2. Racial or Ethnic Residential Segregation Indices
Ian D. Buller (GitHub: @idblr)
-2024-08-30
+2024-08-31
@@ -409,12 +409,10 @@Racial or Ethnic Residential Segregation Indices
hoover() function that computes Delta (DEL)
based on Hoover
(1941) and Duncan, Cuzzort, & Duncan (1961; LC:60007089)massey() function that compute Absolute Clustering
-(ACL) based on Massey & Denton
-(1988)denton() function that compute Relative Clustering
-(RCL) based on Massey & Denton
-(1988)massey_duncan() function that computes Absolute
+Concentration (ACO) based on Massey & Denton
+(1988) and Duncan, Cuzzort, & Duncan (1961; LC:60007089)Centralization:
-
@@ -429,11 +427,15 @@
Racial or Ethnic Residential Segregation Indices
Clustering:
-
-
- Absolute Clustering (Planned) +
massey()function that compute Absolute Clustering +(ACL) based on Massey & Denton +(1988)white_blau()function that computes an index of spatial proximity (SP) based on White (1986) and Blau (1977; ISBN-13:978-0-029-03660-0)
-- Relative Clustering (Planned) +
denton()function that compute Relative Clustering +(RCL) based on Massey & Denton +(1988)- Distance Decay Interaction (Planned)
- Distance Decay Isolation (Planned)
Compute Delta (DEL)
)
-
Compute Absolute Clustering (ACL)
-Compute the racial or ethnic ACL values (2014-2018 5-year -ACS) for census block groups within census tracts of Harris County, TX. +
+
-
+
-Compute Absolute Concentration (ACO)
+Compute the racial or ethnic ACO values (2015-2019 5-year +ACS) for census tracts within core-based statistical areas of Wisconsin. This metric is based on Massey & Denton -(1988). ACL is a measure of clustering of racial or ethnic +(1988) and Duncan, Cuzzort, & Duncan (1961; LC:60007089). +ACO is a measure of concentration 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.
-massey2018HTX <- massey(
- geo_large = 'tract',
- geo_small = 'cbg',
- state = 'TX',
- county = 'Harris County',
- year = 2018,
- subgroup = c('NHoLB', 'HoLB')
-)
-
-# Obtain the 2018 census tracts in Harris County, TX, from the 'tigris' package
-tract2018 <- tracts(year = 2018, state = 'TX')
-# Obtain the 2018 Texas counties from the 'tigris' package
-county2018 <- counties(state = 'TX', year = 2018, cb = TRUE)
-
-# Join the ACL values to the census tract geometries and filter for Harris County, TX
-HTX2010massey <- tract2018 %>%
- left_join(massey2018HTX$acl, by = 'GEOID') %>%
- filter(!st_is_empty(.)) %>%
- filter(!is.na(ACL)) %>%
- st_filter(county2018 %>% filter(NAME == 'Harris')) %>%
- st_make_valid()# Visualize the ACL values (2013-2017 5-year ACS) for census block groups within census tracts of Harris County, TX
+larger geographical units. ACO can range from 0 to 1 and
+represents the relative amount of physical space occupied by a racial or
+ethnic subgroup in a larger geographical unit. A value of 1 indicates
+that a racial or ethnic subgroup has achieved the maximum spatial
+concentration possible (all racial or ethnic subgroup members live in
+the smallest of the smaller geographical units). A value of 0 indicates
+the maximum deconcentration possible (all racial or ethnic subgroup
+members live in the largest of the smaller geographical units).
+massey_duncan2019WI <- massey_duncan(
+ geo_large = 'cbsa',
+ geo_small = 'tract',
+ state = c('WI', 'IL', 'MI', 'MN'),
+ year = 2019,
+ subgroup = c('NHoLB', 'HoLB')
+)
+
+# Obtain the 2019 census-designated places from the 'tigris' package
+cbsa2019 <- core_based_statistical_areas(year = 2019, cb = TRUE)
+# Obtain the 2019 state from the 'tigris' package
+states2019 <- states(year = 2019, cb = TRUE)
+
+# Join the ACO values to the census-designated places geometries and filter for Connecticut
+WI2019massey_duncan <- cbsa2019 %>%
+ left_join(massey_duncan2019WI$aco, by = 'GEOID') %>%
+ filter(!st_is_empty(.)) %>%
+ filter(!is.na(ACO)) %>%
+ st_filter(states2019 %>% filter(STUSPS == 'WI'), .predicate = st_within) %>%
+ st_make_valid()
+# Visualize the ACO values (2015-2019 5-year ACS) for census tracts within core-based statistical areas of Wisconsin.
