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1. Neighborhood Deprivation Indices

Ian D. Buller (GitHub: @idblr)

2025-08-29

Start with the necessary packages for the vignette.

loadedPackages <- c('dplyr', 'ggplot2', 'ndi', 'sf', 'tidycensus', 'tigris')
invisible(lapply(loadedPackages, library, character.only = TRUE))
options(tigris_use_cache = TRUE)

Set your U.S. Census Bureau access key. Follow this link to obtain one. Specify your access key in the messer() or powell_wiley() functions using the key argument of the get_acs() function from the tidycensus package called within each or by using the census_api_key() function from the tidycensus package before running the messer() or powell_wiley() functions (see an example of the latter below).

census_api_key('...') # INSERT YOUR OWN KEY FROM U.S. CENSUS API

Compute NDI (Messer)

Compute the NDI (Messer) values (2006-2010 5-year ACS) for Georgia, U.S.A., census tracts. This metric is based on Messer et al. (2006) with the following socio-economic status (SES) variables:

Characteristic SES dimension ACS table source Description
OCC Occupation C24030 Percent males in management, science, and arts occupation
CWD Housing B25014 Percent of crowded housing
POV Poverty B17017 Percent of households in poverty
FHH Poverty B25115 Percent of female headed households with dependents
PUB Poverty B19058 Percent of households on public assistance
U30 Poverty B19001 Percent households earning <$30,000 per year
EDU Education B06009 Percent earning less than a high school education
EMP Employment B23001 (2010 only); B23025 (2011 onward) Percent unemployed 
messer2010GA <- messer(state = 'GA', year = 2010, round_output = TRUE)

One output from the messer() function is a tibble containing the identification, geographic name, NDI (Messer) values, and raw census characteristics for each tract.

messer2010GA$ndi
## # A tibble: 1,969 × 14
##    GEOID state county tract     NDI NDIQuart   OCC   CWD   POV   FHH   PUB   U30
##    <chr> <chr> <chr>  <chr>   <dbl> <fct>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 1300… Geor… Appli… 9501  -0.0075 2-Below…     0   0     0.1   0.1   0.1   0.3
##  2 1300… Geor… Appli… 9502   0.0458 4-Most …     0   0     0.3   0.1   0.2   0.5
##  3 1300… Geor… Appli… 9503   0.0269 3-Above…     0   0     0.2   0     0.2   0.4
##  4 1300… Geor… Appli… 9504  -0.0083 2-Below…     0   0     0.1   0     0.1   0.3
##  5 1300… Geor… Appli… 9505   0.0231 3-Above…     0   0     0.2   0     0.2   0.4
##  6 1300… Geor… Atkin… 9601   0.0619 4-Most …     0   0.1   0.2   0.2   0.2   0.5
##  7 1300… Geor… Atkin… 9602   0.0593 4-Most …     0   0.1   0.3   0.1   0.2   0.4
##  8 1300… Geor… Atkin… 9603   0.0252 3-Above…     0   0     0.3   0.1   0.2   0.4
##  9 1300… Geor… Bacon… 9701   0.0061 3-Above…     0   0     0.2   0     0.2   0.4
## 10 1300… Geor… Bacon… 9702…  0.0121 3-Above…     0   0     0.2   0.1   0.1   0.5
## # ℹ 1,959 more rows
## # ℹ 2 more variables: EDU <dbl>, EMP <dbl>

A second output from the messer() function is the results from the principal component analysis used to compute the NDI (Messer) values.

messer2010GA$pca
## Principal Components Analysis
## Call: psych::principal(r = ndi_data_pca, nfactors = 1, n.obs = nrow(ndi_data_pca), 
##     covar = FALSE, scores = TRUE, missing = imp)
## Standardized loadings (pattern matrix) based upon correlation matrix
##       PC1   h2   u2 com
## OCC -0.59 0.35 0.65   1
## CWD  0.47 0.22 0.78   1
## POV  0.87 0.76 0.24   1
## FHH  0.67 0.45 0.55   1
## PUB  0.89 0.79 0.21   1
## U30  0.90 0.81 0.19   1
## EDU  0.79 0.62 0.38   1
## EMP  0.46 0.21 0.79   1
## 
##                 PC1
## SS loadings    4.21
## Proportion Var 0.53
## 
## Mean item complexity =  1
## Test of the hypothesis that 1 component is sufficient.
## 
## The root mean square of the residuals (RMSR) is  0.11 
##  with the empirical chi square  1221.09  with prob <  2.3e-246 
## 
## Fit based upon off diagonal values = 0.95

