A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Analyzing wildfire threat counts using a negative binomial regression model
10.1002/env.762.abs
The fire‐monitoring program managed by the Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis collected fire pixel counts from 1998 to 2002 and used them as a measure of wildfire threats for the Amazon region. The objective of the study was to identify the most relevant explanatory variables related to the frequency of fire pixel occurrence. The sample unit was the municipality, the dependent variable was a function of fire pixel counts, and the explanatory variables were related to land management, census, and agricultural data. A generalized longitudinal linear model was used. The most relevant explanatory variables were administrative limits, year, type of region, season, percentages of deforested area and male population, extent of unpaved road, and density of cattle. Approximately 95% of the standardized residuals resulting from fitting the model were in the interval [−2, +2]. Copyright © 2006 John Wiley & Sons, Ltd.
Analyzing wildfire threat counts using a negative binomial regression model
10.1002/env.762.abs
The fire‐monitoring program managed by the Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis collected fire pixel counts from 1998 to 2002 and used them as a measure of wildfire threats for the Amazon region. The objective of the study was to identify the most relevant explanatory variables related to the frequency of fire pixel occurrence. The sample unit was the municipality, the dependent variable was a function of fire pixel counts, and the explanatory variables were related to land management, census, and agricultural data. A generalized longitudinal linear model was used. The most relevant explanatory variables were administrative limits, year, type of region, season, percentages of deforested area and male population, extent of unpaved road, and density of cattle. Approximately 95% of the standardized residuals resulting from fitting the model were in the interval [−2, +2]. Copyright © 2006 John Wiley & Sons, Ltd.
Analyzing wildfire threat counts using a negative binomial regression model
Quintanilha, J. A. (author) / Ho, L. L. (author)
Environmetrics ; 17 ; 529-538
2006-09-01
10 pages
Article (Journal)
Electronic Resource
English
Analyzing wildfire threat counts using a negative binomial regression model
| Online Contents | 2006
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