knitr::opts_chunk$set( fig.width = 5 # Larger figures (default is 3, only legend is visible) ) set.seed(2018)
The dbmss package allows simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, including classical ones (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's $K_d$, Marcon and Puech's $M$). It relies on spatstat for some core calculation.
This vignette contains a quick introduction.
The main data format is wmppp
for weighted, marked point pattern.
It inherits from the ppp
class of the spatstat package.
A wmppp
object can be created from the coordinates of points, their type and their weight.
library("dbmss") # Draw the coordinates of 10 points X <- runif(10) Y <- runif(10) # Draw the point types. PointType <- sample(c("A", "B"), size = 10, replace = TRUE) # Plot the point pattern. Weights are set to 1 ant the window is adjusted autoplot(wmppp(data.frame(X, Y, PointType)))
An example dataset is provided: it is a point pattern from the Paracou forest in French Guiana. Two species of trees are identified, other trees are of type "Other". Point weights are their basal area, in square centimeters.
# Plot (second column of marks is Point Types) autoplot( paracou16, labelSize = expression("Basal area (" ~cm^2~ ")"), labelColor = "Species" )
The main functions of the packages are designed to calculate distance-based measures of spatial structure. Those are non-parametric statistics able to summarize and test the spatial distribution (concentration, dispersion) of points.
The classical, topographic functions such as Ripley's K are provided by the spatstat package and supported by dbmss for convenience.
Relative functions are available in dbmss only. These are the $M$ and $m$ and $K_d$ functions.
The bivariate $M$ function can be calculated for Q. Rosea trees around V. Americana trees:
autoplot( Mhat( paracou16, ReferenceType = "V. Americana", NeighborType = "Q. Rosea" ), main = "" )
Confidence envelopes of various null hypotheses can be calculated. The univariate distribution of Q. Rosea is tested against the null hypothesis of random location.
autoplot( KdEnvelope(paracou16, ReferenceType = "Q. Rosea", Global = TRUE), main = "" )
Significant concentration is detected between about 10 and 20 meters.
https://ericmarcon.github.io/dbmss/
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