This package uses Rcpp to quickly compute population/distance-weighted measures. Geodesic distances can be computed using either Haversine or Vincenty formulas. The package also has functions to return raw distance measures. If you are able to install Rcpp on your machine, you should be able to install this package and use these functions.
Install the latest development version from Github with
NB This package is still in early beta stages. It does not have much in the way of error handling. Data must be pre-processed so that no missing (NA) values are given to the functions.
Available functions
Weighted measures
dist_weighted_mean()
Interpolate values for a vector of locations (X) that are the inverse-distance-weighted average of measures taken at surrounding locations (Y). For each point, x, nearby values of the measure taken at Y are weighted more heavily than those from locations that are farther away.
popdist_weighted_mean()
Interpolate values for a vector of locations (X) that are the population/inverse-distance-weighted average of measures taken at surrounding locations (Y). For each point, x, nearby values of the measure taken at Y are weighted more heavily than those from locations that are farther away. Measures taken in more heavily populated y are given more weight than those with lower populations. This weighting scheme is a compromise between distance and population and is useful for interpolating measures that need to take both into account.
Distances
dist_1to1()
Compute and return the geodesic distance between two spatial points. Returns distance in meters.
dist_1tom()
Compute and return the geodesic distance between one location and a vector of other locations. Returns vector of distances in meters.
dist_mtom()
Compute and return the geodesic distance between each coordinate pair in two vectors. Returns n x k matrix of distances in meters, where n = # of locations in first vector and k = # of locations in second vector.
dist_df()
Compute distance between corresponding coordinate pairs and return vector of distances in meters. For use when creating a new data.frame or dplyr tbl_df() column.
Benchmark
Compare speed with base R function when measuring the distance between every United States population-weighted county centroid as measured in 2010 (N = 3,143 with complete measurements).
Load data
## libraries
libs <- c("tidyverse","microbenchmark","geosphere","distRcpp")
sapply(libs, require, character.only = TRUE)
## read data
df <- get(data(county_centers))
df
## # A tibble: 3,147 x 9
## fips clon00 clat00 clon10 clat10 pclon00 pclat00 pclon10 pclat10
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01001 -86.6 32.5 -86.6 32.5 -86.5 32.5 -86.5 32.5
## 2 01003 -87.7 30.6 -87.7 30.7 -87.8 30.6 -87.8 30.5
## 3 01005 -85.3 31.9 -85.4 31.9 -85.3 31.8 -85.3 31.8
## 4 01007 -87.1 33.0 -87.1 33.0 -87.1 33.0 -87.1 33.0
## 5 01009 -86.6 34.0 -86.6 34.0 -86.6 34.0 -86.6 34.0
## 6 01011 -85.7 32.1 -85.7 32.1 -85.7 32.1 -85.7 32.1
## 7 01013 -86.7 31.7 -86.7 31.8 -86.7 31.8 -86.7 31.8
## 8 01015 -85.8 33.7 -85.8 33.8 -85.8 33.7 -85.8 33.7
## 9 01017 -85.3 32.9 -85.4 32.9 -85.3 32.9 -85.3 32.9
## 10 01019 -85.6 34.2 -85.7 34.1 -85.6 34.2 -85.6 34.2
## # … with 3,137 more rows
## subset to 2010 population-weighted centroids (pclon10, pclat10)
p <- df %>% select(pclon10, pclat10) %>% drop_na() %>% data.frame()Check for equality
