) doi:10.1016/0022-0531(70)90039-6.

duncan Computes the aspatial Dissimilarity Index (D) based on Duncan & Duncan (1955a) doi:10.2307/2088328.

gini Computes the aspatial Gini Index (G) based on Gini (1921) doi:10.2307/2223319.

james_taeuber Computes the aspatial Dissimilarity Index (D) based on James & Taeuber (1985) doi:10.2307/270845.

sudano Computes the aspatial Location Quotient (LQ) based on Merton (1939) doi:10.2307/2084686 and Sudano et al. (2013) doi:10.1016/j.healthplace.2012.09.015.

theil Computes the aspatial Entropy (H) based on Theil (1972; ISBN-13:978-0-444-10378-9) and Theil & Finizza (1971) doi:10.1080/0022250X.1971.9989795.

Indices of Racial or Ethnic Residential Exposure

anthopolos Computes the spatial Racial Isolation Index (RI) based on Anthopolos (2011) doi:10.1016/j.sste.2011.06.002.

bell Computes the aspatial Interaction Index (xPy\*) based on Shevky & Williams (1949; ISBN-13:978-0-837-15637-8) and Bell (1954) doi:10.2307/2574118.

bemanian_beyer Computes the aspatial Local Exposure and Isolation (LEx/Is) based on Bemanian & Beyer (2017) doi:10.1158/1055-9965.EPI-16-0926.

lieberson Computes the aspatial Isolation Index (xPx\*) based on Lieberson (1981; ISBN-13:978-1-032-53884-6) and Bell (1954) doi:10.2307/2574118.

white Computes the aspatial Correlation Ratio (V) based on Bell (1954) doi:10.2307/2574118 and White (1986) doi:10.2307/3644339.

Indices of Racial or Ethnic Residential Concentration

denton_cuzzort Computes the aspatial Relative Concentration (RCO) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281 and Duncan, Cuzzort, & Duncan (1961; LC:60007089).

hoover Computes the aspatial Delta (DEL) based on Hoover (1941) doi:10.1017/S0022050700052980 and Duncan, Cuzzort, & Duncan (1961; LC:60007089).

massey_duncan Computes the aspatial Absolute Concentration (ACO) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281 and Duncan, Cuzzort, & Duncan (1961; LC:60007089).

Indices of Racial or Ethnic Residential Centralization

duncan_cuzzort Computes the aspatial Absolute Centralization (ACE) based on Duncan, Cuzzort, & Duncan (1961; LC:60007089) and Massey & Denton (1988) doi:10.1093/sf/67.2.281.

duncan_duncan Computes the aspatial Relative Centralization (RCE) based on Duncan & Duncan (1955b) doi:10.1086/221609 and Massey & Denton (1988) doi:10.1093/sf/67.2.281.

Indices of Racial or Ethnic Residential Clustering

denton Computes the aspatial Relative Clustering (RCL) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281.

massey Computes the aspatial Absolute Clustering (ACL) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281.

morgan_denton Computes the aspatial Distance-Decay Interaction Index (DPxy\*) based on Morgan (1986) https://www.jstor.org/stable/20001935 and Massey & Denton (1988) doi:10.1093/sf/67.2.281.

morgan_massey Computes the aspatial Distance-Decay Isolation Index (DPxx\*) based on Morgan (1986) https://www.jstor.org/stable/20001935 and Massey & Denton (1988) doi:10.1093/sf/67.2.281.

white_blau Computes an index of spatial proximity (SP) based on White (1986) doi:10.2307/3644339 and Blau (1977; ISBN-13:978-0-029-03660-0).

Additional Indices of Socioeconomic Disparity

atkinson Also computes the aspatial Atkinson Index (A) of income based on Atkinson (1970) doi:10.1016/0022-0531(70)90039-6.

bravo Computes the spatial Educational Isolation Index (EI) based on Bravo (2021) doi:10.3390/ijerph18179384.

gini Also retrieves the aspatial Gini Index (G) of income inequality based on Gini (1921) doi:10.2307/2223319.

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.

Pre-formatted U.S. Census Data

DCtracts2020 A sample dataset containing information about U.S. Census American Community Survey 5-year estimate data for the District of Columbia census tracts (2020). The data are obtained from the get_acs function and formatted for the messer and powell_wiley functions input.

Dependencies

The 'ndi' package relies heavily upon tidycensus-package to retrieve data from the U.S. Census Bureau American Community Survey five-year estimates and the psych-package for computing the neighborhood deprivation indices. The messer function builds upon code developed by Hruska et al. (2022) doi:10.17605/OSF.IO/M2SAV by fictionalizing, adding the percent of households earning <$30,000 per year to the NDI computation, and providing the option for computing the ACS-5 2006-2010 NDI values. There is no code companion to compute NDI included in Andrews et al. (2020) doi:10.1080/17445647.2020.1750066 or Slotman et al. (2022) doi:10.1016/j.dib.2022.108002, but the package author worked directly with the Slotman et al. (2022) doi:10.1016/j.dib.2022.108002 authors to replicate their SAS code in R. The indices of racial or ethnic residential segregation rely heavily on the sf-package and tigris-package packages to assign the smaller geographical units within larger geographical units and, occasionally, perform geospatial projection for distance-based metrics. The computation of RI and EI also relies on the sparseMatrix function to compute the geospatial adjacency matrix between census geographies. Internal function to calculate AI using the Hölder mean is based on Atkinson function.

Author(s)

Ian D. Buller
DLH, LLC (formerly DLH Corporation and Social & Scientific Systems, Inc.), Bethesda, Maryland, USA (current); Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA (original).

Maintainer: I.D.B. ian.buller@alumni.emory.edu

See Also

Useful links:

Formatted U.S. Census American Community Survey 5-year estimate data for DC census tracts (2020) from the 'tidycensus' package

Description

A sample dataset containing information about U.S. Census American Community Survey 5-year estimate data for the District of Columbia census tracts (2020). The data are obtained from the get_acs function and formatted for the messer and powell_wiley functions input.

Usage

DCtracts2020

Format

A data frame with 206 rows and 23 variables:

GEOID

census tract ID

TotalPop

arcsinh-transformed CD3

OCC

percent males in management, science, and arts occupation

CWD

percent of crowded housing

POV

percent of households in poverty

FHH

percent of female headed households with dependents

PUB

percent of households on public assistance

U30

percent of households earning <$30,000 per year

EDU

percent earning less than a high school education

EMP

percent unemployed

logMedHHInc

median household income (dollars), natural log-transformed

PctNoIDRZ

percent of households receiving dividends, interest, or rental income, Z-transformed

PctPubAsstZ

percent of households receiving public assistance, Z-transformed

logMedHomeVal

median home value (dollars), natural log-transformed

PctWorkClassZ

percent in a management, business, science, or arts occupation, Z-transformed

PctFemHeadKidsZ

percent of households that are female headed with any children under 18 years, Z-transformed

PctNotOwnerOccZ

percent of housing units that are owner occupied, Z-transformed

PctNoPhoneZ

percent of households without a telephone, Z-transformed

PctNComPlmbZ

percent of households without complete plumbing facilities, Z-transformed

PctEducLTHSZ

percent with a high school degree or higher (population 25 years and over), Z-transformed

PctEducLTBchZ

percent with a college degree or higher (population 25 years and over), Z-transformed

PctFamBelowPovZ

percent of families with incomes below the poverty level, Z-transformed

PctUnemplZ

percent unemployed, Z-transformed

Source

https://github.com/idblr/ndi/blob/master/README.md

Examples

head(DCtracts2020)

Racial Isolation Index based on Anthopolos et al. (2011)

Description

Compute the spatial Racial Isolation Index (Anthopolos) of selected subgroup(s).

