The goal of geomarker is to facilitate geomarker assessment, or the linkage of environmental exposures and community characteristics with people according to their locations and dates, in R.
You can install the latest release of geomarker from R-universe:
install.packages(
"geomarker",
repos = c(
"https://geomarker-io.r-universe.dev",
"https://cloud.r-project.org"
)
)
You can install the development version of geomarker from GitHub with pak:
pak::pak("geomarker-io/geomarker")
s2cd)The s2cd vector class extends s2::s2_cell to include a list of Date vectors, one for each s2 cell.
This vector is meant to store locations and dates of observations and is used as the input to all geomarker assessment functions.
S2 is a spherical hierarchical cell index: specifically, a quad-based discrete global grid system (DGGS) on the sphere, while the S2 cell ID system is the 64-bit naming scheme that identifies each cell and its resolution within that grid. BigQuery exposes this system through its S2 geography functions.
For geomarker, a full-resolution (level 30) s2_cell can be treated as a point with an approximate spatial resolution of 1 centimeter, although coarser cells can represent regions—for example, when installing a fixture for a level-6 S2 cell.
Traditional longitude and latitude coordinates can be converted to full-resolution S2 cells with s2::as_s2_cell(s2::s2_lnglat(lon, lat)).
Use the S2 Cell Viewer to inspect individual cells and the S2 Region Coverer to explore coarser cells and regional coverings.
| geomarker | geomarker function |
|---|---|
| 🗺️ census block group linkage | get_tiger_bg() |
| ⛰️ elevation | get_elevation_summary() |
| 🏙️ land cover and imperviousness | get_nlcd_fct_imp_summary() |
| 🌫️ MERRA-2 surface PM2.5 | get_merra_data() |
| 🏭 point-source emissions | get_nei_point_summary() |
| 🌿 satellite-based enhanced vegetation index | get_evi_summary() |
| 🚦 traffic | get_traffic_summary() |
| 🌦️ weather and atmospheric conditions |
get_gridmet_data(), get_narr_data()
|
| 🔥 wildfire smoke | get_smoke_summary() |
Most data required for geomarker assessment does not come with the package. On first use, geomarker downloads and, when needed, preprocesses source data into durable managed local copies that remain available across R sessions and projects for the same user.
The stow package manages ordinary source-file downloads. Geomarker supplies package = "geomarker", so its durable managed local copies live beneath the fixed stow directory inside the geomarker package's user data directory (see tools::R_user_dir()):
file.path(tools::R_user_dir("geomarker", "data"), "stow")
Within that directory, each durable managed local copy is organized under the name of the function that consumes it, such as get_elevation_summary or get_tiger_bg. EVI and authenticated MERRA source builds use the same function-named organization while retaining their specialized signed or query-based download transport.
Use stow::stow_path(package = "geomarker") to locate the durable managed local copy directory and stow::stow_info(package = "geomarker") to inspect its contents.
The same organization makes it possible to prepare data in advance for a specific level-6 S2 cell and date range.
The source-specific install_*_geomarker_fixture() helpers download and spatially subset the required assets into an inst/gmrkr--<cell>/R/geomarker/stow directory; see inst/install_cincy_geomarker_fixture.R for a complete example.
For assessments that summarize within a buffer, the corresponding fixture installer accepts a buffer in meters and includes that halo around the level-6 fixture cell.
Set it to the largest buffer that the prepared fixture must support; each installer defaults to the assessment function's default buffer.
A fixture for the Cincinnati-area cell 8841 is included with geomarker.
This pattern can also be used to package data for another study region first and then run geomarker assessments from those prepared assets when source downloads are unavailable, restricted, slow, or otherwise not preferable.
To use a prepared fixture without downloading from the original sources, set R_USER_DATA_DIR to the fixture directory and set R_GEOMARKER_NO_DOWNLOAD=true.
For example, select the fixture included with geomarker for the current R session with:
Sys.setenv(
R_USER_DATA_DIR = system.file("gmrkr--8841", package = "geomarker"),
R_GEOMARKER_NO_DOWNLOAD = "true"
)
When R_GEOMARKER_NO_DOWNLOAD is set to any nonempty value, including false, geomarker assessment functions use only durable managed local copies already available in the selected directory. When a required copy is unavailable, geomarker produces an error rather than downloading it.
Unset offline operation with Sys.unsetenv("R_GEOMARKER_NO_DOWNLOAD").
R_GEOMARKER_NO_DOWNLOAD is honored when geomarker requests durable managed local copies through stow and by its known direct network paths, including Planetary Computer requests and remote raster reads, NASA Earthdata source builds, and release-preparation downloads.
It is a best-effort application control, not a network security boundary; use operating-system or container network controls when network isolation must be guaranteed.
MERRA-2 PM2.5 release data are cataloged in inst/merra-data.dcf as complete half-year assets: January through June (H1) and July through December (H2).
The currently cataloged assets were published with the package's v0.0.1 GitHub release only after every expected daily granule had been discovered and validated.
There is one fixed asset name for each year and half-year. It may be recreated from the latest NASA data while preparing a package release, but the checked-in DCF records exactly one published file and checksum. A later data correction is distributed with a new package and GitHub release.
get_merra_data() first uses a complete monthly artifact retained under the get_merra_data directory as a durable managed local copy and then tries the matching official half-year release.
When neither is present, it returns aligned missing values with one warning rather than substituting an older period.
A user with an Earthdata Login can build any fully elapsed and fully available month directly from NASA. Set the account username and password:
Sys.setenv(EARTHDATA_USER = "...", EARTHDATA_PASSWORD = "...")
install_merra_data("2026-07", source = "earthdata")
The source build discovers the exact daily M2T1NXAER v5.12.4 granules through NASA CMR, downloads authenticated variable-and-CONUS subsets, stages daily source files for resumption, and writes an adjacent DCF with exact granule revisions and subset hashes.
Set overwrite = TRUE to recreate a month from the latest CMR listing; staged daily source files whose granule revision and hash still match are reused.
This is a manual, potentially large build intended for an HPC or similarly provisioned environment; ordinary package checks do not contact NASA.
The bundled Cincinnati MERRA fixtures use deterministic synthetic concentrations solely to exercise the offline API and both half-year branches. They are not scientific data and must not be used for analysis.