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Updated: 26 min 53 sec ago

How to use covariate files for regression kriging

Fri, 07/13/2018 - 15:23
Hi all,

I am new to R and geostatistics.
I have raster files (NDVI and DEM) and I want to use them as covariates for
regression kriging.
Do I need to project them first to same projected coordinate system
and resample
to same cell size before converting them to txt file for use in r.

Thanks

        [[alternative HTML version deleted]]

_______________________________________________
R-sig-Geo mailing list
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Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 06:56
On Fri, 13 Jul 2018, Benjamin Lieberman wrote:

> Roger-
>
> Thank you so much for the help. In our case, first order neighbors are
> all neighbors who are adjacent to a voter. Second order neighbors are
> then all neighbors who are adjacent to the first order neighbors. Hope
> that this could clarify what I have been referencing this time.

So you need to define what you mean by adjacent for the purposes of your
study. This depends on knowing the underlying behavioural patterns
affecting interaction.

Roger

>
> I will try the method you suggested, thank you.
>
> Best,
> Ben
> --
> Benjamin Lieberman
> Muhlenberg College 2019
> Mobile: 301.299.8928
>
>> On Jul 13, 2018, at 7:30 AM, Roger Bivand <[hidden email]> wrote:
>>
>> On Fri, 13 Jul 2018, Benjamin Lieberman wrote:
>>
>>> All-
>>>
>>> I would like to note that as the data is proprietary, and for obvious privacy concerns, the lat/long pairs were randomly generated, and were not taken directly from the data.
>>
>> Thanks for the clarification. Note that if the data are a sample, that is not a complete listing for one or more study areas, you don't know who the first order neighbour (the most proximate other voter) is, because that indidivual may not be in the sample. Your fallback then is to treat the data as aggregates, unless you rule out local sampling variability.
>>
>> Roger
>>
>>>
>>>
>>> --
>>> Benjamin Lieberman
>>> Muhlenberg College 2019
>>> Mobile: 301.299.8928
>>>
>>>> On Jul 13, 2018, at 6:58 AM, Benjamin Lieberman <[hidden email]> wrote:
>>>>
>>>> Roger anf Facu,
>>>>
>>>> Thank you very much for the help. In terms of the data, I only provided the ID and Lat/Long pairs because they were the only covariates which were necessary. The data set we are using was purchased and contains voter registration information, voter history, and census tract information, after some geocoding took place. The locations are the residents houses, in this instance.
>>>>
>>>> I have rerun the knn with longlat = T, but I am still hung up on the idea of the first order neighbors. I have reread the vignette and section 5 discusses High-Order Neighbors, but there isn’t any mention of first or second order neighbors, as you mentioned above (“first order neighbors are not defined”). One of the pieces of literature I found said that polygons are problematic to work with, as are tesslations for precisely the reason you mentioned, non-planarity. For this reason, I am hung up on the idea of how to find all first order neighbors for a point, especially as the number of first order neighbors varies from point to point, and such knearneigh would not be appropriate here.
>>>>
>>>> If this is something that does not seem feasible, maybe another tactic is necessary.
>>>>
>>>> Again, thank you all for the help.
>>>>
>>>> Warmest
>>>> --
>>>> Benjamin Lieberman
>>>> Muhlenberg College 2019
>>>> Mobile: 301.299.8928
>>>>
>>>>> On Jul 13, 2018, at 6:11 AM, Roger Bivand <[hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>> wrote:
>>>>>
>>>>> On Fri, 13 Jul 2018, Facundo Muñoz wrote:
>>>>>
>>>>>> Dear Benjamin,
>>>>>>
>>>>>> I'm not sure how you define "first order neighbors" for a point. The
>>>>>> first thing that comes to my mind is to use their corresponding voronoi
>>>>>> polygons and define neighborhood from there. Following your code:
>>>>>
>>>>> Thanks, the main source of confusion is that "first order neighbors" are not defined. A k=1 neighbour could be (as below), as could k=6, or voronoi neighbours, or sphere of influence etc. So reading vignette("nb") would be a starting point.
>>>>>
>>>>> Also note that voronoi and other graph-based neighbours should only use planar coordinates - including dismo::voronoi, which uses deldir::deldir() - just like spdep::tri2nb(). Triangulation can lead to spurious neighbours on the convex hull.
>>>>>
>>>>>>
>>>>>> v <- dismo::voronoi(coords)
>>>>>> par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
>>>>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>>>>> plot(v, add = TRUE)
>>>>>> text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
>>>>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>>>>> plot(poly2nb(v), coords, add = TRUE, col = "gray")
>>>>>>
>>>>>> ƒacu.-
>>>>>>
>>>>>>
>>>>>> On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
>>>>>>> Hi all,
>>>>>>>
>>>>>>> Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair.
>>>>>
>>>>> Using individual voter data is highly dangerous, and must in every case be subject to the strictest privacy rules. Voter data does not in essence have position - the only valid voting data that has position is of the voting station/precinct, and those data are aggregated to preserve anonymity.
>>>>>
>>>>> Why does position and voter data not have position? Which location should you use - residence, workplace, what? What are these locations proxying? Nothing valid can be drawn from "just voter data" - you can get conclusions from carefully constructed stratified exit polls, but there the key gender/age/ethnicity/social class/etc. confounders are handled by design. Why should voting decisions be influenced by proximity (they are not)? The missing element here is looking carefully at relevant covariates at more aggregated levels (in the US typically zoning controlling social class positional segregation, etc.).
>>>>>
>>>>>>> I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.
>>>>>>>
>>>>>>> While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.
>>>>>>>
>>>>>>> There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.
>>>>>>>
>>>>>>> Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.
>>>>>
>>>>> You mean RStudio, there is no such version of R.
>>>>>
>>>>>>>
>>>>>>> # Create a data frame of 10 voters, picked at random
>>>>>>> voter.1 = c(1, -75.52187, 40.62320)
>>>>>>> voter.2 = c(2,-75.56373, 40.55216)
>>>>>>> voter.3 = c(3,-75.39587, 40.55416)
>>>>>>> voter.4 = c(4,-75.42248, 40.64326)
>>>>>>> voter.5 = c(5,-75.56654, 40.54948)
>>>>>>> voter.6 = c(6,-75.56257, 40.67375)
>>>>>>> voter.7 = c(7, -75.51888, 40.59715)
>>>>>>> voter.8 = c(8, -75.59879, 40.60014)
>>>>>>> voter.9 = c(9, -75.59879, 40.60014)
>>>>>>> voter.10 = c(10, -75.50877, 40.53129)
>>>>>>>
>>>>>
>>>>> These are in geographical coordinates.
>>>>>
>>>>>>> # Bind the vectors together
>>>>>>> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>>>>>>>
>>>>>>> # Rename the columns
>>>>>>> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>>>>>>>
>>>>>>> # Change the class from a matrix to a data frame
>>>>>>> voter.subset = as.data.frame(voter.subset)
>>>>>>>
>>>>>>> # Load in the required packages
>>>>>>> library(spdep)
>>>>>>> library(sp)
>>>>>>>
>>>>>>> # Set the coordinates
>>>>>>> coordinates(voter.subset) = c("Longitude", "Latitude")
>>>>>>> coords = coordinates(voter.subset)
>>>>>>>
>>>>>>> # Jitter to ensure no duplicate points
>>>>>>> coords = jitter(coords, factor = 1)
>>>>>>>
>>>>>
>>>>> jitter does not respect geographical coordinated (decimal degree metric).
>>>>>
>>>>>>> # Find the first nearest neighbor of each point
>>>>>>> one.nn = knearneigh(coords, k=1)
>>>>>
>>>>> See the help page (hint: longlat=TRUE to use Great Circle distances, much slower than planar).
>>>>>
>>>>>>>
>>>>>>> # Convert the first nearest neighbor to format "nb"
>>>>>>> one.nn_nb = knn2nb(one.nn, sym = F)
>>>>>>>
>>>>>>> Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.
>>>>>>>
>>>>>>> Warmest,
>>>>>>> Ben
>>>>>>> --
>>>>>>> Benjamin Lieberman
>>>>>>> Muhlenberg College 2019
>>>>>>> Mobile: 301.299.8928
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> [[alternative HTML version deleted]]
>>>>>
>>>>> Plain text only, please.
>>>>>
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> R-sig-Geo mailing list
>>>>>>> [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo> <https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>>
>>>>>>
>>>>>>
>>>>>> [[alternative HTML version deleted]]
>>>>>>
>>>>>> _______________________________________________
>>>>>> R-sig-Geo mailing list
>>>>>> [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo> <https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>>
>>>>>>
>>>>>
>>>>> --
>>>>> Roger Bivand
>>>>> Department of Economics, Norwegian School of Economics,
>>>>> Helleveien 30, N-5045 Bergen, Norway.
>>>>> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>>> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140> <http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>>
>>>>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________><https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________>>
>>>>> R-sig-Geo mailing list
>>>>> [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo> <https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>>
>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> [hidden email] <mailto:[hidden email]>
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>>
>>
>> --
>> Roger Bivand
>> Department of Economics, Norwegian School of Economics,
>> Helleveien 30, N-5045 Bergen, Norway.
>> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]>
>> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>
>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> --
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: [hidden email]
http://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
_______________________________________________
R-sig-Geo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N-5045 Bergen, Norway

Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 06:44
Roger-

Thank you so much for the help. In our case, first order neighbors are all neighbors who are adjacent to a voter. Second order neighbors are then all neighbors who are adjacent to the first order neighbors. Hope that this could clarify what I have been referencing this time.

I will try the method you suggested, thank you.

