Do you mean something similar to what the bivariate Ripley's K function
does for spatial point distribution?

Hm, if yes I do not know of anything similar (would love to be pointed to
some solution too).

But what I've done once to handle a similar problem was: 1) I calculated
LISA measures for both the cases and the population; 2) calculated the
correlation between both LISA considering each spatial feature as an entry.
Of ourse you could follow such route in different ways, for instance doing
a bivariate regression so to easily extract both the R^2 and the F-test
(for some sort of inference test as you've mentioned).
But this is only my insight on how to have an informal glance at the issue.
I am sure other people in the list can point you to less dirty ways of
achieving what you ask.

Best,

Fabricio


2013/7/21 Ferdinand Bersse <fvbersse@gmail.com>

> Dear list,
>
> Let's say I have a city map divided in neighborhoods (barrios), in shape
> polygon form. For each barrio I had the information of crime occurances
> (cases) in a given year and also of number of people living in the area.
>
> What I would like to do is to compare the spatial pattern of the crime
> cases and spatial pattern of population, so to have a formal check of if
> the clustering of crimes isn't only following the clustering of population.
>
> Is there any such way to test that? Both a measure of how much those
> patterns are correlated or a inference test of the correlation (to test the
> null hypothesis) would be very welcome.
>
> Thanks,
>
> Ferd.
>
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