A third output from the messer() function is a tibble containing a breakdown of the missingness of the census characteristics used to compute the NDI (Messer) values.

messer2010GA$missing
## # A tibble: 8 × 4
##   variable total n_missing percent_missing
##   <chr>    <int>     <int> <chr>          
## 1 CWD       1969        14 0.71 %         
## 2 EDU       1969        13 0.66 %         
## 3 EMP       1969        13 0.66 %         
## 4 FHH       1969        14 0.71 %         
## 5 OCC       1969        15 0.76 %         
## 6 POV       1969        14 0.71 %         
## 7 PUB       1969        14 0.71 %         
## 8 U30       1969        14 0.71 %

We can visualize the NDI (Messer) values geographically by linking them to spatial information from the tigris package and plotting with the ggplot2 package suite.

# Obtain the 2010 counties from the 'tigris' package
county2010GA <- counties(state = 'GA', year = 2010, cb = TRUE)
# Remove first 9 characters from GEOID for compatibility with tigris information
county2010GA$GEOID <- substring(county2010GA$GEO_ID, 10) 

# Obtain the 2010 census tracts from the 'tigris' package
tract2010GA <- tracts(state = 'GA', year = 2010, cb = TRUE)
# Remove first 9 characters from GEOID for compatibility with tigris information
tract2010GA$GEOID <- substring(tract2010GA$GEO_ID, 10) 

# Join the NDI (Messer) values to the census tract geometry
GA2010messer <- tract2010GA %>%
  left_join(messer2010GA$ndi, by = 'GEOID')
# Visualize the NDI (Messer) values (2006-2010 5-year ACS) for Georgia, U.S.A., census tracts 
## Continuous Index
ggplot() +
  geom_sf(
    data = GA2010messer,
    aes(fill = NDI),
    size = 0.05,
    color = 'transparent'
  ) +
  geom_sf(
    data = county2010GA,
    fill = 'transparent',
    color = 'white',
    size = 0.10
  ) +
  theme_minimal() +
  scale_fill_viridis_c() +
  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Messer)',
    subtitle = 'GA census tracts as the referent'
  )

## Categorical Index
### Rename '9-NDI not avail' level as NA for plotting
GA2010messer$NDIQuartNA <-
  factor(
    replace(
      as.character(GA2010messer$NDIQuart),
      GA2010messer$NDIQuart == '9-NDI not avail',
      NA
    ),
    c(levels(GA2010messer$NDIQuart)[-5], NA)
  )

ggplot() +
  geom_sf(
    data = GA2010messer,
    aes(fill = NDIQuartNA),
    size = 0.05,
    color = 'transparent'
  ) +
  geom_sf(
    data = county2010GA,
    fill = 'transparent',
    color = 'white',
    size = 0.10
  ) +
  theme_minimal() +
  scale_fill_viridis_d(guide = guide_legend(reverse = TRUE), na.value = 'grey80') +
  labs(fill = 'Index (Categorical)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Messer) Quartiles',
    subtitle = 'GA census tracts as the referent'
  )

The results above are at the tract level. The NDI (Messer) values can also be calculated at the county level.

messer2010GA_county <- messer(geo = 'county', state = 'GA', year = 2010)

# Join the NDI (Messer) values to the county geometry
GA2010messer_county <- county2010GA %>%
  left_join(messer2010GA_county$ndi, by = 'GEOID')
# Visualize the NDI (Messer) values (2006-2010 5-year ACS) for Georgia, U.S.A., counties
## Continuous Index
ggplot() +
  geom_sf(
    data = GA2010messer_county,
    aes(fill = NDI),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_c() +
  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Messer)',
    subtitle = 'GA counties as the referent'
  )

