| 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. -Author |
| Genre: | Article |
| ProdID: | 70017380 |
| Citation Author: | De Cola, L. |
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| Citation End Page: | 427 |
| Citation Issue: | 5 |
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| Citation Language: | English |
| Citation Larger Work Title: | International Journal of Geographical Information Systems |
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| Citation Number Of Pages: | 17 |
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| Citation Search Results Text: | Simulating and mapping spatial complexity using multi-scale techniques; 1994; Article; Journal; International Journal of Geographical Information Systems; De Cola, L. |
| Citation Start Page: | 411 |
| Citation Volume: | 8 |
| Citation Year: | 1994 |
| Type: | citation/reference |
| Text: | Simulating and mapping spatial complexity using multi-scale techniques; 1994; Article; Journal; International Journal of Geographical Information Systems; De Cola, L. |
| URL (THUMBNAIL): | http://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg |
| Date Other: | Sat, 1 Jan 1994 00:00 -0600 |
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