lehdr (pronounced: lee dur like a metric litre) is an R package that allows users to interface with the Longitudinal and Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) dataset returned as dataframes.

The lehdr package is designed to grab the LODES flat files (origin-destination, workplace, and residential association files) and includes an option to aggregate results to the Census tract level for analysts who find the fuzzing at the block level too great.

You can install the released version of lehdr from CRAN with:

install.packages("lehdr")

And the development version from GitHub with:

# install.packages("pak")
pak::pak("jamgreen/lehdr")

Load the library and optionally set the lehdr_use_cache option to save downloaded files for reuse:

library(lehdr)

options(lehdr_use_cache = TRUE)

The grab_lodes() function can be used to download data for a specific state, year, and LODES version. The LODES table type can be set to the default origin-destination (“od”),residential association (“rac”), or workplace association (“wac”).

For example, Oregon (state = "or") 2020 (year = 2020) from LODES version 8 (version="LODES8", default), origin-destination (lodes_type = "od"), primary jobs including private primary, secondary, and Federal (job_type = "JT01", default), as well as, primary jobs across ages, earnings, and industry (segment = "S000", default), aggregated at the Census Tract level rather than the default Census Block (agg_geo = "tract").

or_od <- grab_lodes(
  state = "or",
  year = 2020,
  version = "LODES8",
  lodes_type = "od",
  job_type = "JT01",
  segment = "S000",
  state_part = "main",
  agg_geo = "tract"
)

head(or_od)

The package can be used to retrieve multiple states and years at the same time by creating a vector or list. This second example pulls the Oregon AND Rhode Island (state = c("or", "ri")) for 2013 and 2014 (year = c(2013, 2014) or year = 2013:2014).

or_ri_od <- grab_lodes(
  state = c("or", "ri"),
  year = c(2013, 2014),
  lodes_type = "od",
  job_type = "JT01",
  segment = "S000",
  state_part = "main",
  agg_geo = "tract"
)

head(or_ri_od)

Not all years are available for each state. To see all options for lodes_type, job_type, and segment and the availability for each state/year, please see the most recent LEHD Technical Document at https://lehd.ces.census.gov/data/lodes/LODES8.

Set geometry = TRUE to download spatial data using the {tigris} package and join to your output data. When lodes_type = "rac" or lodes_type = "wac", grab_lodes() now returns an sf data frame.

ri_rac_geo <- grab_lodes(
  state = "ri",
  year = 2020,
  lodes_type = "rac",
  agg_geo = "county",
  geometry = TRUE
)

plot(ri_rac_geo["C000"])

If lodes_type = "od" the returned data frame contains the geometry for both the origin and destination in sfc class columns. These features can be combined together as the following example shows:

or_od_geo <- grab_lodes(
  state = "or",
  year = 2020,
  lodes_type = "od",
  agg_geo = "county",
  geometry = TRUE,
  state_part = "main"
)

h_to_w_geometry <- lapply(
  seq(nrow(or_od_geo)),
  function(i) {
    sf::st_linestring(
      c(
        sf::st_centroid(or_od_geo[["h_geometry"]][[i]]),
        sf::st_centroid(or_od_geo[["w_geometry"]][[i]])
      )
    )
  }
)

h_to_w_lines <- sf::st_as_sfc(h_to_w_geometry, crs = 4269)

or_od_lines <- sf::st_set_geometry(
  or_od_geo[, c("w_county", "h_county", "h_geometry", "S000")],
  h_to_w_lines
)

multnomah_od_lines <- dplyr::filter(
  or_od_lines,
  w_county == "41051"
)

plot(multnomah_od_lines["S000"], reset = FALSE)
plot(or_od_geo["h_geometry"], lwd = 0.25, add = TRUE)

Using the optional version paramater, users can specify which LODES version to use. Version 8 is default (version="LODES8") is enumerated at 2020 Census blocks. LODES7 (version="LODES7") is enumerated at 2010 Census blocks, but ends in 2019. LODES5 (version="LODES5") is enumerated at 2000 Census blocks, but ends in 2009.

Other common uses might include retrieving Residential or Work Area Characteristics (lodes_type = "rac" or lodes_type = "wac" respectively), low income jobs (segment = "SE01") or good producing jobs (segment = "SI01"). Other common geographies might include retrieving data at the Census Block level (agg_geo = "block", not necessary as it is default).

The LODES dataset is frequently used by transportation and economic development planners, regional economists, disaster managers and other public servants in order to have a fine grained understanding of the distribution of employment.

Such data is integral for regional travel demand models that help to dictate transportation policy options, regional economists and economic development planners interested in the spatial distribution of particular kinds of work use the data to weigh different industrial or workforce policy options.

Finally, as a Census product, the LODES data can be joined to Census Decennial or American Community Survey data to help visualize the interactions between different population groups and work.

In short, the LODES dataset is the only source of detailed geographic information on employment for the country and should be more widely available for researchers and analysts who work on regional development issues.

This package was developed by Jamaal Green, University of Pennsylvania; Dillon Mahmoudi, University of Maryland Baltimore County; and Liming Wang, Portland State University.

This package would not exist in its current format without the inspiration of Bob Rudis’s lodes package