Abstract
Abstract Spatial autocorrelation is a technique designed to measure spatial covariance between neighbouring values on a two-dimensional surface. Hypothetical surfaces used in this paper are point patterns that are subdivided according to a regular grid matrix, thus providing a measurable value for each subarea or grid square. It is shown that although the shape and size of the subareas is constant the coefficient does vary according to the number of grid squares, and consequently the number of joins in the system. The coefficient also varies according to changes in the directional orientation of the grid matrix, and the location and number of non-content or blank grid squares inside the matrix.
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