ggplot() +
geom_sf(
- data = HTX2010massey,
- aes(fill = ACL)
+ data = WI2019massey_duncan,
+ aes(fill = ACO)
) +
geom_sf(
- data = county2018 %>% filter(NAME == 'Harris County'),
+ data = states2019 %>% filter(STUSPS == 'WI'),
fill = 'transparent',
color = 'black',
size = 0.2
) +
theme_minimal() +
- scale_fill_gradient2(
- low = '#998ec3',
- mid = '#f7f7f7',
- high = '#f1a340',
- midpoint = 0
- ) +
- labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2013-2017 estimates') +
- ggtitle(
- 'Absolute Clustering (Massey & Denton)\nCensus block groups within census tracts of Harris County, TX',
- subtitle = 'Black population'
- )
-
+ scale_fill_viridis_c(limits = c(0, 1)) +
+ labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2015-2019 estimates') +
+ ggtitle(
+ 'Absolute Concentration (Massey & Duncan)\ncensus tracts within core-based statistical areas of Wisconsin',
+ subtitle = 'Black population'
+ )
-
-
Compute Relative Clustering (RCL)
-Compute the racial or ethnic RCL values (2014-2018 5-year -ACS) for census block groups within census tracts of Harris County, TX. -This metric is based on Massey & Denton -(1988). 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.
-denton2018HTX <- denton(
- geo_large = 'tract',
+
+Compute Absolute Centralization (ACE)
+Compute the racial or ethnic ACE values (2013-2017 5-year
+ACS) for census block groups within census-designated places of
+Connecticut. This metric is based on Duncan, Cuzzort, & Duncan
+(1961; LC:60007089) and Massey & Denton
+(1988). 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. 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).
+Note: 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.
+duncan_cuzzort2017CT <- duncan_cuzzort(
+ geo_large = 'place',
geo_small = 'cbg',
- state = 'TX',
- county = 'Harris County',
- year = 2018,
- subgroup = 'NHoLB',
- subgroup_ref = 'NHoLW'
-)
-
-# Obtain the 2018 census tracts in Harris County, TX, from the 'tigris' package
-tract2018 <- tracts(year = 2018, state = 'TX')
-# Obtain the 2018 Texas counties from the 'tigris' package
-county2018 <- counties(state = 'TX', year = 2018, cb = TRUE)
-
-# Join the RCL values to the census tract geometries and filter for Harris County, TX
-HTX2010denton <- tract2018 %>%
- left_join(denton2018HTX$rcl, by = 'GEOID') %>%
- filter(!st_is_empty(.)) %>%
- filter(!is.na(RCL)) %>%
- st_filter(county2018 %>% filter(NAME == 'Harris')) %>%
- st_make_valid()
-# Visualize the RCL values (2013-2017 5-year ACS) for census block groups within census tracts of Harris County, TX
+ state = 'CT',
+ year = 2017,
+ subgroup = c('NHoLB', 'HoLB')
+)
+
+# Obtain the 2017 census-designated places in Connecticut from the 'tigris' package
+places2017 <- places(year = 2017, state = 'CT')
+# Obtain the 2017 state from the 'tigris' package
+states2017 <- states(year = 2017, cb = TRUE)
+
+# Join the ACE values to the census-designated places geometries and filter for Connecticut
+CT2010duncan_cuzzort <- places2017 %>%
+ left_join(duncan_cuzzort2017CT$ace, by = 'GEOID') %>%
+ filter(!st_is_empty(.)) %>%
+ filter(!is.na(ACE)) %>%
+ st_filter(states2017 %>% filter(STUSPS == 'CT')) %>%
+ st_make_valid()
+# Visualize the ACE values (2013-2017 5-year ACS) for census block groups within census-designated places of Connecticut
ggplot() +
geom_sf(
- data = HTX2010denton,
- aes(fill = RCL)
+ data = CT2010duncan_cuzzort,
+ aes(fill = ACE)
) +
geom_sf(
- data = county2018 %>% filter(NAME == 'Harris County'),
+ data = states2017 %>% filter(STUSPS == 'CT'),
fill = 'transparent',
color = 'black',
size = 0.2
@@ -1419,66 +1427,71 @@ Compute Relative Clustering (RCL)
low = '#998ec3',
mid = '#f7f7f7',
high = '#f1a340',
- midpoint = 0
- ) +
- labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2013-2017 estimates') +
- ggtitle(
- 'Relative Clustering (Massey & Denton)\nCensus block groups within census tracts of Harris County, TX',
- subtitle = 'Black non-Hispanic vs. white non-Hispanic'
- )
-
+ midpoint = 0,
+ limits = c(-1, 1)
+ ) +
+ labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2013-2017 estimates') +
+ ggtitle(
+ 'Absolute Centralization (Duncan & Cuzzort)\nCensus block groups within census-designated places of Connecticut',
+ subtitle = 'Black population'
+ )
+
-
Compute Absolute Centralization (ACE)
-Compute the racial or ethnic ACE values (2013-2017 5-year +
+
-
-Compute Relative Centralization (RCE)
+Compute the racial or ethnic RCE values (2013-2017 5-year ACS) for census block groups within census-designated places of -Connecticut. This metric is based on Duncan, Cuzzort, & Duncan -(1961; LC:60007089) and Massey & Denton -(1988). ACE is a measure of the degree to which racial or +Connecticut. This metric is based on Duncan & Duncan (1955b) +and Massey & Denton +(1988). 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. ACE can range in +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 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.