## Categorical Index
### Rename '9-NDI not avail' level as NA for plotting
GA2010messer_county$NDIQuartNA <-
  factor(
    replace(
      as.character(GA2010messer_county$NDIQuart),
      GA2010messer_county$NDIQuart == '9-NDI not avail',
      NA
    ),
    c(levels(GA2010messer_county$NDIQuart)[-5], NA)
  )

ggplot() +
  geom_sf(
    data = GA2010messer_county,
    aes(fill = NDIQuartNA),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_d(guide = guide_legend(reverse = TRUE), na.value = 'grey80') +
  labs(fill = 'Index (Categorical)', caption = 'Source: U.S. Census ACS 2006-2010 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Messer) Quartiles',
    subtitle = 'GA counties as the referent'
  )

Compute NDI (Powell-Wiley)

Compute the NDI (Powell-Wiley) values (2016-2020 5-year ACS) for Maryland, Virginia, Washington, D.C., and West Virginia, U.S.A., census tracts. This metric is based on Andrews et al. (2020) and Slotman et al. (2022) with socio-economic status (SES) variables chosen by Roux and Mair (2010):

Characteristic SES dimension ACS table source Description
MedHHInc Wealth and income B19013 Median household income (dollars)
PctRecvIDR Wealth and income B19054 Percent of households receiving dividends, interest, or rental income
PctPubAsst Wealth and income B19058 Percent of households receiving public assistance
MedHomeVal Wealth and income B25077 Median home value (dollars)
PctMgmtBusSciArt Occupation C24060 Percent in a management, business, science, or arts occupation
PctFemHeadKids Occupation B11005 Percent of households that are female headed with any children under 18 years
PctOwnerOcc Housing conditions DP04 Percent of housing units that are owner occupied
PctNoPhone Housing conditions DP04 Percent of households without a telephone
PctNComPlmb Housing conditions DP04 Percent of households without complete plumbing facilities
PctEducHSPlus Education S1501 Percent with a high school degree or higher (population 25 years and over)
PctEducBchPlus Education S1501 Percent with a college degree or higher (population 25 years and over)
PctFamBelowPov Wealth and income S1702 Percent of families with incomes below the poverty level
PctUnempl Occupation S2301 Percent unemployed

More information about the codebook and computation of the NDI (Powell-Wiley) can be found on a GIS Portal for Cancer Research website.

powell_wiley2020DMVW <- powell_wiley(
  state = c('DC', 'MD', 'VA', 'WV'),
  year = 2020,
  round_output = TRUE
)

One output from the powell_wiley() function is a tibble containing the identification, geographic name, NDI (Powell-Wiley) values, and raw census characteristics for each tract.

powell_wiley2020DMVW$ndi
## # A tibble: 4,425 × 20
##    GEOID       state  county tract   NDI NDIQuint MedHHInc PctRecvIDR PctPubAsst
##    <chr>       <chr>  <chr>  <chr> <dbl> <fct>       <dbl>      <dbl>      <dbl>
##  1 11001000101 Distr… Distr… 1.01  -2.13 1-Least…   187839       50.9        0.8
##  2 11001000102 Distr… Distr… 1.02  -2.46 1-Least…   184167       52.2        0.6
##  3 11001000201 Distr… Distr… 2.01  NA    9-NDI n…       NA      NaN        NaN  
##  4 11001000202 Distr… Distr… 2.02  -2.30 1-Least…   164261       49.6        0.9
##  5 11001000300 Distr… Distr… 3     -2.06 1-Least…   156483       46          0.6
##  6 11001000400 Distr… Distr… 4     -2.09 1-Least…   153397       47.8        0  
##  7 11001000501 Distr… Distr… 5.01  -2.11 1-Least…   119911       44.5        0.8
##  8 11001000502 Distr… Distr… 5.02  -2.21 1-Least…   153264       46.8        0.5
##  9 11001000600 Distr… Distr… 6     -2.16 1-Least…   154266       60.8        7.4
## 10 11001000702 Distr… Distr… 7.02  -1.20 1-Least…    71747       22.9        0  
## # ℹ 4,415 more rows
## # ℹ 11 more variables: MedHomeVal <dbl>, PctMgmtBusScArti <dbl>,
## #   PctFemHeadKids <dbl>, PctOwnerOcc <dbl>, PctNoPhone <dbl>,
## #   PctNComPlmb <dbl>, PctEducHSPlus <dbl>, PctEducBchPlus <dbl>,
## #   PctFamBelowPov <dbl>, PctUnempl <dbl>, TotalPop <dbl>

A second output from the powell_wiley() function is the results from the principal component analysis used to compute the NDI (Powell-Wiley) values.

