View source: R/create_slope_cs.R
create_slope_cs  R Documentation 
Creates a cost surface based on the difficulty of moving up/down slope. This function provides the choice of multiple isotropic and anisotropic cost functions that estimate human movement across a landscape. Maximum percentage slope possible for traversal can also be supplied. Lastly, geographical slant exaggeration can be accounted for.
create_slope_cs( dem, cost_function = "tobler", neighbours = 16, crit_slope = 12, max_slope = NULL, percentile = 0.5, exaggeration = FALSE )
dem 

cost_function 

neighbours 

crit_slope 

max_slope 

percentile 

exaggeration 

Tobler's 'Hiking Function' is the most widely used cost function when approximating the difficulty of moving across a landscape (Gorenflo and Gale, 1990; Wheatley and Gillings, 2001). The function assesses the time necessary to traverse a surface and takes into account upslope and downslope (Kantner, 2004; Tobler, 1993). Time unit measured in seconds.
Tobler's offpath Hiking Function reduces the speed of the Tobler's Hiking Function by 0.6 to take into account walking offpath (Tobler, 1993). Time unit measured in seconds.
The Irmischer and Clark cost functions (2018) were modelled from speed estimates of United States Military Academy (USMA) cadets while they navigated on foot over hilly, wooded terrain as part of their summer training in map and compass navigation. Time unit measured in seconds.
The Modified Hiking cost function combines MIDE (París Roche, 2002), a method to calculate walking hours for an average hiker with a light load (MárquezPérez et al. 2017), and Tobler's 'Hiking Function' (Tobler, 1993). Time unit measured in seconds.
Herzog (2013), based on the cost function provided by Llobera and Sluckin (2007), has provided a cost function to approximate the cost for wheeled transport. The cost function is symmetric and is most applicable for use when the same route was taken in both directions.
Herzog's (2010) Sixthdegree polynomial cost function approximates the energy expenditure values (J/(kg*m)) found in Minetti et al. (2002) but eliminates the problem of unrealistic negative energy expenditure values for steep downhill slopes.
Llobera and Sluckin (2007) cost function approximates the metabolic energy expenditure (KJ/m) when moving across a landscape.
Campbell (2019) cost function (Lorentz distribution) approximates the time taken to traverse a surface based on crowdsourced GPS data (1.05 million travel rate records). Data divided into travel rate percentiles (1st, 5th to 95th, by 5, and 99th). max_slope argument is fixed at 30 degrees slope to reflect the maximum slope that the cost function is parametised to. Time unit measured in seconds.
Exaggeration
When observers face directly toward a hill, their awareness of the slant of the hill is greatly overestimated (Pingel, 2009; Proffitt, 1995; Proffitt, 2001). Pingel (2009) identified that downhill slopes are overestimated at approximately 2.3 times, whilst uphill slopes are overestimated at 2 times.
TransitionLayer
(gdistance package) numerically expressing the difficulty of moving up/down slope based on the cost function provided in the cost_function argument.
Joseph Lewis
r < raster::raster(system.file('external/maungawhau.grd', package = 'gdistance')) slope_cs_16 < create_slope_cs(r, cost_function = 'tobler', neighbours = 16, max_slope = NULL) slope_cs_48 < create_slope_cs(r, cost_function = 'tobler', neighbours = 48, max_slope = NULL)
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