swissRain: Swiss rainfall data

Description Usage Format Source Examples

Description

Data from the SIC-97 project: Spatial Interpolation Comparison.

Usage

1
data("swissRain")

Format

swissRain is a SpatialPolygonsDataFrame 100 daily rainfall measurements made in Switzerland on the 8th of May 1986. swissAltitude is a raster of elevation data, and swissLandType is a raster of land cover types.

Source

https://wiki.52north.org/AI_GEOSTATS/AI_GEOSTATSData and http://srtm.csi.cgiar.org and https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table

Examples

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data("swissRain")
plot(swissAltitude, main="elevation")
points(swissRain)
plot(swissBorder, add=TRUE)


# land type, a categorical variable
commonValues  = sort(table(values(swissLandType)),decreasing=TRUE)[1:5]
commonValues=commonValues[!names(commonValues)==0]

thelevels = levels(swissLandType)[[1]]$ID
thebreaks = c(-0.5, 0.5+thelevels)
thecol = rep(NA, length(thelevels))
names(thecol) = as.character(thelevels)

thecol[names(commonValues)] = rainbow(length(commonValues))

plot(swissLandType, breaks=thebreaks, col=thecol,legend=FALSE,
	main="land type")
points(swissRain)
plot(swissBorder, add=TRUE)


legend("topleft",fill=thecol[names(commonValues)],
		legend=levels(swissLandType)[[1]][
						match(as.integer(names(commonValues)),
								levels(swissLandType)[[1]]$ID),
						"Category"],
				bty='n'
				)

# code to assemble the dataset
## Not run: 
dataDir = "/store/patirck/spatialData/"
download.file("http://mldata.org/repository/data/download/spat-interp-comparison-1997/",
	destfile=paste(dataDir, "swiss.zip",sep=""))
unzip(paste(dataDir, 'swiss.zip',sep=""), exdir=dataDir)
swissRain = read.table(paste(dataDir, "sic_obs.dat",sep=""),sep=',', 
	col.names=c('ID','x','y','rain'))
# the following seems to make the coordinates line up with epsg:2056
swissRain$x = swissRain$x - 17791.29 + 2672591 
swissRain$y = swissRain$y - 13224.66 + 1200225
# the readme file says rain is in tenths of mm 
swissRain$rain= swissRain$rain / 10  
library(sp)
library(rgdal)
# create projection without epsg code so rgdal doesn't need to be loaded
theproj = CRSargs(CRS("+init=epsg:2056"))
theproj = gsub("\+init=epsg:[[:digit:]]+ ", "", theproj)
theproj = CRS(theproj)

swissRain = SpatialPointsDataFrame(swissRain[,c('x','y')], data=swissRain[,c('ID','rain')], 
	proj4string=theproj)



#######
# Swiss Border
#######


library('raster')
swissBorder = getData('GADM', country='CHE', level=0)
isChar = which(unlist(lapply(swissBorder@data, is.character)))
isUtf = which(
	unlist(lapply(swissBorder@data[,isChar], 
		Encoding)) == 'UTF-8')
swissBorder = swissBorder[,
	!match(names(swissBorder), names(isUtf), nomatch=0)
	]
library(rgdal)
swissBorder = spTransform(swissBorder, CRS(proj4string(swissRain)))

############
# land type
############
# see loaloa's help file for installation of the MODIS package
library(MODIS)
MODISoptions(gdalPath="/usr/bin/", 
	localArcPath=dataDir, outDirPath=dataDir)
options()[grep("MODIS", names(options()), value=TRUE)]

myProduct = "MCD12Q1"
getProduct(myProduct)

thehdf=getHdf(product=myProduct,
		begin="2002-01-01",end="2002-01-02",
		extent=extent(spTransform(swissBorder, CRS("+init=epsg:4326"))))

layerNames = getSds(thehdf[[1]][1])$SDSnames
ltLayer = grep("Type_1$", layerNames)
theString = rep(0, length(layerNames))
theString[ltLayer] = 1
theString = paste(theString, collapse="")

runGdal(product=myProduct,
		begin="2002-01-01",end="2002-01-02",
		outProj = proj4string(swissRain),
		pixelSize=2000, job="loa",
		SDSstring = theString,
		extent=extent(spTransform(swissBorder, CRS("+init=epsg:4326"))))

# find file name 
thenames = preStack(
		path = paste(options()$MODIS_outDirPath, "loa",sep=""),
		pattern=myProduct)
swissLandType = raster(thenames)
swissLandType = crop(swissLandType, extend(extent(swissBorder),20000))


swissLandType = as.factor(swissLandType)

# labels of land types
library(XML)
labels = readHTMLTable("http://nsidc.org/data/ease/ancillary.html")
labels = labels[[grep("Land Cover Classes", names(labels))]]
classCol = grep("Class Number", names(labels))
labels[,classCol] = as.integer(as.character(labels[,classCol]))

labels[  grep("Water", labels$Category), 
		classCol		
] = 0
labelVec = as.character(labels$Category)
names(labelVec) = as.character(labels[,classCol])


levels(swissLandType)[[1]]$Category = 
		labelVec[as.character(levels(swissLandType)[[1]]$ID)]

levels(swissLandType)[[1]]$col = NA

theForests = grep("forest", levels(swissLandType)[[1]]$Category, 
	ignore.case=TRUE)
	
library(RColorBrewer)	
levels(swissLandType)[[1]][theForests,"col"] = 
	brewer.pal(length(theForests)+1, "Greens")[-1]
	
levels(swissLandType)[[1]][
	grep("snow", levels(swissLandType)[[1]]$Category,ignore.case=TRUE),
	"col"] = "#FFFFFF"

levels(swissLandType)[[1]][
	grep("water", levels(swissLandType)[[1]]$Category,ignore.case=TRUE),
	"col"] = "#0000FF"

levels(swissLandType)[[1]][
	grep("grass", levels(swissLandType)[[1]]$Category,ignore.case=TRUE),
	"col"] = "#CCBB00"

	
stillNA = is.na(levels(swissLandType)[[1]]$col)
levels(swissLandType)[[1]][stillNA, "col"] =
	brewer.pal(sum(stillNA), "Set3")
	
	

swissLandType@legend@colortable = levels(swissLandType)[[1]]$col

levels(swissLandType)[[1]]$n = table(values(swissLandType))

plot(swissLandType)
mostCommon = levels(swissLandType)[[1]]$n >= 700
legend("topright", 	
	fill=levels(swissLandType)[[1]][mostCommon,"col"],
	legend = substr(
		levels(swissLandType)[[1]][mostCommon,"Category"],
		1, 12)
	)
	
table(extract(swissLandType, swissRain), exclude=NULL)


#### 
# SwissAltitude
###
library(raster)
download.file('http://biogeo.ucdavis.edu/data/diva/alt/CHE_alt.zip',
	destfile=paste(dataDir,'CHE_alt.zip',sep=""))
unzip(paste(dataDir, 'CHE_alt.zip',sep=""), exdir=dataDir)
swissAltitude = raster(paste(dataDir, "CHE_alt.gri",sep=""))
swissAltitude = projectRaster(swissAltitude, 
	crs=CRS(proj4string(swissRain)))
swissAltitude = aggregate(swissAltitude,fact=2)
 
 
save(swissRain, swissAltitude, swissBorder, swissLandType,
		file="~/workspace/diseasemapping/pkg/geostatsp/data/swissRain.RData",
		compress="xz")

## End(Not run)

geostatsp documentation built on July 14, 2018, 9:01 a.m.