messer {ndi}

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:

• OCC (C24030): percent males in management, science, and arts occupation

• CWD (B25014): percent of crowded housing

• POV (B17017): percent of households in poverty

• FHH (B25115): percent of female headed households with dependents

• PUB (B19058): percent of households on public assistance

• U30 (B19001): percent of households earning <$30,000 per year

• EDU (B06009): percent earning less than a high school education

• EMP (B23025): percent unemployed (2011 onward)

• EMP (B23001): percent unemployed (2010 only)

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.

See Also

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

Examples

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

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

  # Impute NDI for tracts (2020) with missing census information (median values)
  messer(state = "tract", "GA", year = 2020, imp = TRUE)
  
  # County-level metric (2020)
  messer(geo = "county", state = "GA", year = 2020)
  

## End(Not run)