How to efficiently generate data of neighboring points

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 at 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 at 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 at 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 at nhh.no>
wrote:

On Tue, 2 Jun 2020, Kent Johnson wrote:

Date: Tue, 2 Jun 2020 02:44:17 -0500
From: Lom Navanyo <lomnavasia at gmail.com>
To: r-sig-geo at 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

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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: Roger.Bivand at 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 at 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 at 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 at nhh.no
https://orcid.org/0000-0003-2392-6140
https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en

How to efficiently generate data of neighboring points

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