
Administrator

On Mon, 24 Jul 2017, Rafael Pereira wrote:
> Hi all,
>
> I would like to ask whether some you conducted bivariate spatial
> correlation in R.
>
> I know the bivariate 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/mapofbivariatespatialcorrelationinrbivariatelisa>
> 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 followup, your presumably throwaway
example makes fundamental mistakes. The code in spdep by Virgilio
GómezRubio 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 pvalues < 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

Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N5045 Bergen, Norway.
voice: +47 55 95 93 55; email: [hidden email]
EditorinChief of The R Journal, https://journal.rproject.org/index.htmlhttp://orcid.org/0000000323926140https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N5045 Bergen, Norway


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 bivariate 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 bivariate 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$variable).
best,
Rafael H M Pereira
On Mon, Jul 24, 2017 at 7:56 PM, Roger Bivand < [hidden email]> wrote:
> On Mon, 24 Jul 2017, Rafael Pereira wrote:
>
> Hi all,
>>
>> I would like to ask whether some you conducted bivariate spatial
>> correlation in R.
>>
>> I know the bivariate Moran's I is not implemented in the spdep library.
>> I left a question on SO but also wanted to hear if anyone if the mainlist
>> have come across this.
>> https://stackoverflow.com/questions/45177590/mapofbivariat>> espatialcorrelationinrbivariatelisa
>>
>> I also know Roger Bivand has implemented the L index proposed by Lee
>> (2001)
>> in spdep, but I'm not I'm not sure whether the L local correlation
>> coefficients can be interpreted the same way as the local Moran's I
>> coefficients. I couldn't find any reference commenting on this issue. I
>> would very much appreciate your thoughts this.
>>
>
> In the SO question, and in the followup, your presumably throwaway
> example makes fundamental mistakes. The code in spdep by Virgilio
> GómezRubio is for uni and bivariate L, and produces point values of local
> L. This isn't the main problem, which is rather that you are not taking
> account of the underlying population counts, nor shrinking any estimates of
> significance to accommodate population sizes. Population sizes vary from 0
> to 11858, with the lower quartile at 3164 and upper 5698:
> plot(ecdf(oregon.tract$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 pvalues < 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]]
>>
>> _______________________________________________
>> RsigGeo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>
>>
> 
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N5045 Bergen, Norway.
> voice: +47 55 95 93 55; email: [hidden email]
> EditorinChief of The R Journal, https://journal.rproject.org/index.html> http://orcid.org/0000000323926140> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en [[alternative HTML version deleted]]
_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo

Administrator

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 bivariate 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 bivariate 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
bivariate 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 bivariate 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 FDRcorrected SAD local Moran's I pvalues 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 < [hidden email]> wrote:
>
>> On Mon, 24 Jul 2017, Rafael Pereira wrote:
>>
>> Hi all,
>>>
>>> I would like to ask whether some you conducted bivariate spatial
>>> correlation in R.
>>>
>>> I know the bivariate 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/mapofbivariat>>> espatialcorrelationinrbivariatelisa
>>>
>>> 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 followup, your presumably throwaway
>> example makes fundamental mistakes. The code in spdep by Virgilio
>> GómezRubio 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 pvalues < 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]]
>>>
>>> _______________________________________________
>>> RsigGeo mailing list
>>> [hidden email]
>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>>
>>>
>> 
>> Roger Bivand
>> Department of Economics, Norwegian School of Economics,
>> Helleveien 30, N5045 Bergen, Norway.
>> voice: +47 55 95 93 55; email: [hidden email]
>> EditorinChief of The R Journal, https://journal.rproject.org/index.html>> http://orcid.org/0000000323926140>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> RsigGeo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/rsiggeo
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N5045 Bergen, Norway.
voice: +47 55 95 93 55; email: [hidden email]
EditorinChief of The R Journal, https://journal.rproject.org/index.htmlhttp://orcid.org/0000000323926140https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N5045 Bergen, Norway


