spatstat

spatstat

Spatial point pattern analysis.

Point Patterns

library(spatstat)

# Create point pattern
pp <- ppp(x, y, window = owin(c(0, 1), c(0, 1)))

# From data frame
pp <- ppp(df$x, df$y, window = owin(range(df$x), range(df$y)))

# With marks (attributes)
pp <- ppp(x, y, window = win, marks = factor(types))

Windows

# Rectangular window
win <- owin(c(0, 100), c(0, 100))

# Polygonal window
win <- owin(poly = list(x = c(0, 1, 1, 0), y = c(0, 0, 1, 1)))

# Circular window
win <- disc(radius = 50, centre = c(50, 50))

# From shapefile
library(maptools)
win <- as.owin(sp_polygon)

Summary Statistics

# Summary
summary(pp)

# Intensity (points per unit area)
intensity(pp)

# Quadrat counts
quadratcount(pp, nx = 5, ny = 5)

# Quadrat test (CSR)
quadrat.test(pp, nx = 5, ny = 5)

Density Estimation

# Kernel density
dens <- density(pp)
plot(dens)

# With bandwidth
dens <- density(pp, sigma = 10)

# Adaptive bandwidth
dens <- density(pp, sigma = bw.diggle)

# Bandwidth selection
bw.diggle(pp)
bw.ppl(pp)
bw.scott(pp)

Distance Functions

# G function (nearest neighbor)
G <- Gest(pp)
plot(G)

# F function (empty space)
F <- Fest(pp)
plot(F)

# K function (Ripley's K)
K <- Kest(pp)
plot(K)

# L function (transformed K)
L <- Lest(pp)
plot(L)

# Pair correlation function
g <- pcf(pp)
plot(g)

Envelopes (Significance Testing)

# Monte Carlo envelope for K function
env <- envelope(pp, Kest, nsim = 99)
plot(env)

# For L function
env <- envelope(pp, Lest, nsim = 99)
plot(env)

# Global envelope
env <- envelope(pp, Lest, nsim = 99, global = TRUE)

Point Process Models

# Poisson (CSR)
fit <- ppm(pp ~ 1)

# Inhomogeneous Poisson
fit <- ppm(pp ~ x + y)

# With covariates
fit <- ppm(pp ~ covariate_image)

# Cluster process
fit <- kppm(pp ~ 1, "Thomas")

# Summary
summary(fit)

Marked Point Patterns

# Create marked pattern
pp <- ppp(x, y, window = win, marks = factor(types))

# Split by marks
split(pp)

# Mark correlation
markcorr(pp)

# Cross-type functions
Kcross(pp, "type1", "type2")
Lcross(pp, "type1", "type2")