Spatial autocorrelation describes the extent to which a variable is either dispersed or clustered through space. In the case of a raster image, spatial autocorrelation refers to the similarity in the values of nearby grid cells. This tool measures the spatial autocorrelation of a raster image using the global Moran's I statistic. Moran's I varies from -1 to 1, where I = -1 indicates a dispersed, checkerboard type pattern and I = 1 indicates a clustered (smooth) surface. I = 0 indicates a random distribution of values. Image Autocorrelation computes Moran's I for the first lag only, meaning that it only takes into account the variability among the immediate neighbors of each grid cell.
The user must specify the names of one or more input raster images. In addition, the user must specify the contiguity type (Rook's, King's, or Bishop's), which describes which neighboring grid cells are examined for the analysis. The following figure describes the available cases:
The tool outputs a text report which, for each input image, reports the Moran's I value and the variance, z-score, and p-value (significance) under normal and randomization sampling assumptions.
NoData values in the input image are ignored during the analysis.
The following is an example of a Python script using this tool:
wd = pluginHost.getWorkingDirectory()
# You may have multiple input files but they must
# be separated by semicolons in the string.
inputFiles = wd + "input1.dep" + ";" + wd + "input2.dep" + ";" + wd + "input3.dep"
contiguity = "rook"
args = [inputFiles, contiguity]
pluginHost.runPlugin("ImageAutocorrelation", args, False)
This is a Groovy script also using this tool:
def wd = pluginHost.getWorkingDirectory()
// You may have multiple input files but they must
// be separated by semicolons in the string.
def inputFiles = wd + "input1.dep" + ";" + wd + "input2.dep" + ";" + wd + "input3.dep"
def contiguity = "king"
String[] args = [inputFiles, contiguity]
pluginHost.runPlugin("ImageAutocorrelation", args, false)