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/5348193abf779625f5e8c5090776a228What 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/mapofbivariatespatialcorrelationinrbivariatelisaOn Wed, Jul 26, 2017 at 11:07 AM, Roger Bivand < [hidden email]> 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 bivariate
>> 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 bivariate 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
> bivariate 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 bivariate 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 FDRcorrected SAD local Moran's I pvalues 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 < [hidden email]>
>> wrote:
>>
>> On Mon, 24 Jul 2017, Rafael Pereira wrote:
>>>
>>> Hi all,
>>>
>>>>
>>>> I would like to ask whether some you conducted bivariate spatial
>>>> correlation in R.
>>>>
>>>> I know the bivariate 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/mapofbivariat>>>> espatialcorrelationinrbivariatelisa
>>>>
>>>> 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 followup, your presumably throwaway
>>> example makes fundamental mistakes. The code in spdep by Virgilio
>>> GómezRubio 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 pvalues < 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]]
>>>>
>>>> _______________________________________________
>>>> RsigGeo mailing list
>>>> [hidden email]
>>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>>>
>>>>
>>>> 
>>> Roger Bivand
>>> Department of Economics, Norwegian School of Economics,
>>> Helleveien 30, N5045 Bergen, Norway.
>>> voice: +47 55 95 93 55; email: [hidden email]
>>> EditorinChief of The R Journal, https://journal.rproject.org/>>> index.html
>>> http://orcid.org/0000000323926140>>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> RsigGeo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>
>
> 
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N5045 Bergen, Norway.
> voice: +47 55 95 93 55; email: [hidden email]
> EditorinChief of The R Journal, https://journal.rproject.org/index.html> http://orcid.org/0000000323926140> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>
[[alternative HTML version deleted]]
_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo

Administrator

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: [RsigGeo] bivariate spatial correlation in R
Kopi: Rogério Barbosa, [hidden email]
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/mapofbivariatespatialcorrelationinrbivariatelisa 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 bivariate >> 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 bivariate 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 > bivariate 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 bivariate 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 FDRcorrected SAD local Moran's I pvalues 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 bivariate spatial >>>> correlation in R. >>>> >>>> I know the bivariate 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/mapofbivariat >>>> espatialcorrelationinrbivariatelisa >>>> >>>> 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 followup, your presumably throwaway >>> example makes fundamental mistakes. The code in spdep by Virgilio >>> GómezRubio 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 pvalues < 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]] >>>> >>>> _______________________________________________ >>>> RsigGeo mailing list >>>> [hidden email] >>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo >>>> >>>> >>>>  >>> Roger Bivand >>> Department of Economics, Norwegian School of Economics, >>> Helleveien 30, N5045 Bergen, Norway. >>> voice: +47 55 95 93 55; email: [hidden email] >>> EditorinChief of The R Journal, https://journal.rproject.org/ >>> index.html >>> http://orcid.org/0000000323926140 >>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en >>> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> RsigGeo mailing list >> [hidden email] >> https://stat.ethz.ch/mailman/listinfo/rsiggeo >> > >  > Roger Bivand > Department of Economics, Norwegian School of Economics, > Helleveien 30, N5045 Bergen, Norway. > voice: +47 55 95 93 55; email: [hidden email] > EditorinChief of The R Journal, https://journal.rproject.org/index.html > http://orcid.org/0000000323926140 > https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en > [[alternative HTML version deleted]] _______________________________________________ RsigGeo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/rsiggeo [[alternative HTML version deleted]]
_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N5045 Bergen, Norway


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 < [hidden email]> 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: [RsigGeo] bivariate spatial correlation in R
> Kopi: Rogério Barbosa, [hidden email]
>
>
> 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/mapof> bivariatespatialcorrelationinrbivariatelisa 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
> bivariate >> 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 bivariate 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 > bivariate 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 bivariate 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 FDRcorrected SAD
> local Moran's I pvalues 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
> bivariate spatial >>>> correlation in R. >>>> >>>> I know the bivariate
> 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/mapof> bivariat >>>> espatialcorrelationinrbivariatelisa >>>> >>>> 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 followup, your presumably throwaway >>> example
> makes fundamental mistakes. The code in spdep by Virgilio >>> GómezRubio
> 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 pvalues <
> 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]] >>>> >>>>
> _______________________________________________ >>>> RsigGeo mailing
> list >>>> [hidden email] >>>> https://stat.ethz.ch/mailman/> listinfo/rsiggeo >>>> >>>> >>>>  >>> Roger Bivand >>> Department of
> Economics, Norwegian School of Economics, >>> Helleveien 30, N5045 Bergen,
> Norway. >>> voice: +47 55 95 93 55 <+47%2055%2095%2093%2055>; email:
> [hidden email] >>> EditorinChief of The R Journal,
> https://journal.rproject.org/ >>> index.html >>>
> http://orcid.org/0000000323926140 >>> https://scholar.google.no/> citations?user=AWeghB0AAAAJ&hl=en >>> >> >> [[alternative HTML version
> deleted]] >> >> _______________________________________________ >>
> RsigGeo mailing list >> [hidden email] >>
> https://stat.ethz.ch/mailman/listinfo/rsiggeo >> > >  > Roger Bivand
> > Department of Economics, Norwegian School of Economics, > Helleveien 30,
> N5045 Bergen, Norway. > voice: +47 55 95 93 55 <+47%2055%2095%2093%2055>;
> email: [hidden email] > EditorinChief of The R Journal,
> https://journal.rproject.org/index.html > http://orcid.org/00000003> 23926140 > https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en >
> [[alternative HTML version deleted]] _______________________________________________
> RsigGeo mailing list [hidden email]
> https://stat.ethz.ch/mailman/listinfo/rsiggeo>
>
[[alternative HTML version deleted]]
_______________________________________________
RsigGeo mailing list
[hidden email]
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Administrator

