scholarly journals Generalized Linear Spatial Models to Predict Slate Exploitability

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Angeles Saavedra ◽  
Javier Taboada ◽  
María Araújo ◽  
Eduardo Giráldez
Keyword(s):  
Spatial Distribution ◽  
Generalized Linear Models ◽  
Spatial Dependence ◽  
Spatial Model ◽  
Linear Models ◽  
Spatial Models ◽  
Response Variable ◽  
Explanatory Variables ◽  
Model Residuals

The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatial model (GLSMs) was fitted in order to explore relationship between exploitability and different explanatory variables that characterize slate quality. Modelling the influence of these variables and analysing the spatial distribution of the model residuals yielded a GLSM that allows slate exploitability to be predicted more effectively than when using generalized linear models (GLM), which do not take spatial dependence into account. Studying the residuals and comparing the prediction capacities of the two models lead us to conclude that the GLSM is more appropriate when the response variable presents spatial distribution.

More generalized linear modelling.

2021 ◽  
pp. 171-186
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Generalized Linear Models ◽  
Count Data ◽  
Binary Data ◽  
Linear Models ◽  
Explanatory Variable ◽  
Response Variable ◽  
Explanatory Variables ◽  
Continuous Response ◽  
Generalized Linear Modelling ◽  
Linear Modelling

Abstract This chapter employs generalized linear modelling using the function glm when we know that variances are not constant with one or more explanatory variables and/or we know that the errors cannot be normally distributed, for example, they may be binary data, or count data where negative values are impossible, or proportions which are constrained between 0 and 1. A glm seeks to determine how much of the variation in the response variable can be explained by each explanatory variable, and whether such relationships are statistically significant. The data for generalized linear models take the form of a continuous response variable and a combination of continuous and discrete explanatory variables.

Car Ownership and the Built Environment: A Spatial Modeling Approach

2021 ◽  
pp. 036119812110494
Author(s):  
Jerome Laviolette ◽  
Catherine Morency ◽  
Owen D. Waygood ◽  
Konstadinos G. Goulias
Keyword(s):  
Built Environment ◽  
Spatial Dependence ◽  
Linear Models ◽  
Census Data ◽  
Model Specification ◽  
Car Ownership ◽  
Grocery Stores ◽  
Modeling Framework ◽  
Explanatory Variables ◽  
Environment Variables

Car ownership is linked to higher car use, which leads to important environmental, social and health consequences. As car ownership keeps increasing in most countries, it remains relevant to examine what factors and policies can help contain this growth. This paper uses an advanced spatial econometric modeling framework to investigate spatial dependences in household car ownership rates measured at fine geographical scales using administrative data of registered vehicles and census data of household counts for the Island of Montreal, Canada. The use of a finer level of spatial resolution allows for the use of more explanatory variables than previous aggregate models of car ownership. Theoretical considerations and formal testing suggested the choice of the Spatial Durbin Error Model (SDEM) as an appropriate modeling option. The final model specification includes sociodemographic and built environment variables supported by theory and achieves a Nagelkerke pseudo-R2 of 0.93. Despite the inclusion of those variables the spatial linear models with and without lagged explanatory variables still exhibit residual spatial dependence. This indicates the presence of unobserved autocorrelated factors influencing car ownership rates. Model results indicate that sociodemographic variables explain much of the variance, but that built environment characteristics, including transit level of service and local commercial accessibility (e.g., to grocery stores) are strongly and negatively associated with neighborhood car ownership rates. Comparison of estimates between the SDEM and a non-spatial model indicates that failing to control for spatial dependence leads to an overestimation of the strength of the direct influence of built environment variables.

scholarly journals Species richness and diversity in shrub savanna using ordinary kriging