dist_R <- geosphere::distm(p, p, fun = distHaversine)
dist_Rcpp <- distRcpp::dist_mtom(p[,1],p[,2],p[,1],p[,2])
dist_R[1:5,1:5]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0 248335.5 133369.0 83691.8 162207.0
## [2,] 248335.5 0.0 274424.4 282744.5 394877.3
## [3,] 133369.0 274424.4 0.0 215905.4 263771.5
## [4,] 83691.8 282744.5 215905.4 0.0 114301.5
## [5,] 162207.0 394877.3 263771.5 114301.5 0.0
dist_Rcpp[1:5,1:5]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.0 248335.5 133369.0 83691.8 162207.0
## [2,] 248335.5 0.0 274424.4 282744.5 394877.3
## [3,] 133369.0 274424.4 0.0 215905.4 263771.5
## [4,] 83691.8 282744.5 215905.4 0.0 114301.5
## [5,] 162207.0 394877.3 263771.5 114301.5 0.0
all.equal(dist_R, dist_Rcpp)
## [1] TRUEBenchmark
2018 MacBookPro, 2.9 GHz Intel Core i9, 32 GB 2400 MHz DDR4 SDRAM
microbenchmark(
dist_R = geosphere::distm(p, p, fun = distHaversine),
dist_Rcpp = distRcpp::dist_mtom(p[,1],p[,2],p[,1],p[,2]),
times = 100
)
## Unit: milliseconds
## expr min lq mean median uq max neval
## dist_R 1930.1767 2047.0935 2228.0031 2234.7130 2361.3778 2673.8354 100
## dist_Rcpp 466.6231 480.8235 511.5361 501.9158 526.1218 684.4163 100Big file
## get census block group centers of population
bg <- readr::read_csv("https://www2.census.gov/geo/docs/reference/cenpop2010/blkgrp/CenPop2010_Mean_BG.txt") %>%
setNames(tolower(names(.))) %>%
filter(statefp < 56) %>%
mutate(id = paste0(statefp, countyfp, tractce, blkgrpce),
lon = longitude,
lat = latitude) %>%
select(id, lon, lat) %>%
drop_na()
##
## ── Column specification ──────────────────────────────────────────────────────────────────────
## cols(
## STATEFP = col_character(),
## COUNTYFP = col_character(),
## TRACTCE = col_character(),
## BLKGRPCE = col_double(),
## POPULATION = col_double(),
## LATITUDE = col_double(),
## LONGITUDE = col_double()
## )
ct <- get(data(county_centers)) %>%
rename(id = fips,
lon = pclon10,
lat = pclat10) %>%
drop_na()
bg
## # A tibble: 217,330 x 3
## id lon lat
## <chr> <dbl> <dbl>
## 1 010010201001 -86.5 32.5
## 2 010010201002 -86.5 32.5
## 3 010010202001 -86.5 32.5
## 4 010010202002 -86.5 32.5
## 5 010010203001 -86.5 32.5
## 6 010010203002 -86.5 32.5
## 7 010010204001 -86.4 32.5
## 8 010010204002 -86.4 32.5
## 9 010010204003 -86.4 32.5
## 10 010010204004 -86.4 32.5
## # … with 217,320 more rows
ct
## # A tibble: 3,137 x 9
## id clon00 clat00 clon10 clat10 pclon00 pclat00 lon lat
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01001 -86.6 32.5 -86.6 32.5 -86.5 32.5 -86.5 32.5
## 2 01003 -87.7 30.6 -87.7 30.7 -87.8 30.6 -87.8 30.5
## 3 01005 -85.3 31.9 -85.4 31.9 -85.3 31.8 -85.3 31.8
## 4 01007 -87.1 33.0 -87.1 33.0 -87.1 33.0 -87.1 33.0
## 5 01009 -86.6 34.0 -86.6 34.0 -86.6 34.0 -86.6 34.0
## 6 01011 -85.7 32.1 -85.7 32.1 -85.7 32.1 -85.7 32.1
## 7 01013 -86.7 31.7 -86.7 31.8 -86.7 31.8 -86.7 31.8
## 8 01015 -85.8 33.7 -85.8 33.8 -85.8 33.7 -85.8 33.7
## 9 01017 -85.3 32.9 -85.4 32.9 -85.3 32.9 -85.3 32.9
## 10 01019 -85.6 34.2 -85.7 34.1 -85.6 34.2 -85.6 34.2
## # … with 3,127 more rows
system.time(dist_Rcpp <- distRcpp::dist_min(x_df = ct, y_df = bg))
## user system elapsed
## 34.304 1.526 35.888
