[R-sig-Geo] Inference of local Gi*

[Anaïs Ladoy]

Dear list members,

I'm currently working on a point dataset, from which I want to conduct
a Hot Spot Analysis with local Gi* statistics (Getis-Ord).

I'm trying to find a way of computing its significance. I see two ways
of computing significance in this case:

1) Compare the obtained local Gi from spdep::localG to a normal
distribution. But here I have several questions :
a) In my first case study (BMI value of 15 000 participants in a cohort
study), the distribution of local Gi is far from normal (it is bimodal
with a mode around very negative values and a mode around 0). However,
I do need a normal distribution of Gi in order to compare it with a
normal distribution, right? Or am I missing something here? What should
I do in this case?
b) In my second case study (Years of life lost for 30 000 individuals),
the distribution of Gi returned by spdep::localG is approximately
normal but the standard deviation is far from 1. In fact, in
spdep::localG, the Gi values are supposedly standardized (from what I
understood using an analytical mean and variance). Should I use these
to compare to a normal distribution, or should I use raw G values
(using return_internals=TRUE) and standardize them with the observed
mean and variance of Gi? Does it cause a problem that my observed
variance does not match the analytical variance?

2) Compute permutations
However this is not implemented in R for localG. I tried using PySAL
but the initial file is big and the weight file is huge, and my
computer crashes. Any thoughts to solve this issue?

Thank you for any feedback.
Kind regards,
Anaïs

--
Anaïs Ladoy
PhD student, Laboratory of Geographic Information Systems, Swiss
Federal Institute of Technology in Lausanne (EPFL), Switzerland.