2016 ◽  
Vol 51 (8) ◽  
pp. 958-966 ◽  
Author(s):  
Anderson Pedro Bernardina Batista ◽  
José Márcio de Mello ◽  
Marcel Régis Raimundo ◽  
Henrique Ferraço Scolforo ◽  
Aliny Aparecida dos Reis ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Species Richness ◽  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Diversity Index ◽  
Spatial Models ◽  
Shannon Diversity Index ◽  
Shannon Diversity ◽  
Species Richness And Diversity ◽  
Number Of Species

Abstract: The objective of this work was to analyze the spatial distribution and the behavior of species richness and diversity in a shrub savanna fragment, in 2003 and 2014, using ordinary kriging, in the state of Minas Gerais, Brazil. In both evaluation years, the measurements were performed in a fragment with 236.85 hectares, in which individual trees were measured and identified across 40 plots (1,000 m2). Species richness was determined by the total number of species in each plot, and diversity by the Shannon diversity index. For the variogram study, spatial models were fitted and selected. Then, ordinary kriging was applied and the spatial distribution of the assessed variables was described. A strong spatial dependence was observed between species richness and diversity by the Shannon diversity index (<25% spatial dependence degree). Areas of low and high species diversity and richness were found in the shrub savanna fragment. Spatial distribution behavior shows relative stability regarding the number of species and the Shannon diversity index in the evaluated years.

Climate Influences on Meningitis Incidence in Northwest Nigeria

2014 ◽  
Vol 6 (1) ◽  
pp. 62-76 ◽  
Author(s):  
Auwal F. Abdussalam ◽  
Andrew J. Monaghan ◽  
Vanja M. Dukić ◽  
Mary H. Hayden ◽  
Thomas M. Hopson ◽  
...  
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Skill Score ◽  
Additive Models ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Explanatory Variables ◽  
Score Statistics

Abstract Northwest Nigeria is a region with a high risk of meningitis. In this study, the influence of climate on monthly meningitis incidence was examined. Monthly counts of clinically diagnosed hospital-reported cases of meningitis were collected from three hospitals in northwest Nigeria for the 22-yr period spanning 1990–2011. Generalized additive models and generalized linear models were fitted to aggregated monthly meningitis counts. Explanatory variables included monthly time series of maximum and minimum temperature, humidity, rainfall, wind speed, sunshine, and dustiness from weather stations nearest to the hospitals, and the number of cases in the previous month. The effects of other unobserved seasonally varying climatic and nonclimatic risk factors that may be related to the disease were collectively accounted for as a flexible monthly varying smooth function of time in the generalized additive models, s(t). Results reveal that the most important explanatory climatic variables are the monthly means of daily maximum temperature, relative humidity, and sunshine with no lag; and dustiness with a 1-month lag. Accounting for s(t) in the generalized additive models explains more of the monthly variability of meningitis compared to those generalized linear models that do not account for the unobserved factors that s(t) represents. The skill score statistics of a model version with all explanatory variables lagged by 1 month suggest the potential to predict meningitis cases in northwest Nigeria up to a month in advance to aid decision makers.

Modeling of the spatial distribution of Pacific spiny dogfish (Squalus suckleyi) in the Gulf of Alaska using generalized additive and generalized linear models

2013 ◽  
Vol 70 (9) ◽  
pp. 1372-1385 ◽  
Author(s):  
Jason R. Gasper ◽  
Gordon H. Kruse
Keyword(s):  
Spatial Distribution ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Fish Habitat ◽  
Additive Model ◽  
Gulf Of Alaska ◽  
Spiny Dogfish ◽  
Essential Fish Habitat ◽  
Mortality Estimation

