Least-cost paths through a wind graph — least_cost

Traces the least-cost (fastest) paths between sites through a wind graph, as sequences of grid cell centers. The result has the same structure as wind_trails() output, so it can be drawn with geom_wind_trail().

Usage

least_cost_paths(graph, from, to, pairs = c("all", "nearest", "matched"))

Arguments

graph: A wind_graph, created with wind_graph().
from, to: Origin and destination sites: two-column matrices (or data frames) of longitude and latitude, or SpatVectors of points.
pairs: Which origin-destination pairs to trace: "all" (the default) traces a path from every origin to every destination; "nearest" traces one path from each origin, to the destination it can reach at the lowest cost; "matched" pairs from and to row by row, which requires them to have the same number of sites.

Value

A data frame with one row per path vertex, ordered from origin to destination along each path:

trail: path id.
from, to: row numbers of the path's origin in from and destination in to.
step: vertex number along the path, starting at 0 at the origin.
hours: cumulative travel cost from the origin, in hours (if trans = 1 in wind_rose() and wind speeds are in m/s). Its final value on each path equals the pair's pairwise_least_cost() (with snap = TRUE).
x, y: longitude and latitude of the grid cell center.

Pairs with no path (where the destination can't be reached, e.g. because wind never blows toward it) are omitted with a warning. Pairs whose origin and destination fall in the same grid cell are omitted, since their path has no length.

Details

Paths are computed with gdistance::shortestPath(). They follow the graph's direction: with a downwind graph (the default in wind_graph()), a path from from to to is the fastest downwind route. With an upwind graph, it is the fastest downwind route from to to from, traced in reverse. Because wind graphs are directed, the path from A to B generally differs from the path from B to A.

Paths move between neighboring grid cells, in eight directions, so they appear as segments at 45-degree angles, including occasional staircase patterns where the optimal route lies between two neighbor directions. This reflects the model's structure rather than the plot. Paths from one origin often share segments, showing the main transport corridors.

Examples

rose <- windscape_example("wind_rose")
graph <- wind_graph(rose)
site <- cbind(-105, 40)
destinations <- cbind(c(-95, -115, -100, -110), c(45, 35, 32, 48))
paths <- least_cost_paths(graph, site, destinations)
head(paths)
#>   trail from to step    hours         x        y
#> 1     1    1  1    0  0.00000 -105.1579 39.84127
#> 2     1    1  1    1  6.39227 -104.8421 39.84127
#> 3     1    1  1    2 27.63580 -104.5263 40.15873
#> 4     1    1  1    3 35.55941 -104.2105 40.15873
#> 5     1    1  1    4 44.14945 -103.8947 40.15873
#> 6     1    1  1    5 53.24461 -103.5789 40.15873

library(ggplot2)
ggplot(paths, aes(x, y)) +
  geom_wind_trail(aes(color = hours)) +
  coord_quickmap()
#> Error in wind_trail_layer(mapping, data, stat, GeomWindTrail, position,     seeds, res, fixed_length, length, hours, match.arg(direction),     steps, match.arg(wrap), arrow, na.rm, show.legend, inherit.aes,     ...): Problem while computing aesthetics.
#> ℹ Error occurred in the 1st layer.
#> Caused by error:
#> ! object 'u' not found