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catchmentACS describes the neighborhoods around a set of sites, such as pre-kindergarten (Pre-K) classrooms, with data from the American Community Survey (ACS). For each site and each drive time, such as 5, 10, and 15 minutes, it finds the area that can be reached by car within that time, the site’s catchment. It then combines the ACS estimates of the census tracts that overlap the catchment into estimates for it: how many people live there, how many are below the poverty level, what share of households receive benefits from the Supplemental Nutrition Assistance Program (SNAP), and so on. Every estimate comes with a margin of error, the half-width of its 90 percent confidence interval, and with a record of the settings and inputs behind it.
For a count, each tract contributes the share of its own area that lies inside the drive-time area: a tract half inside contributes half of its count. This assumes that whatever a variable counts is spread evenly over a tract: the people below the poverty level, and not only the population as a whole. Each rate is the ratio of two counts computed this way. Medians and per-person values from three ACS tables (median household income, median home value, and per capita income) are averaged over the overlapping tracts instead, with weights proportional to the area each tract shares with the area. Those weights ignore how many people live in each tract, so such an average can be far from the median of the area itself. A median or per-person value from any other ACS table is added up like a count. The articles listed below describe the calculations and what they assume.
When catchmentACS is available on CRAN,
install.packages("catchmentACS") installs the released
version. The development version is on GitHub:
# install.packages("pak")
pak::pak("joonho112/catchmentACS")pak::pak() does not build the articles; they are on the
package website, https://joonho112.github.io/catchmentACS/articles/.
Building drive-time areas with the default routing service needs the
osrm package, and the package’s maps need the leaflet package. Neither
is installed with catchmentACS:
install.packages(c("osrm", "leaflet"))Downloading ACS data needs a free Census API key, which can be requested at https://api.census.gov/data/key_signup.html and saved with:
tidycensus::census_api_key("YOUR_KEY_HERE", install = TRUE)
readRenviron("~/.Renviron")The first line saves the key in ~/.Renviron for later
sessions; the second makes it usable in the session that is running. A
key already saved there needs overwrite = TRUE.
The key is needed only when catchmentACS downloads estimates: when no
saved result of them matches the call, or when
force_refresh = TRUE. A run that supplies estimates through
acs =, like the example below, needs no key.
This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.
Downloaded estimates and drive-time areas are saved for the rest of
the R session, so a repeated call neither downloads nor routes again. To
keep them between sessions, add this line to the R startup file, for
example with usethis::edit_r_profile():
options(catchmentACS.cache_dir = tools::R_user_dir("catchmentACS", "cache"))Saved results in that folder that go unused for 30 days are deleted.
?cacs_cache_dir gives the details.
Everything the example needs comes from three files that ship with
the package. They hold made-up data: 20 sites on a grid over a rectangle
around Alabama, drive-time areas drawn as circles around the sites, and
random ACS estimates for 911 small squares that stand in for census
tracts. Given the areas and the estimates, cacs_run() skips
the steps that build the areas and download the estimates, so the
example contacts no service and needs no key.
library(catchmentACS)
library(dplyr)
library(sf) # needed to subset the bundled sf objects with [
sites <- readRDS(system.file(
"extdata", "legacy_2025_sites.rds", package = "catchmentACS"
))
iso <- readRDS(system.file(
"extdata", "legacy_2025_isochrones.rds", package = "catchmentACS"
))
acs <- readRDS(system.file(
"extdata", "sample_alabama_subset.rds", package = "catchmentACS"
))
one_site <- "AL_SITE_07"
result <- cacs_run(
sites = sites[sites$site_id == one_site, , drop = FALSE],
state = "AL",
precomputed_isochrones = iso[iso$site_id == one_site, , drop = FALSE],
acs = acs,
verbose = FALSE
)
tibble::as_tibble(result) |>
filter(variable %in% names(cacs_acs_default_rates)) |>
select(site_id, drive_time_min, variable, estimate, moe)The call returns one row for each drive time and rate, with an
estimate and its margin of error, and the same for each ACS variable in
acs. Down the column for this site the poverty rate is 0.62
for the 5-minute area and 0.36 for the 15-minute area, and the share of
households receiving SNAP benefits is 0.04 and 0.11. These numbers
describe no real place. The bundled ACS values are random, and the
made-up squares are spaced apart rather than tiling the state, so the
5-minute area overlaps one square and the two wider areas overlap three:
the change down the column is the change from one square’s values to an
average of three. The file ships with the package, so every reader gets
the same numbers. For some other sites a rate has no value, because one
made-up estimate in twenty is missing; the run then warns and leaves
NA in estimate and moe.
