Simulating and mapping spatial complexity using multi-scale techniques
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Abstract
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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Simulating and mapping spatial complexity using multi-scale techniques |
| Series title | International Journal of Geographical Information Systems |
| DOI | 10.1080/02693799408902011 |
| Volume | 8 |
| Issue | 5 |
| Year Published | 1994 |
| Language | English |
| Publisher | Taylor & Francis |
| Description | 17 p. |
| First page | 411 |
| Last page | 427 |
| Google Analytic Metrics | Metrics page |