433 research outputs found

    Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

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    The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

    Spatio temporal modeling of species distribution

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    The aim of this thesis is study spatial distribution of different groups from different perspectives and to analyse the different approaches to this problem. We move away from the classical approach, commonly used by ecologists, to more complex solutions, already applied in several disciplines. We are focused in applying advanced modelling techniques in order to understand species distribution and species behaviour and the relationships between them and environmental factors and have used first the most common models applied in ecology to move then to more advanced and complex perspectives. From a general perspective and comparing the different models applied during the process, from MaxEnt to spatio-temporal models with INLA, we can affirm that the models that we have developed show better results that the already built. Also, it is difficult to compare between the different approaches, but the Bayesian approach shows more flexibility and also the inclusion of spatial field or the latent spatio-temporal process allows to include residuals as a proxy for unmeasured variables. Compared with additive models with thin plate splines, probably considered one of the greatest methods to analyse species distribution models working with presence-absence data, comparable to MaxEnt, CART and MARS, our results show a better fit and more flexibility in the design. As a natural process we have realised that the Bayesian approach could be a better solution or at least a different approach for consideration. The main advantage of the Bayesian model formulation is the computational ease in model fit and prediction compared to classical geostatistical methods. To do so, instead of MCMC we have used the novel integrated nested Laplace approximation approach through the Stochastic Partial Differential Equation (SPDE) approach. The SPDE approach can be easily implemented providing results in reasonable computing time (comparing with MCMC). We showed how SPDE is a useful tool in the analysis of species distribution. This modelling could be expanded to the spatio-temporal domain by incorporating an extra term for the temporal effect, using parametric or semiparametric constructions to reflect linear, nonlinear, autoregressive or more complex behaviours. We can conclude that spatial and spatio-temporal Bayesian models are a really interesting approach for the understanding of environmental dynamics, not only because of the possibility to develop and solve more complex problems but also for the easy understanding of the implementation processes.The aim of this thesis is study spatial distribution of different groups from different perspectives and to analyse the different approaches to this problem. We move away from the classical approach, commonly used by ecologists, to more complex solutions, already applied in several disciplines. We are focused in applying advanced modelling techniques in order to understand species distribution and species behaviour and the relationships between them and environmental factors and have used first the most common models applied in ecology to move then to more advanced and complex perspectives. From a general perspective and comparing the different models applied during the process, from MaxEnt to spatio-temporal models with INLA, we can affirm that the models that we have developed show better results that the already built. Also, it is difficult to compare between the different approaches, but the Bayesian approach shows more flexibility and also the inclusion of spatial field or the latent spatio-temporal process allows to include residuals as a proxy for unmeasured variables. Compared with additive models with thin plate splines, probably considered one of the greatest methods to analyse species distribution models working with presence-absence data, comparable to MaxEnt, CART and MARS, our results show a better fit and more flexibility in the design. As a natural process we have realised that the Bayesian approach could be a better solution or at least a different approach for consideration. The main advantage of the Bayesian model formulation is the computational ease in model fit and prediction compared to classical geostatistical methods. To do so, instead of MCMC we have used the novel integrated nested Laplace approximation approach through the Stochastic Partial Differential Equation (SPDE) approach. The SPDE approach can be easily implemented providing results in reasonable computing time (comparing with MCMC). We showed how SPDE is a useful tool in the analysis of species distribution. This modelling could be expanded to the spatio-temporal domain by incorporating an extra term for the temporal effect, using parametric or semiparametric constructions to reflect linear, nonlinear, autoregressive or more complex behaviours. We can conclude that spatial and spatio-temporal Bayesian models are a really interesting approach for the understanding of environmental dynamics, not only because of the possibility to develop and solve more complex problems but also for the easy understanding of the implementation processes

    Bayesian Spatio-Temporal Modeling for Forecasting, Trend Assessment and Spatial Trend Filtering

