I would appreciate advice on how to do a spatial statistics task:
detecting points at a high risk of Cases (and cluster analysis). I have
semi-randomly located vector point data with 2 attributes (Cases and
Controls) at each point, i.e. marked point data. I want to apply spatial
smoothing as I hypothesize that the underlying process is such that
clusters of cases are of interest (although I also know that the process
operates on a small enough scale that isolated points with high numbers
of cases are also of interest). Having read the book by Bivand et al
"Applied Spatial Data Analysis with R" I was following the approach of
an inhomogeneous Poisson process analysis (p.175) to calculate a risk
image of log(cases/controls), which incidentally gives a few challenges
with divide by zeros. This risk image (I used data class ppp in R
library SpatStat as I had marked point data) is a raster interpolated
image, even though I originated with vector point data. However, the aim
of my analysis is to score each of the (vector) points with its risk
value, i.e. give each of my original vector points a new attribute
called Risk. For my application I am not interested in what happens in
between my point locations (as the point locations have a meaning). So
my problem is how to get the Risk value at the original point locations.
My current approach involves converting the points to raster, so I lose
the precise point location and I have a large data set so in practice
cannot set a very fine resolution. So my key question is: please, can
someone offer me advice on how to get the Risk value at the original
point locations? 

I wonder if I can avoid raster and do all processing in a vector map
(perhaps by interpolation or spline approximation). This would mean I
can avoid resampling a raster map back to the original vector point
locations as occasionally  the locations are so close (less than
resolution) this will create inaccuracy. 

(Background: I have not done much spatial statistics before so am trying
to keep it simple; I have used GRASS; not much experience with R).

When I originally import the map into R it has class
SpatialPointsDataFrame. Below are a couple of code examples:
> Case <- smooth.ppp(Cases,75, eps=50)   # smooth my Case data with a
kernel width of 75m; 50m pixel dimension in the resulting smoothed ppp.
> plot(eval.im( log((Case+9e-6)/(Control+1e-5)) + 0.3521755)) # to get
the Risk map; the 9e6- and 1e-5 are how I avoid divide by zero type
problems.

Thankyou,
Mike Elliott,
UK.

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