On Thu, Oct 13, 2011 at 1:40 PM, <[email protected]> wrote: > Im still not entirely sure what youre getting at: spatial correlation of > origin/destination locations to each other? Or some characteristic of the > origin/destination pairs? It sounds like the latter, but then the question > is correlated with what?**** > > >
It sounds like you've got a bivariate point pattern - a set of origin points and a set of destination points - and you want to ask questions about the relationship of the underlying processes... Read: http://www.amazon.co.uk/Statistical-Analysis-Spatial-Point-Patterns/dp/0340740701 which should give you an outline. You probably want to use some kind of bivariate K-function. This estimates the number of events that occur within a distance of an event, based on the type. If type 2 and type 1 events are being generated by the same process, then the number of type 1's and type 2's within a distance from a type 1 or a type 2 event should be the same (within estimation errors, in practice). For example, if thefts mostly happen in the east side of town, and recoveries on the other side of the tracks, then you'll see more thefts near thefts than recoveries near to thefts. If the number of recoveries near thefts is the same as the number of other thefts, then you can possibly conclude the reverse, that its all happening everywhere. An interesting complication could be that you can link each theft event with its recovery event, so maybe what you really have is a spatial line process. Possibly you might want to look at the length, direction, orientation, and endpoint-locations of the lines... The spatstat package can do all the point-pattern stuff, and splancs has some K-function code too... Barry [[alternative HTML version deleted]]
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