Usage

anthopolos(
  geo = "tract",
  year = 2020,
  subgroup,
  crs = "ESRI:102008",
  quiet = FALSE,
  ...
)

Arguments

geo

Character string specifying the geography of the data either counties geo = 'county', census tracts geo = 'tract' (the default), or census block groups geo = 'cbg'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the spatial Racial Isolation Index (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 RI for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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:

Use the internal state and county arguments within the 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 RI values in these census geographies may be unstable. A stop-gap solution for the former source of edge effect is to compute the 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 or ethnicity subgroup(s) (e.g., non-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 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 RI value close to 0.

Value

An object of class 'list'. This is a named list with the following components:

ri

An object of class 'tbl' for the GEOID, name, RI, and raw census values of specified census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute RI.

References

Anthopolos, R, James, SA, Gelfand, AE, & Miranda, ML (2011) A Spatial Measure of Neighborhood Level Racial Isolation Applied to Low Birthweight, Preterm Birth, and Birthweight in North Carolina. Spatial and Spatio-temporal Epidemiology, 2(4):235-246. doi:10.1016/j.sste.2011.06.002

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other isolation indices: bemanian_beyer, lieberson, morgan_massey, white

Interaction indices: bell, morgan_denton

Education Isolation Index: bravo

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Racial Isolation Index (a measure of isolation)
  ## of Black populations
  ## in census tracts of Georgia, U.S.A. (2020)
  anthopolos(
    geo = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Atkinson Index based on Atkinson (1970)

Description

Compute the aspatial Atkinson Index of income or selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

atkinson(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  epsilon = 0.5,
  holder = FALSE,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the income or racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

epsilon

Numerical. Shape parameter that denotes the aversion to inequality. Value must be between 0 and 1.0 (the default is 0.5).

holder

Logical. If TRUE, will compute index using the Hölder mean. If FALSE, will not compute with the Hölder mean. The default is FALSE.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Atkinson Index (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 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 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or geo_large = 'metro') but may be available from other U.S. Census Bureau surveys. When subgroup = 'MedHHInc', the metric will be computed for median household income ('B19013_001') using the Hölder mean. The twenty racial or ethnic subgroups (U.S. Census Bureau definitions) are:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

A is a measure of the evenness of residential inequality (e.g., racial or ethnic segregation) when comparing smaller geographical units to larger ones within which the smaller geographical units are located. A can range in value from 0 to 1 with smaller values indicating lower levels of inequality (e.g., less segregation).

The 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 units 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) doi:10.48550/arXiv.2002.05819 for one method to select epsilon.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the A value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the A computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the A computation.

Value

An object of class 'list'. This is a named list with the following components:

a

An object of class 'tbl' for the GEOID, name, and A at specified larger census geographies.

a_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute A.

References

Atkinson, AB (1970) On the Measurement of Inequality. Journal of Economic Theory, 2(3):244-263. doi:10.1016/0022-0531(70)90039-6

James, D, & Taeuber, KE (1985) Measures of Segregation. Sociological Methodology, 15:1-32. doi:10.2307/270845

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other one-group evenness indices: gini, james_taeuber, sudano, theil

Between groups dissimilarity indices: duncan

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Atkinson Index (a measure of the evenness) 
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  atkinson(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
  )
 
  # Atkinson Index (a measure of the evenness) 
  ## of median household income
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  atkinson(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'MedHHInc'
  )


## End(Not run)

Interaction Index based on Shevky & Williams (1949) and Bell (1954)

Description

Compute the aspatial Interaction Index (Bell) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

bell(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ixn,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

subgroup_ixn

Character string specifying the racial or ethnic subgroup(s) as the interaction population. If the same as subgroup, will compute the simple isolation of the group. See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to 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 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 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

xPy\* is some measure of the probability that a member of one subgroup(s) will meet or interact with a member of another subgroup(s) with higher values signifying higher probability of interaction (less isolation). xPy\* can range in value from 0 to 1.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the xPy\* value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the xPy\* computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the xPy\* computation.

Value

An object of class 'list'. This is a named list with the following components:

xpy_star

An object of class 'tbl' for the GEOID, name, and xPy\* at specified larger census geographies.

xpy_star_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute xPy\*.

References

Eshref, S, & Williams, M (1949). The Social Areas of Los Angeles: Analysis and Typology. 1st Ed. Los Angeles:John Randolph Haynes and Dora Haynes Foundation. ISBN-13:978-0-837-15637-8

Bell, W (1954) A probability model for the measurement of ecological segregation. Social Forces, 32(4):357-364. doi:10.2307/2574118

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other interaction indices: morgan_denton

Isolation indices: anthopolos, bemanian_beyer, lieberson, morgan_massey, white

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Interaction Index (a measure of exposure)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  bell(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ixn = 'NHoLW'
   )


## End(Not run)

Local Exposure and Isolation based on Bemanian & Beyer (2017)

Description

Compute the aspatial Local Exposure and Isolation (Bemanian & Beyer) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

bemanian_beyer(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ixn,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

subgroup_ixn

Character string specifying the racial or ethnic subgroup(s) as the interaction population. If the same as subgroup, will compute the simple isolation of the group. See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Local Exposure and Isolation (LEx/Is) 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 LEx/Is for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

LEx/Is is a measure of the probability that two individuals living within a specific smaller geographical unit (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 geographical unit 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 geographical unit. (Note: will adjust data by 0.025 if probabilities are zero, one, or undefined. The output will include a warning if adjusted. See logit for additional details.)

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 geographical unit is equal to the expected probability if the subgroup(s) were perfectly mixed in the larger geographical unit. If LEx/Is is greater than zero then the interaction is more likely to occur within the smaller geographical unit than in the larger geographical unit, and if LEx/Is is less than zero then the interaction is less likely to occur within the smaller geographical unit than in the larger geographical unit. Note: the exponentiation of each LEx/Is results in the odds ratio of the specific exposure or isolation of interest in a smaller geographical unit relative to the larger geographical unit.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the LEx/Is value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the LEx/Is computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the LEx/Is computation.

Value

An object of class 'list'. This is a named list with the following components:

lexis

An object of class 'tbl' for the GEOID, name, and LEx/Is at specified smaller census geographies.

lexis_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute LEx/Is.