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 sureveybased 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 < [hidden email]> 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: [RsigGeo] bivariate spatial correlation in R
>> Kopi: Rogério Barbosa, [hidden email]
>>
>>
>> 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/mapof>> bivariatespatialcorrelationinrbivariatelisa 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
>> bivariate >> 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 bivariate 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 > bivariate 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 bivariate 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 FDRcorrected SAD
>> local Moran's I pvalues 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
>> bivariate spatial >>>> correlation in R. >>>> >>>> I know the bivariate
>> 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/mapof>> bivariat >>>> espatialcorrelationinrbivariatelisa >>>> >>>> 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 followup, your presumably throwaway >>> example
>> makes fundamental mistakes. The code in spdep by Virgilio >>> GómezRubio
>> 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 pvalues <
>> 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]] >>>> >>>>
>> _______________________________________________ >>>> RsigGeo mailing
>> list >>>> [hidden email] >>>> https://stat.ethz.ch/mailman/>> listinfo/rsiggeo >>>> >>>> >>>>  >>> Roger Bivand >>> Department of
>> Economics, Norwegian School of Economics, >>> Helleveien 30, N5045 Bergen,
>> Norway. >>> voice: +47 55 95 93 55 <+47%2055%2095%2093%2055>; email:
>> [hidden email] >>> EditorinChief of The R Journal,
>> https://journal.rproject.org/ >>> index.html >>>
>> http://orcid.org/0000000323926140 >>> https://scholar.google.no/>> citations?user=AWeghB0AAAAJ&hl=en >>> >> >> [[alternative HTML version
>> deleted]] >> >> _______________________________________________ >>
>> RsigGeo mailing list >> [hidden email] >>
>> https://stat.ethz.ch/mailman/listinfo/rsiggeo >> > >  > Roger Bivand
>>> Department of Economics, Norwegian School of Economics, > Helleveien 30,
>> N5045 Bergen, Norway. > voice: +47 55 95 93 55 <+47%2055%2095%2093%2055>;
>> email: [hidden email] > EditorinChief of The R Journal,
>> https://journal.rproject.org/index.html > http://orcid.org/00000003>> 23926140 > https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en >
>> [[alternative HTML version deleted]] _______________________________________________
>> RsigGeo mailing list [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>
>>
>

Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N5045 Bergen, Norway.
voice: +47 55 95 93 55; email: [hidden email]
EditorinChief of The R Journal, https://journal.rproject.org/index.htmlhttp://orcid.org/0000000323926140https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N5045 Bergen, Norway


