Displays a concentration area plot example for the Kola data. This procedure ist useful for determining if mulitple populations that are spatially dependent are present in a data set. For a more general function see concarea.
x: name of the x-axis spatial coordinate, the eastings
y: name of the y-axis spatial coordinate, the northings
z: name of the variable to be processed and plotted
zname: a title for the x-axes of the qp-plot and concentration area plot.
caname: a title for the image of interpolated data.
borders: either NULL or character string with the name of the list with list elements x and y for x- and y-coordinates of map borders
logx: if it is required to make a logarithmis data transformation for the interpolation
ifrev: if FALSE the empirical function ist plotted from highest value to lowest
ngrid: default value is 100
xlim: the range for the x-axis
xcoord: a title for the x-axis, defaults to "Easting"
ycoord: a title for the y-axis, defaults to "Northing"
ifbw: if the plot is drawn in black and white
x.logfinetick: how fine are the tick marks on log-scale on x-axis
y.logfinetick: how fine are the tick marks on log-scale on y-axis
ifjit: default value is FALSE
ncp: default value is 0
Details
The function assumes that the area is proportional to the count of grid points. To be a reasonable model the data points should be 'evenly' spread over the plane. The interpolated grid size ist computed as (max(x) - min(x))/ngrid, with a default value of 100 for ngrid. Akima's interpolation function is used to obtain a linear interpolation between the spatial data values.
Returns
An example concentration area plot for Kola is created.
References
C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.