gpc: geographical pattern causality

In spEDM: Spatial Empirical Dynamic Modeling

geographical pattern causality

Description

geographical pattern causality

Usage

## S4 method for signature 'sf'
gpc(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  boot = 99,
  random = TRUE,
  seed = 42,
  dist.metric = "L2",
  zero.tolerance = k,
  relative = TRUE,
  weighted = TRUE,
  threads = detectThreads(),
  detrend = FALSE,
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE,
  nb = NULL
)

## S4 method for signature 'SpatRaster'
gpc(
  data,
  cause,
  effect,
  libsizes = NULL,
  E = 3,
  k = E + 2,
  tau = 1,
  style = 1,
  lib = NULL,
  pred = NULL,
  boot = 99,
  random = TRUE,
  seed = 42,
  dist.metric = "L2",
  zero.tolerance = k,
  relative = TRUE,
  weighted = TRUE,
  threads = detectThreads(),
  detrend = FALSE,
  parallel.level = "low",
  bidirectional = TRUE,
  progressbar = TRUE,
  grid.coord = TRUE
)

Arguments

data	observation data.
cause	name of causal variable.
effect	name of effect variable.
libsizes	(optional) number of spatial units used (input needed: vector - spatial vector, matrix - spatial raster).
E	(optional) embedding dimensions.
k	(optional) number of nearest neighbors.
tau	(optional) step of spatial lags.
style	(optional) embedding style (0 includes current state, 1 excludes it).
lib	(optional) libraries indices (input requirement same as libsizes).
pred	(optional) predictions indices (input requirement same as libsizes).
boot	(optional) number of bootstraps to perform.
random	(optional) whether to use random sampling.
seed	(optional) random seed.
dist.metric	(optional) distance metric (L1: Manhattan, L2: Euclidean).
zero.tolerance	(optional) maximum number of zeros tolerated in signature space.
relative	(optional) whether to calculate relative changes in embeddings.
weighted	(optional) whether to weight causal strength.
threads	(optional) number of threads to use.
detrend	(optional) whether to remove the linear trend.
parallel.level	(optional) level of parallelism, low or high.
bidirectional	(optional) whether to examine bidirectional causality.
progressbar	(optional) whether to show the progress bar.
nb	(optional) neighbours list.
grid.coord	(optional) whether to detrend using cell center coordinates (TRUE) or row/column numbers (FALSE).

Value

A list

xmap

cross mapping results (only present if libsizes is not NULL)

causality

per-sample causality statistics (present if libsizes is NULL)

summary

overall causal strength (present if libsizes is NULL)

pattern

pairwise pattern relationships (present if libsizes is NULL)

varname

names of causal and effect variables

bidirectional

whether to examine bidirectional causality

References

Zhang, Z., Wang, J., 2025. A model to identify causality for geographic patterns. International Journal of Geographical Information Science 1–21.

Examples

columbus = sf::read_sf(system.file("case/columbus.gpkg",package="spEDM"))

gpc(columbus,"hoval","crime",E = 6,k = 9)

# convergence diagnostics
g = gpc(columbus,"hoval","crime",libsizes = seq(5,45,5),E = 6,k = 9)
plot(g)

spEDM documentation built on Nov. 30, 2025, 5:07 p.m.