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

[Rafael Pereira]

Hi Roger,

you're correct in pointing out that the code would much more work to be
incorporated in the spdep library. This is something that Rogerio and I are
considering to do in the next few months as both of us find more time in
our agendas.

Thank you again for the discussion. The amount of work and attention you
put into this community is exceptional for someone as busy as you are and
this is much appreciated!

best,

Rafa

On Thu, Aug 3, 2017 at 10:13 AM, Roger Bivand <Roger.Bivand at nhh.no> wrote:

> On Wed, 2 Aug 2017, Rafael Pereira wrote:
>
> Roger,
>>
>>
>> Thank you for your response.
>>
>>
>> I recognize the data is not ideal and the analysis has limitations because
>> of the lack of information on population displacements that might have
>> occurred over the years. Nonetheless, there are plenty of data +
>> literature
>> showing how the spatial distribution of income classes and land use
>> patterns is fairly stable over time in this city, particularly for a short
>> timescales like in this analysis. Having said this, I believe these two
>> questions (1) what socioeconomic classes have gained more accessibility?
>> and (2) “were wealthier areas in 2010 able to attract more changes to
>> accessibility?” in the end ask the same thing but with different
>> phrasings,
>> though your phrasing (2) is more precise/correct.
>>
>>
>>
>> On the more technical discussion, I see your point that spatial AND
>> temporal correlation in my data would make permutation bootstrap
>> inappropriate to generate significance levels, thus making bivariate
>> Moran’s I biased. Thank you very much for the clarifications! This has
>> been
>> very helpful and I will have a closer look at which spatial regression
>> models are more appropriate for my analysis.
>>
>>
>> On a side note, do you think the function to calculate bivariate Moran’s I
>> is correct?  And could it be incorporated in the next version of spdep? If
>> so, please give credit to Rogério Barbosa, the researcher who proposed the
>> code in Stack Overflow.
>>
>
> Perhaps, but SO is usually ephemeral (nobody takes responsibility for
> documenting code and fixing bugs found later). I don't see any tests,
> documentation or accommodation of what spdep expects for edge cases - the
> function as it stands would need a lot of work to protect users from
> obvious blunders. There are no references to literature, nor to proven test
> cases which this implementation should match. We have an implementation of
> Lee (2001), but this is not the same, right? Which article gives the formal
> statistical development of the bivariate local Moran's I? Do we know that
> conditional simulation (permutation bootstrap) is valid in some cases, if
> so which? Is there a development of parametric bootstrap?
>
>
> Roger
>
>
>> best,
>>
>> Rafael HM Pereira
>> http://urbandemographics.blogspot.com
>>
>>
>> On Mon, Jul 31, 2017 at 10:52 PM, Roger Bivand <Roger.Bivand at nhh.no>
>> wrote:
>>
>> Rafael,
>>>
>>> I'm sorry, but there is no way you can logically "analyze who benefits
>>> the
>>> recent changes in the transport system in terms of access to jobs" from
>>> the
>>> data you have.
>>>
>>> Even if you had aggregate household income data for 2014 and 2017 (not
>>> for
>>> 2010 only), you would not know whether wealthier families had not
>>> dispaced
>>> poorer families as accessibility improved. You need individual data,
>>> either
>>> survey or register, preferably panel, to show that changes in
>>> accessibility
>>> change the economic welfare of households controlling for movement of
>>> households. The timestamps on the data make any attempt to do this very
>>> risky; the real findings from a hypothetical surevey-based panel might be
>>> completely different, especially if poorer households were displaced
>>> (also
>>> indirectly, through rising house prices driven by improved
>>> accessibility).
>>> Gauging the welfare effects of transport investments is very hard to
>>> instrument.
>>>
>>> The closest I could get was to map deciles of the change in access (more
