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Simulating and mapping spatial complexity using multi-scale techniques

January 1, 1994

A central problem in spatial analysis is the mapping of data for complex spatial fields using relatively simple data structures, such as those of a conventional GIS. This complexity can be measured using such indices as multi-scale variance, which reflects spatial autocorrelation, and multi-fractal dimension, which characterizes the values of fields. These indices are computed for three spatial processes: Gaussian noise, a simple mathematical function, and data for a random walk. Fractal analysis is then used to produce a vegetation map of the central region of California based on a satellite image. This analysis suggests that real world data lie on a continuum between the simple and the random, and that a major GIS challenge is the scientific representation and understanding of rapidly changing multi-scale fields. -Author

Publication Year 1994
Title Simulating and mapping spatial complexity using multi-scale techniques
Authors L. De Cola
Publication Type Article
Publication Subtype Journal Article
Series Title International Journal of Geographical Information Systems
Index ID 70017380
Record Source USGS Publications Warehouse