[R-sig-Geo] bivariate spatial correlation in R

[Rafael Pereira]

Roger,

This example was provided only for the sake or making the code easily
reproducible for others and I'm more interested in how the bi-variate Moran
could be implemented in R, but your comments are very much welcomed and
I've made changes to the question.

My actual case study looks at bi-variate spatial correlation between (a)
average household income per capita and (b) proportion of jobs in the city
that are accessible under 60 minutes by transit. I don't think I could use
rates in this case but I will normalize the variables using
scale(data$variable).

best,

Rafael H M Pereira

On Mon, Jul 24, 2017 at 7:56 PM, Roger Bivand <Roger.Bivand at nhh.no> wrote:

> On Mon, 24 Jul 2017, Rafael Pereira wrote:
>
> Hi all,
>>
>> I would like to ask whether some you conducted bi-variate spatial
>> correlation in R.
>>
>> I know the bi-variate Moran's I is not implemented in the spdep library.
>> I left a question on SO but also wanted to hear if anyone if the mainlist
>> have come across this.
>> https://stackoverflow.com/questions/45177590/map-of-bivariat
>> e-spatial-correlation-in-r-bivariate-lisa
>>
>> I also know Roger Bivand has implemented the L index proposed by Lee
>> (2001)
>> in spdep, but I'm not I'm not sure whether the L local correlation
>> coefficients can be interpreted the same way as the local Moran's I
>> coefficients. I couldn't find any reference commenting on this issue. I
>> would very much appreciate your thoughts this.
>>
>
> In the SO question, and in the follow-up, your presumably throw-away
> example makes fundamental mistakes. The code in spdep by Virgilio
> Gómez-Rubio is for uni- and bivariate L, and produces point values of local
> L. This isn't the main problem, which is rather that you are not taking
> account of the underlying population counts, nor shrinking any estimates of
> significance to accommodate population sizes. Population sizes vary from 0
> to 11858, with the lower quartile at 3164 and upper 5698:
> plot(ecdf(oregon.tract$pop2000)). Should you be comparing rates in stead?
> These are also compositional variables (sum to pop2000, or 1 if rates) with
> the other missing components. You would probably be better served by tools
> examining spatial segregation, such as for example the seg package.
>
> The 0 count populations cause problems for an unofficial alternative, the
> black/white ratio:
>
> oregon.tract1 <- oregon.tract[oregon.tract$white > 0,]
> oregon.tract1$rat <- oregon.tract1$black/oregon.tract1$white
> nb <- poly2nb(oregon.tract1)
> lw <- nb2listw(nb)
>
> which should still be adjusted by weighting:
>
> lm0 <- lm(rat ~ 1, weights=pop2000, data=oregon.tract1)
>
> I'm not advising this, but running localmoran.sad on this model output
> yields SAD p-values < 0.05 after FDR correction only in contiguous tracts
> on the Washington state line in Portland between the Columbia and
> Williamette rivers. So do look at the variables you are using before
> rushing into things.
>
> Hope this clarifies,
>
> Roger
>
>
>> best,
>>
>> Rafael HM Pereira
>> http://urbandemographics.blogspot.com
>>
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>>
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>>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: Roger.Bivand at nhh.no
> Editor-in-Chief of The R Journal, https://journal.r-project.org/index.html
> http://orcid.org/0000-0003-2392-6140
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en

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