Best,
Ben
--
Benjamin Lieberman
Muhlenberg College 2019
Mobile: 301.299.8928

> On Jul 13, 2018, at 7:30 AM, Roger Bivand <[hidden email]> wrote:
>
> On Fri, 13 Jul 2018, Benjamin Lieberman wrote:
>
>> All-
>>
>> I would like to note that as the data is proprietary, and for obvious privacy concerns, the lat/long pairs were randomly generated, and were not taken directly from the data.
>
> Thanks for the clarification. Note that if the data are a sample, that is not a complete listing for one or more study areas, you don't know who the first order neighbour (the most proximate other voter) is, because that indidivual may not be in the sample. Your fallback then is to treat the data as aggregates, unless you rule out local sampling variability.
>
> Roger
>
>>
>>
>> --
>> Benjamin Lieberman
>> Muhlenberg College 2019
>> Mobile: 301.299.8928
>>
>>> On Jul 13, 2018, at 6:58 AM, Benjamin Lieberman <[hidden email]> wrote:
>>>
>>> Roger anf Facu,
>>>
>>> Thank you very much for the help. In terms of the data, I only provided the ID and Lat/Long pairs because they were the only covariates which were necessary. The data set we are using was purchased and contains voter registration information, voter history, and census tract information, after some geocoding took place. The locations are the residents houses, in this instance.
>>>
>>> I have rerun the knn with longlat = T, but I am still hung up on the idea of the first order neighbors. I have reread the vignette and section 5 discusses High-Order Neighbors, but there isn’t any mention of first or second order neighbors, as you mentioned above (“first order neighbors are not defined”). One of the pieces of literature I found said that polygons are problematic to work with, as are tesslations for precisely the reason you mentioned, non-planarity. For this reason, I am hung up on the idea of how to find all first order neighbors for a point, especially as the number of first order neighbors varies from point to point, and such knearneigh would not be appropriate here.
>>>
>>> If this is something that does not seem feasible, maybe another tactic is necessary.
>>>
>>> Again, thank you all for the help.
>>>
>>> Warmest
>>> --
>>> Benjamin Lieberman
>>> Muhlenberg College 2019
>>> Mobile: 301.299.8928
>>>
>>>> On Jul 13, 2018, at 6:11 AM, Roger Bivand <[hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>> wrote:
>>>>
>>>> On Fri, 13 Jul 2018, Facundo Muñoz wrote:
>>>>
>>>>> Dear Benjamin,
>>>>>
>>>>> I'm not sure how you define "first order neighbors" for a point. The
>>>>> first thing that comes to my mind is to use their corresponding voronoi
>>>>> polygons and define neighborhood from there. Following your code:
>>>>
>>>> Thanks, the main source of confusion is that "first order neighbors" are not defined. A k=1 neighbour could be (as below), as could k=6, or voronoi neighbours, or sphere of influence etc. So reading vignette("nb") would be a starting point.
>>>>
>>>> Also note that voronoi and other graph-based neighbours should only use planar coordinates - including dismo::voronoi, which uses deldir::deldir() - just like spdep::tri2nb(). Triangulation can lead to spurious neighbours on the convex hull.
>>>>
>>>>>
>>>>> v <- dismo::voronoi(coords)
>>>>> par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
>>>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>>>> plot(v, add = TRUE)
>>>>> text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
>>>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>>>> plot(poly2nb(v), coords, add = TRUE, col = "gray")
>>>>>
>>>>> ƒacu.-
>>>>>
>>>>>
>>>>> On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
>>>>>> Hi all,
>>>>>>
>>>>>> Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair.
>>>>
>>>> Using individual voter data is highly dangerous, and must in every case be subject to the strictest privacy rules. Voter data does not in essence have position - the only valid voting data that has position is of the voting station/precinct, and those data are aggregated to preserve anonymity.
>>>>
>>>> Why does position and voter data not have position? Which location should you use - residence, workplace, what? What are these locations proxying? Nothing valid can be drawn from "just voter data" - you can get conclusions from carefully constructed stratified exit polls, but there the key gender/age/ethnicity/social class/etc. confounders are handled by design. Why should voting decisions be influenced by proximity (they are not)? The missing element here is looking carefully at relevant covariates at more aggregated levels (in the US typically zoning controlling social class positional segregation, etc.).
>>>>
>>>>>> I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.
>>>>>>
>>>>>> While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.
>>>>>>
>>>>>> There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.
>>>>>>
>>>>>> Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.
>>>>
>>>> You mean RStudio, there is no such version of R.
>>>>
>>>>>>
>>>>>> # Create a data frame of 10 voters, picked at random
>>>>>> voter.1 = c(1, -75.52187, 40.62320)
>>>>>> voter.2 = c(2,-75.56373, 40.55216)
>>>>>> voter.3 = c(3,-75.39587, 40.55416)
>>>>>> voter.4 = c(4,-75.42248, 40.64326)
>>>>>> voter.5 = c(5,-75.56654, 40.54948)
>>>>>> voter.6 = c(6,-75.56257, 40.67375)
>>>>>> voter.7 = c(7, -75.51888, 40.59715)
>>>>>> voter.8 = c(8, -75.59879, 40.60014)
>>>>>> voter.9 = c(9, -75.59879, 40.60014)
>>>>>> voter.10 = c(10, -75.50877, 40.53129)
>>>>>>
>>>>
>>>> These are in geographical coordinates.
>>>>
>>>>>> # Bind the vectors together
>>>>>> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>>>>>>
>>>>>> # Rename the columns
>>>>>> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>>>>>>
>>>>>> # Change the class from a matrix to a data frame
>>>>>> voter.subset = as.data.frame(voter.subset)
>>>>>>
>>>>>> # Load in the required packages
>>>>>> library(spdep)
>>>>>> library(sp)
>>>>>>
>>>>>> # Set the coordinates
>>>>>> coordinates(voter.subset) = c("Longitude", "Latitude")
>>>>>> coords = coordinates(voter.subset)
>>>>>>
>>>>>> # Jitter to ensure no duplicate points
>>>>>> coords = jitter(coords, factor = 1)
>>>>>>
>>>>
>>>> jitter does not respect geographical coordinated (decimal degree metric).
>>>>
>>>>>> # Find the first nearest neighbor of each point
>>>>>> one.nn = knearneigh(coords, k=1)
>>>>
>>>> See the help page (hint: longlat=TRUE to use Great Circle distances, much slower than planar).
>>>>
>>>>>>
>>>>>> # Convert the first nearest neighbor to format "nb"
>>>>>> one.nn_nb = knn2nb(one.nn, sym = F)
>>>>>>
>>>>>> Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.
>>>>>>
>>>>>> Warmest,
>>>>>> Ben
>>>>>> --
>>>>>> Benjamin Lieberman
>>>>>> Muhlenberg College 2019
>>>>>> Mobile: 301.299.8928
>>>>>>
>>>>>>
>>>>>>
>>>>>> [[alternative HTML version deleted]]
>>>>
>>>> Plain text only, please.
>>>>
>>>>>>
>>>>>> _______________________________________________
>>>>>> R-sig-Geo mailing list
>>>>>> [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo> <https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>>
>>>>>
>>>>>
>>>>> [[alternative HTML version deleted]]
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-Geo mailing list
>>>>> [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo> <https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>>
>>>>>
>>>>
>>>> --
>>>> Roger Bivand
>>>> Department of Economics, Norwegian School of Economics,
>>>> Helleveien 30, N-5045 Bergen, Norway.
>>>> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140> <http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>>
>>>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________><https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________>>
>>>> R-sig-Geo mailing list
>>>> [hidden email] <mailto:[hidden email]> <mailto:[hidden email] <mailto:[hidden email]>>
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo> <https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>>
>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email] <mailto:[hidden email]>
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]>
> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en>
        [[alternative HTML version deleted]]

_______________________________________________
R-sig-Geo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/r-sig-geo

Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 06:30
On Fri, 13 Jul 2018, Benjamin Lieberman wrote:

> All-
>
> I would like to note that as the data is proprietary, and for obvious
> privacy concerns, the lat/long pairs were randomly generated, and were
> not taken directly from the data.

Thanks for the clarification. Note that if the data are a sample, that is
not a complete listing for one or more study areas, you don't know who the
first order neighbour (the most proximate other voter) is, because that
indidivual may not be in the sample. Your fallback then is to treat the
data as aggregates, unless you rule out local sampling variability.

