R build
status R-CMD-check

The goal of zctaDB is to add ZIP code-level geomarkers to your data.

Available geomarkers include

Category Variables More information
Population and racial composition total population of ZCTA
number and percentage of white non-Hispanic residents
number and percentage of black non-Hispanic residents
racial index of concentration at the extremes (ICE)
racial ICE published
Road proximity length and density of primary roads
length and density of secondary roads
DeGAUSS roads container
EJ Screen traffic proximity
ozone concentration
PM_{2.5} concentration
diesel PM concentration
respiratory hazard index
EJ Screen
Land Cover percent impervious land
percent green land
percent tree canopy
Community Deprivation fraction assisted income
fraction high school education
median household income
fraction no health insurance
fraction poverty
fraction vacant housing
deprivation index
deprivation index
NARR Cell Identifier narr cell *Note that this is based on centroids of 2010 ZCTAs, but there are alternative ways to join these spatial datasets that may be better, such as all overlapping cells and corresponding area-based weights or using the the cell that most overlaps the ZCTA
Average Annual Daily Traffic Density density of roads with moving traffic (meters of road per square meter of area)
density of roads with stop and go traffic (meters of road per square meter of area)
moving traffic density (vehicle-meters per square meter of area)
stop and go traffic density (vehicle-meters per square meter of area)
moving truck traffic density (truck-meters per square meter of area)
stop and go truck traffic density (truck-meters per square meter of area)

For more information on these variables and how they are used in this package, please view the documentation for the corresponding function (e.g., ?zctaDB::add_road_data).


You can install zctaDB from from GitHub with:

# install.packages("remotes")


Each function in zctaDB adds variables to your data based on a column named zcta that contains a 5-digit ZIP code.

d <- data.frame(id = c('abc', 'def', 'ghi'),
            zcta = c('45229', '45056', '47012'))

add_road_data(data = d)
#> no column called 'year' -- assuming 2010 ZCTAs.
#>    id  zcta year primary_road_length primary_road_density secondary_road_length
#> 1 abc 45229 2018            3721.548         5.263361e-04              8508.693
#> 2 def 45056 2018               0.000         0.000000e+00             97575.410
#> 3 ghi 47012 2018            8783.797         2.049049e-05             69904.720
#>   secondary_road_density
#> 1           0.0012033790
#> 2           0.0004956594
#> 3           0.0001630709