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.
best,
Rafael HM Pereira
http://urbandemographics.blogspot.comOn Mon, Jul 31, 2017 at 10:52 PM, Roger Bivand < [hidden email]> 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 sureveybased 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 < [hidden email]>
>> 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: [RsigGeo] bivariate spatial correlation in R
>>> Kopi: Rogério Barbosa, [hidden email]
>>>
>>>
>>> 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/mapof>>> bivariatespatialcorrelationinrbivariatelisa 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
>>> bivariate >> 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 bivariate 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 > bivariate 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 bivariate 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 FDRcorrected SAD
>>> local Moran's I pvalues 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
>>>>>>
>>>>> bivariate spatial >>>> correlation in R. >>>> >>>> I know the
>>> bivariate
>>> 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/mapof>>> bivariat >>>> espatialcorrelationinrbivariatelisa >>>> >>>> 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 followup, your presumably throwaway >>> example
>>> makes fundamental mistakes. The code in spdep by Virgilio >>> GómezRubio
>>> 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 pvalues <
>>> 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]] >>>> >>>>
>>> _______________________________________________ >>>> RsigGeo mailing
>>> list >>>> [hidden email] >>>> https://stat.ethz.ch/mailman/>>> listinfo/rsiggeo >>>> >>>> >>>>  >>> Roger Bivand >>> Department of
>>> Economics, Norwegian School of Economics, >>> Helleveien 30, N5045
>>> Bergen,
>>> Norway. >>> voice: +47 55 95 93 55 <+47%2055%2095%2093%2055>; email:
>>> [hidden email] >>> EditorinChief of The R Journal,
>>> https://journal.rproject.org/ >>> index.html >>>
>>> http://orcid.org/0000000323926140 >>> https://scholar.google.no/>>> citations?user=AWeghB0AAAAJ&hl=en >>> >> >> [[alternative HTML version
>>> deleted]] >> >> _______________________________________________ >>
>>> RsigGeo mailing list >> [hidden email] >>
>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo >> > >  > Roger Bivand
>>>
>>>> Department of Economics, Norwegian School of Economics, > Helleveien 30,
>>>>
>>> N5045 Bergen, Norway. > voice: +47 55 95 93 55
>>> <+47%2055%2095%2093%2055>;
>>> email: [hidden email] > EditorinChief of The R Journal,
>>> https://journal.rproject.org/index.html > http://orcid.org/00000003>>> 23926140 > https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>>> >
>>> [[alternative HTML version deleted]] ______________________________
>>> _________________
>>> RsigGeo mailing list [hidden email]
>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>>
>>>
>>>
>>
> 
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N5045 Bergen, Norway.
> voice: +47 55 95 93 55; email: [hidden email]
> EditorinChief of The R Journal, https://journal.rproject.org/index.html> http://orcid.org/0000000323926140> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>
[[alternative HTML version deleted]]
_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo

Administrator

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 < [hidden email]> 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 sureveybased 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 < [hidden email]>
>>> 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: [RsigGeo] bivariate spatial correlation in R
>>>> Kopi: Rogério Barbosa, [hidden email]
>>>>
>>>>
>>>> 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/mapof>>>> bivariatespatialcorrelationinrbivariatelisa 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
>>>> bivariate >> 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 bivariate 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 > bivariate 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 bivariate 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 FDRcorrected SAD
>>>> local Moran's I pvalues 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
>>>>>>>
>>>>>> bivariate spatial >>>> correlation in R. >>>> >>>> I know the
>>>> bivariate
>>>> 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/mapof>>>> bivariat >>>> espatialcorrelationinrbivariatelisa >>>> >>>> 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 followup, your presumably throwaway >>> example
>>>> makes fundamental mistakes. The code in spdep by Virgilio >>> GómezRubio
>>>> 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 pvalues <
>>>> 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]] >>>> >>>>
>>>> _______________________________________________ >>>> RsigGeo mailing
>>>> list >>>> [hidden email] >>>> https://stat.ethz.ch/mailman/>>>> listinfo/rsiggeo >>>> >>>> >>>>  >>> Roger Bivand >>> Department of
>>>> Economics, Norwegian School of Economics, >>> Helleveien 30, N5045
>>>> Bergen,
>>>> Norway. >>> voice: +47 55 95 93 55 <+47%2055%2095%2093%2055>; email:
>>>> [hidden email] >>> EditorinChief of The R Journal,
>>>> https://journal.rproject.org/ >>> index.html >>>
>>>> http://orcid.org/0000000323926140 >>> https://scholar.google.no/>>>> citations?user=AWeghB0AAAAJ&hl=en >>> >> >> [[alternative HTML version
>>>> deleted]] >> >> _______________________________________________ >>
>>>> RsigGeo mailing list >> [hidden email] >>
>>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo >> > >  > Roger Bivand
>>>>
>>>>> Department of Economics, Norwegian School of Economics, > Helleveien 30,
>>>>>
>>>> N5045 Bergen, Norway. > voice: +47 55 95 93 55
>>>> <+47%2055%2095%2093%2055>;
>>>> email: [hidden email] > EditorinChief of The R Journal,
>>>> https://journal.rproject.org/index.html > http://orcid.org/00000003>>>> 23926140 > https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>>>>>
>>>> [[alternative HTML version deleted]] ______________________________
>>>> _________________
>>>> RsigGeo mailing list [hidden email]
>>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>>>
>>>>
>>>>
>>>
>> 
>> Roger Bivand
>> Department of Economics, Norwegian School of Economics,
>> Helleveien 30, N5045 Bergen, Norway.
>> voice: +47 55 95 93 55; email: [hidden email]
>> EditorinChief of The R Journal, https://journal.rproject.org/index.html>> http://orcid.org/0000000323926140>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>>
>

Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N5045 Bergen, Norway.
voice: +47 55 95 93 55; email: [hidden email]
EditorinChief of The R Journal, https://journal.rproject.org/index.htmlhttp://orcid.org/0000000323926140https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________
RsigGeo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/rsiggeo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N5045 Bergen, Norway


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 < [hidden email]> 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 < [hidden email]>
>> 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 sureveybased 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 < [hidden email]>
>>>> 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: [RsigGeo] bivariate spatial correlation in R
>>>>> Kopi: Rogério Barbosa, [hidden email]
>>>>>
>>>>>
>>>>> 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/mapof>>>>> bivariatespatialcorrelationinrbivariatelisa 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
>>>>> bivariate >> 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 bivariate 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 > bivariate 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 bivariate 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 FDRcorrected SAD
>>>>> local Moran's I pvalues 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
>>>>>>
>>>>>>>
>>>>>>>> bivariate spatial >>>> correlation in R. >>>> >>>> I know the
>>>>>>>
>>>>>> bivariate
>>>>> 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/mapof>>>>> bivariat >>>> espatialcorrelationinrbivariatelisa >>>> >>>> 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 followup, your presumably throwaway >>>
>>>>> example
>>>>> makes fundamental mistakes. The code in spdep by Virgilio >>>
>>>>> GómezRubio
>>>>> 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
>>>>> pvalues <
>>>>> 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]] >>>> >>>>
>>>>> _______________________________________________ >>>> RsigGeo mailing
>>>>> list >>>> [hidden email] >>>> https://stat.ethz.ch/mailman/>>>>> listinfo/rsiggeo >>>> >>>> >>>>  >>> Roger Bivand >>> Department of
>>>>> Economics, Norwegian School of Economics, >>> Helleveien 30, N5045
>>>>> Bergen,
>>>>> Norway. >>> voice: +47 55 95 93 55 <+47%2055%2095%2093%2055>; email:
>>>>> [hidden email] >>> EditorinChief of The R Journal,
>>>>> https://journal.rproject.org/ >>> index.html >>>
>>>>> http://orcid.org/0000000323926140 >>> https://scholar.google.no/>>>>> citations?user=AWeghB0AAAAJ&hl=en >>> >> >> [[alternative HTML version
>>>>> deleted]] >> >> _______________________________________________ >>
>>>>> RsigGeo mailing list >> [hidden email] >>
>>>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo >> > >  > Roger
>>>>> Bivand
>>>>>
>>>>> Department of Economics, Norwegian School of Economics, > Helleveien
>>>>>> 30,
>>>>>>
>>>>>> N5045 Bergen, Norway. > voice: +47 55 95 93 55
>>>>> <+47%2055%2095%2093%2055>;
>>>>> email: [hidden email] > EditorinChief of The R Journal,
>>>>> https://journal.rproject.org/index.html > http://orcid.org/00000003>>>>> 23926140 > https://scholar.google.no/cita>>>>> tions?user=AWeghB0AAAAJ&hl=en
>>>>>
>>>>>>
>>>>>> [[alternative HTML version deleted]] ______________________________
>>>>> _________________
>>>>> RsigGeo mailing list [hidden email]
>>>>> https://stat.ethz.ch/mailman/listinfo/rsiggeo>>>>>
>>>>>
>>>>>
>>>>>
>>>> 
>>> Roger Bivand
>>> Department of Economics, Norwegian School of Economics,
>>> Helleveien 30, N5045 Bergen, Norway.
>>> voice: +47 55 95 93 55; email: [hidden email]
>>> EditorinChief of The R Journal, https://journal.rproject.org/>>> index.html
>>> http://orcid.org/0000000323926140>>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>>>
>>>
>>
> 
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N5045 Bergen, Norway.
> voice: +47 55 95 93 55; email: [hidden email]
> EditorinChief of The R Journal, https://journal.rproject.org/index.html> http://orcid.org/0000000323926140> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>
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