The Pacific spiny dogfish (Squalus suckleyi) is a common bycatch species in the Gulf of Alaska. Their spatial distribution is poorly understood, as most catch is discarded at sea. We analyzed spiny dogfish spatial distribution from fishery-dependent and -independent observations of longline gear between 1996 and 2008 using generalized additive and generalized linear models. Poisson, negative binomial, and quasi-Poisson error structures were investigated; the quasi-Poisson generalized additive model fit best. Models showed that spiny dogfish catches were concentrated east of Kodiak Island in waters ≤100 m deep. Results facilitate design of future spiny dogfish assessment surveys and identification of areas in which to focus at-sea observations for fishing mortality estimation, and provide the basis for first-ever designation of spiny dogfish essential fish habitat, despite US legal requirements for essential fish habitat designations since 1996. Identified areas of high bycatch may expedite spatial management by indicating areas in which directed spiny dogfish fisheries could be focused or, conversely, areas in which heightened conservation and catch accounting efforts would be most effective to prevent overfishing of this long-lived, late-maturing species.

scholarly journals Penalized Likelihood Estimation of Gamma Distributed Response Variable via Corrected Solution of Regression Coefficients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Rasaki Olawale Olanrewaju
Keyword(s):  
Generalized Linear Models ◽  
Linear Models ◽  
Penalized Likelihood ◽  
Likelihood Estimation ◽  
Regression Coefficients ◽  
Response Variable ◽  
Penalized Likelihood Estimation ◽  
Selection Operator ◽  
Minimax Concave Penalty

A Gamma distributed response is subjected to regression penalized likelihood estimations of Least Absolute Shrinkage and Selection Operator (LASSO) and Minimax Concave Penalty via Generalized Linear Models (GLMs). The Gamma related disturbance controls the influence of skewness and spread in the corrected path solutions of the regression coefficients.

scholarly journals Modeling land use impacts on spatial distribution of floating plastic litter in surface water of Southeast Asian archipelago

2021 ◽  
Author(s):  
Andri Wibowo
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Surface Water ◽  
Population Density ◽  
Southeast Asia ◽  
Spatial Model ◽  
Spatial Models ◽  
Southeast Asian ◽  
Land Use Impacts

AbstractPlastics are present in many ecosystems including floating in surface water of remote archipelago and this can lead to the increase in plastic litter density. Whereas the spatial model of plastic litter density related to the population inhabits isolated archipelago is still limited. And what are the underlying factors driving the presence of plastic litter is also poorly understood. This study is trying to find the answers of those questions. The study was implemented in Thousand Island archipelago located in North of Java Island, one of populated islands in Southeast Asia. The studied surface water covers an area of 10000 Ha and consists of 10 islands with 3 islands are occupied by settlements and the remaining islands are occupied by vegetation. This study has recorded 3 types of floating macro-litter from water that consist of PET, HDPE, and LDPE litter. The plastic litter was observed concentrated in the east sides of archipelago where the populated islands were located. The spatial models show LDPE litter was distributed in the vast areas in comparison to PET and HDPE litter. Beside land use variables, the model has confirmed that the population density was the main underlying factors contribute to the plastic litter density in Thousand Island archipelago. The model can be applied to estimate PET (AIC = −0.53060) and HDPE (AIC = 18.28828) litter density. While LDPE litter density was influenced by population (AIC = 22.60201) rather than population density factors.

scholarly journals QML estimation with non-summable weight matrices

2020 ◽  
Vol 22 (4) ◽  
pp. 469-495
Author(s):  
Jakub Olejnik ◽  
Alicja Olejnik
Keyword(s):  
Maximum Likelihood ◽  
Spatial Dependence ◽  
Spatial Models ◽  
Interaction Patterns ◽  
Likelihood Estimator ◽  
General Group ◽  
Spatial Weight Matrix ◽  
Spatial Weight ◽  
Quasi Maximum Likelihood ◽  
Model Residuals

Abstract This paper revisits the theory of asymptotic behaviour of the well-known Gaussian Quasi-Maximum Likelihood estimator of parameters in mixed regressive, high-order autoregressive spatial models. We generalise the approach previously published in the econometric literature by weakening the assumptions imposed on the spatial weight matrix. This allows consideration of interaction patterns with a potentially larger degree of spatial dependence. Moreover, we broaden the class of admissible distributions of model residuals. As an example application of our new asymptotic analysis we also consider the large sample behaviour of a general group effects design.

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