On 20/10/14 17:03, Nicolas Meurisse wrote:
Hi all,
I want to sample locations within a defined area, but also optimize
the spatial arrangement of my sample locations in order to:
1. sample some parts of the area more intensively (e.g. as a
function of previous findings intensity, as it could be illustrated
by a density map)
2. sample at regular intervals within each area, as opposed to
pure random sampling (e.g. "regular" or "stratified random" as
defined in sp package)
Here an example dataset:
n <- 10000
x1 <- matrix(rnorm(n), ncol = 2)
x2 <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2)
x <- rbind(x1, x2)
# scatterplot with smoothed densities color representation
smoothScatter(x)
# random sampling (not appropriate, as we want an arrangement that
optimizes conditions 1 and 2)
points(sample(x[,1],20), sample(x[,2],20), col="red", pch=16)
I would greatly appreciate any insight someone might have as it seems
there are lot of potential applications using such "stratified"
sampling.
It seems to that desideratum #1 can easily be effected using the rthin()
function from the spatstat package.
I'm not clear what you actually want to do in respect of desideratum #2,
but it is at least conceivable that this could be effected in the same
way: Provide an image or function specifying the intervals (regions?) from
which you wish to sample. I.e. make an image (object of class "im") or
function which takes the value 1 within the regions from which you wish to
sample and 0 outside those regions.
cheers,
Rolf Turner
--
Rolf Turner
Technical Editor ANZJS