+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.Note: 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.
-duncan_cuzzort2017CT <- duncan_cuzzort(
+duncan_duncan2017CT <- duncan_duncan(
geo_large = 'place',
geo_small = 'cbg',
state = 'CT',
year = 2017,
- subgroup = c('NHoLB', 'HoLB')
-)
-
-# Obtain the 2017 census-designated places in Connecticut from the 'tigris' package
-places2017 <- places(year = 2017, state = 'CT')
-# Obtain the 2017 state from the 'tigris' package
-states2017 <- states(year = 2017, cb = TRUE)
-
-# Join the ACE values to the census-designated places geometries and filter for Connecticut
-CT2010duncan_cuzzort <- places2017 %>%
- left_join(duncan_cuzzort2017CT$ace, by = 'GEOID') %>%
- filter(!st_is_empty(.)) %>%
- filter(!is.na(ACE)) %>%
- st_filter(states2017 %>% filter(STUSPS == 'CT')) %>%
- st_make_valid()
+ subgroup = 'NHoLB',
+ subgroup_ref = 'NHoLW'
+)
+
+# Obtain the 2017 census-designated places in Connecticut from the 'tigris' package
+places2017 <- places(year = 2017, state = 'CT')
+# Obtain the 2017 state from the 'tigris' package
+states2017 <- states(year = 2017, cb = TRUE)
+
+# Join the ACE values to the census-designated places geometries and filter for Connecticut
+CT2010duncan_duncan <- places2017 %>%
+ left_join(duncan_duncan2017CT$rce, by = 'GEOID') %>%
+ filter(!st_is_empty(.)) %>%
+ filter(!is.na(RCE)) %>%
+ st_filter(states2017 %>% filter(STUSPS == 'CT')) %>%
+ st_make_valid()# Visualize the ACE values (2013-2017 5-year ACS) for census block groups within census-designated places of Connecticut
ggplot() +
geom_sf(
- data = CT2010duncan_cuzzort,
- aes(fill = ACE)
+ data = CT2010duncan_duncan,
+ aes(fill = RCE)
) +
geom_sf(
data = states2017 %>% filter(STUSPS == 'CT'),
@@ -1496,69 +1509,54 @@ Compute Absolute Centralization (ACE)
) +
labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2013-2017 estimates') +
ggtitle(
- 'Absolute Centralization (Duncan & Cuzzort)\nCensus block groups within census-designated places of Connecticut',
- subtitle = 'Black population'
+ 'Relative Centralization (Duncan & Duncan)\nCensus block groups within census-designated places of Connecticut',
+ subtitle = 'Black non-Hispanic vs. white non-Hispanic'
)
-
+
Compute Relative Centralization (RCE)
-Compute the racial or ethnic RCE values (2013-2017 5-year -ACS) for census block groups within census-designated places of -Connecticut. This metric is based on Duncan & Duncan (1955b) -and Massey & Denton -(1988). 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.
-Note: 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.
-duncan_duncan2017CT <- duncan_duncan(
- geo_large = 'place',
+
+Compute Absolute Clustering (ACL)
+Compute the racial or ethnic ACL values (2014-2018 5-year
+ACS) for census block groups within census tracts of Harris County, TX.
+This metric is based on Massey & Denton
+(1988). 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.