References

Bemanian, A, & Beyer, KMM (2017) Measures Matter: The Local Exposure/Isolation (LEx/Is) Metrics and Relationships between Local-Level Segregation and Breast Cancer Survival. Cancer Epidemiology, Biomarkers & Prevention, 26(4):516-524. doi:10.1158/1055-9965.EPI-16-0926

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other isolation indices: anthopolos, lieberson, morgan_massey, white

Interaction indices: bell, morgan_denton

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Local Exposure and Isolation 
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  bemanian_beyer(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ixn = 'NHoLW'
   )


## End(Not run)

Educational Isolation Index based on Bravo et al. (2021)

Description

Compute the spatial Educational Isolation Index (Bravo) of selected educational attainment category(ies).

Usage

bravo(
  geo = "tract",
  year = 2020,
  subgroup,
  crs = "ESRI:102008",
  quiet = FALSE,
  ...
)

Arguments

geo

Character string specifying the geography of the data either census tracts geo = 'tract' (the default) or counties geo = 'county'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the educational attainment category(ies). See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the spatial Educational Isolation Index (EI) of U.S. census tracts or counties for a specified geographical extent (e.g., the entire U.S. or a single state) based on Bravo et al. (2021) doi:10.3390/ijerph18179384 who originally designed the metric for the educational isolation of individual without a college degree. This function provides the computation of EI for any of the U.S. Census Bureau educational attainment levels.

The function uses the get_acs 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 five educational attainment levels (U.S. Census Bureau definitions) are:

Note: If year = 2009, then the ACS-5 data (2005-2009) are from the B15002 question.

Use the internal state and county arguments within the 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 EI values in these census geographies may be unstable. A stop-gap solution for the former source of edge effect is to compute the EI 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 with the specified educational attainment category (e.g., a Bachelor's degree or more) will have an EI value close to 1. In contrast, a census geography (and its neighbors) that is nearly none of its population with the specified educational attainment category (e.g., less than a Bachelor's degree) will have an EI value close to 0.

Value

An object of class 'list'. This is a named list with the following components:

ei

An object of class 'tbl' for the GEOID, name, EI, and raw census values of specified census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute EI.

References

Bravo, MA, Leong, MC, Gelfand, AE, & Miranda, ML (2021) Assessing Disparity Using Measures of Racial and Educational Isolation. International Journal of Environmental Research and Public Health, 18(17):9384. doi:10.3390/ijerph18179384

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Racial Isolation Index: anthopolos

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Educational Isolation Index (a measure of exposure)
  ## of less than some college or associate's degree attainment
  ## in census tracts of Georgia, U.S.A. (2020)
  bravo(
    geo = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('LtHS', 'HSGiE')
   )


## End(Not run)

Relative Clustering based on Massey & Denton (1988)

Description

Compute the aspatial Relative Clustering (Massey & Denton) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

denton(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ref,
  crs = "ESRI:102008",
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

subgroup_ref

Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Relative Clustering (RCL) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281. This function provides the computation of RCL for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

RCL is a measure of clustering of racial or ethnic populations within smaller geographical units that are located within larger geographical units. RCL can range in value from -Inf to Inf and represents the degree to which an area is a racial or ethnic enclave. RCL equals 0 when the racial or ethnic subgroup population displays the same amount of clustering as the referent racial or ethnic subgroup population, and is positive whenever the racial or ethnic subgroup population members display greater clustering than is typical of the the referent racial or ethnic subgroup population. If the racial or ethnic subgroup population members were less clustered than the the referent racial or ethnic subgroup population, then RCL would be negative.

The metric uses the exponential transform of a distance matrix (kilometers) between smaller geographical area centroids, with a diagonal defined as (0.6*a_{i})^{0.5} where a_{i} is the area (square kilometers) of smaller geographical unit i as defined by White (1983) doi:10.1086/227768.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the RCL value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the RCL computation.

Value

An object of class 'list'. This is a named list with the following components:

rcl

An object of class 'tbl' for the GEOID, name, and RCL at specified larger census geographies.

rcl_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute RCL.

References

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Absolute Clustering: massey

Proximity measures: morgan_massey, morgan_denton, white_blau

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Relative Clustering (a measure of clustering)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  denton(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ref = 'NHoLW'
   )


## End(Not run)

Relative Concentration based on Massey & Denton (1988) and Duncan, Cuzzort, & Duncan (1961)

Description

Compute the aspatial Relative Concentration (Massey & Denton) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

denton_cuzzort(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ref,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

subgroup_ref

Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Relative Concentration (RCO) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281 and Duncan, Cuzzort, & Duncan (1961; LC:60007089). This function provides the computation of RCO for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

RCO is a measure of concentration of racial or ethnic populations within smaller geographical units that are located within larger geographical units. RCO is a measure of concentration of racial or ethnic populations within smaller geographical units that are located within larger geographical units. RCO can range from -1 to 1 and represents the share of a larger geographical unit occupied by a racial or ethnic subgroup compared to a referent racial or ethnic subgroup. A value of 1 indicates that the concentration of the racial or ethnic subgroup exceeds the concentration of the referent racial or ethnic subgroup at the maximum extent possible. A value of -1 is the converse. Note: Computed as designed, but values smaller than -1 are possible if the racial or ethnic subgroup population is larger than the referent racial or ethnic subgroup population.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the RCO value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the RCO computation.

Value

An object of class 'list'. This is a named list with the following components:

rco

An object of class 'tbl' for the GEOID, name, and RCO at specified larger census geographies.

rco_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute RCO.

References

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

Duncan, OD, Cuzzort, RP, & Duncan, B (1961) Statistical Geography: Problems in Analyzing Area Data. Free Press. LC:60007089

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Absolute Concentration: massey_duncan

Delta: hoover

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Relative Concentration (a measure of concentration)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  denton_cuzzort(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ref = 'NHoLW'
   )


## End(Not run)

Dissimilarity Index based on Duncan & Duncan (1955)

Description

Compute the aspatial Dissimilarity Index (Duncan & Duncan) of selected racial or ethnic subgroup(s) and U.S. geographies

Usage

duncan(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ref,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

subgroup_ref

Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Dissimilarity Index (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 D for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

D is a measure of the evenness of racial or ethnic residential segregation when comparing smaller geographical units to larger ones within which the smaller geographical units 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.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the D value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the D computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the D computation.

Value

An object of class 'list'. This is a named list with the following components:

d

An object of class 'tbl' for the GEOID, name, and D at specified larger census geographies.

d_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute D.