powell_wiley2020DMVW$pca
## $loadings
## 
## Loadings:
##                 PC1    PC2    PC3   
## logMedHHInc     -0.638 -0.364       
## PctNoIDRZ        0.612  0.319       
## PctPubAsstZ      0.379  0.615       
## logMedHomeVal   -0.893              
## PctWorkClassZ    0.974              
## PctFemHeadKidsZ  0.128  0.697 -0.233
## PctNotOwnerOccZ -0.375  0.923       
## PctNoPhoneZ             0.329  0.524
## PctNComPlmbZ           -0.141  0.869
## PctEducLTHSZ     0.642  0.164       
## PctEducLTBchZ    1.020 -0.121       
## PctFamBelowPovZ  0.219  0.698       
## PctUnemplZ              0.596       
## 
##                  PC1   PC2   PC3
## SS loadings    4.340 2.971 1.102
## Proportion Var 0.334 0.229 0.085
## Cumulative Var 0.334 0.562 0.647
## 
## $rotmat
##             [,1]       [,2]       [,3]
## [1,]  0.68516447  0.4403017 0.04517229
## [2,] -0.95446519  1.0806634 0.03196818
## [3,] -0.09078904 -0.2028145 1.02053725
## 
## $rotation
## [1] "promax"
## 
## $Phi
##           [,1]      [,2]      [,3]
## [1,] 1.0000000 0.5250277 0.1649550
## [2,] 0.5250277 1.0000000 0.1923867
## [3,] 0.1649550 0.1923867 1.0000000
## 
## $Structure
##             [,1]        [,2]        [,3]
##  [1,] -0.8432264 -0.71542812 -0.26037289
##  [2,]  0.7750210  0.63477178  0.13357439
##  [3,]  0.7001489  0.81237803  0.17181801
##  [4,] -0.8931597 -0.46211477 -0.19777858
##  [5,]  0.9253217  0.42037509  0.12424330
##  [6,]  0.4559503  0.71962940 -0.07780352
##  [7,]  0.1195748  0.73799969  0.17788881
##  [8,]  0.2552300  0.42753150  0.58693047
##  [9,]  0.1155170  0.05008712  0.84948765
## [10,]  0.7325510  0.50620197  0.16403994
## [11,]  0.9557341  0.41335682  0.13787215
## [12,]  0.5881663  0.81650181  0.18717861
## [13,]  0.3841044  0.63036827  0.09925687
## 
## $communality
##  [1] 0.5468870 0.4774810 0.5220533 0.8012746 0.9576793 0.5566938 0.9968490
##  [8] 0.3829550 0.7774209 0.4398519 1.0560478 0.5359573 0.3616523
## 
## $uniqueness
##     logMedHHInc       PctNoIDRZ     PctPubAsstZ   logMedHomeVal   PctWorkClassZ 
##     0.453113047     0.522518997     0.477946655     0.198725386     0.042320711 
## PctFemHeadKidsZ PctNotOwnerOccZ     PctNoPhoneZ    PctNComPlmbZ    PctEducLTHSZ 
##     0.443306153     0.003150984     0.617045044     0.222579117     0.560148142 
##   PctEducLTBchZ PctFamBelowPovZ      PctUnemplZ 
##    -0.056047809     0.464042693     0.638347726 
## 
## $Vaccounted
##                            [,1]      [,2]       [,3]
## SS loadings           4.5987837 3.2131788 1.10386926
## Proportion Var        0.3537526 0.2471676 0.08491302
## Cumulative Var        0.3537526 0.6009202 0.68583321
## Proportion Explained  0.5157997 0.3603902 0.12381002
## Cumulative Proportion 0.5157997 0.8761900 1.00000000

A third output from the powell_wiley() function is a tibble containing a breakdown of the missingness of the census characteristics used to compute the NDI (Powell-Wiley) values.