Roger

>
>
> --
> Benjamin Lieberman
> Muhlenberg College 2019
> Mobile: 301.299.8928
>
>> On Jul 13, 2018, at 6:58 AM, Benjamin Lieberman <[hidden email]> wrote:
>>
>> Roger anf Facu,
>>
>> Thank you very much for the help. In terms of the data, I only provided the ID and Lat/Long pairs because they were the only covariates which were necessary. The data set we are using was purchased and contains voter registration information, voter history, and census tract information, after some geocoding took place. The locations are the residents houses, in this instance.
>>
>> I have rerun the knn with longlat = T, but I am still hung up on the idea of the first order neighbors. I have reread the vignette and section 5 discusses High-Order Neighbors, but there isn’t any mention of first or second order neighbors, as you mentioned above (“first order neighbors are not defined”). One of the pieces of literature I found said that polygons are problematic to work with, as are tesslations for precisely the reason you mentioned, non-planarity. For this reason, I am hung up on the idea of how to find all first order neighbors for a point, especially as the number of first order neighbors varies from point to point, and such knearneigh would not be appropriate here.
>>
>> If this is something that does not seem feasible, maybe another tactic is necessary.
>>
>> Again, thank you all for the help.
>>
>> Warmest
>> --
>> Benjamin Lieberman
>> Muhlenberg College 2019
>> Mobile: 301.299.8928
>>
>>> On Jul 13, 2018, at 6:11 AM, Roger Bivand <[hidden email] <mailto:[hidden email]>> wrote:
>>>
>>> On Fri, 13 Jul 2018, Facundo Muñoz wrote:
>>>
>>>> Dear Benjamin,
>>>>
>>>> I'm not sure how you define "first order neighbors" for a point. The
>>>> first thing that comes to my mind is to use their corresponding voronoi
>>>> polygons and define neighborhood from there. Following your code:
>>>
>>> Thanks, the main source of confusion is that "first order neighbors" are not defined. A k=1 neighbour could be (as below), as could k=6, or voronoi neighbours, or sphere of influence etc. So reading vignette("nb") would be a starting point.
>>>
>>> Also note that voronoi and other graph-based neighbours should only use planar coordinates - including dismo::voronoi, which uses deldir::deldir() - just like spdep::tri2nb(). Triangulation can lead to spurious neighbours on the convex hull.
>>>
>>>>
>>>> v <- dismo::voronoi(coords)
>>>> par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
>>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>>> plot(v, add = TRUE)
>>>> text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
>>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>>> plot(poly2nb(v), coords, add = TRUE, col = "gray")
>>>>
>>>> ƒacu.-
>>>>
>>>>
>>>> On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
>>>>> Hi all,
>>>>>
>>>>> Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair.
>>>
>>> Using individual voter data is highly dangerous, and must in every case be subject to the strictest privacy rules. Voter data does not in essence have position - the only valid voting data that has position is of the voting station/precinct, and those data are aggregated to preserve anonymity.
>>>
>>> Why does position and voter data not have position? Which location should you use - residence, workplace, what? What are these locations proxying? Nothing valid can be drawn from "just voter data" - you can get conclusions from carefully constructed stratified exit polls, but there the key gender/age/ethnicity/social class/etc. confounders are handled by design. Why should voting decisions be influenced by proximity (they are not)? The missing element here is looking carefully at relevant covariates at more aggregated levels (in the US typically zoning controlling social class positional segregation, etc.).
>>>
>>>>> I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.
>>>>>
>>>>> While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.
>>>>>
>>>>> There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.
>>>>>
>>>>> Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.
>>>
>>> You mean RStudio, there is no such version of R.
>>>
>>>>>
>>>>> # Create a data frame of 10 voters, picked at random
>>>>> voter.1 = c(1, -75.52187, 40.62320)
>>>>> voter.2 = c(2,-75.56373, 40.55216)
>>>>> voter.3 = c(3,-75.39587, 40.55416)
>>>>> voter.4 = c(4,-75.42248, 40.64326)
>>>>> voter.5 = c(5,-75.56654, 40.54948)
>>>>> voter.6 = c(6,-75.56257, 40.67375)
>>>>> voter.7 = c(7, -75.51888, 40.59715)
>>>>> voter.8 = c(8, -75.59879, 40.60014)
>>>>> voter.9 = c(9, -75.59879, 40.60014)
>>>>> voter.10 = c(10, -75.50877, 40.53129)
>>>>>
>>>
>>> These are in geographical coordinates.
>>>
>>>>> # Bind the vectors together
>>>>> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>>>>>
>>>>> # Rename the columns
>>>>> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>>>>>
>>>>> # Change the class from a matrix to a data frame
>>>>> voter.subset = as.data.frame(voter.subset)
>>>>>
>>>>> # Load in the required packages
>>>>> library(spdep)
>>>>> library(sp)
>>>>>
>>>>> # Set the coordinates
>>>>> coordinates(voter.subset) = c("Longitude", "Latitude")
>>>>> coords = coordinates(voter.subset)
>>>>>
>>>>> # Jitter to ensure no duplicate points
>>>>> coords = jitter(coords, factor = 1)
>>>>>
>>>
>>> jitter does not respect geographical coordinated (decimal degree metric).
>>>
>>>>> # Find the first nearest neighbor of each point
>>>>> one.nn = knearneigh(coords, k=1)
>>>
>>> See the help page (hint: longlat=TRUE to use Great Circle distances, much slower than planar).
>>>
>>>>>
>>>>> # Convert the first nearest neighbor to format "nb"
>>>>> one.nn_nb = knn2nb(one.nn, sym = F)
>>>>>
>>>>> Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.
>>>>>
>>>>> Warmest,
>>>>> Ben
>>>>> --
>>>>> Benjamin Lieberman
>>>>> Muhlenberg College 2019
>>>>> Mobile: 301.299.8928
>>>>>
>>>>>
>>>>>
>>>>> [[alternative HTML version deleted]]
>>>
>>> Plain text only, please.
>>>
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-Geo mailing list
>>>>> [hidden email] <mailto:[hidden email]>
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>>>
>>>>
>>>> [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-Geo mailing list
>>>> [hidden email] <mailto:[hidden email]>
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>>>
>>>
>>> --
>>> Roger Bivand
>>> Department of Economics, Norwegian School of Economics,
>>> Helleveien 30, N-5045 Bergen, Norway.
>>> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]>
>>> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>
>>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________>
>>> R-sig-Geo mailing list
>>> [hidden email] <mailto:[hidden email]>
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> --
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: [hidden email]
http://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
_______________________________________________
R-sig-Geo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N-5045 Bergen, Norway

Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 06:26
On Fri, 13 Jul 2018, Benjamin Lieberman wrote:

> Roger anf Facu,
>
> Thank you very much for the help. In terms of the data, I only provided
> the ID and Lat/Long pairs because they were the only covariates which
> were necessary. The data set we are using was purchased and contains
> voter registration information, voter history, and census tract
> information, after some geocoding took place. The locations are the
> residents houses, in this instance.
>
> I have rerun the knn with longlat = T, but I am still hung up on the
> idea of the first order neighbors. I have reread the vignette and
> section 5 discusses High-Order Neighbors, but there isn’t any mention of
> first or second order neighbors, as you mentioned above (“first order
> neighbors are not defined”). One of the pieces of literature I found
> said that polygons are problematic to work with, as are tesslations for
> precisely the reason you mentioned, non-planarity. For this reason, I am
> hung up on the idea of how to find all first order neighbors for a
> point, especially as the number of first order neighbors varies from
> point to point, and such knearneigh would not be appropriate here. So project them, and use Euclidean distances in distance or graph-based
methods (or knn). You still have not defined "first order neighbors". That
is your call alone. If you believe that voter behaviour is like a
contagious disease, define contagion, and from that "first order
neighbours". If you are simply accounting for missing background
covariates that have a larger spatial footprint rather than voter-voter
interaction, it probably doesn't matter much. What is the implied model
here - that voters behave by observing the behaviour of their proximate
neighbours (giving similar behaviour for near neighbours) or that voters
are patched/segregated by residence, and near neighbours behave similarly
not because of information spillovers between voters, but because the
voters are subject to aggregate social/economic conditions?

Roger

>
> If this is something that does not seem feasible, maybe another tactic
> is necessary.
>
> Again, thank you all for the help.
>
> Warmest
> --
> Benjamin Lieberman
> Muhlenberg College 2019
> Mobile: 301.299.8928
>
>> On Jul 13, 2018, at 6:11 AM, Roger Bivand <[hidden email]> wrote:
>>
>> On Fri, 13 Jul 2018, Facundo Muñoz wrote:
>>
>>> Dear Benjamin,
>>>
>>> I'm not sure how you define "first order neighbors" for a point. The
>>> first thing that comes to my mind is to use their corresponding voronoi
>>> polygons and define neighborhood from there. Following your code:
>>
>> Thanks, the main source of confusion is that "first order neighbors" are not defined. A k=1 neighbour could be (as below), as could k=6, or voronoi neighbours, or sphere of influence etc. So reading vignette("nb") would be a starting point.
>>
>> Also note that voronoi and other graph-based neighbours should only use planar coordinates - including dismo::voronoi, which uses deldir::deldir() - just like spdep::tri2nb(). Triangulation can lead to spurious neighbours on the convex hull.
>>
>>>
>>> v <- dismo::voronoi(coords)
>>> par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>> plot(v, add = TRUE)
>>> text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>> plot(poly2nb(v), coords, add = TRUE, col = "gray")
>>>
>>> ƒacu.-
>>>
>>>
>>> On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
>>>> Hi all,
>>>>
>>>> Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair.
>>
>> Using individual voter data is highly dangerous, and must in every case be subject to the strictest privacy rules. Voter data does not in essence have position - the only valid voting data that has position is of the voting station/precinct, and those data are aggregated to preserve anonymity.
>>
>> Why does position and voter data not have position? Which location should you use - residence, workplace, what? What are these locations proxying? Nothing valid can be drawn from "just voter data" - you can get conclusions from carefully constructed stratified exit polls, but there the key gender/age/ethnicity/social class/etc. confounders are handled by design. Why should voting decisions be influenced by proximity (they are not)? The missing element here is looking carefully at relevant covariates at more aggregated levels (in the US typically zoning controlling social class positional segregation, etc.).
>>
>>>> I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.
>>>>
>>>> While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.
>>>>
>>>> There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.
>>>>
>>>> Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.
>>
>> You mean RStudio, there is no such version of R.
>>
>>>>
>>>> # Create a data frame of 10 voters, picked at random
>>>> voter.1 = c(1, -75.52187, 40.62320)
>>>> voter.2 = c(2,-75.56373, 40.55216)
>>>> voter.3 = c(3,-75.39587, 40.55416)
>>>> voter.4 = c(4,-75.42248, 40.64326)
>>>> voter.5 = c(5,-75.56654, 40.54948)
>>>> voter.6 = c(6,-75.56257, 40.67375)
>>>> voter.7 = c(7, -75.51888, 40.59715)
>>>> voter.8 = c(8, -75.59879, 40.60014)
>>>> voter.9 = c(9, -75.59879, 40.60014)
>>>> voter.10 = c(10, -75.50877, 40.53129)
>>>>
>>
>> These are in geographical coordinates.
>>
>>>> # Bind the vectors together
>>>> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>>>>
>>>> # Rename the columns
>>>> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>>>>
>>>> # Change the class from a matrix to a data frame
>>>> voter.subset = as.data.frame(voter.subset)
>>>>
>>>> # Load in the required packages
>>>> library(spdep)
>>>> library(sp)
>>>>
>>>> # Set the coordinates
>>>> coordinates(voter.subset) = c("Longitude", "Latitude")
>>>> coords = coordinates(voter.subset)
>>>>
>>>> # Jitter to ensure no duplicate points
>>>> coords = jitter(coords, factor = 1)
>>>>
>>
>> jitter does not respect geographical coordinated (decimal degree metric).
>>
>>>> # Find the first nearest neighbor of each point
>>>> one.nn = knearneigh(coords, k=1)
>>
>> See the help page (hint: longlat=TRUE to use Great Circle distances, much slower than planar).
>>
>>>>
>>>> # Convert the first nearest neighbor to format "nb"
>>>> one.nn_nb = knn2nb(one.nn, sym = F)
>>>>
>>>> Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.
>>>>
>>>> Warmest,
>>>> Ben
>>>> --
>>>> Benjamin Lieberman
>>>> Muhlenberg College 2019
>>>> Mobile: 301.299.8928
>>>>
>>>>
>>>>
>>>> [[alternative HTML version deleted]]
>>
>> Plain text only, please.
>>
>>>>
>>>> _______________________________________________
>>>> R-sig-Geo mailing list
>>>> [hidden email] <mailto:[hidden email]>
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> [hidden email] <mailto:[hidden email]>
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>>
>>
>> --
>> Roger Bivand
>> Department of Economics, Norwegian School of Economics,
>> Helleveien 30, N-5045 Bergen, Norway.
>> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]>
>> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>
>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________>
>> R-sig-Geo mailing list
>> [hidden email] <mailto:[hidden email]>
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> --
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: [hidden email]
http://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
_______________________________________________
R-sig-Geo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N-5045 Bergen, Norway

Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 06:16
All-

I would like to note that as the data is proprietary, and for obvious privacy concerns, the lat/long pairs were randomly generated, and were not taken directly from the data.


--
Benjamin Lieberman
Muhlenberg College 2019
Mobile: 301.299.8928

> On Jul 13, 2018, at 6:58 AM, Benjamin Lieberman <[hidden email]> wrote:
>
> Roger anf Facu,
>
> Thank you very much for the help. In terms of the data, I only provided the ID and Lat/Long pairs because they were the only covariates which were necessary. The data set we are using was purchased and contains voter registration information, voter history, and census tract information, after some geocoding took place. The locations are the residents houses, in this instance.
>
> I have rerun the knn with longlat = T, but I am still hung up on the idea of the first order neighbors. I have reread the vignette and section 5 discusses High-Order Neighbors, but there isn’t any mention of first or second order neighbors, as you mentioned above (“first order neighbors are not defined”). One of the pieces of literature I found said that polygons are problematic to work with, as are tesslations for precisely the reason you mentioned, non-planarity. For this reason, I am hung up on the idea of how to find all first order neighbors for a point, especially as the number of first order neighbors varies from point to point, and such knearneigh would not be appropriate here.
>
> If this is something that does not seem feasible, maybe another tactic is necessary.
>
> Again, thank you all for the help.
>
> Warmest
> --
> Benjamin Lieberman
> Muhlenberg College 2019
> Mobile: 301.299.8928
>
>> On Jul 13, 2018, at 6:11 AM, Roger Bivand <[hidden email] <mailto:[hidden email]>> wrote:
>>
>> On Fri, 13 Jul 2018, Facundo Muñoz wrote:
>>
>>> Dear Benjamin,
>>>
>>> I'm not sure how you define "first order neighbors" for a point. The
>>> first thing that comes to my mind is to use their corresponding voronoi
>>> polygons and define neighborhood from there. Following your code:
>>
>> Thanks, the main source of confusion is that "first order neighbors" are not defined. A k=1 neighbour could be (as below), as could k=6, or voronoi neighbours, or sphere of influence etc. So reading vignette("nb") would be a starting point.
>>
>> Also note that voronoi and other graph-based neighbours should only use planar coordinates - including dismo::voronoi, which uses deldir::deldir() - just like spdep::tri2nb(). Triangulation can lead to spurious neighbours on the convex hull.
>>
>>>
>>> v <- dismo::voronoi(coords)
>>> par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>> plot(v, add = TRUE)
>>> text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
>>> plot(coords, type = "n", xlab = NA, ylab = NA)
>>> plot(poly2nb(v), coords, add = TRUE, col = "gray")
>>>
>>> ƒacu.-
>>>
>>>
>>> On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
>>>> Hi all,
>>>>
>>>> Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair.
>>
>> Using individual voter data is highly dangerous, and must in every case be subject to the strictest privacy rules. Voter data does not in essence have position - the only valid voting data that has position is of the voting station/precinct, and those data are aggregated to preserve anonymity.
>>
>> Why does position and voter data not have position? Which location should you use - residence, workplace, what? What are these locations proxying? Nothing valid can be drawn from "just voter data" - you can get conclusions from carefully constructed stratified exit polls, but there the key gender/age/ethnicity/social class/etc. confounders are handled by design. Why should voting decisions be influenced by proximity (they are not)? The missing element here is looking carefully at relevant covariates at more aggregated levels (in the US typically zoning controlling social class positional segregation, etc.).
>>
>>>> I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.
>>>>
>>>> While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.
>>>>
>>>> There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.
>>>>
>>>> Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.
>>
>> You mean RStudio, there is no such version of R.
>>
>>>>
>>>> # Create a data frame of 10 voters, picked at random
>>>> voter.1 = c(1, -75.52187, 40.62320)
>>>> voter.2 = c(2,-75.56373, 40.55216)
>>>> voter.3 = c(3,-75.39587, 40.55416)
>>>> voter.4 = c(4,-75.42248, 40.64326)
>>>> voter.5 = c(5,-75.56654, 40.54948)
>>>> voter.6 = c(6,-75.56257, 40.67375)
>>>> voter.7 = c(7, -75.51888, 40.59715)
>>>> voter.8 = c(8, -75.59879, 40.60014)
>>>> voter.9 = c(9, -75.59879, 40.60014)
>>>> voter.10 = c(10, -75.50877, 40.53129)
>>>>
>>
>> These are in geographical coordinates.
>>
>>>> # Bind the vectors together
>>>> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>>>>
>>>> # Rename the columns
>>>> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>>>>
>>>> # Change the class from a matrix to a data frame
>>>> voter.subset = as.data.frame(voter.subset)
>>>>
>>>> # Load in the required packages
>>>> library(spdep)
>>>> library(sp)
>>>>
>>>> # Set the coordinates
>>>> coordinates(voter.subset) = c("Longitude", "Latitude")
>>>> coords = coordinates(voter.subset)
>>>>
>>>> # Jitter to ensure no duplicate points
>>>> coords = jitter(coords, factor = 1)
>>>>
>>
>> jitter does not respect geographical coordinated (decimal degree metric).
>>
>>>> # Find the first nearest neighbor of each point
>>>> one.nn = knearneigh(coords, k=1)
>>
>> See the help page (hint: longlat=TRUE to use Great Circle distances, much slower than planar).
>>
>>>>
>>>> # Convert the first nearest neighbor to format "nb"
>>>> one.nn_nb = knn2nb(one.nn, sym = F)
>>>>
>>>> Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.
>>>>
>>>> Warmest,
>>>> Ben
>>>> --
>>>> Benjamin Lieberman
>>>> Muhlenberg College 2019
>>>> Mobile: 301.299.8928
>>>>
>>>>
>>>>
>>>> [[alternative HTML version deleted]]
>>
>> Plain text only, please.
>>
>>>>
>>>> _______________________________________________
>>>> R-sig-Geo mailing list
>>>> [hidden email] <mailto:[hidden email]>
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>>
>>>
>>> [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> [hidden email] <mailto:[hidden email]>
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>>
>>
>> --
>> Roger Bivand
>> Department of Economics, Norwegian School of Economics,
>> Helleveien 30, N-5045 Bergen, Norway.
>> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]>
>> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>
>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________>
>> R-sig-Geo mailing list
>> [hidden email] <mailto:[hidden email]>
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>

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Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 05:58
Roger anf Facu,

Thank you very much for the help. In terms of the data, I only provided the ID and Lat/Long pairs because they were the only covariates which were necessary. The data set we are using was purchased and contains voter registration information, voter history, and census tract information, after some geocoding took place. The locations are the residents houses, in this instance.

I have rerun the knn with longlat = T, but I am still hung up on the idea of the first order neighbors. I have reread the vignette and section 5 discusses High-Order Neighbors, but there isn’t any mention of first or second order neighbors, as you mentioned above (“first order neighbors are not defined”). One of the pieces of literature I found said that polygons are problematic to work with, as are tesslations for precisely the reason you mentioned, non-planarity. For this reason, I am hung up on the idea of how to find all first order neighbors for a point, especially as the number of first order neighbors varies from point to point, and such knearneigh would not be appropriate here.

If this is something that does not seem feasible, maybe another tactic is necessary.

Again, thank you all for the help.