References

Duncan, OD, & Duncan, B (1955) Residential Distribution and Occupational Stratification. American Journal of Sociology, 60(5):493-503. doi:10.2307/2088328

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

One-group evenness indices: atkinson, gini, james_taeuber, sudano, theil

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Dissimilarity Index (Duncan & Duncan; a measure of evenness)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  duncan(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ref = 'NHoLW'
   )


## End(Not run)

Absolute Centralization based on Duncan, Cuzzort, & Duncan (1961) and Massey & Denton (1988)

Description

Compute the aspatial Absolute Centralization (Duncan & Cuzzort) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

duncan_cuzzort(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  crs = "ESRI:102008",
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Absolute Centralization (ACE) 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, Cuzzort, & Duncan (1961; LC:60007089) and Massey & Denton (1988) doi:10.1093/sf/67.2.281. This function provides the computation of ACE for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

ACE is a measure of the degree to which racial or ethnic populations within smaller geographical units are located near the center of a larger geographical unit. ACE can range in value from -1 to 1 and represents the spatial distribution of racial or ethnic populations within smaller geographical units compared to the distribution of land area around the center of a larger geographical unit. Positive values indicate a tendency for racial or ethnic populations to reside close to the center of a larger geographical unit, while negative values indicate a tendency to live in outlying areas. A score of 0 means that racial or ethnic populations have a uniform distribution throughout a larger geographical unit. ACE gives the proportion of racial or ethnic populations required to change residence to achieve a uniform distribution of population around the center of a larger geographical unit.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the ACE value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the ACE computation.

Important consideration: The original metric used the location of the central business district (CBD) to compute the metric, but the U.S. Census Bureau has not defined CBDs for U.S. cities since the 1982 Census of Retail Trade. Therefore, this function uses the the centroids of each larger geographical unit as the 'centre', but may not represent the current CBD.

Value

An object of class 'list'. This is a named list with the following components:

ace

An object of class 'tbl' for the GEOID, name, and ACE at specified larger census geographies.

ace_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute ACE.

References

Duncan, OD, Cuzzort, RP, & Duncan, B (1961) Statistical Geography: Problems in Analyzing Area Data. Free Press. LC:60007089

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Relative Centralization: duncan_duncan

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Absolute Centralization (a measure of centralization)
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  duncan_cuzzort(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Relative Centralization based on Duncan, Cuzzort, & Duncan (1961) and Massey & Denton (1988)

Description

Compute the aspatial Relative Centralization (Duncan & Duncan) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

duncan_duncan(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ref,
  crs = "ESRI:102008",
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

subgroup_ref

Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Relative Centralization (RCE) 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:10.1086/221609 and Massey & Denton (1988) doi:10.1093/sf/67.2.281. This function provides the computation of RCE for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

RCE is a measure of the degree to which racial or ethnic populations within smaller geographical units are located near the center of a larger geographical unit. RCE can range in value from -1 to 1 and represents the spatial distribution of racial or ethnic populations within smaller geographical units relative to the compared to the distribution of the referent racial or ethnic population around the center of a larger geographical unit. Positive values indicate a tendency for racial or ethnic populations to reside closer to the center of a larger geographical unit than the referent racial or ethnic population, while negative values indicate the racial or ethnic population is distributed farther from the center of a larger geographical unit than the referent racial or ethnic population. A score of 0 means that racial or ethnic populations have a uniform distribution throughout a larger geographical unit. RCE gives the proportion of racial or ethnic populations required to change residence to match the degree of centralization of the referent racial or ethnic population.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the RCE value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the RCE computation.

Important consideration: The original metric used the location of the central business district (CBD) to compute the metric, but the U.S. Census Bureau has not defined CBDs for U.S. cities since the 1982 Census of Retail Trade. Therefore, this function uses the the centroids of each larger geographical unit as the 'centre', but may not represent the current CBD.

Value

An object of class 'list'. This is a named list with the following components:

rce

An object of class 'tbl' for the GEOID, name, and RCE at specified larger census geographies.

rce_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute RCE.

References

Duncan, OD, Cuzzort, RP, & Duncan, B (1961) Statistical Geography: Problems in Analyzing Area Data. Free Press. LC:60007089

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Absolute Centralization: duncan_cuzzort

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Relative Centralization (a measure of centralization)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  duncan_duncan(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ref = 'NHoLW'
   )


## End(Not run)

Gini Index based on Gini (1921)

Description

Compute the aspatial racial or ethnic Gini Index and retrieve the aspatial income Gini Index

Usage

gini(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will retrieve the aspatial Gini Index (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 get_acs function to obtain U.S. Census Bureau 5-year American Community Survey estimates of G for the geospatial computation. The yearly estimates are available for 2009 onward when ACS-5 data are available (2010 onward for geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or geo_large = 'metro') but are available from other U.S. Census Bureau surveys. The function will retrieve the provided income inequality metric (B19083) and the twenty racial or ethnic subgroups (U.S. Census Bureau definitions) are:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

According to the U.S. Census Bureau 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 geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the V value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the V computation.

Value

An object of class 'list'. This is a named list with the following components:

g

An object of class 'tbl' for the GEOID, name, and G_re metrics of specified census geographies.

g_data

An object of class 'tbl' for the raw census values at specified smaller census geographies including G_inc.

missing

An object of class 'tbl' of the count and proportion of missingness for G_inc and each census variable used to compute G_re.

References

Gini, C (1921) Measurement of Inequality of Incomes. The Economic Journal, 31(121):124-126. doi:10.2307/2223319

Duncan, OD, & Duncan, B (1955) Residential Distribution and Occupational Stratification. American Journal of Sociology, 60(5):493-503. doi:10.2307/2088328

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other one-group evenness indices: atkinson, james_taeuber, sudano, theil

Between groups dissimilarity indices: duncan

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Gini Index (a metric of evenness) 
  ## of Black populations
  ## in census tracts of Georgia, U.S.A. (2020)
  gini(
    geo_large = 'county',
    geo_small = 'tract', 
    state = 'GA',
    year = 2020, 
    subgroup = c('NHoLB', 'HoLB')
   )
   

## End(Not run)

Delta based on Hoover (1941) and Duncan, Cuzzort, & Duncan (1961)

Description

Compute the aspatial Delta (Hoover) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

hoover(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Delta (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 DEL for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

DEL is a measure of the proportion of members of one subgroup(s) residing in geographic units with above average density of members of the subgroup(s). The index provides the proportion of a subgroup population that would have to move across geographic units to achieve a uniform density. DEL can range in value from 0 to 1.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the DEL value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the DEL computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the DEL computation.

Value

An object of class 'list'. This is a named list with the following components:

del

An object of class 'tbl' for the GEOID, name, and DEL at specified larger census geographies.

del_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute DEL.

References

Hoover, EM (1941) Interstate Redistribution of Population, 1850-1940. Journal of Economic History, 1:199-205. doi:10.2307/2223319

Duncan, OD, Cuzzort, RP, & Duncan, B (1961) Statistical Geography: Problems in Analyzing Area Data. Free Press. LC:60007089

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Absolute Concentration: massey_duncan

Relative Concentration: denton_cuzzort

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.
  
  # Delta (a measure of concentration) 
  ## of non-Hispanic Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  hoover(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB'
   )
  

## End(Not run)

Dissimilarity Index based on James & Taeuber (1985)

Description

Compute the aspatial Dissimilarity Index (James & Taeuber) of selected racial or ethnic subgroup(s) and U.S. geographies

Usage

james_taeuber(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Dissimilarity Index (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 James & Taeuber (1985) doi:10.2307/270845. This function provides the computation of D for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

D is a measure of the evenness of racial or ethnic residential segregation when comparing smaller geographical units to larger ones within which the smaller geographical units 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.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the D value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the D computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the D computation.

