sequential.hotspots: Calculate roadkill hotspots for a series of (sub)sampling...

In DeadCanMove: Assess How Spatial Roadkill Patterns Change with Temporal Sampling Scheme

Description

This function applies hotspots sequencially to a given set of submatrices to identify the hotspot regions in each dataset, using an adaptation of the method of Malo et al. (2004).

Usage

sequential.hotspots(dataset, submats, region.column, 
first.subsampl.col, confidence = 0.95)

Arguments

dataset	name of the matrix or dataframe containing the complete data
submats	a list of the submatrices for which to calculate the hotspots (result of the sequential.submatrix function)
region.column	name or index number of the column containing the regions (road segments, sites) to classify as hotspots or non-hotspots
first.subsampl.col	index number of the first column containing subsampling data
confidence	confidence threshold to consider hotspots. The default is 0.95

Value

A list of 2 elements:

hotspots.thresholds	A named integer vector
hotspots.maps	A list of data frames, each showing the total number of events (deaths) per region and whether or not it was considered a hospot.

Author(s)

A. Marcia Barbosa

References

Malo, J.E., Suarez, F., Diez, A. (2004) Can we mitigate animal-vehicle accidents using predictive models? J. Appl. Ecol. 41, 701-710 (doi: 10.1111/j.0021-8901.2004.00929.x)

See Also

hotspots

Examples

data(roadkills)

submats <- sequential.submatrix(dataset = roadkills,
sampl.columns = 4:ncol(roadkills), window.sizes = 1, gap.sizes = 1:3,
group.column = "taxon", include.all.together = TRUE, 
remove.zeros = TRUE, keep.nonsampl.columns = TRUE, 
n.subsampl.columns = 85)

shs <- sequential.hotspots(dataset = roadkills, submats = submats,
region.column = "segment", first.subsampl.col = 4, confidence = 0.95)

shs
str(shs)

DeadCanMove documentation built on May 2, 2019, 6:48 p.m.