README.md

leastcostpath - version 1.8.7 Build Status CRAN status CRAN Downloads Month CRAN Downloads Total =============================

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()

Getting Started

Installation

#install.packages("devtools")
library(devtools)
install_github("josephlewis/leastcostpath")
library(leastcostpath)

Usage

Creation of Cost Surfaces

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)

Least Cost Path computation

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 1

Cost Corridors

cc <- create_cost_corridor(slope_cs, loc1, loc2)

plot(cc)
plot(loc1, add = T)
plot(loc2, add = T)

From-Everywhere-to-Everywhere Least Cost Paths

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)

Cumulative Cost Paths

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)

Banded Least Cost Paths

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)

Least Cost Path Density

cumulative_lcps <- create_lcp_density(lcps = lcp_network, raster = r, rescale = FALSE)

plot(cumulative_lcps)

Least Cost Path Network

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)

Stochastic Least Cost Path

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)

Probabilistic Least Cost Path

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)

Wide Least Cost Path

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)

Common Errors

Error in if (is.numeric(v) && any(v < 0)) { : 
missing value where TRUE/FALSE needed

Error caused when trying to calculate a Least Cost Path using SpatialPoints outside of the Cost Surface Extent:

  1. Check SpatialPoints used in the LCP calculation coincide with Raster / Cost Surface

  2. 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 value

Error 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:

  1. Check if there are negative values via:

    quantile(your_cost_surface@transitionMatrix@x)

Contributing

If you would like to contribute to the R Package leastcostpath, please follow the "fork-and-pull" Git workflow:

  1. Fork the rep on Github
  2. Clone the project to your own machine
  3. Commit the changes to your own branch
  4. Push your work back to your fork
  5. Submit a pull request so that the changes can be reviewed

Issues

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

Case Studies Using leastcostpath

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

Versioning

See NEWS.md for a summary of Version updates

Authors

Citation

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


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leastcostpath documentation built on June 3, 2022, 9:06 a.m.