Figure 2.4 Two point patterns in absolute space

figures

code

Although it is labelled ‘point patterns in absolute space’, the real (ahem) point of this figure is that spatial analysis often works, in effect, with a relative concept of space, since it is based on notions as here, such as the distances between points.

If you want point patterns, you need the fantastic spatstat by Adrian Baddeley, Ege Rubak, and Rolf Turner.

Code

library(spatstat)

Make a couple of point patterns, one evenly-space using sequential spatial inhibition, and one clustered using a Thomas process. For the latter, see

Thomas M. 1949. A generalisation of Poisson’s binomial limit for use in ecology. Biometrika 36, 18–25.

Code

pp1 <- rSSI(0.07, n = 105)
pp2 <- rThomas(10, 0.03, 10)

Calculate nearest neighbour distances.

Code

nn1 <- nndist(pp1)
nn2 <- nndist(pp2)
max_d <- ceiling(max(c(nn1, nn2)) * 10) / 10
breaks <- seq(0, max_d, length.out = 10)

And plot them.

Code

par(mai = rep(0.3, 4))
layout(matrix(1:4, 2, 2, byrow = TRUE))

plot(pp1, main = "")
hist(nn1, main = "", xlab = "Distance", ylab = "Frequency", breaks = breaks)
plot(pp2, main = "")
hist(nn2, main = "", xlab = "Distance", ylab = "Frequency", breaks = breaks)

Code

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