AL_SITE_07 has no such row.
For a run on your own sites, the call is the same without
precomputed_isochrones = and acs =. The sites
go in as a data frame with the columns site_id,
lon, and lat, or as an sf object of points
with a site_id column. The run then needs a routing
service, a Census API key, and an internet connection. Getting
started with catchmentACS goes through the result column by column,
and Routing
services, API keys, and offline use covers the routing services and
their keys.
ORS_API_KEY
environment variable, ors_api_key = in
cacs_isochrone(), or
iso_args = list(ors_api_key = ...) in
cacs_run(). The openrouteservice route has been checked
only with simulated responses from openrouteservice.Not implemented yet: Mapbox and r5r as routing services, weighting by
population, rates other than the five above, and ACS pulls of 1-year
estimates, block groups, counties, or several states at once. Asking for
any of these stops the run with an error naming what is missing.
Weighting by population, a different rates list, and
several states in state = give the error no matter what
else is supplied. The other five give it only when the step that would
use them runs. With precomputed_isochrones = supplied the
routing step does not run, so provider = "mapbox" is
accepted and left unused; with acs = supplied the download
step does not run, so acs_args = list(survey = "acs1") is
too.
The articles below are on the package website.
vignette("name", package = "catchmentACS") opens one
locally when the installed package includes the articles. A version
installed from CRAN includes them. A version installed from GitHub
includes them only when they are built during installation, as with
remotes::install_github("joonho112/catchmentACS", build_vignettes = TRUE),
or with devtools::install(build_vignettes = TRUE) from a
local copy.
| Article | What it covers |
|---|---|
| Getting started with catchmentACS | A first run on the example data, and how to read the result. |
| A worked example with Alabama Pre-K sites | The five steps one at a time, then tables for a report. |
| Routing services, API keys, and offline use | Which services are implemented, what keys they need, and how to run without a network. |
| Mapping each step for one Birmingham site | Leaflet maps of the drive-time areas, the tracts, the weighted estimates, and the rates. |
| Article | What it covers |
|---|---|
| How the estimates are computed | The calculation from tracts to catchment estimates, and its assumptions. |
| Area weighting of tract estimates | The two weights, and when an area-weighted average stands in for a median. |
| Margins of error for combined estimates | How the margins of error of the tracts combine, and what the formulas leave out. |
| Rates and their margins of error | The five rate definitions and the two formulas for the margin of error of a rate. |
| Article | What it covers |
|---|---|
| Updating code written for versions 0.1 and 0.2 | The ten changes that reach code written for 0.1 or 0.2. |
| Updating code written for version 0.3 | What changed in 0.4. |
| Updating code written for version 0.4 | What changed in 0.5.0, including the corrected medians and per-person values. |
Help pages for the functions are on the website as well.
?cacs_run describes every argument of the one call that
runs the whole calculation.
catchmentACS is written by JoonHo Lee (ORCID:
0009-0006-4019-8703). citation("catchmentACS") gives
the citation for the installed version, and
toBibtex(citation("catchmentACS")) gives it as a BibTeX
entry.
MIT © JoonHo Lee
Bug reports and feature requests are welcome at https://github.com/joonho112/catchmentACS/issues.
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.