Download Geographic Data

Description

Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.

Marine data

Description

Marine data from Bio-Oracle

Usage

bio_oracle(path, var, stat, benthic=FALSE, 
		depth="Mean", time="Present", rcp, ...)

Arguments

Value

SpatRaster

References

Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E.A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27: 277-284.

See Also

https://bio-oracle.org/

Examples

# this is a large download
x <- bio_oracle(path=tempdir(), "Salinity", "Max", 
		benthic=TRUE, depth="Mean", time="Present")

CMIP6 climate model data

Description

Download downscaled and calibrated CMIP6 climate data for projected future climates. Either for the entire world or for a 30 degrees tile. For more information see https://www.worldclim.org/

Usage

cmip6_world(model, ssp, time, var, res, path, ...)

cmip6_tile(lon, lat, model, ssp, time, var, path, ...)

Arguments

Value

SpatRaster

See Also

vrt to combine tiles

Examples

# download of large files takes a while
tmin10 <- cmip6_world("CNRM-CM6-1", "585", "2061-2080", 
					var="tmin", res=10, path=tempdir())

Get country codes

Description

Get country codes for all countries in the world.

Usage

country_codes(query=NULL)

Arguments

Value

data.frame

Examples

cc <- country_codes()
head(cc)

p <- country_codes(query="Per")
p

Sacks crop calendar data

Description

Download Sacks crop calendar data. The crops available are returned by sacksCrops

Usage

crop_calendar_sacks(crop="", path, ...)

sacksCrops()

Arguments

Value

SpatRaster

References

Sacks, W.J., D. Deryng, J.A. Foley, and N. Ramankutty, 2010. Crop planting dates: an analysis of global patterns. Global Ecology and Biogeography 19: 607-620. doi:10.1111/j.1466-8238.2010.00551.x.

See Also

https://sage.nelson.wisc.edu/data-and-models/datasets/crop-calendar-dataset/

Examples

# download may take > 5s
cas <- crop_calendar_sacks("cassava", path=tempdir())

Monfreda crop data

Description

Monfreda global crop data (area, yield) for 175 crops.

Data may be freely used for research, study, or teaching, but must be cited appropriately (see below). Re-release of the data, or incorporation of the data into a commercial product, is allowed only with explicit permission.

Usage

monfredaCrops()
crop_monfreda(crop="", var="area_ha", path, ...)

Arguments

Value

SpatRaster

References

Monfreda, C., N. Ramankutty, and J. A. Foley (2008), Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022, doi:10.1029/2007GB002947.

See Also

http://www.earthstat.org/harvested-area-yield-175-crops/

Examples

# download may take > 5s
mcas <- crop_monfreda("cassava", path=tempdir())
mcas
names(mcas)

SPAM crop data

Description

SPAM crop data. For each of 42 crops or crop groups get a 10-minute spatial resolution raster with the crop area, yield, production or value by cropping system (rainfed or irrigated, and subsistence, low-input or high-input).

The global data are for 2010. The Africa dataset is for 2017.

Usage

spamCrops()
crop_spam(crop="", var="area", path, africa=FALSE, ...)

Arguments

Value

SpatRaster

References

International Food Policy Research Institute, 2019. Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0. https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V4.

International Food Policy Research Institute, 2020. Spatially-Disaggregated Crop Production Statistics Data in Africa South of the Sahara for 2017. https://doi.org/10.7910/DVN/FSSKBW, Harvard Dataverse, V3.

See Also

https://www.mapspam.info/data/

Examples

# downloads a large file
cas <- crop_spam("cassava", "area", path=tempdir(), TRUE)

Cropland distribution data

Description

Cropland distribution data at a 30-seconds spatial resolution from three sources:

worldcover is derived from the ESA WorldCover data set at 0.3-seconds resolution. (License CC BY 4.0), see https://esa-worldcover.org/en. Values were aggregated and represent the fraction cropland in each cell.

glad is derived from the "Global cropland expansion in the 21st century" (Potatov et al) data available here. Values were aggregated and resampled. They represent the fraction cropland in each cell. There are five layers representing the following years: 2003, 2007, 2011, 2015, and 2019.

