loa (version 0.3.1.1)

3.1.example.data: Example data for use with loa

Description

Example data intended for use with examples in loa.

Usage

lat.lon.meuse
   roadmap.meuse

Format

lat.lon.meuse is a modified form of the meuse data set taken from the sp package. Here, coordinate (x,y) elements have been transformed to latitudes and longtiudes and the object class has been converted from SpatialPointsDataFrame to data.frame.

roadmap.meuse is a previously downloaded map intended for use with off-line plot examples using lat.lon.meuse.

Details

lat.lon.meuse was generated using method based on mzn object production in https://github.com/etes/Geoprocessing/blob/master/heatmap.R.

library(sp); library(gstat); library(rgdal)
data(meuse)
coordinates(meuse) =~ x + y
proj4string(meuse) = CRS("+init=epsg:28992")
meuse1 = spTransform(meuse, CRS("+init=epsg:4326"))
meuse2=as.data.frame(meuse1)
mzn=meuse2[,c(14,13,4)]
names(mzn)<-c("Latitude","Longitude","zinc")

roadmap.meuse was generated using:

RgoogleMapsPlot(zinc~latitude*longitude, data=lat.lon.meuse, size=c(450,500), maptype="roadmap")
roadmap.meuse <- loaMapArg()

References

For meuse:

M G J Rikken and R P G Van Rijn, 1993. Soil pollution with heavy metals - an inquiry into spatial variation, cost of mapping and the risk evaluation of copper, cadmium, lead and zinc in the floodplains of the Meuse west of Stein, the Netherlands. Doctoraalveldwerkverslag, Dept. of Physical Geography, Utrecht University

P.A. Burrough, R.A. McDonnell, 1998. Principles of Geographical Information Systems. Oxford University Press.

Stichting voor Bodemkartering (Stiboka), 1970. Bodemkaart van Nederland : Blad 59 Peer, Blad 60 West en 60 Oost Sittard: schaal 1 : 50 000. Wageningen, Stiboka.

For sp:

Roger S. Bivand, Edzer J. Pebesma, Virgilio Gomez-Rubio, 2008. Applied spatial data analysis with R. Springer, NY. http://www.asdar-book.org/

Pebesma, E.J., R.S. Bivand, 2005. Classes and methods for spatial data in R. R News 5 (2), http://cran.r-project.org/doc/Rnews/.

Examples

## data structure of lat.lon.meuse

head(lat.lon.meuse)

##  Use a subsample of lat.lon.meuse

temp <- lat.lon.meuse[sample(1:155, 15),]


## various loaPlot examples 
## using lat.lon.meuse

loaPlot(~longitude*latitude, data=temp)

loaPlot(cadmium~longitude*latitude, data=temp)

loaPlot(cadmium~longitude*latitude, col.regions=c("green", "red"), 
        data=temp)

loaPlot(cadmium*50+copper*10+lead*2+zinc~longitude*latitude, panel.zcases = TRUE, 
        col.regions=c("green", "red"), 
        key.z.main="Concentrations", data=temp)


## (off line) GoogleMap example
## using lat.lon.meuse and roadmap.meuse

GoogleMap(zinc~latitude*longitude, data=temp, 
          map=roadmap.meuse, col.regions=c("grey", "darkred"))

#  Note 1:
#  With loaPlot and GoogleMap, note latitude, longitude axes 
#  assignments: 
#  loaPlot plots z ~ x * y | cond. 
#  GoogleMap plots z ~ lat * lon | cond (z ~ y * x | cond)

#  Note 2:
#  Here, the map argument is supplied so example works off-line. 
#  If not supplied and R is on-line, GoogleMap will (try to) get map 
#  from the Google API. Look at:
if (FALSE) {
   GoogleMap(zinc~latitude*longitude, data=lat.lon.meuse, 
             col.regions=c("grey", "darkred"))}
#  (The map will appear slightly different, because non-default 
#  size and maptype settings were used to make roadmap.meuse. See above.)