Logistic Regression for Southern Pine Beetle Outbreaks with Spatial and Temporal Autocorrelation
By M. Gumpertz, C. Wu, J. Pye. Forestry sciences, 2000
89 citations recorded by the source corpus.
Regional outbreaks of southern pine beetle (Dendroctonus frontalis Zimm.) show marked spatial and temporal patterns. While these patterns are of interest in themselves, we focus on statistical methods for estimating the effects of underlying environmental factors in the presence of spatial and temporal autocorrelation. The most comprehensive available information on outbreaks consists of binary data, specifically, annual presence or absence of outbreak for individual counties within the southern United States. We demonstrate a method for modeling spatially correlated proportions, such as the proportion of years that a county experiences outbreak, based on annual outbreak presence or absence data for counties in three states (NC, SC, and GA) over 31 yr. In this method, the proportion of years in outbreak is predicted using a marginal logistic regression model with spatial autocorrelation among counties, with adjustment of variance terms to account for temporal autocorrelation. This type of model describes the probability of outbreak as a function of explanatory variables such as host availability, physiography, climate, hurricane incidence, and management type. Explicitly including spatial autocorrelation in the model improves estimates of the probability of outbreak for a particular county and of the importance of the various explanatory variables. For. Sci. 46(1):95-107.
Subfields: Environmental Chemistry, Nature and Landscape Conservation, Plant Science
View Paper: https://www.semanticscholar.org/paper/5d69645e306ff57d39b9560f39162b7d98b87114
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In Environmental Chemistry
In Nature and Landscape Conservation
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Citations in the corpus
- Predicting Conflict in Space and Time (2010, methodology): “This problem is typically resolved using a strategy suggested by Geyer and Thompson (1992) and used by Hoeting, Leecaster, and Bowden (1999); Huffer and Wu (1998); Gumpertz, Wu, and Pye (2000); and Ward and Gleditsch (2002), among others.”
- Climate change and the outbreak ranges of two North American bark beetles (2002, background): “However, individual grid cells on a map and county areas do not represent independent observations, and the high level of statistical significance was undoubtedly affected by the spatial autocorrelation inherent in the map data (Gumpertz et al., 2000).”
- Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery (2007, unknown): “Under this continuity assumption, an estimator for ρ may be obtained using the variogram approach of Gumpertz et al. (2000): (1) calculate the standardized residuals, ̂di ¼ yi ỹîri ; (2) construct an empirical semi-variogram, ̂g dð Þ ¼ 1 2jjNðdÞjj X NðdÞ ̂di ̂dj 2 where N(d) denotes a collection…”