Thanks Roger for your feedback and clarification.

Best regards.

El lun., 27 abr. 2020 a las 5:04, Roger Bivand (<

> On Sat, 25 Apr 2020, Jose Ramon Martinez Batlle wrote:

>

> > Dear Anaïs.

> >

> > I am sure more experienced members will give you a better answer, but

> until

> > that I will try to help.

> >

> > 1) If I understood correctly, the spatial objects have 15 000 and 30 000

> > points in each case study, respectively. If this is the case, I am afraid

> > that nb objects of such large datasets surely would have an impact on the

> > system performance when used in subsequent tasks. The best I can suggest

> is

> > to try some sort of spatial binning if possible (e.g. hexbins), but at

> the

> > same time accounting for the modifiable areal unit problem.

> >

> > 2) The spdep:localG help page states that "For inference, a

> Bonferroni-type

> > test is suggested in the references, where tables of critical values may

> be

> > found". The source mentioned is free access, and can be found here:

> >

> > Ord, J. K. and Getis, A. 1995 Local spatial autocorrelation statistics:

> > distributional issues and an application. Geographical Analysis, 27,

> 286–306

> >

>

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1995.tb00912.x> >

> > Standard measures (critical values) for selected percentiles and number

> of

> > entities, are included in Table 3 of the cited reference. Since the

> values

> > returned from localG are Z-values, you can use them to determine whether

> > the critical value chosen is exceeded and thus infer significant local

> > spatial association for each entity.

>

> Thanks, José, you are quite correct that false discovery rate problems are

> among the main reasons why so-called "hot-spot" analyses may be very

> misleading, in appearing to give an inferential basis for apparent map

> pattern.

>

> In our survey paper with David Wong referenced on ?localG,

>

https://doi.org/10.1007/s11749-018-0599-x, we show that the analytical

> and

> bootstrap-based inferences are similar - the normality is related not to

> the underlying variable seen globally, but the the local behaviour of the

> statistic. For this reason, bootstrap permutation implementations are not

> included in spdep, though the code is available if need be. Please

> indicate whether users would like this code included for comparative

> purposes here or in a github issue on

>

https://github.com/r-spatial/spdep/issues/.

>

> Further, the LOSH statistic, which is a measure of local spatial

> heteroscedasticity building on local G, provides a little insight into the

> problems raised for so-called "hot-spot" analyses by variability across

> the study area in the behaviour of the variable of interest. If, for

> example, the variable of interest is influenced by a background variable

> with a spatial pattern, we will probably find "hot-spots" which look like

> the omitted background variable on a map.

>

> While local G cannot take residuals of a linear model, local Moran's I can

> do so. For local G, we do not have exact case-by-case standard deviates;

> we do have these for local Moran's I as discussed in the article with

> David Wong, and they very typically reduce strongly the counts of

> apparently significant local statistcs even before adjusting p-values for

> FDR. Finally, only some local measures can adjust for global

> autocorrelation - unadjusted local measures also respond to the presence

> of global autocorrelation.

>

> On balance, judicious choice of class intervals in mapping a variable of

> interest may prove more helpful than trying to present wobbly inferences

> from ESDA.

>

> Hope this isn't too discouraging,

>

> Roger

>

>

> >

> > Kind regards.

> > José

> >

> > El vie., 24 abr. 2020 a las 14:00, Anaïs Ladoy (<

[hidden email]>)

> > escribió:

> >

> >> 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.

> >>

> >> _______________________________________________

> >> R-sig-Geo mailing list

> >>

[hidden email]
> >>

https://stat.ethz.ch/mailman/listinfo/r-sig-geo> >>

> >

> >

> >

>

> --

> Roger Bivand

> Department of Economics, Norwegian School of Economics,

> Helleveien 30, N-5045 Bergen, Norway.

> voice: +47 55 95 93 55; e-mail:

[hidden email]
>

https://orcid.org/0000-0003-2392-6140>

https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en