Value

An object of class 'list'. This is a named list with the following components:

d

An object of class 'tbl' for the GEOID, name, and D at specified larger census geographies.

d_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute D.

References

James, D, & Taeuber, KE (1985) Measures of Segregation. Sociological Methodology, 15:1-32. doi:10.2307/270845

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other one-group evenness indices: atkinson, gini, sudano, theil

Between groups dissimilarity indices: duncan

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Dissimilarity Index (James & Taeuber; a measure of evenness)
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  james_taeuber(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Index of Concentration at the Extremes based on Feldman et al. (2015) and Krieger et al. (2016)

Description

Compute the aspatial Index of Concentration at the Extremes (Krieger).

Usage

krieger(geo = "tract", year = 2020, quiet = FALSE, ...)

Arguments

geo

Character string specifying the geography of the data either census tracts geo = 'tract' (the default) or counties geo = 'county'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute three aspatial Index of Concentration at the Extremes (ICE) of U.S. census tracts or counties for a specified geographical extent (e.g., entire U.S. or a single state) based on Feldman et al. (2015) doi:10.1136/jech-2015-205728 and Krieger et al. (2016) doi:10.2105/AJPH.2015.302955. The authors expanded the metric designed by Massey in a chapter of Booth & Crouter (2001) doi:10.4324/9781410600141 who initially designed the metric for residential segregation. This function computes five ICE metrics:

The function uses the 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 ICE metrics are:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

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.

Value

An object of class 'list'. This is a named list with the following components:

ice

An object of class 'tbl' for the GEOID, name, ICE metrics, and raw census values of specified census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute the ICE metrics.

References

Feldman, JM, Waterman, PD, Coull, BA, & Krieger, N (2015) Spatial Social Polarisation: Using the Index of Concentration at the Extremes Jointly for Income and Race/Ethnicity to Analyse Risk of Hypertension. Journal of Epidemiology and Community Health, 69(12):1199-207. doi:10.1136/jech-2015-205728

Waterman, PD, Spasojevic, J, Li, W, Maduro, G, & Wye, GV (2016) Public Health Monitoring of Privilege and Deprivation With the Index of Concentration at the Extremes. American Journal of Public Health, 106(2):256-263. doi:10.2105/AJPH.2015.302955

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other concentration metrics: denton_cuzzort, hoover, massey_duncan

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Tract-level metrics (2020)
  krieger(geo = 'tract', state = 'GA', year = 2020)

  # County-level metrics (2020)
  krieger(geo = 'county', state = 'GA', year = 2020)


## End(Not run)

Isolation Index based on Lieberson (1981) and Bell (1954)

Description

Compute the aspatial Isolation Index (Lieberson) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

lieberson(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to 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 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 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

xPx\* is some measure of the probability that a member of one subgroup(s) will meet or interact with a member of their subgroup(s) with higher values signifying higher probability of interaction (less isolation). xPx\* can range in value from 0 to 1.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the xPx\* value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the xPx\* computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the xPx\* computation.

Value

An object of class 'list'. This is a named list with the following components:

xpx_star

An object of class 'tbl' for the GEOID, name, and xPx\* at specified larger census geographies.

xpx_star_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute xPx\*.

References

Lieberson, S (1981). "An Asymmetrical Approach to Segregation." Pp. 61-82 in Ethnic Segregation in Cities, edited by Peach, C, Robinson, V, & Smith, S. 1st Ed. London:Croom Helm. ISBN-13:978-1-032-53884-6

Bell, W (1954) A probability model for the measurement of ecological segregation. Social Forces, 32(4):357-364. doi:10.2307/2574118

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other isolation indices: anthopolos, bemanian_beyer, morgan_massey, white

Interaction indices: bell, morgan_denton

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Interaction (a measure of exposure)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  bell(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB'
   )


## End(Not run)

Absolute Clustering based on Massey & Denton (1988)

Description

Compute the aspatial Absolute Clustering (Massey & Denton) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

massey(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  crs = "ESRI:102008",
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Absolute Clustering (ACL) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281. This function provides the computation of ACL for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

ACL is a measure of clustering of racial or ethnic populations within smaller geographical units that are located within larger geographical units. ACL can range in value from 0 to Inf and represents the degree to which an area is a racial or ethnic enclave. A value of 1 indicates there is no differential clustering of the racial or ethnic subgroup. A value greater than 1 indicates the racial or ethnic subgroup live nearer to one another. A value less than 1 indicates the racial or ethnic subgroup do not live near one another.

The metric uses the exponential transform of a distance matrix (kilometers) between smaller geographical area centroids, with a diagonal defined as (0.6*a_{i})^{0.5} where a_{i} is the area (square kilometers) of smaller geographical unit i as defined by White (1983) doi:10.1086/227768.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the ACL value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the ACL computation.

Value

An object of class 'list'. This is a named list with the following components:

acl

An object of class 'tbl' for the GEOID, name, and ACL at specified larger census geographies.

acl_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute ACL.

References

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Relative Clustering Index: denton

Proximity measures: morgan_denton, morgan_massey, white_blau

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Absolute Clustering (a measure of clustering)
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  massey(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Absolute Concentration based on Massey & Denton (1988) and Duncan, Cuzzort, & Duncan (1961)

Description

Compute the aspatial Absolute Concentration (Massey & Denton) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

massey_duncan(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Absolute Concentration (ACO) of selected racial or ethnic subgroups and U.S. geographies for a specified geographical extent (e.g., the entire U.S. or a single state) based on Massey & Denton (1988) doi:10.1093/sf/67.2.281 and Duncan, Cuzzort, & Duncan (1961; LC:60007089). This function provides the computation of ACO for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

ACO is a measure of concentration of racial or ethnic populations within smaller geographical units that are located within larger geographical units. ACO can range from 0 to 1 and represents the relative amount of physical space occupied by a racial or ethnic subgroup in a larger geographical unit. A value of 1 indicates that a racial or ethnic subgroup has achieved the maximum spatial concentration possible (all racial or ethnic subgroup members live in the smallest of the smaller geographical units). A value of 0 indicates the maximum deconcentration possible (all racial or ethnic subgroup members live in the largest of the smaller geographical units).

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the ACO value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the ACO computation.

Value

An object of class 'list'. This is a named list with the following components:

aco

An object of class 'tbl' for the GEOID, name, and ACO at specified larger census geographies.

aco_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute ACO.

References

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

Duncan, OD, Cuzzort, RP, & Duncan, B (1961) Statistical Geography: Problems in Analyzing Area Data. Free Press. LC:60007089

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Relative Concentration: denton_cuzzort

Delta: hoover

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Absolute Concentration (a measure of concentration)
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  massey_duncan(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Neighborhood Deprivation Index based on Messer et al. (2006)

Description

Compute the aspatial Neighborhood Deprivation Index (Messer).

