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amadeus
is a
mechanism for data,
environments, and user
setup for common environmental and climate health
datasets in R. amadeus
has been developed to improve access
to and utility with large scale, publicly available environmental data
in R.
amadeus
is not yet available from CRAN, but it can be
installed with the devtools
, remotes
, or
pak
packages.
::install_github("NIEHS/amadeus") devtools
::install_github("NIEHS/amadeus") remotes
::pak("NIEHS/amadeus") pak
To add or edit functionality for new data sources or datasets, open a
Pull request into
the main branch with a detailed description of the proposed changes.
Pull requests must pass all status checks, and then will be approved or
rejected by amadeus
’s authors.
Utilize Issues to notify the authors of bugs, questions, or recommendations. Identify each issue with the appropriate label to help ensure a timely response.
download_data
accesses and downloads raw geospatial data
from a variety of open source data repositories. The function is a
wrapper that calls source-specific download functions, each of which
account for the source’s unique combination of URL, file naming
conventions, and data types. Download functions cover the following
sources:
Data Source | File Type | Data Genre |
---|---|---|
Climatology Lab TerraClimate | netCDF | Meteorology |
Climatology Lab GridMet | netCDF | Climate Water |
Köppen-Geiger Climate Classification | GeoTIFF | Climate Classification |
MRLC1 Consortium National Land Cover Database (NLCD) | GeoTIFF | Land Use |
NASA2 Moderate Resolution Imaging Spectroradiometer (MODIS) | HDF | Atmosphere Meteorology Land Use Satellite |
NASA Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) | netCDF | Atmosphere Meteorology |
NASA SEDAC3 UN WPP-Adjusted Population Density | GeoTIFF netCDF |
Population |
NASA SEDAC Global Roads Open Access Data Set | Shapefile Geodatabase |
Roadways |
NASA Goddard Earth Observing System Composition Forcasting (GEOS-CF) | netCDF | Atmosphere Meteorology |
NOAA Hazard Mapping System Fire and Smoke Product | Shapefile KML |
Wildfire Smoke |
NOAA NCEP4 North American Regional Reanalysis (NARR) | netCDF | Atmosphere Meteorology |
OpenGeoHub Foundation OpenLandMap | GeoTIFF | Climate Elevation Soil Land Use Satellite |
Parameter Elevation Regression on Independent Slopes Model (PRISM) | BIL ASCII |
Climate |
US EPA5 Air Data Pre-Generated Data Files | CSV | Air Pollution |
US EPA Ecoregions | Shapefile | Climate Regions |
US EPA National Emissions Inventory (NEI) | CSV | Emissions |
US EPA Toxic Release Inventory (TRI) Program | CSV | Chemicals Pollution |
USGS6 Global Multi-resolution Terrain Elevation Data (GMTED2010) | ESRI ASCII Grid | Elevation |
USGS National Hydrography Dataset (NHD) | Geopackage Geodatabase |
Hydrography |
See the “download_data” vignette for a detailed description of source-specific download functions.
Example use of download_data
using NOAA NCEP North
American Regional Reanalysis’s (NARR) “weasd” (Daily Accumulated Snow at
Surface) variable.
<- "/ EXAMPLE / FILE / PATH /"
directory download_data(
dataset_name = "narr",
year = c(2022, 2022),
variable = "weasd",
directory_to_save = directory,
acknowledgement = TRUE,
download = TRUE
)
Downloading requested files...
Requested files have been downloaded.
list.files(paste0(directory, "weasd"))
[1] "weasd.2022.nc"
process_covariates
imports and cleans raw geospatial
data (downloaded with download_data
), and returns a single
SpatRaster
or SpatVector
into the user’s R
environment. process_covariates
“cleans” the data by
defining interpretable layer names, ensuring a coordinate reference
system is present, and managing `timedata (if applicable).
To avoid errors when using process_covariates
,
do not edit the raw downloaded data files. Passing
user-generated or edited data into process_covariates
may
result in errors as the underlying functions are adapted to each
sources’ raw data file type.
Example use of process_covariates
using the downloaded
“weasd” data.
<- process_covariates(
weasd covariate = "narr",
date = c("2022-01-01", "2022-01-05"),
variable = "weasd",
path = paste0(directory, "weasd"),
extent = NULL
)
Cleaning weasd data for January, 2022...
Detected monolevel data...
Returning daily weasd data from 2022-01-01 to 2022-01-05.
weasd
class : SpatRaster
dimensions : 277, 349, 5 (nrow, ncol, nlyr)
resolution : 32462.99, 32463 (x, y)
extent : -16231.49, 11313351, -16231.5, 8976020 (xmin, xmax, ymin, ymax)
coord. ref. : +proj=lcc +lat_0=50 +lon_0=-107 +lat_1=50 +lat_2=50 +x_0=5632642.22547 +y_0=4612545.65137 +datum=WGS84 +units=m +no_defs
source : weasd.2022.nc:weasd
varname : weasd (Daily Accumulated Snow at Surface)
names : weasd_20220101, weasd_20220102, weasd_20220103, weasd_20220104, weasd_20220105
unit : kg/m^2, kg/m^2, kg/m^2, kg/m^2, kg/m^2
time : 2022-01-01 to 2022-01-05 UTC
calc_covariates
stems from the beethoven
project’s need for various types of data extracted at precise locations.
calc_covariates
, therefore, extracts data from the
“cleaned” SpatRaster
or SpatVector
object at
user defined locations. Users can choose to buffer the locations. The
function returns a data.frame
or SpatVector
with data extracted at all locations for each layer or row in the
SpatRaster
or SpatVector
object,
respectively.
Example of calc_covariates
using processed “weasd”
data.
<- data.frame(id = "001", lon = -78.8277, lat = 35.95013)
locs <- calc_covariates(
weasd_covar covariate = "narr",
from = weasd_process,
locs = locs,
locs_id = "id",
radius = 0,
geom = FALSE
)
Detected `data.frame` extraction locations...
Calculating weasd covariates for 2022-01-01...
Calculating weasd covariates for 2022-01-02...
Calculating weasd covariates for 2022-01-03...
Calculating weasd covariates for 2022-01-04...
Calculating weasd covariates for 2022-01-05...
Returning extracted covariates.
weasd_covar
id time weasd_0
1 0001 2022-01-01 0.000000000
2 0001 2022-01-02 0.000000000
3 0001 2022-01-03 0.000000000
4 0001 2022-01-04 0.000000000
5 0001 2022-01-05 0.001953125
The following R packages can also be used to access climate and
weather data in R, but each differs from amadeus
in the
data sources covered or type of functionality provided.
Multi-Resolution Land Characteristics↩︎
National Aeronautics and Space Administration↩︎
Socioeconomic Data and Applications Center↩︎
National Centers for Environmental Prediction↩︎
United States Environmental Protection Agency↩︎
United States Geological Survey↩︎
Last updated more than two years ago.↩︎
Archived; no longer maintained.↩︎
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.