+massey2018HTX <- massey(
+ geo_large = 'tract',
geo_small = 'cbg',
- state = 'CT',
- year = 2017,
- subgroup = 'NHoLB',
- subgroup_ref = 'NHoLW'
+ state = 'TX',
+ county = 'Harris County',
+ year = 2018,
+ subgroup = c('NHoLB', 'HoLB')
)
-# Obtain the 2017 census-designated places in Connecticut from the 'tigris' package
-places2017 <- places(year = 2017, state = 'CT')
-# Obtain the 2017 state from the 'tigris' package
-states2017 <- states(year = 2017, cb = TRUE)
+# Obtain the 2018 census tracts in Harris County, TX, from the 'tigris' package
+tract2018 <- tracts(year = 2018, state = 'TX')
+# Obtain the 2018 Texas counties from the 'tigris' package
+county2018 <- counties(state = 'TX', year = 2018, cb = TRUE)
-# Join the ACE values to the census-designated places geometries and filter for Connecticut
-CT2010duncan_duncan <- places2017 %>%
- left_join(duncan_duncan2017CT$rce, by = 'GEOID') %>%
+# Join the ACL values to the census tract geometries and filter for Harris County, TX
+HTX2010massey <- tract2018 %>%
+ left_join(massey2018HTX$acl, by = 'GEOID') %>%
filter(!st_is_empty(.)) %>%
- filter(!is.na(RCE)) %>%
- st_filter(states2017 %>% filter(STUSPS == 'CT')) %>%
+ filter(!is.na(ACL)) %>%
+ st_filter(county2018 %>% filter(NAME == 'Harris')) %>%
st_make_valid()
-# Visualize the ACE values (2013-2017 5-year ACS) for census block groups within census-designated places of Connecticut
+# Visualize the ACL values (2013-2017 5-year ACS) for census block groups within census tracts of Harris County, TX
ggplot() +
geom_sf(
- data = CT2010duncan_duncan,
- aes(fill = RCE)
+ data = HTX2010massey,
+ aes(fill = ACL)
) +
geom_sf(
- data = states2017 %>% filter(STUSPS == 'CT'),
+ data = county2018 %>% filter(NAME == 'Harris County'),
fill = 'transparent',
color = 'black',
size = 0.2
@@ -1568,15 +1566,14 @@ Compute Relative Centralization (RCE)
low = '#998ec3',
mid = '#f7f7f7',
high = '#f1a340',
- midpoint = 0,
- limits = c(-1, 1)
- ) +
- labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2013-2017 estimates') +
- ggtitle(
- 'Relative Centralization (Duncan & Duncan)\nCensus block groups within census-designated places of Connecticut',
- subtitle = 'Black non-Hispanic vs. white non-Hispanic'
- )
-
+ midpoint = 0
+ ) +
+ labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2013-2017 estimates') +
+ ggtitle(
+ 'Absolute Clustering (Massey & Denton)\nCensus block groups within census tracts of Harris County, TX',
+ subtitle = 'Black population'
+ )
+
Compute an index of spatial proximity (SP)
@@ -1642,7 +1639,68 @@ Compute an index of spatial proximity (SP)
subtitle = 'Black non-Hispanic vs. white non-Hispanic'
)
-
+
+
+
+
Compute Relative Clustering (RCL)
+Compute the racial or ethnic RCL values (2014-2018 5-year +ACS) for census block groups within census tracts of Harris County, TX. +This metric is based on Massey & Denton +(1988). 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.
+denton2018HTX <- denton(
+ geo_large = 'tract',
+ geo_small = 'cbg',
+ state = 'TX',
+ county = 'Harris County',
+ year = 2018,
+ subgroup = 'NHoLB',
+ subgroup_ref = 'NHoLW'
+)
+
+# Obtain the 2018 census tracts in Harris County, TX, from the 'tigris' package
+tract2018 <- tracts(year = 2018, state = 'TX')
+# Obtain the 2018 Texas counties from the 'tigris' package
+county2018 <- counties(state = 'TX', year = 2018, cb = TRUE)
+
+# Join the RCL values to the census tract geometries and filter for Harris County, TX
+HTX2010denton <- tract2018 %>%
+ left_join(denton2018HTX$rcl, by = 'GEOID') %>%
+ filter(!st_is_empty(.)) %>%
+ filter(!is.na(RCL)) %>%
+ st_filter(county2018 %>% filter(NAME == 'Harris')) %>%
+ st_make_valid()# Visualize the RCL values (2013-2017 5-year ACS) for census block groups within census tracts of Harris County, TX
+ggplot() +
+ geom_sf(
+ data = HTX2010denton,
+ aes(fill = RCL)
+ ) +
+ geom_sf(
+ data = county2018 %>% filter(NAME == 'Harris County'),
+ fill = 'transparent',
+ color = 'black',
+ size = 0.2
+ ) +
+ theme_minimal() +
+ scale_fill_gradient2(
+ low = '#998ec3',
+ mid = '#f7f7f7',
+ high = '#f1a340',
+ midpoint = 0
+ ) +
+ labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2013-2017 estimates') +
+ ggtitle(
+ 'Relative Clustering (Massey & Denton)\nCensus block groups within census tracts of Harris County, TX',
+ subtitle = 'Black non-Hispanic vs. white non-Hispanic'
+ )
## R version 4.4.1 (2024-06-14 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 10 x64 (build 19045)
@@ -1664,7 +1722,7 @@ Compute an index of spatial proximity (SP)
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
-## [1] tigris_2.1 tidycensus_1.6.5 sf_1.0-16 ndi_0.1.6.9010
+## [1] tigris_2.1 tidycensus_1.6.5 sf_1.0-16 ndi_0.1.6.9011
## [5] ggplot2_3.5.1 dplyr_1.1.4 knitr_1.48
##
## loaded via a namespace (and not attached):