Warmest
--
Benjamin Lieberman
Muhlenberg College 2019
Mobile: 301.299.8928

> On Jul 13, 2018, at 6:11 AM, Roger Bivand <[hidden email]> wrote:
>
> On Fri, 13 Jul 2018, Facundo Muñoz wrote:
>
>> Dear Benjamin,
>>
>> I'm not sure how you define "first order neighbors" for a point. The
>> first thing that comes to my mind is to use their corresponding voronoi
>> polygons and define neighborhood from there. Following your code:
>
> Thanks, the main source of confusion is that "first order neighbors" are not defined. A k=1 neighbour could be (as below), as could k=6, or voronoi neighbours, or sphere of influence etc. So reading vignette("nb") would be a starting point.
>
> Also note that voronoi and other graph-based neighbours should only use planar coordinates - including dismo::voronoi, which uses deldir::deldir() - just like spdep::tri2nb(). Triangulation can lead to spurious neighbours on the convex hull.
>
>>
>> v <- dismo::voronoi(coords)
>> par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
>> plot(coords, type = "n", xlab = NA, ylab = NA)
>> plot(v, add = TRUE)
>> text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
>> plot(coords, type = "n", xlab = NA, ylab = NA)
>> plot(poly2nb(v), coords, add = TRUE, col = "gray")
>>
>> ƒacu.-
>>
>>
>> On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
>>> Hi all,
>>>
>>> Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair.
>
> Using individual voter data is highly dangerous, and must in every case be subject to the strictest privacy rules. Voter data does not in essence have position - the only valid voting data that has position is of the voting station/precinct, and those data are aggregated to preserve anonymity.
>
> Why does position and voter data not have position? Which location should you use - residence, workplace, what? What are these locations proxying? Nothing valid can be drawn from "just voter data" - you can get conclusions from carefully constructed stratified exit polls, but there the key gender/age/ethnicity/social class/etc. confounders are handled by design. Why should voting decisions be influenced by proximity (they are not)? The missing element here is looking carefully at relevant covariates at more aggregated levels (in the US typically zoning controlling social class positional segregation, etc.).
>
>>> I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.
>>>
>>> While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.
>>>
>>> There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.
>>>
>>> Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.
>
> You mean RStudio, there is no such version of R.
>
>>>
>>> # Create a data frame of 10 voters, picked at random
>>> voter.1 = c(1, -75.52187, 40.62320)
>>> voter.2 = c(2,-75.56373, 40.55216)
>>> voter.3 = c(3,-75.39587, 40.55416)
>>> voter.4 = c(4,-75.42248, 40.64326)
>>> voter.5 = c(5,-75.56654, 40.54948)
>>> voter.6 = c(6,-75.56257, 40.67375)
>>> voter.7 = c(7, -75.51888, 40.59715)
>>> voter.8 = c(8, -75.59879, 40.60014)
>>> voter.9 = c(9, -75.59879, 40.60014)
>>> voter.10 = c(10, -75.50877, 40.53129)
>>>
>
> These are in geographical coordinates.
>
>>> # Bind the vectors together
>>> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>>>
>>> # Rename the columns
>>> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>>>
>>> # Change the class from a matrix to a data frame
>>> voter.subset = as.data.frame(voter.subset)
>>>
>>> # Load in the required packages
>>> library(spdep)
>>> library(sp)
>>>
>>> # Set the coordinates
>>> coordinates(voter.subset) = c("Longitude", "Latitude")
>>> coords = coordinates(voter.subset)
>>>
>>> # Jitter to ensure no duplicate points
>>> coords = jitter(coords, factor = 1)
>>>
>
> jitter does not respect geographical coordinated (decimal degree metric).
>
>>> # Find the first nearest neighbor of each point
>>> one.nn = knearneigh(coords, k=1)
>
> See the help page (hint: longlat=TRUE to use Great Circle distances, much slower than planar).
>
>>>
>>> # Convert the first nearest neighbor to format "nb"
>>> one.nn_nb = knn2nb(one.nn, sym = F)
>>>
>>> Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.
>>>
>>> Warmest,
>>> Ben
>>> --
>>> Benjamin Lieberman
>>> Muhlenberg College 2019
>>> Mobile: 301.299.8928
>>>
>>>
>>>
>>> [[alternative HTML version deleted]]
>
> Plain text only, please.
>
>>>
>>> _______________________________________________
>>> R-sig-Geo mailing list
>>> [hidden email] <mailto:[hidden email]>
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>
>>
>> [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email] <mailto:[hidden email]>
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
>>
>
> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; e-mail: [hidden email] <mailto:[hidden email]>
> http://orcid.org/0000-0003-2392-6140 <http://orcid.org/0000-0003-2392-6140>
> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________ <https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en_______________________________________________>
> R-sig-Geo mailing list
> [hidden email] <mailto:[hidden email]>
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo <https://stat.ethz.ch/mailman/listinfo/r-sig-geo>
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Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 05:11
On Fri, 13 Jul 2018, Facundo Muñoz wrote:

> Dear Benjamin,
>
> I'm not sure how you define "first order neighbors" for a point. The
> first thing that comes to my mind is to use their corresponding voronoi
> polygons and define neighborhood from there. Following your code:

Thanks, the main source of confusion is that "first order neighbors" are
not defined. A k=1 neighbour could be (as below), as could k=6, or voronoi
neighbours, or sphere of influence etc. So reading vignette("nb") would be
a starting point.

Also note that voronoi and other graph-based neighbours should only use
planar coordinates - including dismo::voronoi, which uses deldir::deldir()
- just like spdep::tri2nb(). Triangulation can lead to spurious neighbours
on the convex hull.

>
> v <- dismo::voronoi(coords)
> par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
> plot(coords, type = "n", xlab = NA, ylab = NA)
> plot(v, add = TRUE)
> text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
> plot(coords, type = "n", xlab = NA, ylab = NA)
> plot(poly2nb(v), coords, add = TRUE, col = "gray")
>
> ƒacu.-
>
>
> On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
>> Hi all,
>>
>> Currently, I am working with U.S. voter data. Below, I included a brief
>> example of the structure of the data with some reproducible code. My
>> data set consists of roughly 233,000 (233k) entries, each specifying a
>> voter and their particular latitude/longitude pair. Using individual voter data is highly dangerous, and must in every case be
subject to the strictest privacy rules. Voter data does not in essence
have position - the only valid voting data that has position is of the
voting station/precinct, and those data are aggregated to preserve
anonymity.

Why does position and voter data not have position? Which location should
you use - residence, workplace, what? What are these locations proxying?
Nothing valid can be drawn from "just voter data" - you can get
conclusions from carefully constructed stratified exit polls, but there
the key gender/age/ethnicity/social class/etc. confounders are handled by
design. Why should voting decisions be influenced by proximity (they are
not)? The missing element here is looking carefully at relevant covariates
at more aggregated levels (in the US typically zoning controlling social
class positional segregation, etc.).

>> I have been using the spdep package with the hope of creating a CAR
>> model. To begin the analysis, we need to find all first order neighbors
>> of every point in the data.
>>
>> While spdep has fantastic commands for finding k nearest neighbors
>> (knearneigh), and a useful command for finding lag of order 3 or more
>> (nblag), I have yet to find a method which is suitable for our purposes
>> (lag = 1, or lag =2). Additionally, I looked into altering the nblag
>> command to accommodate maxlag = 1 or maxlag = 2, but the command relies
>> on an nb format, which is problematic as we are looking for the
>> underlying neighborhood structure.
>>
>> There has been numerous work done with polygons, or data which already
>> is in “nb” format, but after reading the literature, it seems that
>> polygons are not appropriate, nor are distance based neighbor
>> techniques, due to density fluctuations over the area of interest.
>>
>> Below is some reproducible code I wrote. I would like to note that I am
>> currently working in R 1.1.453 on a MacBook. You mean RStudio, there is no such version of R.

>>
>> # Create a data frame of 10 voters, picked at random
>> voter.1 = c(1, -75.52187, 40.62320)
>> voter.2 = c(2,-75.56373, 40.55216)
>> voter.3 = c(3,-75.39587, 40.55416)
>> voter.4 = c(4,-75.42248, 40.64326)
>> voter.5 = c(5,-75.56654, 40.54948)
>> voter.6 = c(6,-75.56257, 40.67375)
>> voter.7 = c(7, -75.51888, 40.59715)
>> voter.8 = c(8, -75.59879, 40.60014)
>> voter.9 = c(9, -75.59879, 40.60014)
>> voter.10 = c(10, -75.50877, 40.53129)
>> These are in geographical coordinates.

>> # Bind the vectors together
>> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>>
>> # Rename the columns
>> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>>
>> # Change the class from a matrix to a data frame
>> voter.subset = as.data.frame(voter.subset)
>>
>> # Load in the required packages
>> library(spdep)
>> library(sp)
>>
>> # Set the coordinates
>> coordinates(voter.subset) = c("Longitude", "Latitude")
>> coords = coordinates(voter.subset)
>>
>> # Jitter to ensure no duplicate points
>> coords = jitter(coords, factor = 1)
>> jitter does not respect geographical coordinated (decimal degree metric).

>> # Find the first nearest neighbor of each point
>> one.nn = knearneigh(coords, k=1)

See the help page (hint: longlat=TRUE to use Great Circle distances, much
slower than planar).

>>
>> # Convert the first nearest neighbor to format "nb"
>> one.nn_nb = knn2nb(one.nn, sym = F)
>>
>> Thank you in advance for any help you may offer, and for taking the
>> time to read this. I have consulted Applied Spatial Data Analysis with
>> R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the
>> spdep documentation, and the nb vignette (Bivand, April 3, 2018) from
>> earlier this year.
>>
>> Warmest,
>> Ben
>> --
>> Benjamin Lieberman
>> Muhlenberg College 2019
>> Mobile: 301.299.8928
>>
>>
>>
>> [[alternative HTML version deleted]] Plain text only, please.