QED has cropland distribution data for Africa. The values are probabilities of cropland presence estimated with a neural network that was trained on an initial 1-million point Geosurvey conducted in 2015. License: CC-BY-SA 4.0; https://about.maps.qed.ai/

Usage

cropland(source, path, year, ...) 

Arguments

Value

SpatRaster

References

WorldCover: Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020 v100. doi:10.5281/zenodo.5571936.

GLAD: Potapov, P., S. Turubanova, M.C. Hansen, A. Tyukavina, V. Zalles, A. Khan, X.-P. Song, A. Pickens, Q. Shen, J. Cortez, 2021. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food. doi:10.1038/s43016-021-00429-z

See Also

landcover

Elevation

Description

Elevation data for any country. The main data source is Shuttle Radar Topography Mission (SRTM) , specifically the hole-filled CGIAR-SRTM (90 m resolution) from https://srtm.csi.cgiar.org/. These data are only available for latitudes between -60 and 60.

The 1 km (30 arc seconds) data were aggregated from SRTM 90 m resolution data and supplemented with the GTOP30 data for high latitudes (>60 degrees).

Usage

elevation_3s(lon, lat, path, ...)
elevation_30s(country, path, mask=TRUE, subs="", ...)
elevation_global(res, path, ...)

Arguments

Value

SpatRaster

Examples

be <- elevation_30s(country="BEL", path=tempdir() )

Human footprint

Description

The "human footprint" is an estimate of the direct and indirect human pressures on the environment. The human pressure is measured using eight variables including built-up environments, population density, electric power infrastructure, crop lands, pasture lands, roads, railways, and navigable waterways. It is expressed on a scale of 0 (low) to 50 (high footprint).

See https://www.nature.com/articles/sdata201667 for the details.

The original data are available here:

https://sedac.ciesin.columbia.edu/data/collection/wildareas-v3

Data are available for two years: 1993 and 2009, for all terrestrial areas except Antarctica. The footprint of seas and oceans was set to zero. The original data was in the Mollweide projection at a 1000 m spatial resolution. The data available through this function was transformed to a longitude/latitude grid at 30-seconds resolution.

Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.

Usage

footprint(year=2009, path, ...) 

Arguments

Value

SpatRaster

References

Venter, O., E. W. Sanderson, A. Magrach, J. R. Allan, J. Beher, K. R. Jones, H. P. Possingham, W. F. Laurance, P. Wood, B. M. Fekete, M. A. Levy, and J. E. Watson. 2016. Sixteen Years of Change in the Global Terrestrial Human Footprint and Implications for Biodiversity Conservation. Nature Communications 7:12558. https://doi.org/10.1038/ncomms12558.

See Also

landcover

Administrative boundaries

Description

Get administrative boundaries for any country in the world. Data are read from files that are downloaded if necessary.

Usage

gadm(country, level=1, path, version="latest", resolution=1, ...)

Arguments

Details

The data are from https://gadm.org

Value

SpatVector

See Also

world

Examples

bel <- gadm(country="BEL", level=1, path=tempdir())

Set the data path

Description

This function allows you set or get the default download path for the geodata package. By setting this path you can avoid downloading the same data many times over. This also guards against website service interruptions.

The default path is ignored if you use the path variable in a function.

To save the default path across sessions, you can add a line like this:

options( geodata_default_path = "c:/your/geodata/path")

to the file returned by

file.path( R.home(), "etc/Rprofile.site")

Alternatively, you can also set a system variable "GEODATA_PATH" to the desired path.