powell_wiley2020DMVW$missing
## # A tibble: 13 × 4
##    variable        total n_missing percent_missing
##    <chr>           <int>     <int> <chr>          
##  1 PctEducLTBchZ    4425        47 1.06 %         
##  2 PctEducLTHSZ     4425        47 1.06 %         
##  3 PctFamBelowPovZ  4425        63 1.42 %         
##  4 PctFemHeadKidsZ  4425        60 1.36 %         
##  5 PctNComPlmbZ     4425        60 1.36 %         
##  6 PctNoIDRZ        4425        60 1.36 %         
##  7 PctNoPhoneZ      4425        60 1.36 %         
##  8 PctNotOwnerOccZ  4425        60 1.36 %         
##  9 PctPubAsstZ      4425        60 1.36 %         
## 10 PctUnemplZ       4425        57 1.29 %         
## 11 PctWorkClassZ    4425        57 1.29 %         
## 12 logMedHHInc      4425        73 1.65 %         
## 13 logMedHomeVal    4425       148 3.34 %

A fourth output from the powell_wiley() function is a character string or numeric value of a standardized Cronbach’s alpha. A value greater than 0.7 is desired.

powell_wiley2020DMVW$cronbach
## [1] 0.9321693

We can visualize the NDI (Powell-Wiley) values geographically by linking them to spatial information from the tigris package and plotting with the ggplot2 package suite.

# Obtain the 2020 counties from the 'tigris' package
county2020 <- counties(cb = TRUE)
county2020DMVW <- county2020[county2020$STUSPS %in% c('DC', 'MD', 'VA', 'WV'), ]

# Obtain the 2020 census tracts from the 'tigris' package
tract2020D <- tracts(state = 'DC', year = 2020, cb = TRUE)
tract2020M <- tracts(state = 'MD', year = 2020, cb = TRUE)
tract2020V <- tracts(state = 'VA', year = 2020, cb = TRUE)
tract2020W <- tracts(state = 'WV', year = 2020, cb = TRUE)
tracts2020DMVW <- rbind(tract2020D, tract2020M, tract2020V, tract2020W)

# Join the NDI (Powell-Wiley) values to the census tract geometry
DMVW2020pw <- tracts2020DMVW %>%
  left_join(powell_wiley2020DMVW$ndi, by = 'GEOID')
# Visualize the NDI (Powell-Wiley) values (2016-2020 5-year ACS) 
## Maryland, Virginia, Washington, D.C., and West Virginia, U.S.A., census tracts 
## Continuous Index
ggplot() +
  geom_sf(
    data = DMVW2020pw,
    aes(fill = NDI),
    color = NA
  ) +
  geom_sf(
    data = county2020DMVW,
    fill = 'transparent',
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_c(na.value = 'grey80') +
  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley)',
    subtitle = 'DC, MD, VA, and WV tracts as the referent'
  )

## Categorical Index (Population-weighted quintiles)
### Rename '9-NDI not avail' level as NA for plotting
DMVW2020pw$NDIQuintNA <-
  factor(replace(
    as.character(DMVW2020pw$NDIQuint),
    DMVW2020pw$NDIQuint == '9-NDI not avail',
    NA
  ),
  c(levels(DMVW2020pw$NDIQuint)[-6], NA))

ggplot() +
  geom_sf(data = DMVW2020pw, aes(fill = NDIQuintNA), color = NA) +
  geom_sf(data = county2020DMVW, fill = 'transparent', color = 'white') +
  theme_minimal() +
  scale_fill_viridis_d(guide = guide_legend(reverse = TRUE), na.value = 'grey80') +
  labs(fill = 'Index (Categorical)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley) Population-weighted Quintiles',
    subtitle = 'DC, MD, VA, and WV tracts as the referent'
  )

Like the NDI (Messer), we also compute county-level NDI (Powell-Wiley).

# Obtain the 2020 counties from the 'tigris' package
county2020DMVW <- counties(state = c('DC', 'MD', 'VA', 'WV'), year = 2020, cb = TRUE)

# NDI (Powell-Wiley) at the county level (2016-2020)
powell_wiley2020DMVW_county <- powell_wiley(
  geo = 'county',
  state = c('DC', 'MD', 'VA', 'WV'),
  year = 2020
)

# Join the NDI (Powell-Wiley) values to the county geometry
DMVW2020pw_county <- county2020DMVW %>%
  left_join(powell_wiley2020DMVW_county$ndi, by = 'GEOID')
# Visualize the NDI (Powell-Wiley) values (2016-2020 5-year ACS)
## Maryland, Virginia, Washington, D.C., and West Virginia, U.S.A., counties
## Continuous Index
ggplot() +
  geom_sf(
    data = DMVW2020pw_county,
    aes(fill = NDI),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_c() +
  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley)',
    subtitle = 'DC, MD, VA, and WV counties as the referent'
  )

