How to efficiently generate data of neighboring points
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 at 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 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