Usage

geodata_path(path)

Arguments

Value

character

Examples

geodata_path()

Landcover data

Description

Landcover data at 30-seconds spatial resolution for (most of) the world. Values are the fraction of a landcover class in each cell. The values are derived from the ESA WorldCover data set at 0.3-seconds resolution. (License CC BY 4.0). See https://esa-worldcover.org/en for more information.

Usage

landcover(var, path, ...) 

Arguments

Value

SpatRaster

References

Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. ESA WorldCover 10 m 2020 v100. doi:10.5281/zenodo.5571936.

See Also

landcover

OpenStreetMap data

Description

Get OpenStreetMap (OSM) data

Usage

osm(country, var, path, proxy=FALSE, ...)

Arguments

Details

License: Open Data Commons Open Database License (ODbL).

See https://www.openstreetmap.org/copyright

Value

SpatVector

Examples

aruba <- osm(country="Aruba", "places", path=tempdir())

population density

Description

Download population density data.

Source: Gridded Population of the World (GPW), v4. Documentation:

http://sedac.ciesin.columbia.edu/data/collection/gpw-v4/documentation

Usage

population(year, res, path, ...)

Arguments

Value

SpatRaster

References

Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). doi:10.7927/H49C6VHW. Accessed 6 July 2021.

Examples

# download may take > 5s
pop <- population(2020, 10, path=tempdir())

crop calendar for rice

Description

Get crop calendar and production data for rice

Usage

rice_calendar(path, ...)

Arguments

Value

SpatVectorCollection

References

Laborte, A.G.; Gutierrez, M.A.; Balanza, J.G.; Saito, K.; Zwart, S.J.; Boschetti, M.; Murty, MVR; Villano, L.; Aunario, J.K.; Reinke, R.; Koo, J.; Hijmans, R.J.; Nelson, A., 2017. RiceAtlas, a spatial database of global rice calendars and production. Scientific Data 4: 170074 doi:10.1038/sdata.2017.74

Examples

# first time api call takes a while
rice <- rice_calendar(path=tempdir())
cal <- rice[1]

Soil data for Africa

Description

Download chemical soil properties data for Africa for different soil depths. The spatial resolution is 30 arc-seconds (about 1 km2), aggregated from the original 250m resolution.

There are more recent estimations for some of the properties available in other data sets. See soil_af_isda and soil_world.

For more info, see https://www.isric.org/projects/soil-property-maps-africa-250-m-resolution

The data have a CC-BY 4.0 NC license

Usage

soil_af(var, depth, path, ...)

Arguments

Details

Value

SpatRaster

References

Hengl T, Heuvelink GBM, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, et al. (2015) Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6): e0125814. doi:10.1371/journal.pone.0125814

See Also

soil_af_elements, soil_af_isda, soil_world_vsi

Examples

# downloads a large file
aph <- soil_af(var="ph", depth=5, path=tempdir())

Soil elements data for Africa

Description

Connect to or download chemical soil element concentration (for the 0-30 cm topsoil) data for Africa. The spatial resolution is 30 arc-seconds (about 1 km2), aggregated from the original 250 m spatial resolution.

The data have an Open Data Commons Open Database License (ODbL)

For more information, see https://www.isric.org/projects/soil-property-maps-africa-250-m-resolution

Usage

soil_af_elements(var, path, ...)

Arguments

Details

Value

SpatRaster

References

Hengl T, Heuvelink GBM, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, et al. (2015) Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6): e0125814. doi:10.1371/journal.pone.0125814

See Also

soil_af, soil_af_isda, soil_world

Examples

# downloads a large file
fe <- soil_af_elements("Fe", path=tempdir(), quiet=TRUE)

iSDA soil data for Africa

Description

Download soil data for Africa derived from the iDSA data set. The original data were aligned and aggregated to 30 arc-seconds (about 1 km2). The original spatial resolution was 30m.

For more info see:

https://envirometrix.nl/isdasoil-open-soil-data-for-africa/

https://zenodo.org/search?page=1&size=20&q=iSDAsoil

Usage

soil_af_isda(var, depth=20, error=FALSE, path, virtual=FALSE, ...) 

