Modeling Spatiotemporal Forest Health Monitoring Data

Augustin, Nicole; Monachini, Monica; Wilpert, Klaus von; Kublin, Edgar; Wood, Simon N.; Schumacher, Martin

doi:10.1198/jasa.2009.ap07058

Cited by 85 publications

(94 citation statements)

References 27 publications

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“…Hence, a major objective of the future inventories will be to gain insight into the spatio-temporal pattern of larva density. For this purpose the methodology developed can be used for time series data by estimating inventory-specific spatial trends or, in the case of a number of inventories, by integrating a space-time effect (Augustin et al 2009).…”

Section: Discussionmentioning

confidence: 99%

A spatially-explicit count data regression for modeling the density of forest cockchafer ( Melolontha hippocastani ) larvae in the Hessian Ried (Germany)

Matthias¹,

Schmidt²,

Rainer³

et al. 2014

Forest Ecosyst

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Background: In this paper, a regression model for predicting the spatial distribution of forest cockchafer larvae in the Hessian Ried region (Germany) is presented. The forest cockchafer, a native biotic pest, is a major cause of damage in forests in this region particularly during the regeneration phase. The model developed in this study is based on a systematic sample inventory of forest cockchafer larvae by excavation across the Hessian Ried. These forest cockchafer larvae data were characterized by excess zeros and overdispersion. Methods: Using specific generalized additive regression models, different discrete distributions, including the Poisson, negative binomial and zero-inflated Poisson distributions, were compared. The methodology employed allowed the simultaneous estimation of non-linear model effects of causal covariates and, to account for spatial autocorrelation, of a 2-dimensional spatial trend function. In the validation of the models, both the Akaike information criterion (AIC) and more detailed graphical procedures based on randomized quantile residuals were used. Results: The negative binomial distribution was superior to the Poisson and the zero-inflated Poisson distributions, providing a near perfect fit to the data, which was proven in an extensive validation process. The causal predictors found to affect the density of larvae significantly were distance to water table and percentage of pure clay layer in the soil to a depth of 1 m. Model predictions showed that larva density increased with an increase in distance to the water table up to almost 4 m, after which it remained constant, and with a reduction in the percentage of pure clay layer. However this latter correlation was weak and requires further investigation. The 2-dimensional trend function indicated a strong spatial effect, and thus explained by far the highest proportion of variation in larva density.

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Section: Discussionmentioning

confidence: 99%

A spatially-explicit count data regression for modeling the density of forest cockchafer ( Melolontha hippocastani ) larvae in the Hessian Ried (Germany)

Matthias¹,

Schmidt²,

Rainer³

et al. 2014

Forest Ecosyst

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“…Note that, in our case, the second-order cubic penalty for f age (age) is J age (f age ) = ∂ 2 f age /∂age 2 2 dage, which is analogous for f gdp (gdp). Following the derivations in Augustin et al (2009), an overall penalty for the tensor product smooth can be obtained by applying the penalties of f gdp (gdp) to the varying coefficients of the marginal smooth f age (age), α l (gdp):…”

Section: Endnotesmentioning

confidence: 99%

Interaction effects of region-level GDP per capita and age on labour market transition rates in Italy

Zanin

Calabrese

2017

IZA J Labor Econ

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The aim of this paper is to measure the effect of the interaction between age for the population of males and females aged 18 to 74 and region-level GDP per capita on labour market transition probabilities in Italy. We compare different occupational states in a sample of males and females who remained in their region of residence at two points in time (12 months apart). We estimate the transition probabilities using a flexible hierarchical logit model with interaction effects between worker age and region-level GDP per capita. We apply this model using longitudinal data from the Italian Labour Force Survey that cover the 2004-2013 period. We find empirical support for the assumption that people in the same age cohort have different labour market opportunities based on the level of GDP per capita in their region of residence. These differences are particularly relevant among younger workers. JEL Classification: C14, J01, J21, J68

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“…Such data are prevalent across many disciplines, including geophysical and environmental sciences [15,3,1], medical informatics [12], and computational fluid dynamics [29]. A geospatio-temporal prediction task typically requires making predictions for a response variable at multiple locations.…”

Section: Introductionmentioning

confidence: 99%

GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction

Tan²,

Liu

et al. 2016

Proceedings of the 2016 SIAM International Conference on Data Mining

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This paper presents a novel geospatio-temporal prediction framework called GSpartan to simultaneously build local regression models at multiple locations. The framework assumes that the local models share a common, low-rank representation, which makes them amenable to multi-task learning. GSpartan learns a set of base models to capture the spatio-temporal variabilities of the data and represents each local model as a linear combination of the base models. A graph Laplacian regularization is used to enforce constraints on the local models based on their spatial autocorrelation. We also introduce sparsity-inducing norms to perform feature selection for the base models and model selection for the local models. Experimental results using historical climate data from 37 weather stations showed that, on average, GSpartan outperforms single-task learning and other existing multi-task learning methods in more than 65% of the stations, which increases to 81% when there are fewer training examples.

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Modeling Spatiotemporal Forest Health Monitoring Data

Cited by 85 publications

References 27 publications

A spatially-explicit count data regression for modeling the density of forest cockchafer ( Melolontha hippocastani ) larvae in the Hessian Ried (Germany)

A spatially-explicit count data regression for modeling the density of forest cockchafer ( Melolontha hippocastani ) larvae in the Hessian Ried (Germany)

Interaction effects of region-level GDP per capita and age on labour market transition rates in Italy

GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction

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