Usage

messer(
  geo = "tract",
  year = 2020,
  imp = FALSE,
  quiet = FALSE,
  round_output = FALSE,
  df = NULL,
  ...
)

Arguments

geo

Character string specifying the geography of the data either census tracts geo = 'tract' (the default) or counties geo = 'county'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2010 onward are currently available.

imp

Logical. If TRUE, will impute missing census characteristics within the internal principal. If FALSE (the default), will not impute.

quiet

Logical. If TRUE, will display messages about potential missing census information and the proportion of variance explained by principal component analysis. The default is FALSE.

round_output

Logical. If TRUE, will round the output of raw census and NDI values from the get_acs at one and four significant digits, respectively. The default is FALSE.

df

Optional. Pass a pre-formatted 'dataframe' or 'tibble' with the desired variables through the function. Bypasses the data obtained by get_acs. The default is NULL. See Details below.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Neighborhood Deprivation Index (NDI) of U.S. census tracts or counties for a specified geographical referent (e.g., US-standardized) based on Messer et al. (2006) doi:10.1007/s11524-006-9094-x.

The function uses the get_acs function to obtain U.S. Census Bureau 5-year American Community Survey characteristics used for computation involving a principal component analysis with the principal function. The yearly estimates are available for 2010 and after when all census characteristics became available. The eight characteristics are:

Use the internal state and county arguments within the get_acs function to specify the referent for standardizing the NDI (Messer) values. For example, if all U.S. states are specified for the state argument, then the output would be a U.S.-standardized index.

The continuous NDI (Messer) values are z-transformed, i.e., 'standardized,' and the categorical NDI (Messer) values are quartiles of the standardized continuous NDI (Messer) values.

Check if the proportion of variance explained by the first principal component is high (more than 0.5).

Users can bypass get_acs by specifying a pre-formatted data frame or tibble using the df argument. This function will compute an index using the first component of a principal component analysis (PCA) with a Varimax rotation (the default for principal) and only one factor (note: PCA set-up not unspecified in Messer et al. (2006)). The recommended structure of the data frame or tibble is an ID (e.g., GEOID) in the first feature (column), followed by the variables of interest (in any order) and no additional information (e.g., omit state or county names from the df argument input).

Value

An object of class 'list'. This is a named list with the following components:

ndi

An object of class 'tbl' for the GEOID, name, NDI (standardized), NDI (quartile), and raw census values of specified census geographies.

pca

An object of class 'principal', returns the output of principal used to compute the NDI values.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute NDI.

References

Messer, LC, Laraia, BA, Kaufman, JS, Eyster, J, Holzman, C, Culhane, J, Elo, I, Burke, J, O'Campo, P (2006) The Development of a Standardized Neighborhood Deprivation Index. Journal of Urban Health, 83(6):1041-1062. doi:10.1007/s11524-006-9094-x

See Also

get_acs for additional arguments for geographic referent selection (i.e., state and county).

Neighborhood Deprivation Index: powell_wiley

Examples


messer(df = DCtracts2020[ , c(1, 3:10)])

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.
  
  # Tract-level NDI (Messer; 2020)
  messer(geo = 'tract', state = 'GA', year = 2020)

  # Impute NDI (Messer; 2020) for tracts with missing census information (median values)
  messer(state = 'tract', state = 'GA', year = 2020, imp = TRUE)
  

## End(Not run)

Distance-Decay Interaction Index based on Morgan (1983) and Massey & Denton (1988)

Description

Compute the aspatial Distance-Decay Interaction Index (Morgan) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

morgan_denton(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ixn,
  crs = "ESRI:102008",
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

subgroup_ixn

Character string specifying the racial or ethnic subgroup(s) as the interaction population. If the same as subgroup, will compute the simple isolation of the group. See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Distance-Decay Interaction Index (DPxy\*) 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 Morgan (1986) https://www.jstor.org/stable/20001935 and Massey & Denton (1988) doi:10.1093/sf/67.2.281. This function provides the computation of DPxy\* for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

DPxy\* is a measure of clustering of racial or ethnic populations within smaller geographical units that are located within larger geographical units. DPxy\* is some measure of the probability that a member of a racial or ethnic subgroup will meet or interact with a member of another racial or ethnic subgroup(s). DPxy\* can range in value from 0 to 1 with higher values signifying higher probability of interaction.

The metric uses the exponential transform of a distance matrix (kilometers) between smaller geographical area centroids, with a diagonal defined as (0.6*a_{i})^{0.5} where a_{i} is the area (square kilometers) of smaller geographical unit i as defined by White (1983) doi:10.1086/227768.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the DPxy\* value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the DPxy\* computation.

Value

An object of class 'list'. This is a named list with the following components:

dpxy_star

An object of class 'tbl' for the GEOID, name, and DPxy\* at specified larger census geographies.

dpxy_star_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute DPxy\*.

References

Morgan, BS (1983) A Distance-Decay Based Interaction Index to Measure Residential Segregation. Area, 15(3):211-217. https://www.jstor.org/stable/20001935

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other proximity measures: morgan_massey, white_blau

Other interaction indices: bell

Isolation indices: anthopolos, bemanian_beyer, lieberson, morgan_massey, white

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Distance-Decay Interaction Index (a measure of clustering)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  morgan_denton(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ixn = 'NHoLW'
   )


## End(Not run)

Distance-Decay Isolation Index based on Morgan (1983) and Massey & Denton (1988)

Description

Compute the aspatial Distance-Decay Isolation Index (Morgan) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

morgan_massey(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  crs = "ESRI:102008",
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Distance-Decay Isolation Index (DPxx\*) 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 Morgan (1986) https://www.jstor.org/stable/20001935 and Massey & Denton (1988) doi:10.1093/sf/67.2.281. This function provides the computation of DPxx\* for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

DPxx\* is a measure of clustering of racial or ethnic populations within smaller geographical units that are located within larger geographical units. DPxx\* is some measure of the probability that a member of one racial or ethnic subgroup will meet or interact with a member of the same racial or ethnic subgroup. DPxx\* can range in value from 0 to 1 with higher values signifying higher probability of isolation (less isolation).

The metric uses the exponential transform of a distance matrix (kilometers) between smaller geographical area centroids, with a diagonal defined as (0.6*a_{i})^{0.5} where a_{i} is the area (square kilometers) of smaller geographical unit i as defined by White (1983) doi:10.1086/227768.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the DPxx\* value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the DPxx\* computation.

Value

An object of class 'list'. This is a named list with the following components:

dpxx_star

An object of class 'tbl' for the GEOID, name, and DPxx\* at specified larger census geographies.

dpxx_star_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute DPxx\*.

References

Morgan, BS (1983) A Distance-Decay Based Interaction Index to Measure Residential Segregation. Area, 15(3):211-217. https://www.jstor.org/stable/20001935

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other proximity measures: morgan_denton, white_blau

Other isolation indices: anthopolos, bemanian_beyer, lieberson, white

Interaction indices: bell, morgan_denton

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Distance-Decay Isolation Index (a measure of clustering)
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  morgan_massey(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Neighborhood Deprivation Index based on Andrews et al. (2020) and Slotman et al. (2022)

Description

Compute the aspatial Neighborhood Deprivation Index (Powell-Wiley).

