[R-sig-Geo] How to efficiently generate data of neighboring points
Roger Bivand
Roger@B|v@nd @end|ng |rom nhh@no
Thu Jun 4 10:48:00 CEST 2020
On Thu, 4 Jun 2020, Lom Navanyo wrote:
> Thank you very much for your support. This gives me what I need and I must
> say listw2sn() is really great.
>
> Why do I need the data in the format as in dataout? I am trying to test
> spatial dependence (or neighborhood effect) by running a regression
> model that entails pop_size_it = beta_1*sum of pop_size of point i's
> neighbors within a specified radius. So my plan is to get the neighbors
> for each focal point as per the specified bands and their attributes (eg
> pop_size) so I can can add them (attribute) by the bands.
Thanks, clarifies a good deal. Maybe look at the original localG articles
for exploring distance relationships (Getis and Ord looked at HIV/AIDS);
?spdep::localG or https://r-spatial.github.io/spdep/reference/localG.html.
Further note at OLS is biased as you have y = f(y) + e, so y on both
sides. The nearest equivalent for a single band is spatialreg::lagsarlm()
with listw=nb2listw(wd1, style="B") to get the neighbour sums through the
weights matrix. So both your betas and their standard errors are unusable,
I'm afraid. You are actually very much closer to ordinary kriging, looking
at the way in which distance attenuates the correlation in value of
proximate observations.
Hope this clarifies,
Roger
>
> I am totally new to the area of spatial econometrics, so I am taking things
> one step at a time. Some readings suggest I may need distance matrix or
> weight matrix but for now I think I should try the current approach.
>
> Thank you.
>
> -------------
> Lom
>
> On Wed, Jun 3, 2020 at 8:18 AM Roger Bivand <Roger.Bivand using nhh.no> wrote:
>
>> On Wed, 3 Jun 2020, Lom Navanyo wrote:
>>
>>> I had the errors with rtree using R 3.6.3. I have since changed to R
>> 4.0.0
>>> but I got the same error.
>>>
>>> And yes, for Roger's example, I have the objects wd1, ... wd4, all with
>>> length 101. I think my difficulty is my inability to output the list
>>> detailing the point IDs t50_fid.
>>
>> library(spData)
>> library(sf)
>> projdata<-st_transform(nz_height, 32759)
>> pts <- st_coordinates(projdata)
>> library(spdep)
>> bufferR <- c(402.336, 1609.34, 3218.69, 4828.03, 6437.38)
>> bds <- c(0, bufferR)
>> wd1 <- dnearneigh(pts, bds[1], bds[2])
>> wd2 <- dnearneigh(pts, bds[2], bds[3])
>> wd3 <- dnearneigh(pts, bds[3], bds[4])
>> wd4 <- dnearneigh(pts, bds[4], bds[5])
>> sn_band1 <- listw2sn(nb2listw(wd1, style="B", zero.policy=TRUE))
>> sn_band1$band <- paste(attr(wd1, "distances"), collapse="-")
>> sn_band2 <- listw2sn(nb2listw(wd2, style="B", zero.policy=TRUE))
>> sn_band2$band <- paste(attr(wd2, "distances"), collapse="-")
>> sn_band3 <- listw2sn(nb2listw(wd3, style="B", zero.policy=TRUE))
>> sn_band3$band <- paste(attr(wd3, "distances"), collapse="-")
>> sn_band4 <- listw2sn(nb2listw(wd4, style="B", zero.policy=TRUE))
>> sn_band4$band <- paste(attr(wd4, "distances"), collapse="-")
>> data_out <- do.call("rbind", list(sn_band1, sn_band2, sn_band3, sn_band4))
>> class(data_out) <- "data.frame"
>> table(data_out$band)
>> data_out$ID_from <- projdata$t50_fid[data_out$from]
>> data_out$ID_to <- projdata$t50_fid[data_out$to]
>> data_out$elev_from <- projdata$elevation[data_out$from]
>> data_out$elev_to <- projdata$elevation[data_out$to]
>> str(data_out)
>>
>> The "spatial.neighbour" representation was that used in the S-Plus
>> SpatialStats module, with "from" and "to" columns, and here drops
>> no-neighbour cases gracefully. So listw2sn() comes in useful
>> for creating the output, and from there, just look-up in the
>> input data.frame. Observations here cannot be their own neighbours.
>>
>> It would be relevant to know why you need these, are you looking at
>> variogram clouds?