## Categorical Index
### Rename '9-NDI not avail' level as NA for plotting
DMVW2020pw_county$NDIQuintNA <-
  factor(
    replace(
      as.character(DMVW2020pw_county$NDIQuint),
      DMVW2020pw_county$NDIQuint == '9-NDI not avail',
      NA
    ),
    c(levels(DMVW2020pw_county$NDIQuint)[-6], NA)
  )

ggplot() +
  geom_sf(
    data = DMVW2020pw_county,
    aes(fill = NDIQuint),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_d(guide = guide_legend(reverse = TRUE), na.value = 'grey80') +
  labs(fill = 'Index (Categorical)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley) Population-weighted Quintiles',
    subtitle = 'DC, MD, VA, and WV counties as the referent'
  )

Advanced Features

Imputing missing census variables

In the messer() and powell_wiley() functions, missing census characteristics can be imputed using the missing and impute arguments of the pca() function in the psych package called within the messer() and powell_wiley() functions. Impute values using the logical imp argument (currently only calls impute = 'median' by default, which assigns the median values of each missing census variable for a geography).

powell_wiley2020DC <- powell_wiley(state = 'DC', year = 2020) # without imputation
powell_wiley2020DCi <- powell_wiley(state = 'DC', year = 2020, imp = TRUE) # with imputation

table(is.na(powell_wiley2020DC$ndi$NDI)) # n=13 tracts without NDI (Powell-Wiley) values
table(is.na(powell_wiley2020DCi$ndi$NDI)) # n=0 tracts without NDI (Powell-Wiley) values

# Obtain the 2020 census tracts from the 'tigris' package
tract2020DC <- tracts(state = 'DC', year = 2020, cb = TRUE)

# Join the NDI (Powell-Wiley) values to the census tract geometry
DC2020pw <- tract2020DC %>%
  left_join(powell_wiley2020DC$ndi, by = 'GEOID')
DC2020pw <- DC2020pw %>%
  left_join(powell_wiley2020DCi$ndi, by = 'GEOID', suffix = c('_nonimp', '_imp'))
# Visualize the NDI (Powell-Wiley) values (2016-2020 5-year ACS) for 
## Washington, D.C., census tracts
## Continuous Index
ggplot() +
  geom_sf(
    data = DC2020pw,
    aes(fill = NDI_nonimp),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_c() +
  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley), Non-Imputed',
    subtitle = 'DC census tracts as the referent'
  )

ggplot() +
  geom_sf(
    data = DC2020pw,
    aes(fill = NDI_imp),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_c() +
  labs(fill = 'Index (Continuous)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley), Imputed',
    subtitle = 'DC census tracts as the referent'
  )

## Categorical Index
### Rename '9-NDI not avail' level as NA for plotting
DC2020pw$NDIQuintNA_nonimp <-
  factor(
    replace(
      as.character(DC2020pw$NDIQuint_nonimp),
      DC2020pw$NDIQuint_nonimp == '9-NDI not avail',
      NA
    ),
    c(levels(DC2020pw$NDIQuint_nonimp)[-6], NA)
  )

ggplot() +
  geom_sf(
    data = DC2020pw,
    aes(fill = NDIQuintNA_nonimp),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_d(guide = guide_legend(reverse = TRUE), na.value = 'grey80') +
  labs(fill = 'Index (Categorical)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley) Quintiles, Non-Imputed',
    subtitle = 'DC census tracts as the referent'
  )

### Rename '9-NDI not avail' level as NA for plotting
DC2020pw$NDIQuintNA_imp <-
  factor(
    replace(
      as.character(DC2020pw$NDIQuint_imp),
      DC2020pw$NDIQuint_imp == '9-NDI not avail',
      NA
    ),
    c(levels(DC2020pw$NDIQuint_imp)[-6], NA)
  )

ggplot() +
  geom_sf(
    data = DC2020pw,
    aes(fill = NDIQuintNA_imp),
    size = 0.10,
    color = 'white'
  ) +
  theme_minimal() +
  scale_fill_viridis_d(guide = guide_legend(reverse = TRUE), na.value = 'grey80') +
  labs(fill = 'Index (Categorical)', caption = 'Source: U.S. Census ACS 2016-2020 estimates') +
  ggtitle(
    'Neighborhood Deprivation Index (Powell-Wiley) Quintiles, Imputed',
    subtitle = 'DC census tracts as the referent'
  )

