Simulating and mapping spatial complexity using multi-scale techniques
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.
Citation Information
Publication Year | 1994 |
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Title | Simulating and mapping spatial complexity using multi-scale techniques |
DOI | 10.1080/02693799408902011 |
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 |