Dear SpatStat Experts,

My goal with spatstat is to identify clusters of incidents.  That is, to
identify from among the noise those incidents which are very similar with
respect to space and time.  To this end I have built a ppx object and my
aim is to perform the clustering analysis.

Having searched through the many pages of spatstat documentation, I was
disappointed to find the clusterset() function takes only a ppp object as
input.  On a ppx object, the best I have found is the nearest neighbour
distances function, nndist().

While nndist() provides the Euclidean distance from each point to its
nearest neighbour, I am struggling with how to proceed from here to
identify clusters.  Many traditional clustering analyses will require an
object of the class 'dist' as input, and the output of nndist() is a simple
numeric vector.  Hence, I am uncertain about how to best pre-process the
Euclidean distances for input into clustering analysis.

What's more, I am curious to receive your advice around the optimal method
for defining units of measure for the time dimension.  With each incident
recorded in POSIXC date/time format, I can produce a "time dimension" as a
numerical vector of "the number of seconds since the beginning of 1970."
 However, with spatial XY coordinates falling relatively close to each
other (all within the few kilometers boxx of the San Francisco city
limits), this approach risks producing dramatically large values for the time
dimension relative to the spatial XY distance between points.

So, my questions for you are:

Given an incidents data set with X,Y, and date/time-stamps (along with
other factor variables that can optionally be used as input),

   1. which methods in the spatstat package are most suitable for
identifying spatio-temporal clusters ?
   2. which methods are most suitable for defining the ideal units of
measure for the time dimension ?
   3. how to convert nndist() values into an object of class 'dist' and /
or any other class that is suitable for clustering analysis ?

Your assistance is very much appreciated.

Thank you very much for your help!


*Keith Helfrich*
Business Intelligence
bi.keithhelfrich@gmail.com
 <http://www.linkedin.com/in/keithhelfrich> Tel: *415-400-6640
<415-400-6640>*

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