>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> --
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: [hidden email]
http://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
_______________________________________________
R-sig-Geo mailing list
[hidden email]
https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N-5045 Bergen, Norway

Re: How to find all first order neighbors of a collection of points

Fri, 07/13/2018 - 04:32
Dear Benjamin,

I'm not sure how you define "first order neighbors" for a point. The
first thing that comes to my mind is to use their corresponding voronoi
polygons and define neighborhood from there. Following your code:

v <- dismo::voronoi(coords)
par(mfrow = c(1, 2), xaxt = "n", yaxt = "n", mgp = c(0, 0, 0))
plot(coords, type = "n", xlab = NA, ylab = NA)
plot(v, add = TRUE)
text(x = coords[, 1], y = coords[, 2], labels = voter.subset$Voter.ID)
plot(coords, type = "n", xlab = NA, ylab = NA)
plot(poly2nb(v), coords, add = TRUE, col = "gray")

ƒacu.-


On 07/12/2018 09:00 PM, Benjamin Lieberman wrote:
> Hi all,
>
> Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair. I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.
>
> While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.
>
> There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.
>
> Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.
>
> # Create a data frame of 10 voters, picked at random
> voter.1 = c(1, -75.52187, 40.62320)
> voter.2 = c(2,-75.56373, 40.55216)
> voter.3 = c(3,-75.39587, 40.55416)
> voter.4 = c(4,-75.42248, 40.64326)
> voter.5 = c(5,-75.56654, 40.54948)
> voter.6 = c(6,-75.56257, 40.67375)
> voter.7 = c(7, -75.51888, 40.59715)
> voter.8 = c(8, -75.59879, 40.60014)
> voter.9 = c(9, -75.59879, 40.60014)
> voter.10 = c(10, -75.50877, 40.53129)
>
> # Bind the vectors together
> voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)
>
> # Rename the columns
> colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")
>
> # Change the class from a matrix to a data frame
> voter.subset = as.data.frame(voter.subset)
>
> # Load in the required packages
> library(spdep)
> library(sp)
>
> # Set the coordinates
> coordinates(voter.subset) = c("Longitude", "Latitude")
> coords = coordinates(voter.subset)
>
> # Jitter to ensure no duplicate points
> coords = jitter(coords, factor = 1)
>
> # Find the first nearest neighbor of each point
> one.nn = knearneigh(coords, k=1)
>
> # Convert the first nearest neighbor to format "nb"
> one.nn_nb = knn2nb(one.nn, sym = F)
>
> Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.
>
> Warmest,
> Ben
> --
> Benjamin Lieberman
> Muhlenberg College 2019
> Mobile: 301.299.8928
>
>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo

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How to find all first order neighbors of a collection of points

Thu, 07/12/2018 - 14:00
Hi all,

Currently, I am working with U.S. voter data. Below, I included a brief example of the structure of the data with some reproducible code. My data set consists of roughly 233,000 (233k) entries, each specifying a voter and their particular latitude/longitude pair. I have been using the spdep package with the hope of creating a CAR model. To begin the analysis, we need to find all first order neighbors of every point in the data.

While spdep has fantastic commands for finding k nearest neighbors (knearneigh), and a useful command for finding lag of order 3 or more (nblag), I have yet to find a method which is suitable for our purposes (lag = 1, or lag =2). Additionally, I looked into altering the nblag command to accommodate maxlag = 1 or maxlag = 2, but the command relies on an nb format, which is problematic as we are looking for the underlying neighborhood structure.

There has been numerous work done with polygons, or data which already is in “nb” format, but after reading the literature, it seems that polygons are not appropriate, nor are distance based neighbor techniques, due to density fluctuations over the area of interest.

Below is some reproducible code I wrote. I would like to note that I am currently working in R 1.1.453 on a MacBook.

# Create a data frame of 10 voters, picked at random
voter.1 = c(1, -75.52187, 40.62320)
voter.2 = c(2,-75.56373, 40.55216)
voter.3 = c(3,-75.39587, 40.55416)
voter.4 = c(4,-75.42248, 40.64326)
voter.5 = c(5,-75.56654, 40.54948)
voter.6 = c(6,-75.56257, 40.67375)
voter.7 = c(7, -75.51888, 40.59715)
voter.8 = c(8, -75.59879, 40.60014)
voter.9 = c(9, -75.59879, 40.60014)
voter.10 = c(10, -75.50877, 40.53129)

# Bind the vectors together
voter.subset = rbind(voter.1, voter.2, voter.3, voter.4, voter.5, voter.6, voter.7, voter.8, voter.9, voter.10)

# Rename the columns
colnames(voter.subset) = c("Voter.ID", "Longitude", "Latitude")

# Change the class from a matrix to a data frame
voter.subset = as.data.frame(voter.subset)

# Load in the required packages
library(spdep)
library(sp)

# Set the coordinates
coordinates(voter.subset) = c("Longitude", "Latitude")
coords = coordinates(voter.subset)

# Jitter to ensure no duplicate points
coords = jitter(coords, factor = 1)

# Find the first nearest neighbor of each point
one.nn = knearneigh(coords, k=1)

# Convert the first nearest neighbor to format "nb"
one.nn_nb = knn2nb(one.nn, sym = F)

Thank you in advance for any help you may offer, and for taking the time to read this. I have consulted Applied Spatial Data Analysis with R (Bivand, Pebesma, Gomez-Rubio), as well as other Sig-Geo threads, the spdep documentation, and the nb vignette (Bivand, April 3, 2018) from earlier this year.

Warmest,
Ben
--
Benjamin Lieberman
Muhlenberg College 2019
Mobile: 301.299.8928



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Re: Distance from not NA cells in a raster

Thu, 07/12/2018 - 07:12
Hi,

You may have solved this already, but I get tripped up on the "the distance from all not NA cells in a raster".  Is it the distance each NA cell is from each non-NA cell?  Also, I'm wondering why you want to know the distance to ALL non-NA cells - what is your big-picture purpose for wanting these distances?  

Cheers,
Ben

> On Jul 6, 2018, at 12:22 PM, Gregovich, Dave P (DFG) <[hidden email]> wrote:
>
> Hi,
> I would like to obtain the distance from all not NA cells in a raster. This works for smaller rasters, but seems difficult for the size of rasters (~ 8000 pixel square)  I am working with.
> Below is what I've tried. I would be OK calling other software from R, or using some parallelization, if it might help.
> Thanks so much for your help!  If I could just calculate this distance in two hours or less (or so) I would be satisfied.
> Dave.
>
> rm(list=ls())
> library(raster)
>
> #make raster
> rast <- raster(nrow = 8000, ncol = 8000, ext = extent(0,1,0,1))
>
> #generate cells to calculate distance from.
> rast[sample(8000^2, 10000)] <- 1
>
> #try two different methods...
> dist1 <- gridDistance(rast, origin = 1)#throws an error after x minutes
> #'Error: cannot allocate vector of size 3.8 Gb'
> dist2 <- distance(rast)#ran all night, R was hung up in the morning and had to force shutdown.
>
> ___________________________________________
> Dave Gregovich
> Research Analyst
> Alaska Department of Fish and Game
> Division of Wildlife Conservation
> 802 3rd Street
> Douglas, AK 99824
> 907-465-4291
> ___________________________________________
>
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
Ben Tupper
Bigelow Laboratory for Ocean Sciences
60 Bigelow Drive, P.O. Box 380
East Boothbay, Maine 04544
http://www.bigelow.org

Ecological Forecasting: https://eco.bigelow.org/






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Re: Spatial autocorrelation help

Thu, 07/12/2018 - 06:44
Hi Orcun

I am not quite sure if im doing this correctly but I do understand that i first need to check spatial autocorrelation occurs in my data. so i did this below steps and after that checked it again in best model residuals

# Another approach to find SAC by creating neighbors first, then get distances between each point and neighbors, then
# inverse the distance and then check the SAC using mora's I
coord <- cbind(data$long, data$lat)
coords <- coordinates(coord)

# creates a matrix of nn indexes - knearneigh to get nearest neighbors
nn5 <- knearneigh(coords, k=5)  
mi5.nlist <- knn2nb(nn5, row.names = NULL, sym=FALSE)

# creates a sp weights matrix
mi5.sw <- nb2listw(mi5.nlist)

# cal moran's I using distance as weights
# calculates the distance
mi5.dist <- nbdists(mi5.nlist, coords)

# now invert the distnace to determine weights (closer =higher)
mi5.dist1 <- lapply(mi5.dist, function(x){ifelse(is.finite(1/x), (1/x), (1/0.001))})
mi5.dist2 <- lapply(mi5.dist, function(x){ifelse(is.finite(1/x^2), (1/x^2), (1/0.001^2))})

# check the distance between the distribution
summary(unlist(mi5.dist1))

# now create sp weights matrix weighted on distance
mi5.d1sw <- nb2listw(mi5.nlist, glist=mi5.dist1)
mi5.d2sw <- nb2listw(mi5.nlist, glist=mi5.dist2)

# morans test
moran.test(as.numeric(data$response), mi5.d1sw)
moran.test(as.numeric(data$response), mi5.d2sw)

This first moran’s test gives :
Moran I statistic standard deviate = 2.0328, p-value = 0.02104
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance
      0.105850408      -0.004608295       0.002952729

Second morans test gives:

Moran I statistic standard deviate = 2.3848, p-value = 0.008545
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance
      0.154097396      -0.004608295       0.004428848

And both indicates presence of spatial autocorrelation in the raw data.