Arguments

Details

Value

SpatRaster

References

Tomislav Hengl, Matthew A. E. Miller, Josip Križan, Keith D. Shepherd, Andrew Sila, Milan Kilibarda, Ognjen Antonijevic, Luka Glušica, Achim Dobermann, Stephan M. Haefele, Steve P. McGrath, Gifty E. Acquah, Jamie Collinson, Leandro Parente, Mohammadreza Sheykhmousa, Kazuki Saito, Jean-Martial Johnson, Jordan Chamberlin, Francis B.T. Silatsa, Martin Yemefack, John Wendt, Robert A. MacMillan, Ichsani Wheeler & Jonathan Crouch, 2021. African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning. Scientific Reports 11: 6130.

See Also

soil_af_elements, soil_af, soil_world

Examples

# downloads a large file
afph <- soil_af_isda("ph.h2o", path=tempdir(), quiet=TRUE)

Soil data for water balance computation (Africa only)

Description

Download physical soil properties data for Africa that can be used in water balance computation. The values are for a soil depth of 0 to 30 cm. The spatial resolution is 30 arc-seconds (about 1 km2), aggregated from the original 250m resolution.

For other properties see soil_af, soil_af_elements, soil_af_isda.

For more info, see https://www.isric.org/projects/soil-property-maps-africa-250-m-resolution

The data have a CC-BY 4.0 NC license

Usage

soil_af_water(var, depth = "30cm", path, ...)

Arguments

Details

Value

SpatRaster

See Also

soil_af_elements, soil_af_isda, soil_world

Examples

# this downloads a large file
tetaS <- soil_af_water(var="tetas", depth="erzd", path=tempdir())

Global soil data

Description

Download global soils data. The data are derived from the SoilGRIDS database. The data were aggregated and transformed to a longitude/latitude coordinate reference system with 30-second spatial resolution.

See https://www.isric.org/explore/soilgrids for more info.

data license: CC-BY 4.0

Usage

soil_world(var, depth, stat="mean", name="", path, ...)

Arguments

Details

Value

SpatRaster

References

Poggio L., de Sousa L.M., Batjes N.H., Heuvelink G.B.M., Kempen B., Ribeiro E., Rossiter D., 2021. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil 7:217-240, 2021. doi:10.5194/soil-7-217-2021

See Also

For virtual access to the original data: soil_world_vsi For Africa: soil_af_isda, soil_af, soil_af_elements

Examples

# this downloads a large file 
gph <- soil_world(var="phh2o", depth=5, path=tempdir())

soil grids_vsi

Description

Virtually connect to the global soilgrids data. See https://www.isric.org/explore/soilgrids for more info.

data license: CC-BY 4.0

Usage

soil_world_vsi(var, depth, stat="mean", name="")

Arguments

Details

The below table lists the variable names, a description, and the units of the variables. Note that these units are not standard units, and are different from the data for other soil data available through this package.

Value

SpatRaster

References

Poggio, L., de Sousa, L.M., Batjes, N.H., Heuvelink, G.B.M., Kempen, B., Ribeiro, E., and Rossiter, D., 2021. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty. Soil 7:217-240, 2021. doi:10.5194/soil-7-217-2021

See Also

soil_world to download these data at 30-seconds spatial resolution.

For Africa: soil_af_isda, soil_af, soil_af_elements

Examples

ph <- soil_world_vsi(var="phh2o", depth=5)
ph

Download species occurrence data from GBIF

Description

Download data from the Global Biodiversity Information Facility (GBIF) data portal.

sp_genus returns a data.frame with all the species names associated with a genus.

sp_occurrence downloads species occurrence records. You can download data for a single species or for an entire genus by using species="". Note that the maximum number of records that can be downloaded for a single search is 100,000.

You can check the number of records returned by using the option download=FALSE.