Usage

powell_wiley(
  geo = "tract",
  year = 2020,
  imp = FALSE,
  quiet = FALSE,
  round_output = FALSE,
  df = NULL,
  ...
)

Arguments

geo

Character string specifying the geography of the data either census tracts geo = 'tract' (the default) or counties geo = 'county'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2010 onward are currently available.

imp

Logical. If TRUE, will impute missing census characteristics within the internal principal using median values of variables. If FALSE (the default), will not impute.

quiet

Logical. If TRUE, will display messages about potential missing census information, standardized Cronbach's alpha, and proportion of variance explained by principal component analysis. The default is FALSE.

round_output

Logical. If TRUE, will round the output of raw census and NDI values from the get_acs at one and four significant digits, respectively. The default is FALSE.

df

Optional. Pass a pre-formatted 'dataframe' or 'tibble' with the desired variables through the function. Bypasses the data obtained by get_acs. The default is NULL. See Details below.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Neighborhood Deprivation Index (NDI) of U.S. census tracts or counties for a specified geographical referent (e.g., US-standardized) based on Andrews et al. (2020) doi:10.1080/17445647.2020.1750066 and Slotman et al. (2022) doi:10.1016/j.dib.2022.108002.

The function uses the get_acs function to obtain U.S. Census Bureau 5-year American Community Survey characteristics used for computation involving a factor analysis with the principal function. The yearly estimates are available in 2010 and after when all census characteristics became available. The thirteen characteristics chosen by Roux and Mair (2010) doi:10.1111/j.1749-6632.2009.05333.x are:

Use the internal state and county arguments within the get_acs function to specify the referent for standardizing the NDI (Powell-Wiley) values. For example, if all U.S. states are specified for the state argument, then the output would be a U.S.-standardized index. Please note: the NDI (Powell-Wiley) values will not exactly match (but will highly correlate with) those found in Andrews et al. (2020) doi:10.1080/17445647.2020.1750066 and Slotman et al. (2022) doi:10.1016/j.dib.2022.108002 because the two studies used a different statistical platform (i.e., SPSS and SAS, respectively) that intrinsically calculate the principal component analysis differently from R.

The categorical NDI (Powell-Wiley) values are population-weighted quintiles of the continuous NDI (Powell-Wiley) values. NOTE: As of version 0.2.0, population-weighted quintiles are computed using a weighted quantile function wtd.quantile where values are calculated NDI and weights are Total Population instead of previously using quantile of the product of the calculated NDI and natural logarithm transformed total population.

Check if the proportion of variance explained by the first principal component is high (more than 0.5).

Users can bypass get_acs by specifying a pre-formatted data frame or tibble using the df argument. This function will compute an index using the first component of a principal component analysis (PCA) with a Promax (oblique) rotation and a minimum Eigenvalue of 1, omitting variables with absolute loading score < 0.4. The recommended structure of the data frame or tibble is an ID (e.g., GEOID) in the first feature (column), an estimate of the total population in the second feature (column), followed by the variables of interest (in any order) and no additional information (e.g., omit state or county names from the df argument input).

Value

An object of class 'list'. This is a named list with the following components:

ndi

An object of class 'tbl' for the GEOID, name, NDI continuous, NDI quintiles, and raw census values of specified census geographies.

pca

An object of class 'principal', returns the output of principal used to compute the NDI values.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute NDI.

cronbach

An object of class 'character' or 'numeric' for the results of the Cronbach's alpha calculation. If only one factor is computed, a message is returned. If more than one factor is computed, Cronbach's alpha is calculated and should check that it is >0.7 for respectable internal consistency between factors.

References

Andrews, MA, Tomura, K, Claudel, SE, Xu, S, Ceasar, JN, Collins, BS, Langerman, S, Mitchell, VM, Baumer, Y, & Powell-Wiley TM (2022) Geospatial Analysis of Neighborhood Deprivation Index (NDI) for the United States by County. Journal of Maps, 16(1):101-112. doi:10.1080/17445647.2020.1750066

Slotman, BA, Stinchcomb, DG, Powell-Wiley, TM, Ostendorf, DM, Saelens, BE, Gorin, AA, Zenk, SN, & Berrigan, D (2022) Environmental Data and Methods from the Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Environmental Working Group. Data in Brief, 41:108002. doi:10.1016/j.dib.2022.108002

See Also

get_acs for additional arguments for geographic referent selection (i.e., state and county).

Neighborhood Deprivation Index: messer

Examples


powell_wiley(df = DCtracts2020[ , -c(3:10)])

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Tract-level NDI (Powell-Wiley; 2020)
  powell_wiley(geo = 'tract', state = 'GA', year = 2020)

  # Impute NDI (Powell-Wiley; 2020) for tracts with missing census information (median values)
  powell_wiley(state = 'tract', state = 'GA', year = 2020, imp = TRUE)


## End(Not run)

Location Quotient based on Merton (1938) and Sudano et al. (2013)

Description

Compute the aspatial Location Quotient (Sudano) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

sudano(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Location Quotient (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 LQ for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

LQ is some measure of relative racial homogeneity of each smaller geographical units within a larger geographical unit. LQ can range in value from 0 to infinity because it is ratio of two proportions in which the numerator is the proportion of subgroup population in a smaller geographical unit and the denominator is the proportion of subgroup population in its larger geographical unit. For example, a smaller geographical unit with an LQ of 5 means that the proportion of the subgroup population living in the smaller geographical unit is five times the proportion of the subgroup population in its larger geographical unit.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the LQ value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the LQ computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the LQ computation.

Value

An object of class 'list'. This is a named list with the following components:

lq

An object of class 'tbl' for the GEOID, name, and LQ at specified smaller census geographies.

lq_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute LQ.

References

Merton, RK (1938) Social Structure and Anomie. American Sociological Review, 3(5):672-682. doi:10.2307/2084686

Sudano, JJ, Perzynski, A, Wong, DW, Colabianchi, N, Litaker, D (2013) Neighborhood Racial Residential Segregation and Changes in Health or Death Among Older Adults. Health & Place, 19:80-88. doi:10.1016/j.healthplace.2012.09.015

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other one-group evenness indices: atkinson, gini, james_taeuber, theil

Between groups dissimilarity indices: duncan

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Location Quotient (a measure of relative homogeneity) 
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  sudano(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Entropy based on Theil (1972) and Theil & Finizza (1971)

Description

Compute the aspatial Entropy (Theil) of selected racial or ethnic subgroup(s) and U.S. geographies

Usage

theil(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Entropy (H) 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 Theil (1972; ISBN-13:978-0-444-10378-9) and Theil & Finizza (1971) doi:10.1080/0022250X.1971.9989795. This function provides the computation of H for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

H is a measure of the evenness of racial or ethnic residential segregation when comparing smaller geographical units to larger ones within which the smaller geographical units are located. H can range in value from 0 to 1 and represents the (weighted) average deviation of each smaller geographical unit from the larger geographical unit's "entropy" or racial and ethnic diversity, which is greatest when each group is equally represented in the larger geographical unit. H varies between 0, when all smaller geographical units have the same racial or ethnic composition as the larger geographical area (i.e., maximum integration), to a high of 1, when all smaller geographical units contain one group only (maximum segregation).

