Spatial pattern and spatial autocorrelation

The spatial pattern of a distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The capability of evaluating spatial patterns is a prerequisite to understanding the complicated spatial processes underlying the distribution of a phenomenon. Spatial autocorrelation indicates the extent to which the occurrence of one feature is influenced by similar features in the adjacent area. As such, statistics of spatial autocorrelation provide a useful indicator of spatial patterns. This study shows that quadrat analysis of spatial autocorrelation is not suitable for evaluating point patterns. The evaluation of spatial patterns for area features, using Moran's I coefficient, must take into consideration the log-linear relationship between map resolution and spatial autocorrelation. The topological structure of a complex spatial pattern can be revealed by the correlograms constructed based on higher-order spatial relationships. The spatial pattern can be characterized by the behavior of the correlogram's wavelength and amplitude within a specific range of spatial orders.