To avoid getting more than 100,000 records, you can do separate queries for different geographic areas. This has been automated in sp_occurrence_split. This function recursively splits the area of interest into smaller areas until the number of records in an area is less than 50,000. It then downloads these records and saves them in a folder called "gbif". After all areas have been evaluated, the data are combined into a single file and returned as a data.frame). If the function is interrupted, it can be run again, and it will resume where it left off.

If you want to download data for an entire genus, first run sp_genus and then download data for the returned species names one by one.

Before using this function, please first check the GBIF data use agreement and see the note below about how to cite these data.

Usage

sp_genus(genus, simple=TRUE, ...)

sp_occurrence(genus, species="", ext=NULL, args=NULL, geo=TRUE, 
	removeZeros=FALSE, download=TRUE, ntries=5, nrecs=300, 
	start=1, end=Inf, fixnames=TRUE, ...)

sp_occurrence_split(genus, species="", path=".", ext=c(-180,180,-90,90),
	args=NULL, geo=TRUE, removeZeros=FALSE, ntries=5, nrecs=300,
	fixnames=TRUE, prefix=NULL, ...)

Arguments

Value

data.frame

Note

Under the terms of the GBIF data user agreement, users who download data agree to cite a DOI. Citation rewards data-publishing institutions and individuals and provides support for sharing open data [1][2]. You can get a DOI for the data you downloaded by creating a "derived" dataset. For this to work, you need to keep the "datasetKey" variable in your dataset.

References

https://www.gbif.org/occurrence https://www.gbif.org/derived-dataset/about

Examples

sp_occurrence("solanum", download=FALSE)
sp_occurrence("solanum", "acaule", download=FALSE)

sp_occurrence("Batrachoseps", "" , down=FALSE)
sp_occurrence("Batrachoseps", "luciae", down=FALSE)
g <- sp_occurrence("Batrachoseps", "luciae", geo=TRUE, end=25)
#plot(g[, c("lon", "lat")])


## args
a1 <- sp_occurrence("Elgaria", "multicarinata", 
			args="recordNumber=Robert J. Hijmans RH-2")
a2 <- sp_occurrence("Batrachoseps", "luciae",
			args=c("year=2023", "identifiedBy=Anthony Ye"))

## year supports "range queries"
a3 <- sp_occurrence("Batrachoseps", "luciae", 
			args=c("year=2020,2023", "identifiedBy=Kuoni W"))
#table(a3[,c("year")])

Travel time to a city or port

Description

Download global travel time to a city or port data on rasters at a 30 arc-seconds (about 1 km2) resolution.

Usage

travel_time(to="city", size=1, up=FALSE, path, ...) 

Arguments

Details

Description of the the size argument.

to="city"

to="port"

Value

SpatRaster

References

Nelson, A., D.J. Weiss, J. van Etten, A. Cattaneo, T.S. McMenomy & J. Koo, 2019. A suite of global accessibility indicators. Scientific Data 6: 266. doi:10.1038/s41597-019-0265-5

Version 3 (2019-05-15) from https://figshare.com/articles/dataset/Travel_time_to_cities_and_ports_in_the_year_2015/7638134/3

Administrative boundaries

Description

Get the borders for all the countries in the world. Data are read from files that are downloaded if necessary.

Usage

world(resolution=5, level=0, path, version="latest", ...)

Arguments

Details

The data are from https://gadm.org

Value

SpatVector

See Also

gadm

Examples

w <- world(path=tempdir())

WorldClim climate data

Description

Download climate data from WorldClim version 2.1. See Details for variables and units.

Usage

worldclim_global(var, res, path, version="2.1", ...)
worldclim_country(country, var, path, version="2.1", ...)
worldclim_tile(var, lon, lat, path, version="2.1", ...)

Arguments

Details

These are the WorldClim monthly average climate data.

Value

SpatRaster

See Also

https://www.worldclim.org/

Examples

lux <- worldclim_country("Luxembourg", var="tmin", path=tempdir())