SIMULATING AND MAPPING SPATIAL COMPLEXITY USING MULTISCALE TECHNIQUES

A central problem in spatial analysis is the mapping of data for compl ex spatial fields using relatively simple data structures, such as tho se of a conventional GIS. This complexity can be measured using such i ndices as multi-scale variance, which reflects spatial autocorrelation , and multi-fractal dimension, which characterizes the values of field s. These indices are computed for three spatial processes: Gaussian no ise, a simple mathematical function, and data for a random walk. Fract al analysis is then used to produce a vegetation map of the central re gion of California based on a satellite image. This analysis suggests that real world data lie on a continuum between the simple and the ran dom, and that a major GIS challenge is the scientific representation a nd understanding of rapidly changing multi-scale fields.