This repository contains the adjacency structure for US counties. The
information is stored in a .rds file, to be read into R using the
Matrix package as follows:
library(Matrix)
library(readr)
A <- readr::read_rds("county-connectivity-2019.rds")
class(A)
#> [1] "dgCMatrix"
#> attr(,"package")
#> [1] "Matrix"Or:
url <- "https://github.com/ConnorDonegan/county-adjacency/raw/main/county-connectivity-2019.rds"
A <- readr::read_rds(url)The county FIPS codes (GEOIDs) are strored in the column and row names so that the adjacency structure can be properly aligned to other data sets:
head(colnames(A))
#> [1] "31039" "53069" "35011" "31109" "31129" "46099"
head(rownames(A))
#> [1] "31039" "53069" "35011" "31109" "31129" "46099"The file make-county-adjacency-file.R can be used (with some caution)
to reproduce this for any year, 2010-2019 (and futher on as they become
available). There are some slight changes over time in US counties.
The adjacency file was created by calling spdep::poly2nb on the county
shapefile returned by tigris::counties(year = 2019), dropping U.S.
territories, and connecting the islands of Hawaii to each other.
The following code visualizes the connectivity structure:
library(tigris)
#> To enable
#> caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
library(sf)
#> Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1; sf_use_s2() is TRUE
sdf <- counties() %>%
dplyr::filter(as.numeric(STATEFP) < 57) %>%
shift_geometry(preserve_area = TRUE)
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E <- Matrix::summary(Matrix::Matrix(A))
E <- E[which(E$i < E$j), ]
G <- list(np = nrow(A), # confrom to spdep graph structure, then back to nb to map it
from = E$i,
to = E$j,
nedges = nrow(E)
)
class(G) <- "Graph"
nb <- spdep::graph2nb(G)
plot(nb, sf::st_geometry(sdf))