Hey!

Today I will show how powerful the R {raster} package is on another example. The raster package is not only a great tool for raster processing and calculation but also very useful for data acquisition. With the function getData() you can download the following data directly into R and process it:

• SRTM 90 (elevation data with 90m resolution between latitude  -60 and 60)
• World Climate Data (Tmin, Tmax, Precip, BioClim)
• Global adm. boundaries (different levels)

Global adm. boundaries

Lets start with the global adm. boundaries:

Install the raster package and load it first. To be able to use the getData() function to acquire data about global amd. boundaries we have to specify three arguments:

install.packages("raster")
library(raster)
austria0 <- getData('GADM', country='AUT', level=0)

1. Select Dataset: The first argument specifies the dataset. ‘GADM’ returns the global administrative boundaries.
2. Select country: The second argument provides the country name of the boundaries by using its ISO A3 country code (more info here)
3. Specify level: The third argument specifies the level of of administrative subdivision (0=country, 1=first level subdivision).

The code above returns the boundaries for Austria for the level 0. Lets compare them to the Level 1 subdivision by plotting both of them:

#Get Data
austria0 <- getData('GADM' , country="AUT", level=0)
austria1 <- getData('GADM' , country="AUT", level=1)

#Plot
par(mfrow(2,1))
plot(austria0, main="Adm. Boundaries Austria Level 0")
plot(austria1, main="Adm. Boundaries Austria Level 1")

World Climate

Lets do the same with the World Climate data, here you also have to specify three arguments:

climate <- getData('worldclim', var='bio', res=2.5)

1. Select Dataset: The first argument specifies the dataset. ‘worldclim’ returns the World Climate Data.
2. Select variable: The second argument specifies the variable: ‘tmin’, ‘tmax’, ‘prec’ and ‘bio’ (more info here).
3. Specify resolution:  0.5, 2.5, 5, and 10 (minutes of a degree). In the case of res=0.5, you must also provide a lon and lat argument for a tile.

The code above returns a raster with the 18 bioclimate variables covering the whole world with a resoltion of 2.5 minutes of degrees:

BIO1 = Annual Mean Temperature
BIO2 = Mean Diurnal Range (Mean of monthly (max temp – min temp))
BIO3 = Isothermality (BIO2/BIO7) (* 100)
BIO4 = Temperature Seasonality (standard deviation *100)
BIO5 = Max Temperature of Warmest Month
BIO6 = Min Temperature of Coldest Month
BIO7 = Temperature Annual Range (BIO5-BIO6)
BIO8 = Mean Temperature of Wettest Quarter
BIO9 = Mean Temperature of Driest Quarter
BIO10 = Mean Temperature of Warmest Quarter
BIO11 = Mean Temperature of Coldest Quarter
BIO12 = Annual Precipitation
BIO13 = Precipitation of Wettest Month
BIO14 = Precipitation of Driest Month
BIO15 = Precipitation Seasonality (Coefficient of Variation)
BIO16 = Precipitation of Wettest Quarter
BIO17 = Precipitation of Driest Quarter
BIO18 = Precipitation of Warmest Quarter
BIO19 = Precipitation of Coldest Quarter

Lets plot the first indicator “Annual Mean Temperature”:

#Plot
plot(climate$bio1, main="Annual Mean Temperature")

SRTM 90 Elevation

Last but not least, lets have a look at the SRTM 90 Data. We will use the getData() function one last time:

srtm <- getData('SRTM', lon=16, lat=48)

1. Select Dataset: The first argument specifies the dataset. ‘SRTM’ returns the SRTM 90 elevation data.
2. Specify Lon: The second argument specifies the lon of the SRTM tile.
3. Specify Lat:  The second argument specifies the lat of the SRTM tile.

The code above will return one SRTM Tile somewhere around Vienna. Let’s plot the adm. boundaries of Austria together with the SRTM Tile in one plot:

plot(srtm)
plot(austria0, add=TRUE)

As you can see, not all of Austria is covered by this tile. Lets download two more eastern tiles and mosaic them to get the full extent of Austria. You can get more info on the tile extent here.

#Download two more tiles
srtm2 <- getData('SRTM', lon=13, lat=48)
srtm3 <- getData('SRTM', lon=9, lat=48)

#Mosaic/merge srtm tiles
srtmmosaic <- mosaic(srtm, srtm2, srtm3, fun=mean)

Lets plot the result:

plot(srtmmosaic, main="Elevation (SRTM)")
plot(austria0, add=TRUE)

I hope this gave you a good first impression on how to acquire spatial data with R.  More useful tutorials will follow, until then you can get more information here:

http://www.inside-r.org/packages/cran/raster/docs/getData