>>> negatives than positives) and compare the aspatial income distributions:
>>>
>>> library(spdep)
>>> library(rgdal)
>>> map <- readOGR(dsn=".", layer="test_map")
>>> library(classInt)
>>> cI <- classIntervals(map$diffaccess, n=10, style="quantile")
>>> library(RColorBrewer)
>>> ybrpal <- brewer.pal(6, "YlOrBr")
>>> fC <- findColours(cI, ybrpal)
>>> qtm(map, fill="diffaccess", fill.breaks=cI$brks, format="Europe2")
>>> map$faccess <- factor(findInterval(map$diffaccess, cI$brks,
>>>   all.inside=TRUE), labels=names(attr(fC, "table")))
>>> qtm(map, fill="diffaccess", fill.breaks=cI$brks, format="Europe2")
>>> acc_income <- split(map$income, map$faccess)
>>> do.call("rbind", lapply(acc_income, summary))
>>> dens <- lapply(acc_income, density)
>>> plot(1, ylab="", xlab="", type="n", xlim=c(-2000, 11000), ylim=c(0,
>>>   0.002))
>>> for (i in seq(along=dens)) lines(dens[[i]], col=i)
>>> legend("topright", legend=names(dens), col=1:length(dens), lty=1,
>>> bty="n")
>>>
>>> These density curves really do not suggest any clear relationship, other
>>> than that some areas with increased accessibility had higher incomes in
>>> 2010.
>>>
>>> You can examine the reverse relationship - were aggregate areas that were
>>> more wealthy in 2010 able to attract more changes to accessibility? The
>>> answer seems to be yes, they were able to do this:
>>>
>>> nb <- poly2nb(map)
>>> lw <- nb2listw(nb, style = "W", zero.policy = T)
>>> lm.morantest(lm(diffaccess ~ I(income/1000), map), lw)
>>> # SLX model
>>> summary(lmSLX(diffaccess ~ I(income/1000), map, lw))
>>> lm.morantest(lmSLX(diffaccess ~ I(income/1000), map, lw), lw)
>>> # Spatial Durbin error model - SDEM
>>> obj <- errorsarlm(diffaccess ~ I(income/1000), map, lw, etype="emixed")
>>> summary(impacts(obj))
>>> summary(impacts(lmSLX(diffaccess ~ I(income/1000), map, lw)))
>>> LR.sarlm(lmSLX(diffaccess ~ I(income/1000), map, lw), obj)
>>>
>>> It would be possible to run lm.morantest.sad() on the output of the SDEM
>>> model taking global spatial autocorrelation into account. If you need
>>> that,
>>> follow up in this thread.
>>>
>>> Bivariate Moran's I should not be used in this case, but could be used in
>>> other cases - use in change over time is troubling because randomisation
>>> will not be a good guide as t=1 and t=2 are subject to temporal as well
>>> as
>>> spatial autocorrelation, so you cannot use permutation bootstrap to find
>>> a
>>> usable measure of significance.
>>>
>>> Hope this clarifies, and thanks for the code.
>>>
>>> Roger
>>>
>>> On Sun, 30 Jul 2017, Rafael Pereira wrote:
>>>
>>> Roger,
>>>
>>>>
>>>> Population and income data are single point in time and come from the
>>>> 2010
>>>> Census.
>>>>
>>>> Accessibility variables in 2014 and 2017 show the proportion of jobs
>>>> accessible by public transport under 60 minutes. The variable diffaccess
>>>> shows the difference between these two. It's in percentage points
>>>> (access2017 - access2014)
>>>>
>>>> best,
>>>>
>>>> Rafael H M Pereira
>>>> urbandemographics.blogspot.com
>>>>
>>>> On Sun, Jul 30, 2017 at 7:41 AM, Roger Bivand <Roger.Bivand at nhh.no>
>>>> wrote:
>>>>
>>>> Thanks, I'll get back when able, offline now. What are the units of
>>>>
>>>>> observation, and are aggregate household incomes observed only once?
>>>>>
>>>>> Roger
>>>>>
>>>>> Roger Bivand
>>>>> Norwegian School of Economics
>>>>> Bergen, Norway
>>>>>
>>>>>
>>>>>
>>>>> Fra: Rafael Pereira
>>>>> Sendt: søndag 30. juli, 00.39
>>>>> Emne: Re: [R-sig-Geo] bivariate spatial correlation in R
>>>>> Kopi: Rogério Barbosa, r-sig-geo at r-project.org
>>>>>
>>>>>
>>>>> Hi all, here is a reproducible example to calculate in R bivariate
>>>>> Moran's
>>>>> I and LISA clusters. This example is based on a this answer provided in
>>>>> SO*
>>>>> and it uses a toy model of my data. The R script and the shape file
>>>>> with
>>>>> the data are available on this link. https://gist.github.com/
>>>>> rafapereirabr/5348193abf779625f5e8c5090776a228 What this example does
>>>>> is
>>>>> to estimate the spatial association between household income per capita
