How to find all first order neighbors of a collection of points
On Fri, 13 Jul 2018, Facundo Mu?oz 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") ?acu.- 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.
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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 http://orcid.org/0000-0003-2392-6140 https://scholar.google.no/citations?user=AWeghB0AAAAJ&hl=en