Overview

The distanceto package is designed to quickly sample distances from points features to other vector layers. Normally the approach for calculating distance to (something) involves generating distance surfaces using raster based approaches eg. raster::distance or gdal_proximity and subsequently point sampling these surfaces. Since raster based approaches are a costly method that frequently leads to memory issues or long and slow run times with high resolution data or large study sites, we have opted to compute these distances using vector based approaches. As a helper, there’s a decidedly low-res raster based approach for visually inspecting your region’s distance surface. But the workhorse is distance_to.

The distanceto package provides two functions:

distance_to
distance_raster

Install

# Enable the robitalec universe
options(repos = c(
    robitalec = 'https://robitalec.r-universe.dev',
    CRAN = 'https://cloud.r-project.org'))

# Install distanceto
install.packages('distanceto')

`distance_to`

library(distanceto)
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE

Long-lat / unprojected coordinates

# Load nc data
nc <- st_read(system.file("shape/nc.shp", package="sf"))
#> Reading layer `nc' from data source `/github/workspace/pkglib/sf/shape/nc.shp' using driver `ESRI Shapefile'
#> Simple feature collection with 100 features and 14 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
#> Geodetic CRS:  NAD27

# Set number of sampling points
npts <- 1e3

# Sample points in nc
ncpts <- st_sample(nc, npts)

# Select first 5 of nc
ncsub <- nc[1:5,]

# Measure distance from ncpts to first 5 of nc
dist <- distance_to(ncpts, ncsub, measure = 'geodesic')

# or add to ncpts
ncpts$dist <- dist

head(dist, 30)
#>  [1]  86602.821 225255.315  23180.430  14303.934  74146.245  37797.486
#>  [7]  78697.785 164216.902 169194.822  49642.578 109982.003   5659.561
#> [13] 138036.462  24041.704  40204.860  93561.970  63165.072 126628.811
#> [19]  68865.047 122436.527 249407.147  73454.059  58556.261  85167.917
#> [25]  88872.924  40969.136 141723.182 107266.497 152430.802  31830.876
hist(dist)

Projected coordinates

# Transform nc data to local projected coordinates (UTM 18N)
nc_utm <- st_transform(nc, 32618)

# Set number of sampling points
npts <- 1e2

# Sample points within nc data
nc_utm_pts <- st_sample(nc_utm, npts)

# Select one polygon within nc data
nc_utm_select <- nc_utm[1, ]

# Measure distance from seine points to seine
dist <- distance_to(nc_utm_pts, nc_utm_select)

# or add to seine points
nc_utm_pts$dist <- dist

head(dist, 30)
#>  [1] 529234.18 140466.84 297218.45 235912.56 405825.45 366228.14 387845.82
#>  [8] 307230.50  56810.10 188559.62 384656.43  56039.33 197939.39  64590.64
#> [15] 380677.50 424683.97 306211.64 329921.62 350576.52  10862.22  27425.74
#> [22] 147273.29 205025.82 314994.06  31493.89 345919.82  81208.42 272412.99
#> [29] 179467.55  96501.76
hist(dist)

`distance_raster`

library(raster)
#> Loading required package: sp

rdist <- distance_raster(nc_utm_select, 1e4, extent = st_bbox(nc_utm))

plot(rdist)

Introduction to distance-to