Moving-Window and Direct Data Aggregation

Data aggregation via moving window or direct methods. Aggregate a fine-resolution raster to a grid. The moving window method smooths the surface using a specified function within a moving window of a specified size and shape prior to aggregation. The direct method simply aggregates to the grid using the specified function. The package differs from other packages offering moving window analysis in 2 key ways: 1. The moving window code has been optimised so that it runs more quickly than \code{raster::focal} or other packages that build on this; 2. It combines moving window and aggregation in a way which can be effectively parallelised.

The grainchanger package provides functionality for data aggregation to a grid via moving-window or direct methods.

Moving-window data aggregation

The moving-window data aggregation method smooths the surface using a specified function within a moving window of a specified size and shape prior to aggregation.

The moving window approach allows users to capture some information about landscape structure at the scale at which the process acts in the landscape.

Direct data aggregation

The direct method simply aggregates to the grid using the specified function, essentially acting as a wrapper for the raster aggregate function.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("laurajanegraham/grainchanger")

Example

Moving-window data aggregation

The below example shows the moving-window data aggregation in action. It aggregates a categorical raster to a grid using Shannon evenness as the function calculated within a square moving window of 5 units. This value is included as a column on the grid sf object.

library(grainchanger)
library(ggplot2)
library(landscapetools)
 
# categorical landscape
show_landscape(cat_ls)
#> Loading required package: raster
#> Loading required package: sp

 
# moving-window aggregation using Shannon evenness
g_sf$mwda <- winmove_agg(g = g_sf,
            dat = cat_ls, 
            d = 5,
            type = "rectangle",
            fun = "shei",
            lc_class = 0:3)
 
ggplot(g_sf) + geom_sf(aes(fill = mwda))

Direct data aggregation

The below example shows the direct data aggregation in action. It aggregates a continuous raster to a grid using the range as the function calculated for each cell of the larger grid. This value is included as a column on the grid sf object. var_range is an inbuilt function in the grainchanger package.

# continuous landscape
show_landscape(cont_ls)

 
# direct aggregation using range
g_sf$range <- nomove_agg(g = g_sf,
            dat = cat_ls, 
            fun = "var_range")
 
ggplot(g_sf) + geom_sf(aes(fill = range))

Functions

There are a number of inbuilt functions in the grainchanger package, with their useage outlined below. While it is possible to use user-defined functions within both winmove_agg and nomove_agg, we welcome suggestions for additional functions. Please add as an issue - doing it this way means we can maximise the speed of the function.

Additional utilities

Create torus

The create_torus function takes as input a square or rectangular landscape and pads it by a specified radius, creating the effect of a torus. We developed this function in order to avoid edge effects when testing methods on simulated landscapes (such as those from NLMR).

torus <- create_torus(cat_ls, 5)
 
show_landscape(torus)

Meta

• Please report any issues or bugs.
• License: GPL3
• Get citation information for grainchanger in R doing citation(package = 'grainchanger')
• Please note that the ‘grainchanger’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.