[R-sig-Geo] How to find all first order neighbors of a collection of points

[Facundo Muñoz]

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:

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")

ƒacu.-


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. 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.
>
> # 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)
>
> # 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)
>
> # Find the first nearest neighbor of each point
> one.nn = knearneigh(coords, k=1)
>
> # 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
>
>
>
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>
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