Generalized linear modelling in periglacial studies: terrain parameters and patterned ground
Abstract Generalized linear models (GLM) are mathematical extensions of linear models. GLM models are more flexible and better suited for analysing relationships of spatial data, which can often be poorly represented by classical Gaussian distributions such as least‐square‐regression techniques. Thi...
| Published in: | Permafrost and Periglacial Processes |
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| Main Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2004
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1002/ppp.482 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fppp.482 https://onlinelibrary.wiley.com/doi/pdf/10.1002/ppp.482 |
| Summary: | Abstract Generalized linear models (GLM) are mathematical extensions of linear models. GLM models are more flexible and better suited for analysing relationships of spatial data, which can often be poorly represented by classical Gaussian distributions such as least‐square‐regression techniques. This paper demonstrates GLM model‐building procedures step‐by‐step for the distribution and abundance of active patterned ground in northern Finland. The exercise is based on data from an area of 200 km 2 (800 modelling squares of 0.25 km 2 ). Both the distribution and abundance models clearly indicate an increasing activity of patterned ground with (1) increasing soil moisture and (2) proportion of concave topography. Activity decreases with increasing altitude. We conclude that GLM techniques combined with a geographic information system can play an important role in analysing and modelling periglacial data sets. Copyright © 2004 John Wiley & Sons, Ltd. |
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