Spatial Autocorrelation Analysis (Moran's I, Geary's C)

Comprehensive spatial autocorrelation analysis for tissue pattern analysis and digital pathology. Implements Moran's I, Geary's C, and local indicators of spatial association (LISA) for detecting spatial clustering and patterns in histopathological data. Essential for analyzing spatial dependencies in tissue architecture and cellular distributions.

Usage

spatialautocorrelation(
  data,
  measurement,
  x_coordinate,
  y_coordinate,
  region_id,
  time_point,
  autocorr_method = "morans_i",
  spatial_weights = "k_nearest",
  distance_threshold = 50,
  k_neighbors = 8,
  bandwidth = 25,
  significance_test = "permutation_test",
  permutations = 999,
  confidence_level = 0.95,
  local_analysis = TRUE,
  cluster_detection = TRUE,
  hotspot_analysis = TRUE,
  spatial_regimes = FALSE,
  temporal_analysis = FALSE,
  multivariate_analysis = FALSE,
  robustness_check = TRUE,
  edge_effects = TRUE,
  clinical_interpretation = TRUE,
  spatial_plots = TRUE,
  autocorr_plots = TRUE,
  lisa_plots = TRUE
)

Arguments

data

the data as a data frame

measurement

Continuous measurement for spatial autocorrelation analysis

x_coordinate

X spatial coordinate for location data

y_coordinate

Y spatial coordinate for location data

region_id

Region or tissue identifier for stratified analysis

time_point

Time point identifier for temporal spatial analysis

autocorr_method

Spatial autocorrelation method to compute

spatial_weights

Method for constructing spatial weights matrix

distance_threshold

Distance threshold for spatial neighbor definition

k_neighbors

Number of nearest neighbors for spatial weights

bandwidth

Bandwidth for Gaussian kernel weights

significance_test

Method for statistical significance testing

permutations

Number of permutations for significance testing

confidence_level

Confidence level for statistical inference

local_analysis

Compute local indicators of spatial association

cluster_detection

Detect and characterize spatial clusters

hotspot_analysis

Identify spatial hot spots and cold spots

spatial_regimes

Identify spatially distinct regimes or patterns

temporal_analysis

Analyze changes in spatial autocorrelation over time

multivariate_analysis

Analyze spatial relationships between multiple variables

robustness_check

Test robustness to different spatial weight specifications

edge_effects

Apply corrections for edge effects in spatial analysis

clinical_interpretation

Provide clinical interpretation of spatial patterns

spatial_plots

Generate spatial pattern visualization maps

autocorr_plots

Create spatial autocorrelation function plots

lisa_plots

Generate LISA cluster and significance maps

Value

A results object containing:

results$instructions					a html
results$dataInfo					a table
results$globalAutocorrelation					a table
results$localIndicators					a table
results$spatialClusters					a table
results$hotspotAnalysis					a table
results$spatialRegimes					a table
results$robustnessAnalysis					a table
results$temporalAnalysis					a table
results$clinicalInterpretation					a table
results$spatialMap					an image
results$autocorrPlot					an image
results$lisaMap					an image
results$methodExplanation					a html

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$dataInfo$asDF

as.data.frame(results$dataInfo)

Examples