leastcostpath - version 1.8.7
=============================
The leastcostpath is built on the classes and functions provided in the R package gdistance (Van Etten, 2017).
NOTE: The R library leastcostpath requires the use of projected coordinate systems. The package does not account for geographic coordinate systems.
leastcostpath provides the functionality to calculate Least Cost Paths (LCPs) using numerous time- and energy-based cost functions that approximate the difficulty of moving across a landscape. Additional cost surfaces can be incorporated into the analysis via create_barrier_cs() or create_feature_cs().
leastcostpath also provides the functionality to calculate Stochastic Least Cost Paths (Pinto and Keitt, 2009), and Probabilistic Least Cost Paths (Lewis, 2020).
leastcostpath also provides the functionality to calculate movement potential within a landscape through the implementation of From-Everywhere-to-Everywhere (White and Barber, 2012), Cumulative Cost Paths (Verhagen 2013), and Least Cost Path calculation within specified distance bands (Llobera, 2015).
Lastly, leastcostpath provides the functionality to validate the accuracy of the computed Least Cost Path relative to another path via validate_lcp() (Goodchild and Hunter, 1997) and PDI_validation() (Jan et al. 1999).
Functions currently in development:
Functions recently added: * check_locations()
#install.packages("devtools")
library(devtools)
install_github("josephlewis/leastcostpath")
library(leastcostpath)library(leastcostpath)
r <- raster::raster(system.file('external/maungawhau.grd', package = 'gdistance'))
slope_cs <- create_slope_cs(r, cost_function = 'tobler')
slope_cs_10 <- create_slope_cs(r, cost_function = 'tobler', max_slope = 10)
slope_cs_exagg <- create_slope_cs(r, cost_function = 'tobler', exaggeration = TRUE)
distance_cs <- create_distance_cs(r, neighbours = 16)loc1 = cbind(2667670, 6479000)
loc1 = sp::SpatialPoints(loc1)
loc2 = cbind(2667800, 6479400)
loc2 = sp::SpatialPoints(loc2)
lcps <- create_lcp(cost_surface = slope_cs, origin = loc1, destination = loc2, directional = FALSE)
plot(raster(slope_cs))
plot(lcps[1,], add = T, col = "red") # location 1 to location 2
plot(lcps[2,], add = T, col = "blue") # location 2 to location 1cc <- create_cost_corridor(slope_cs, loc1, loc2)
plot(cc)
plot(loc1, add = T)
plot(loc2, add = T)locs <- sp::spsample(as(raster::extent(r), 'SpatialPolygons'),n=10,'regular')
lcp_network <- create_FETE_lcps(cost_surface = slope_cs, locations = locs,
cost_distance = FALSE, parallel = FALSE)
plot(raster(slope_cs))
plot(locs, add = T)
plot(lcp_network, add = T)locs <- sp::spsample(as(raster::extent(r), 'SpatialPolygons'),n=1,'random')
lcp_network <- create_CCP_lcps(cost_surface = slope_cs, location = locs, distance = 50,
radial_points = 10, cost_distance = FALSE, parallel = FALSE)
plot(raster(slope_cs))
plot(locs, add = T)
plot(lcp_network, add = T)locs <- sp::spsample(as(raster::extent(r), 'SpatialPolygons'),n=1,'random')
lcp_network <- create_banded_lcps(cost_surface = slope_cs, location = locs, min_distance = 20,
max_distance = 50, radial_points = 10, cost_distance = FALSE, parallel = FALSE)
plot(raster(slope_cs))
plot(locs, add = T)
plot(lcp_network, add = T)cumulative_lcps <- create_lcp_density(lcps = lcp_network, raster = r, rescale = FALSE)
plot(cumulative_lcps)locs <- sp::spsample(as(raster::extent(r), 'SpatialPolygons'),n=5,'regular')
mat <- cbind(c(1, 4, 2, 1), c(2, 2, 4, 3))
