[R-sig-Geo] How to efficiently generate data of neighboring points
Lom Navanyo
|omn@v@@|@ @end|ng |rom gm@||@com
Thu Jun 4 20:50:25 CEST 2020
Thank you. Yes, the OLS is biased and my plan is to use a 2SLS approach. I
have a variable I intend to use as an IV for y.
I have seen a few papers use this approach. Will this approach not correct
for the endogeneity?
Actually, I am not sure if this is a right forum or perhaps if it's
appropriate or acceptable to you to take this one-on-one with you for help:
My model actually looks like this: y= f(y, x) + e.
Aside the endogeneity of y (which I intend to instrument by another
variable z), there is simultaneity between y and x.
I intend to use the lag of x as instrument for x. Given that I am seeking
to test spatial dependency, do you see some fatal flaws with my approach?
I have also seen other empirical approaches like static and dynamic spatial
panel data modelling. I will be reviewing them also to see suitability for
my objective.
But, any further directions or suggestions are highly appreciated.
Thanks,
-------------------
Lom
On Thu, Jun 4, 2020 at 3:48 AM Roger Bivand <Roger.Bivand using nhh.no> wrote:
> 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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