>>>>> and
>>>>> the gains in accessibility to jobs. The aim is to analyze who benefits
>>>>> the
>>>>> recent changes in the transport system in terms of access to jobs. So
>>>>> the
>>>>> idea is not to find causal relationships, but spatial association
>>>>> between
>>>>> areas of high/low income who had high/low gains in accessibility. The
>>>>> variables in the data show info on the proportion of jobs accessible in
>>>>> both years 2014 and 2017 (access2014, access2017) and the difference
>>>>> between the two years in percentage points (diffaccess). Roger, I know
>>>>> you
>>>>> have shown to be a bit sceptical about this application of bivariate
>>>>> Moran's I. Do you still think a spatial regression would be more
>>>>> appropriate? Also, I would be glad to hear if others have comments on
>>>>> the
>>>>> code. This function is not implemented in any package so it would be
>>>>> great
>>>>> to have some feedback. Rafael H M Pereira
>>>>> urbandemographics.blogspot.com
>>>>> * https://stackoverflow.com/questions/45177590/map-of-
>>>>> bivariate-spatial-correlation-in-r-bivariate-lisa On Wed, Jul 26, 2017
>>>>> at
>>>>> 11:07 AM, Roger Bivand wrote: > On Wed, 26 Jul 2017, Rafael Pereira
>>>>> wrote:
>>>>>
>>>>> 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). >> > > Please provide a reproducible example,
>>>>> either
>>>>> with a link to a data > subset, or using a builtin data set. My guess
>>>>> is
>>>>> that you do not need > bi-variate spatial correlation at all, but
>>>>> rather
>>>>> a
>>>>> spatial regression. > > The "causal" variable would then the the
>>>>> proportion
>>>>> of jobs accessible > within 60 minutes by transit, though this is
>>>>> extremely
>>>>> blunt, and lots of > other covariates (demography, etc.) impact average
>>>>> household income per > capita (per block/tract?). Since there are many
>>>>> missing variables in your > specification, any spatial correlation
>>>>> would
>>>>> be
>>>>> most closely associated > with them (demography, housing costs,
>>>>> education,
>>>>> etc.), and the choice of > units of measurement would dominate the
>>>>> outcome.
>>>>>
>>>>> This is also why bi-variate spatial correlation is seldom a good idea,
>>>>>>
>>>>>>>
>>>>>>> I > believe. It can be done, but most likely shouldn't, unless it can
>>>>>>
>>>>> be >
>>>>> motivated properly. > > By the way, the weighted and FDR-corrected SAD
>>>>> local Moran's I p-values of > the black/white ratio for Oregon (your
>>>>> toy
>>>>> example) did deliver the goods - > if you zoom in in mapview::mapview,
>>>>> you
>>>>> can see that it detects a rate > hotspot between the rivers. > > Roger
>>>>> >
>>>>>
>>>>>>
>>>>>>
>>>>> best, >> >> Rafael H M Pereira >> >> On Mon, Jul 24, 2017 at 7:56 PM,
>>>>>>
>>>>>>>
>>>>>>>> Roger Bivand >> 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 0,] >>> oregon.tract1$rat >> nb >> lw >> >>> which
>>>>> should
>>>>> still be adjusted by weighting: >>> >>> lm0 >> >>> 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 >>>> >>>>
>>>>>>>
>>>>>> [[alternative HTML version deleted]] >>>> >>>>
>>>>> _______________________________________________ >>>> R-sig-Geo mailing
>>>>> list >>>> R-sig-Geo at r-project.org >>>> 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 <+47%2055%2095%2093%2055>; 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 >>> >> >> [[alternative HTML version
>>>>> deleted]] >> >> _______________________________________________ >>
>>>>> R-sig-Geo mailing list >> R-sig-Geo at r-project.org >>
>>>>> 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
>>>>> <+47%2055%2095%2093%2055>;
>>>>> 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/cita
>>>>> tions?user=AWeghB0AAAAJ&hl=en
>>>>>
>>>>>>
>>>>>> [[alternative HTML version deleted]] ______________________________
>>>>> _________________
>>>>> R-sig-Geo mailing list R-sig-Geo at r-project.org
>>>>> 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: 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
>>>
>>>
>>
> --
> 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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