>>
>> Hope this clarifies,
>>
>> Roger
>>
>>>
>>> ---------
>>> Lom
>>>
>>> On Tue, Jun 2, 2020 at 8:02 PM Kent Johnson <kent3737 using gmail.com> wrote:
>>>
>>>> Roger's example works for me and gives a list of length 101. I did have
>>>> some issues that were resolved by updating packages. I'm using R 3.6.3
>> on
>>>> macOS 10.15.4. I also use rtree successfully on Windows 10 with R 3.6.3.
>>>>
>>>> Kent
>>>>
>>>> On Tue, Jun 2, 2020 at 12:29 PM Roger Bivand <Roger.Bivand using nhh.no>
>> wrote:
>>>>
>>>>> On Tue, 2 Jun 2020, Kent Johnson wrote:
>>>>>
>>>>>> rtree uses Euclidean distance so the points should be in a coordinate
>>>>>> system where this makes sense at least as a reasonable approximation.
>>>>>
>>>>> I tried the original example:
>>>>>
>>>>> remotes::install_github("hunzikp/rtree")
>>>>> library(spData)
>>>>> library(sf)
>>>>> projdata<-st_transform(nz_height, 32759)
>>>>> library(rtree)
>>>>> pts <- st_coordinates(projdata)
>>>>> rt <- RTree(st_coordinates(projdata))
>>>>> bufferR <- c(402.336, 1609.34, 3218.69, 4828.03, 6437.38)
>>>>> wd1 <- withinDistance(rt, pts, bufferR[1])
>>>>>
>>>>> but unfortunately failed (maybe newer Boost headers than yours?):
>>>>>
>>>>> Error in UseMethod("withinDistance", rTree) :
>>>>> no applicable method for 'withinDistance' applied to an object of
>>>>> class
>>>>> "c('list', 'RTree')"
>>>>>
>>>>>>
>>>>>> Kent
>>>>>>
>>>>>> On Tue, Jun 2, 2020 at 9:59 AM Roger Bivand <Roger.Bivand using nhh.no>
>>>>> wrote:
>>>>>>
>>>>>>> On Tue, 2 Jun 2020, Kent Johnson wrote:
>>>>>>>
>>>>>>>>> Date: Tue, 2 Jun 2020 02:44:17 -0500
>>>>>>>>> From: Lom Navanyo <lomnavasia using gmail.com>
>>>>>>>>> To: r-sig-geo using r-project.org
>>>>>>>>> Subject: [R-sig-Geo] How to efficiently generate data of
>> neighboring
>>>>>>>>> points within specified radii (distances) for each point
>> in a
>>>>>>> given
>>>>>>>>> points data set.
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>> Hello,
>>>>>>>>> I have data set of about 3400 location points with which I am
>> trying
>>>>> to
>>>>>>>>> generate data of each point and their neighbors within defined
>> radii
>>>>>>> (eg,
>>>>>>>>> 0.25, 1, and 3 miles).
>>>>>>>>>
>>>>>>>>
>>>>>>>> The rtree package is very fast and memory-efficient for
>>>>> within-distance
>>>>>>>> calculations.
>>>>>>>> https://github.com/hunzikp/rtree
>>>>>>>
>>>>>>> Thanks! Does this also apply when the input points are in
>> geographical
>>>>>>> coordinates?
>>>>>>>
>>>>>>> Roger
>>>>>>>
>>>>>>>>
>>>>>>>> Kent Johnson
>>>>>>>> Cambridge, MA
>>>>>>>>
>>>>>>>> [[alternative HTML version deleted]]
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> R-sig-Geo mailing list
>>>>>>>> R-sig-Geo using r-project.org
>>>>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Roger Bivand
>>>>>>> Department of Economics, Norwegian School of Economics,
>>>>>>> Helleveien 30, N-5045 Bergen, Norway.
>>>>>>> voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
>>>>>>> https://orcid.org/0000-0003-2392-6140
>>>>>>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>>>>>>>
>>>>>>
>>>>>
>>>>> --
>>>>> Roger Bivand
>>>>> Department of Economics, Norwegian School of Economics,
>>>>> Helleveien 30, N-5045 Bergen, Norway.
>>>>> voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
>>>>> https://orcid.org/0000-0003-2392-6140
>>>>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>>>>>
>>>>
>>>
>>
>> --
>> Roger Bivand
>> Department of Economics, Norwegian School of Economics,
>> Helleveien 30, N-5045 Bergen, Norway.
>> voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
>> https://orcid.org/0000-0003-2392-6140
>> https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
>>
>
--
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; e-mail: Roger.Bivand using nhh.no
https://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en
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