variocloudmap: Interactive variocloud and map
In GeoXp: Interactive exploratory spatial data analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

The function variocloudmap() draws a semi-variocloud (directional or omnidirectional) and a map. It is used to detect spatial autocorrelation. Possibility to draw the empirical semi-variogram and a robust empirical semi-variogram.

variocloudmap(sp.obj, name.var, bin=NULL, quantiles=TRUE,
names.attr=names(sp.obj), criteria=NULL, carte=NULL, identify=FALSE, cex.lab=0.8,
pch=16, col="lightblue3", xlab="", ylab="", axes=FALSE, lablong="", lablat="",
xlim=NULL, ylim=NULL)

`sp.obj`	object of class extending Spatial-class
`name.var`	a character; attribute name or column number in attribute table
`bin`	a vector of numeric values where empirical variogram will be evaluated
`quantiles`	a boolean to represent the Additive Quantile Regression Smoothing
`names.attr`	names to use in panel (if different from the names of variable used in sp.obj)
`criteria`	a vector of boolean of size the number of Spatial Units, which permit to represent preselected sites with a cross, using the tcltk window
`carte`	matrix with 2 columns for drawing spatial polygonal contours : x and y coordinates of the vertices of the polygon
`identify`	if not FALSE, identify plotted objects (currently only working for points plots). Labels for identification are the row.names of the attribute table row.names(as.data.frame(sp.obj)).
`cex.lab`	character size of label
`pch`	16 by default, symbol for selected points
`col`	"lightblue3" by default, color of bars on the cloud map
`xlab`	a title for the graphic x-axis
`ylab`	a title for the graphic y-axis
`axes`	a boolean with TRUE for drawing axes on the map
`lablong`	name of the x-axis that will be printed on the map
`lablat`	name of the y-axis that will be printed on the map
`xlim`	the x limits of the plot
`ylim`	the y limits of the plot

For some couple of sites (s_i,s_j), the graph represents on the y-axis the semi squared difference between var_i and var_j :

gamma_ij=0.5(var_i-var_j)^2

and on the x-absis the distance h_(ij) between s_i and s_j. The semi Empirical variogram has been calculated as :

gamma(h)=0.5/|N(h)|sum_(N(h))(Z(s_i)-Z(s_j))^2

where

N(h)={(s_i,s_j):s_i-s_j=h;i,j=1,...,n}

and the robust version :

gamma(h)=frac(1)(2(0.457+frac(0.494)(|N(h)|)))(frac(1)(|N(h)|)sum_(N(h))|Z(s_i)-Z(s_j)|^(1/2))^4

The number N of points to evaluate the empirical variogram and the distance epsilon between points are set as follows :

N=frac(1)(max(30/n^2,0.08,d/D))

and :

epsilon=frac(D)(N)

with :

D=max(h_ij)-min(h_ij)

and :

d=max(h_ij^(l)-h_ij^(l+1)),

where h^(l) is the vector of sorted distances. In options, possibility to represent a regression quantile smoothing spline g_alpha (in that case the points below this quantile curve are not drawn).

In the case where user click on save results button, a matrix of integer is created as a global variable in last.select object. It corresponds to the numbers of spatial unit corresponding to couple of sites selected just before leaving the Tk window.

Thomas-Agnan C., Aragon Y., Ruiz-Gazen A., Laurent T., Robidou L.

Thibault Laurent, Anne Ruiz-Gazen, Christine Thomas-Agnan (2012), GeoXp: An R Package for Exploratory Spatial Data Analysis. Journal of Statistical Software, 47(2), 1-23.

Cressie N. and Hawkins D. (1980), Robust estimation of the variogram, in Journal of the international association for mathematical geology, 13, 115-125.

angleplotmap, driftmap

#####
# Data Meuse
data(meuse)

# meuse is a data.frame object. We have to create
# a Spatial object, by using first the longitude and latitude
# to create Spatial Points object ...
meuse.sp = SpatialPoints(cbind(meuse$x,meuse$y))
# ... and then by integrating other variables to create SpatialPointsDataFrame
meuse.spdf = SpatialPointsDataFrame(meuse.sp, meuse)

# meuse.riv is used for contour plot
data(meuse.riv)

# example of use of variocloudmap
variocloudmap(meuse.spdf, "zinc", quantiles=TRUE, bin=seq(0,2000,100),
xlim=c(0,2000),ylim=c(0,500000),pch=2,carte=meuse.riv[c(21:65,110:153),],
criteria=(meuse$lime==1))