This function averages the spatial data locally. It divides
the input matrix into submatrices of dimension
averages the spatial data in these submatrices. By doing this, the
dimension of resultant matrix is reduced by a factor of
Dimension of the submatrix. This has to be a positive integer smaller than the dimension of input matrix.
If the data is classified into discrete units, the calculation of variance and skewness can give spurious results irrelevant to the proximity to transition. Therefore, discrete data should be 'coarse-grained' before calculating the spatial early warning signals. However, this can also be applied to continuous state data.
A matrix of reduced dimension.
Sankaran, S., Majumder, S., Kefi, S. and Guttal, V. (2017). Implications of being discrete and spatial for detecting early warning signals of regime shifts. Ecological Indicators.
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