lcp_network <- create_lcp_network(slope_cs, locations = locs,
nb_matrix = mat, cost_distance = FALSE, parallel = FALSE)locs <- sp::spsample(as(raster::extent(r), 'SpatialPolygons'),n=2,'random')
stochastic_lcp <- replicate(n = 10, create_stochastic_lcp(cost_surface = slope_cs,
origin = locs[1,], destination = locs[2,], directional = FALSE))
stochastic_lcp <- do.call(rbind, stochastic_lcp)locs <- sp::spsample(as(raster::extent(r), 'SpatialPolygons'),n=2,'random')
RMSE <- 5
n <- 10
lcps <- list()
for (i in 1:n) {
lcps[[i]] <- leastcostpath::create_lcp(cost_surface = leastcostpath::create_slope_cs(dem = leastcostpath::add_dem_error(dem = r, rmse = RMSE, size = "auto", vgm_model = "Sph"), cost_function = "tobler", neighbours = 16), origin = locs[1,], destination = locs[2,], directional = FALSE, cost_distance = TRUE)
}
lcps <- do.call(rbind, lcps)n <- 3
slope_cs <- create_slope_cs(r, cost_function = 'tobler', neighbours = wide_path_matrix(n))
loc1 = cbind(2667670, 6479000)
loc1 = sp::SpatialPoints(loc1)
loc2 = cbind(2667800, 6479400)
loc2 = sp::SpatialPoints(loc2)
lcps <- create_wide_lcp(cost_surface = slope_cs, origin = loc1,
destination = loc2, path_ncells = n)Error in if (is.numeric(v) && any(v < 0)) { :
missing value where TRUE/FALSE neededError caused when trying to calculate a Least Cost Path using SpatialPoints outside of the Cost Surface Extent:
Check SpatialPoints used in the LCP calculation coincide with Raster / Cost Surface
Check coordinate system of the Raster/Cost Surface is the same as the SpatialPoints
Error in get.shortest.paths(adjacencyGraph, indexOrigin, indexGoal):
At structural_properties.c:4521 :
Weight vector must be non-negative, Invalid valueError caused when calculating a Least Cost Path using a Cost Surface that contains negative values. Error due to Djikstra’s algorithm requiring non-negative values:
quantile(*your_cost_surface*@transitionMatrix@x)
If you would like to contribute to the R Package leastcostpath, please follow the “fork-and-pull” Git workflow:
Please submit issues and enhancement requests via github Issues * If submitting an issue, please clearly describe the issue, including steps to reproduce when it is a bug, or a justification for the proposed enhancement request
Fjellström, M., Seitsonen, O., Wallén, H., 2022. Mobility in Early Reindeer Herding, in: Salmi, A.-K. (Ed.), Domestication in Action, Arctic Encounters. Springer International Publishing, Cham, pp. 187–212. https://doi.org/10.1007/978-3-030-98643-8_7
Field, S., Glowacki, D.M., Gettler, L.T., 2022. The Importance of Energetics in Archaeological Least Cost Analysis. J Archaeol Method Theory. https://doi.org/10.1007/s10816-022-09564-8
Herzog, I., 2022. Issues in Replication and Stability of Least-cost Path Calculations. SDH 5, 131–155. https://doi.org/10.14434/sdh.v5i2.33796
Lewis, J., 2021. Probabilistic Modelling for Incorporating Uncertainty in Least Cost Path Results: a Postdictive Roman Road Case Study. Journal of Archaeological Method and Theory. https://doi.org/10.1007/s10816-021-09522-w
Ludwig, B., 2020. Reconstructing the Ancient Route Network in Pergamon’s Surroundings. Land 9, 241. https://doi.org/10.3390/land9080241
See NEWS.md for a summary of Version updates
Please cite as:
Lewis, J. (2022) leastcostpath: Modelling Pathways and Movement Potential Within a Landscape (version 1.8.7).
Available at: https://cran.r-project.org/web/packages/leastcostpath/index.html