GeoXp (version 1.6.2)

variocloudmap: Interactive variocloud and map

Description

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.

Usage

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)

Arguments

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

Value

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.

Details

For some couple of sites $(s_i,s_j)$, the graph represents on the y-axis the semi squared difference between $var_i$ and $var_j$ : $$\gamma_{ij}=\frac{1}{2}(var_i-var_j)^2$$ and on the x-absis the distance $h_(ij)$ between $s_i$ and $s_j$. The semi Empirical variogram has been calculated as : $$\gamma(h)=\frac{1}{2|N(h)|}\sum_{N(h)}(Z(s_i)-Z(s_j))^2$$ 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).

References

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.

See Also

angleplotmap, driftmap

Examples

#####
# 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))