Should i account for this in all models or if i perform logistic mixed model it is fine……help is much appreciated. Difficult to understand what the problem is and how to solve it



> On 11 Jul 2018, at 7:01 PM, Orcun Morali <[hidden email]> wrote:
>
> Hi Dechen,
>
> As for measuring spatial autocorrelation, one thing I noticed about your output is that you are using the randomization assumption in spdep::moran.test. Randomization assumption is not appropriate for Moran's I of regression residuals and spdep::lm.morantest is the function to correctly calculate moments of the measure for regression residuals anyway. Before using lm.morantest though, if I were you, I would check whether its inference applies to logistic regression residuals as well, since the theory was initially based on the classical regression.
>
> As for fitting a spatial logistic model if you need it, McSpatial package in R might help you.
>
> Best Regards,
>
> Orcun
>
> On 10/07/18 20:46, Dechen Lham wrote:
>> Hello all,
>>
>> I would like some help in my problem below:
>>
>> I am running a logistic regression and my best model residuals has spatial autocorrelation  (SAC) when checked as below and also on the raw data of the response type. My response is binary 0 and 1 (type of prey and to be predicted by several predictors). These type of prey are obtained from  a total of  200 locations (where the faecal samples are collected from).   In order to account for this SAC , I used the auto_covdist function from spdep package. But when i use this as a new predictor in my model, and then check for spatial autocorrelation in the residues of the model, there is still spatial autocorrelation,…..could u see if i am doing something wrong please?
>>
>> #account for SAC in the model using weights
>> # auto_covariate is a distance weighted covariate
>> data$response <- as.numeric(data$response)
>> auto_weight <- autocov_dist(data$prey.type, xy=coords, nbs=1, type="inverse", zero.policy = TRUE,style="W", longlat = TRUE)
>>
>> m5_auto <- glm(response ~  predictor1 + predictor2 + predictor3 + predictor4 + predictor1:predictor4, weight=auto_weight, family=quasibinomial("logit"), data=data)
>>
>> # check spatial autocorrelation - first convert data to spatial points dataframe
>> dat <- SpatialPointsDataFrame(cbind(data$long, data$lat), data)
>> lstw  <- nb2listw(knn2nb(knearneigh(dat, k = 2)))
>>
>> # check SAC in model residuals
>> moran.test(residuals.glm(m5_auto), lstw) # and gives the below:
>>
>> Moran I test under randomisation
>>
>> data:  residuals.glm(m5)
>> weights: lstw
>>
>> Moran I statistic standard deviate = 1.9194, p-value = 0.02747
>> alternative hypothesis: greater
>> sample estimates:
>> Moran I statistic       Expectation          Variance
>>      0.160824328      -0.004608295       0.007428642
>>
>> -Someone said its stupid to account for spatial autocorrelation in a logistic regression when you have a significant SAC using moran’s I. So i am now wondering how this can be solved? or does a SAC in a logistic regression be just ignored?
>>
>> I am new to spatial statistics and now idea how to move with such. I only know that my data has spatial
>>  autocorrelation (which i hope to have checked correctly using morans I as above) and now need to account for this in my analysis. Some advice would be greatly appreciated by people who have used to account for SAC in their logistic models.  Is a logistic mixed models an option to consider?especially if your covariates are spatial in nature,…i read somewhere that if you cant account for SAC in glm then you can move to mixed models esp if your covariates are spatial which is expected to digest the SAC.
>>
>> Help and advice would be greatly appreciated.
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> [hidden email]
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
> _______________________________________________
> R-sig-Geo mailing list
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> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
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Re: Spatial autocorrelation help

Wed, 07/11/2018 - 12:01
Hi Dechen,

As for measuring spatial autocorrelation, one thing I noticed about your
output is that you are using the randomization assumption in
spdep::moran.test. Randomization assumption is not appropriate for
Moran's I of regression residuals and spdep::lm.morantest is the
function to correctly calculate moments of the measure for regression
residuals anyway. Before using lm.morantest though, if I were you, I
would check whether its inference applies to logistic regression
residuals as well, since the theory was initially based on the classical
regression.

As for fitting a spatial logistic model if you need it, McSpatial
package in R might help you.

Best Regards,

Orcun

On 10/07/18 20:46, Dechen Lham wrote:
> Hello all,
>
> I would like some help in my problem below:
>
> I am running a logistic regression and my best model residuals has spatial autocorrelation  (SAC) when checked as below and also on the raw data of the response type. My response is binary 0 and 1 (type of prey and to be predicted by several predictors). These type of prey are obtained from  a total of  200 locations (where the faecal samples are collected from).   In order to account for this SAC , I used the auto_covdist function from spdep package. But when i use this as a new predictor in my model, and then check for spatial autocorrelation in the residues of the model, there is still spatial autocorrelation,…..could u see if i am doing something wrong please?
>
> #account for SAC in the model using weights
> # auto_covariate is a distance weighted covariate
> data$response <- as.numeric(data$response)
> auto_weight <- autocov_dist(data$prey.type, xy=coords, nbs=1, type="inverse", zero.policy = TRUE,style="W", longlat = TRUE)
>
> m5_auto <- glm(response ~  predictor1 + predictor2 + predictor3 + predictor4 + predictor1:predictor4, weight=auto_weight, family=quasibinomial("logit"), data=data)
>
> # check spatial autocorrelation - first convert data to spatial points dataframe
> dat <- SpatialPointsDataFrame(cbind(data$long, data$lat), data)
> lstw  <- nb2listw(knn2nb(knearneigh(dat, k = 2)))
>
> # check SAC in model residuals
> moran.test(residuals.glm(m5_auto), lstw) # and gives the below:
>
> Moran I test under randomisation
>
> data:  residuals.glm(m5)
> weights: lstw
>
> Moran I statistic standard deviate = 1.9194, p-value = 0.02747
> alternative hypothesis: greater
> sample estimates:
> Moran I statistic       Expectation          Variance
>       0.160824328      -0.004608295       0.007428642
>
> -Someone said its stupid to account for spatial autocorrelation in a logistic regression when you have a significant SAC using moran’s I. So i am now wondering how this can be solved? or does a SAC in a logistic regression be just ignored?
>
> I am new to spatial statistics and now idea how to move with such. I only know that my data has spatial
>   autocorrelation (which i hope to have checked correctly using morans I as above) and now need to account for this in my analysis. Some advice would be greatly appreciated by people who have used to account for SAC in their logistic models.  Is a logistic mixed models an option to consider?especially if your covariates are spatial in nature,…i read somewhere that if you cant account for SAC in glm then you can move to mixed models esp if your covariates are spatial which is expected to digest the SAC.
>
> Help and advice would be greatly appreciated.
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
_______________________________________________
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Re: Spatial autocorrelation help

Tue, 07/10/2018 - 14:57
Hi Patrick

Thank you for your quick response and i went through your thesis and its very useful information. One thing that i was wondering was, you could potentially also use quadratic terms of the predictors which may have non-linear relation with the response variable right? rather than to use GAMM.

Besides i need to still figure out how to check the SAC correctly in my data as there is the global morans I and a local morans I right? Further need to figure out how to plot them correctly to see the patterns. I did make a correlogram of the raw data and from the residuals of the best model but both looked very similar and also after accounting for SAC, the morans I was significant and SAC was not accounted for. So it would be great if you can see I am doing something wrong while accepting for the SAC below…please


regards


> On 10 Jul 2018, at 8:38 PM, Patrick Schratz <[hidden email]> wrote:
>
> Hi Dechen,
>
> it is very important to account for SAC in any model. This can be done in various ways. In log.reg it is common to include spatial autocorrelation structures that describe the underlying SAC. To do so, you can use mixed models, e.g. MASS::glmmPQL().
>
> Also have a look at Wood (2017) Generalized Additive Models in R.
>
> I did account for it in my master thesis.Even though the code is not attached, it may help you: https://zenodo.org/record/814262 <https://zenodo.org/record/814262>
> Cheers, Patrick
> On Jul 10 2018, at 7:46 pm, Dechen Lham <[hidden email]> wrote:
>
> Hello all,
>
> I would like some help in my problem below:
>
> I am running a logistic regression and my best model residuals has spatial autocorrelation (SAC) when checked as below and also on the raw data of the response type. My response is binary 0 and 1 (type of prey and to be predicted by several predictors). These type of prey are obtained from a total of 200 locations (where the faecal samples are collected from). In order to account for this SAC , I used the auto_covdist function from spdep package. But when i use this as a new predictor in my model, and then check for spatial autocorrelation in the residues of the model, there is still spatial autocorrelation,…..could u see if i am doing something wrong please?
>
> #account for SAC in the model using weights
> # auto_covariate is a distance weighted covariate
> data$response <- as.numeric(data$response)
> auto_weight <- autocov_dist(data$prey.type, xy=coords, nbs=1, type="inverse", zero.policy = TRUE,style="W", longlat = TRUE)
>
> m5_auto <- glm(response ~ predictor1 + predictor2 + predictor3 + predictor4 + predictor1:predictor4, weight=auto_weight, family=quasibinomial("logit"), data=data)
>
> # check spatial autocorrelation - first convert data to spatial points dataframe
> dat <- SpatialPointsDataFrame(cbind(data$long, data$lat), data)
> lstw <- nb2listw(knn2nb(knearneigh(dat, k = 2)))
>
> # check SAC in model residuals
> moran.test(residuals.glm(m5_auto), lstw) # and gives the below:
>
> Moran I test under randomisation
>
> data: residuals.glm(m5)
> weights: lstw
>
> Moran I statistic standard deviate = 1.9194, p-value = 0.02747
> alternative hypothesis: greater
> sample estimates:
> Moran I statistic Expectation Variance
> 0.160824328 -0.004608295 0.007428642
>
> -Someone said its stupid to account for spatial autocorrelation in a logistic regression when you have a significant SAC using moran’s I. So i am now wondering how this can be solved? or does a SAC in a logistic regression be just ignored?
>
> I am new to spatial statistics and now idea how to move with such. I only know that my data has spatial
> autocorrelation (which i hope to have checked correctly using morans I as above) and now need to account for this in my analysis. Some advice would be greatly appreciated by people who have used to account for SAC in their logistic models. Is a logistic mixed models an option to consider?especially if your covariates are spatial in nature,…i read somewhere that if you cant account for SAC in glm then you can move to mixed models esp if your covariates are spatial which is expected to digest the SAC.
>
> Help and advice would be greatly appreciated.
>
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo

