Hi LS,
you can us function tess to create a tesselation and then split your ppp
with it. See this example:
data(lansing)
lansing
gridA<- tess(xgrid=seq(0,1, by=0.1), ygrid=seq(0,1,by=0.1))
gridA
plot(lansing)
plot(gridA, add=T)
lansing.g=split(lansing, gridA)
lansing.g
length(lansing.g)
lansing.g[[1]]
lansing.g[[1]]$n
Kest(lansing.g[[1]])
Kest(lansing.g[[2]])
...
Kest(lansing.g[[100]])
HTH,
Marcelino
El 30/06/2013 19:43, L S escribió:
Hi,
I am not very savvy with spatial data analysis (though I am doing my best
to learn). I have embarked on a spatial analysis project and am not sure
whether what I would like to do is possible.
My dataset is over 500,000 points (with lat, long attributes as well as a
single numerical attribute). The data was not collected in a spatially
regular or predictable way--the points are all over the place, with some
areas having greater concentration of points.
I created a point process object with my data, using a shape file as the
observation window (owin). I have been trying, with no success, to divide
this owin into a regular grid. For example, could I create 0.5 x 0.5
degree (or in any other measurement) gridded squares over the owin and then
access the points in each square? The purpose is to be able to analyze the
points in each grid independently and to be able to visualize the data
using the grids (such plotting the mean of points within the grid cell
instead of each individual point).
The most promising r function I have found thus far is the quadrantcount
function in spatstat where the owin is divided into grid cells and the
number of points in each cell is counted. However, I am unable to access
the points in each cell created by quadrantcount--is this possible at all?
For example, one cell may have 2000 points...can I then put those points
in a data frame and do data analysis on just those? Or, alternatively, to
use those cells for plotting the mean value of the points?
Any advice would be greatly appreciated. I look forward to hearing your
feedback and guidance.
Thanks,
LS
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