These functions are similar to accessors and predicates, but instead of
recycling x and y to a common length and returning a vector of that
length, these functions return a vector of length x with each element
i containing information about how the entire vector y relates to
the feature at x[i].
s2_closest_feature(x, y)
s2_closest_edges(
x,
y,
k,
min_distance = -1,
max_distance = Inf,
radius = s2_earth_radius_meters()
)
s2_farthest_feature(x, y)
s2_distance_matrix(x, y, radius = s2_earth_radius_meters())
s2_max_distance_matrix(x, y, radius = s2_earth_radius_meters())
s2_contains_matrix(x, y, options = s2_options(model = "open"))
s2_within_matrix(x, y, options = s2_options(model = "open"))
s2_covers_matrix(x, y, options = s2_options(model = "closed"))
s2_covered_by_matrix(x, y, options = s2_options(model = "closed"))
s2_intersects_matrix(x, y, options = s2_options())
s2_disjoint_matrix(x, y, options = s2_options())
s2_equals_matrix(x, y, options = s2_options())
s2_touches_matrix(x, y, options = s2_options())
s2_dwithin_matrix(x, y, distance, radius = s2_earth_radius_meters())
s2_may_intersect_matrix(x, y, max_edges_per_cell = 50, max_feature_cells = 4)Geography vectors, coerced using as_s2_geography().
x is considered the source, where as y is considered the target.
The number of closest edges to consider when searching. Note that in S2 a point is also considered an edge.
The minimum distance to consider when searching for
edges. This filter is applied after the search is complete (i.e.,
may cause fewer than k values to be returned).
The maximum distance to consider when searching for edges. This filter is applied before the search.
Radius of the earth. Defaults to the average radius of
the earth in meters as defined by s2_earth_radius_meters().
An s2_options() object describing the polygon/polyline
model to use and the snap level.
A distance on the surface of the earth in the same units
as radius.
For s2_may_intersect_matrix(),
this values controls the nature of the index on y, with higher values
leading to coarser index. Values should be between 10 and 50; the default
of 50 is adequate for most use cases, but for specialized operations users
may wish to use a lower value to increase performance.
For s2_may_intersect_matrix(), this value
controls the approximation of x used to identify potential intersections
on y. The default value of 4 gives the best performance for most operations,
but for specialized operations users may wish to use a higher value to increase
performance.
A vector of length x.
See pairwise predicate functions (e.g., s2_intersects()).
city_names <- c("Vatican City", "San Marino", "Luxembourg")
cities <- s2_data_cities(city_names)
country_names <- s2_data_tbl_countries$name
countries <- s2_data_countries()
# closest feature returns y indices of the closest feature
# for each feature in x
country_names[s2_closest_feature(cities, countries)]
#> [1] "Italy" "Italy" "Luxembourg"
# farthest feature returns y indices of the farthest feature
# for each feature in x
country_names[s2_farthest_feature(cities, countries)]
#> [1] "New Zealand" "New Zealand" "New Zealand"
# use s2_closest_edges() to find the k-nearest neighbours
nearest <- s2_closest_edges(cities, cities, k = 2, min_distance = 0)
city_names
#> [1] "Vatican City" "San Marino" "Luxembourg"
city_names[unlist(nearest)]
#> [1] "San Marino" "Vatican City" "San Marino"
# predicate matrices
country_names[s2_intersects_matrix(cities, countries)[[1]]]
#> [1] "Italy"
# distance matrices
s2_distance_matrix(cities, cities)
#> [,1] [,2] [,3]
#> [1,] 0.0 226040.9 986762.6
#> [2,] 226040.9 0.0 792700.1
#> [3,] 986762.6 792700.1 0.0
s2_max_distance_matrix(cities, countries[1:4])
#> [,1] [,2] [,3] [,4]
#> [1,] 5284474 6716222 729253.1 4488169
#> [2,] 5225941 6939199 798367.2 4551290
#> [3,] 5543951 7659266 1564998.8 5198533