        [[alternative HTML version deleted]]

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Re: Spatial autocorrelation help

Tue, 07/10/2018 - 13:38
Hi Dechen,

it is very important to account for SAC in any model. This can be done in various ways. In log.reg it is common to include spatial autocorrelation structures that describe the underlying SAC. To do so, you can use mixed models, e.g. MASS::glmmPQL().
Also have a look at Wood (2017) Generalized Additive Models in R.
I did account for it in my master thesis.Even though the code is not attached, it may help you: https://zenodo.org/record/814262
Cheers, Patrick
On Jul 10 2018, at 7:46 pm, Dechen Lham <[hidden email]> wrote:
>
> Hello all,
> I would like some help in my problem below:
> I am running a logistic regression and my best model residuals has spatial autocorrelation (SAC) when checked as below and also on the raw data of the response type. My response is binary 0 and 1 (type of prey and to be predicted by several predictors). These type of prey are obtained from a total of 200 locations (where the faecal samples are collected from). In order to account for this SAC , I used the auto_covdist function from spdep package. But when i use this as a new predictor in my model, and then check for spatial autocorrelation in the residues of the model, there is still spatial autocorrelation,…..could u see if i am doing something wrong please?
> #account for SAC in the model using weights
> # auto_covariate is a distance weighted covariate
> data$response <- as.numeric(data$response)
> auto_weight <- autocov_dist(data$prey.type, xy=coords, nbs=1, type="inverse", zero.policy = TRUE,style="W", longlat = TRUE)
>
> m5_auto <- glm(response ~ predictor1 + predictor2 + predictor3 + predictor4 + predictor1:predictor4, weight=auto_weight, family=quasibinomial("logit"), data=data)
> # check spatial autocorrelation - first convert data to spatial points dataframe
> dat <- SpatialPointsDataFrame(cbind(data$long, data$lat), data)
> lstw <- nb2listw(knn2nb(knearneigh(dat, k = 2)))
>
> # check SAC in model residuals
> moran.test(residuals.glm(m5_auto), lstw) # and gives the below:
>
> Moran I test under randomisation
> data: residuals.glm(m5)
> weights: lstw
>
> Moran I statistic standard deviate = 1.9194, p-value = 0.02747
> alternative hypothesis: greater
> sample estimates:
> Moran I statistic Expectation Variance
> 0.160824328 -0.004608295 0.007428642
>
> -Someone said its stupid to account for spatial autocorrelation in a logistic regression when you have a significant SAC using moran’s I. So i am now wondering how this can be solved? or does a SAC in a logistic regression be just ignored?
> I am new to spatial statistics and now idea how to move with such. I only know that my data has spatial
> autocorrelation (which i hope to have checked correctly using morans I as above) and now need to account for this in my analysis. Some advice would be greatly appreciated by people who have used to account for SAC in their logistic models. Is a logistic mixed models an option to consider?especially if your covariates are spatial in nature,…i read somewhere that if you cant account for SAC in glm then you can move to mixed models esp if your covariates are spatial which is expected to digest the SAC.
>
> Help and advice would be greatly appreciated.
> _______________________________________________
> R-sig-Geo mailing list
> [hidden email]
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>

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Spatial autocorrelation help

Tue, 07/10/2018 - 12:46
Hello all,

I would like some help in my problem below:

I am running a logistic regression and my best model residuals has spatial autocorrelation  (SAC) when checked as below and also on the raw data of the response type. My response is binary 0 and 1 (type of prey and to be predicted by several predictors). These type of prey are obtained from  a total of  200 locations (where the faecal samples are collected from).   In order to account for this SAC , I used the auto_covdist function from spdep package. But when i use this as a new predictor in my model, and then check for spatial autocorrelation in the residues of the model, there is still spatial autocorrelation,…..could u see if i am doing something wrong please?

#account for SAC in the model using weights
# auto_covariate is a distance weighted covariate
data$response <- as.numeric(data$response)
auto_weight <- autocov_dist(data$prey.type, xy=coords, nbs=1, type="inverse", zero.policy = TRUE,style="W", longlat = TRUE)

m5_auto <- glm(response ~  predictor1 + predictor2 + predictor3 + predictor4 + predictor1:predictor4, weight=auto_weight, family=quasibinomial("logit"), data=data)

# check spatial autocorrelation - first convert data to spatial points dataframe
dat <- SpatialPointsDataFrame(cbind(data$long, data$lat), data)
lstw  <- nb2listw(knn2nb(knearneigh(dat, k = 2)))

# check SAC in model residuals
moran.test(residuals.glm(m5_auto), lstw) # and gives the below:

Moran I test under randomisation

data:  residuals.glm(m5)  
weights: lstw  

Moran I statistic standard deviate = 1.9194, p-value = 0.02747
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance
     0.160824328      -0.004608295       0.007428642

-Someone said its stupid to account for spatial autocorrelation in a logistic regression when you have a significant SAC using moran’s I. So i am now wondering how this can be solved? or does a SAC in a logistic regression be just ignored?

I am new to spatial statistics and now idea how to move with such. I only know that my data has spatial
 autocorrelation (which i hope to have checked correctly using morans I as above) and now need to account for this in my analysis. Some advice would be greatly appreciated by people who have used to account for SAC in their logistic models.  Is a logistic mixed models an option to consider?especially if your covariates are spatial in nature,…i read somewhere that if you cant account for SAC in glm then you can move to mixed models esp if your covariates are spatial which is expected to digest the SAC.

Help and advice would be greatly appreciated.

_______________________________________________
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[hidden email]
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Re: converting from MSL EGM2008 to RH2000

Tue, 07/10/2018 - 08:14
On Tue, 10 Jul 2018, Harp, Nathen (DOT) via R-sig-Geo wrote:

> I don't believe R has geodetic packages but that is what you need.  See NGS for examples,,
> https://www.ngs.noaa.gov/PC_PROD/pc_prod.shtml

While this is currently the case, we hope that PROJ >=5 will make this
possible - for now please also consider using PROJ command line tools with
geodetic pipelines, maybe ask on the proj list, and keep everyone
informed!

Roger

> ________________________________
> From: R-sig-Geo <[hidden email]> on behalf of Francis Freire <[hidden email]>
> Sent: Tuesday, July 10, 2018 7:20:59 AM
> To: '[hidden email]'
> Subject: [R-sig-Geo] converting from MSL EGM2008 to RH2000
>
> ATTENTION: This email came from an external source. Do not open attachments or click on links from unknown senders or unexpected emails.
>
>
> Hi,
>
> I am particularly new to R and would like to ask a question. I have looking all over the net for a way to convert the vertical reference system of our z values in our xyz text file from MSL EGM2008 to RH2000 using R. Has anyone done this before or can point me in the right directions?
>
> Best,
>
> Francis
> _______________________________________________
> R-sig-Geo mailing list
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>
> [[alternative HTML version deleted]]
>
> _______________________________________________
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> [hidden email]
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>
--
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: [hidden email]
http://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en

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Roger Bivand
Department of Economics
Norwegian School of Economics
Helleveien 30
N-5045 Bergen, Norway

Re: converting from MSL EGM2008 to RH2000

Tue, 07/10/2018 - 07:45
I don't believe R has geodetic packages but that is what you need.  See NGS for examples,,
https://www.ngs.noaa.gov/PC_PROD/pc_prod.shtml
________________________________
From: R-sig-Geo <[hidden email]> on behalf of Francis Freire <[hidden email]>
Sent: Tuesday, July 10, 2018 7:20:59 AM
To: '[hidden email]'
Subject: [R-sig-Geo] converting from MSL EGM2008 to RH2000

ATTENTION: This email came from an external source. Do not open attachments or click on links from unknown senders or unexpected emails.


Hi,

I am particularly new to R and would like to ask a question. I have looking all over the net for a way to convert the vertical reference system of our z values in our xyz text file from MSL EGM2008 to RH2000 using R. Has anyone done this before or can point me in the right directions?

Best,

Francis
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converting from MSL EGM2008 to RH2000

Tue, 07/10/2018 - 06:20

Hi,

I am particularly new to R and would like to ask a question. I have looking all over the net for a way to convert the vertical reference system of our z values in our xyz text file from MSL EGM2008 to RH2000 using R. Has anyone done this before or can point me in the right directions?

Best,

Francis
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Memory issues using raster::subs

Mon, 07/09/2018 - 08:44
Hi all,

I am having issues with using the subs function in the raster package. In the past, I have successfully used the function to reclassify a raster, but now when I try to use it, I am receiving the error " Error: cannot allocate vector of size 2.0 Gb". The code is the same as what I had used before with a larger raster and data.frame.

For example, this code works:

segments = raster(D:/path/To/InputRaster.tif) ### objects raster
obj_predicted = data.frame(zone,predicted)
filename="D:/path/To/Raster.tif"
subs(segments,obj_predicted,by=1,which=2,filename=filename,progress="text")

The segments raster is of size 71026 by 78701. obj_predicted is a 1,693,839 X 2 column data frame, with each row of the first column of the data frame corresponding to a pixel value in the segments raster.

However, when I replace the segments raster with another raster that is 14157 by 11923  and the obj_predicted data frame is 6588 x 2, I receive the error message. The crs of both rasters is the same, both data frames are essentially the same etc.

Sorry that I can't really provide the data to attempt reproduction. Any help would be appreciated. I am going to attempt to process by block, but this seems

Wade

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