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

To conduct a contiguous US-standardized index, compute an NDI for all states as in the example below that replicates the nationally standardized NDI (Powell-Wiley) values (2013-2017 ACS-5) found in Slotman et al. (2022) and available from a GIS Portal for Cancer Research website. To replicate the nationally standardized NDI (Powell-Wiley) values (2006-2010 ACS-5) found in Andrews et al. (2020) change the year argument to 2010 (i.e., year = 2010).

us <- states()
n51 <- c(
  'Commonwealth of the Northern Mariana Islands',
  'Guam',
  'American Samoa',
  'Puerto Rico',
  'United States Virgin Islands'
)
y51 <- us$STUSPS[!(us$NAME %in% n51)]

start_time <- Sys.time() # record start time
powell_wiley2017US <- powell_wiley(state = y51, year = 2017)
end_time <- Sys.time() # record end time
time_srr <- end_time - start_time # Calculate run time
ggplot(powell_wiley2017US$ndi, aes(x = NDI)) +
  geom_histogram(color = 'black', fill = 'white') +
  theme_minimal() +
  ggtitle(
    'Histogram of US-standardized NDI (Powell-Wiley) values (2013-2017)',
    subtitle = 'U.S. census tracts as the referent (including AK, HI, and DC)'
  )

The process to compute a US-standardized NDI (Powell-Wiley) took about 12.7 minutes to run on a machine with the features listed at the end of the vignette.

sessionInfo()
## R version 4.5.1 (2025-06-13 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26100)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] tigris_2.2.1     tidycensus_1.7.3 sf_1.0-21        ndi_0.2.0       
## [5] ggplot2_3.5.2    dplyr_1.1.4      knitr_1.50      
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1   psych_2.5.6        viridisLite_0.4.2  farver_2.1.2      
##  [5] fastmap_1.2.0      digest_0.6.37      rpart_4.1.24       lifecycle_1.0.4   
##  [9] cluster_2.1.8.1    Cairo_1.6-2        magrittr_2.0.3     compiler_4.5.1    
## [13] rlang_1.1.6        Hmisc_5.2-3        sass_0.4.10        tools_4.5.1       
## [17] utf8_1.2.6         yaml_2.3.10        data.table_1.17.8  labeling_0.4.3    
## [21] htmlwidgets_1.6.4  classInt_0.4-11    mnormt_2.1.1       curl_6.4.0        
## [25] xml2_1.3.8         RColorBrewer_1.1-3 abind_1.4-8        KernSmooth_2.23-26
## [29] withr_3.0.2        foreign_0.8-90     purrr_1.1.0        nnet_7.3-20       
## [33] grid_4.5.1         e1071_1.7-16       colorspace_2.1-1   scales_1.4.0      
## [37] MASS_7.3-65        cli_3.6.5          rmarkdown_2.29     crayon_1.5.3      
## [41] generics_0.1.4     rstudioapi_0.17.1  httr_1.4.7         tzdb_0.5.0        
## [45] DBI_1.2.3          cachem_1.1.0       proxy_0.4-27       stringr_1.5.1     
## [49] rvest_1.0.4        parallel_4.5.1     base64enc_0.1-3    vctrs_0.6.5       
## [53] Matrix_1.7-3       jsonlite_2.0.0     carData_3.0-5      car_3.1-3         
## [57] hms_1.1.3          Formula_1.2-5      htmlTable_2.4.3    jquerylib_0.1.4   
## [61] tidyr_1.3.1        units_0.8-7        glue_1.8.0         stringi_1.8.7     
## [65] gtable_0.3.6       tibble_3.3.0       pillar_1.11.0      rappdirs_0.3.3    
## [69] htmltools_0.5.8.1  R6_2.6.1           evaluate_1.0.5     lattice_0.22-7    
## [73] readr_2.1.5        backports_1.5.0    bslib_0.9.0        class_7.3-23      
## [77] Rcpp_1.1.0         uuid_1.2-1         gridExtra_2.3      nlme_3.1-168      
## [81] checkmate_2.3.2    xfun_0.52          pkgconfig_2.0.3

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.