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the H value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the H computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the H computation.

Note: The computation differs from Massey & Denton (1988) doi:10.1093/sf/67.2.281 by taking the absolute value of (E-E_{i}) so extent of the output is {0, 1} as designed by Theil (1972; ISBN-13:978-0-444-10378-9) instead of {-Inf, Inf} as described in Massey & Denton (1988) doi:10.1093/sf/67.2.281.

Value

An object of class 'list'. This is a named list with the following components:

h

An object of class 'tbl' for the GEOID, name, and H at specified larger census geographies.

h_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute H.

References

Theil, H (1972) Statistical decomposition analysis: with applications in the social and administrative. Amsterdam: North-Holland Publishing Company. ISBN-13:978-1-032-53884-6

Theil, H, & Finizza, AJ (1971) A Note on the Measurement of Racial Integration of Schools by Means of Informational Concepts. Journal of Mathematical Sociology, 1:187-194. doi:10.1080/0022250X.1971.9989795

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other one-group evenness indices: atkinson, gini, james_taeuber, sudano

Between groups dissimilarity indices: duncan

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Entropy (a measure of evenness) 
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  theil(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = c('NHoLB', 'HoLB')
   )


## End(Not run)

Correlation Ratio based on Bell (1954) and White (1986)

Description

Compute the aspatial Correlation Ratio (White) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

white(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s). See Details for available choices.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute the aspatial Correlation Ratio (V or Eta^{2}) 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 Bell (1954) doi:10.2307/2574118 and White (1986) doi:10.2307/3644339. This function provides the computation of V for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

V removes the asymmetry from the Isolation Index (Bell) by controlling for the effect of population composition. The Isolation Index (Bell) is some measure of the probability that a member of one subgroup(s) will meet or interact 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.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the V value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the V computation.

Value

An object of class 'list'. This is a named list with the following components:

v

An object of class 'tbl' for the GEOID, name, and V at specified larger census geographies.

v_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute V.

References

Bell, W (1954) A probability model for the measurement of ecological segregation. Social Forces, 32(4):357-364. doi:10.2307/2574118

White, MJ (1986) Segregation and Diversity Measures in Population Distribution. Population Index, 52(2):198-221. doi:10.2307/3644339

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other isolation indices: anthopolos, bemanian_beyer, lieberson, morgan_massey

Interaction indices: bell, morgan_denton

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.
  
  # Correlation Ratio (a measure of exposure) 
  ## of Black populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  white(
    geo_large = 'county',
    geo_small = 'tract', 
    state = 'GA',
    year = 2020, 
    subgroup = c('NHoLB', 'HoLB')
   )
  

## End(Not run)

An index of spatial proximity based on White (1986) and Blau (1977)

Description

Compute an index of spatial proximity (White) of a selected racial or ethnic subgroup(s) and U.S. geographies.

Usage

white_blau(
  geo_large = "county",
  geo_small = "tract",
  year = 2020,
  subgroup,
  subgroup_ref,
  crs = "ESRI:102008",
  omit_NAs = TRUE,
  quiet = FALSE,
  ...
)

Arguments

geo_large

Character string specifying the larger geographical unit of the data. The default is counties geo_large = 'county'.

geo_small

Character string specifying the smaller geographical unit of the data. The default is census tracts geo_small = 'tract'.

year

Numeric. The year to compute the estimate. The default is 2020, and the years 2009 onward are currently available.

subgroup

Character string specifying the racial or ethnic subgroup(s) as the comparison population. See Details for available choices.

subgroup_ref

Character string specifying the racial or ethnic subgroup(s) as the reference population. See Details for available choices.

crs

Numeric or character string specifying the coordinate reference system to compute the distance-based metric. The default is Albers North America crs = 'ESRI:102008'.

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.

quiet

Logical. If TRUE, will display messages about potential missing census information. The default is FALSE.

...

Arguments passed to get_acs to select state, county, and other arguments for census characteristics

Details

This function will compute an index of spatial proximity (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 SP for any of the U.S. Census Bureau race or ethnicity subgroups (including Hispanic and non-Hispanic individuals).

The function uses the 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 geo_large = 'cbsa' and 2011 onward for geo_large = 'place', geo_large = 'csa', or 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:

Use the internal state and county arguments within the get_acs function to specify geographic extent of the data output.

SP is a measure of clustering of racial or ethnic populations within smaller geographical units that are located within larger geographical units. 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 (0.6*a_{i})^{0.5} where a_{i} is the area (square kilometers) of smaller geographical unit i as defined by White (1983) doi:10.1086/227768.

Larger geographical units available include states geo_large = 'state', counties geo_large = 'county', census tracts geo_large = 'tract', census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', and metropolitan divisions geo_large = 'metro'. Smaller geographical units available include, counties geo_small = 'county', census tracts geo_small = 'tract', and census block groups geo_small = 'cbg'. If a larger geographical unit is comprised of only one smaller geographical unit (e.g., a U.S county contains only one census tract), then the SP value returned is NA. If the larger geographical unit is census-designated places geo_large = 'place', core-based statistical areas geo_large = 'cbsa', combined statistical areas geo_large = 'csa', or metropolitan divisions geo_large = 'metro', only the smaller geographical units completely within a larger geographical unit are considered in the V computation (see internal st_within function for more information) and recommend specifying all states within which the interested larger geographical unit are located using the internal state argument to ensure all appropriate smaller geographical units are included in the SP computation.

Value

An object of class 'list'. This is a named list with the following components:

sp

An object of class 'tbl' for the GEOID, name, and SP at specified larger census geographies.

sp_data

An object of class 'tbl' for the raw census values at specified smaller census geographies.

missing

An object of class 'tbl' of the count and proportion of missingness for each census variable used to compute SP.

References

White, MJ (1986) Segregation and Diversity Measures in Population Distribution. Population Index, 52(2):198-221. doi:10.2307/3644339

Blau, PM (1977) Inequality and Heterogeneity: A Primitive Theory of Social Structure. Free Press. ISBN-13:978-0-029-03660-0

Massey, DS, & Denton, NA (1988) The Dimensions of Residential Segregation. Social Forces, 67(1):281-315. doi:10.1093/sf/67.2.281

See Also

get_acs for additional arguments for geographic extent selection (i.e., state and county).

Other proximity measures: morgan_denton, morgan_massey

Relative Clustering: denton

Absolute Clustering: massey

Examples

## Not run: 
# Wrapped in \dontrun{} because these examples require a Census API key.

  # Index of spatial proximity (a measure of clustering)
  ## of non-Hispanic Black vs. non-Hispanic white populations
  ## in census tracts within counties of Georgia, U.S.A. (2020)
  white_blau(
    geo_large = 'county',
    geo_small = 'tract',
    state = 'GA',
    year = 2020,
    subgroup = 'NHoLB',
    subgroup_ref = 'NHoLW'
   )


## End(Not run)