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    This work develops Bayesian spatio-temporal modeling techniques specifically aimed at studying several aspects of our motivating applications, to include vector-borne disease incidence and air pollution levels. A key attribute of the proposed techniques are that they are scalable to extremely large data sets which consist of spatio-temporally oriented observations. The scalability of our modeling strategies is accomplished in two primary ways. First, through the introduction of carefully constructed latent random variables we are able to develop Markov chain Monte Carlo (MCMC) sampling algorithms that consist primarily of Gibbs steps. This leads to the fast and easy updating of the model parameters from common distributions. Second, for the spatio-temporal aspects of the models, a novel sampling strategy for Gaussian Markov random fields (GRMFs) that can be easily implemented (in parallel) within MCMC sampling algorithms is used. The performance of the proposed modeling strategies are demonstrated through extensive numerical studies and are further used to analyze vector-borne disease data measured on canines throughout the conterminous United States and PM 2.5 levels measured at weather stations throughout the Eastern United States. In particular, we begin by developing a Poisson regression model that can be used to forecast the incidence of vector-borne disease throughout a large geographic area. The proposed model accounts for spatio-temporal dependence through a vector autoregression and is fit through a Metropolis-Hastings based Markov chain Monte Carlo (MCMC) sampling algorithm. The model is used to forecast the prevalence of Lyme disease (Chapter 2) and Anaplasmosis (Chapter 3) in canines throughout the United States. As a part of these studies we also evaluate the significance of various climatic and socio-economic drivers of disease. We then present (Chapter 4) the development of the \u27chromatic sampler\u27 for GMRFs. The chromatic sampler is an MCMC sampling technique that exploits the Markov property of GMRFs to sample large groups of parameters in parallel. A greedy algorithm for finding such groups of parameters is presented. The methodology is found to be superior, in terms of computational effort, to both full block and single-site updating. For assessing spatio-temporal trends, we develop (Chapter 5) a binomial regression model with spatially varying coefficients. This model uses Gaussian predictive processes to estimate spatially varying coefficients and a conditional autoregressive structure embedded in a vector autoregression to account for spatio-temporal dependence in the data. The methodology is capable of estimating both widespread regional and small scale local trends. A data augmentation strategy is used to develop a Gibbs based MCMC sampling routine. The approach is made computationally feasible through adopting the chromatic sampler for GMRFs to sample the spatio-temporal random effects. The model is applied to a dataset consisting of 16 million test results for antibodies to Borrelia burgdoferi and used to identify several areas of the United States experiencing increasing Lyme disease risk. For nonparametric functional estimation, we develop (Chapter 6) a Bayesian multidimensional trend filter (BMTF). The BMTF is a flexible nonparameteric estimator that extends traditional one dimensional trend filtering methods to multiple dimensions. The methodology is computationally scalable to a large support space and the expense of fitting the model is nearly independent of the number of observations. The methodology involves discretizing the support space and estimating a multidimensional step function over the discretized support. Two adaptive methods of discretization which allows the data to determine the resolution of the resulting function is presented. The BMTF is then used (Chapter 7) to allow for spatially varying coefficients within a quantile regression model. A data augmentation strategy is introduced which facilitates the development of a Gibbs based MCMC sampling routine. This methodology is developed to study various meteorological drivers of high levels of PM 2.5, a particularly hazardous form of air pollution consisting of particles less than 2.5 micrometers in diameter

    Multi-scalar remote sensing of the northern mixed prairie vegetation

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    Optimal scale of study and scaling are fundamental to ecological research, and have been made easier with remotely sensed (RS) data. With access to RS data at multiple scales, it is important to identify how they compare and how effectively information at a specific scale will potentially transfer between scales. Therefore, my research compared the spatial, spectral, and temporal aspects of scale of RS data to study biophysical properties and spatio-temporal dynamics of the northern mixed prairie vegetation. I collected ground cover, dominant species, aboveground biomass, and leaf area index (LAI) from 41 sites and along 3 transects in the West Block of Grasslands National Park of Canada (GNPC; +49°, -107°) between June-July of 2006 and 2007. Narrowband (VIn) and broadband vegetation indices (VIb) were derived from RS data at multiple scales acquired through field spectroradiometry (1 m) and satellite imagery (10, 20, 30 m). VIs were upscaled from their native scales to coarser scales for spatial comparison, and time-series imagery at ~5-year intervals was used for temporal comparison. Results showed VIn, VIb, and LAI captured the spatial variation of plant biophysical properties along topographical gradients and their spatial scales ranged from 35-200 m. Among the scales compared, RS data at finer scales showed stronger ability than coarser scales to estimate ground vegetation. VIn were found to be better predictors than VIb in estimating LAI. Upscaling at all spatial scales showed similar weakening trends for LAI prediction using VIb, however spatial regression methods were necessary to minimize spatial effects in the RS data sets and to improve the prediction results. Multiple endmember spectral mixture analysis (MESMA) successfully captured the spatial heterogeneity of vegetation and effective modeling of sub-pixel spectral variability to produce improved vegetation maps. However, the efficiency of spectral unmixing was found to be highly dependent on the identification of optimal type and number of region-specific endmembers, and comparison of spectral unmixing on imagery at different scales showed spectral resolution to be important over spatial resolution. With the development of a comprehensive endmember library, MESMA may be used as a standard tool for identifying spatio-temporal changes in time-series imagery. Climatic variables were found to affect the success of unmixing, with lower success for years of climatic extremes. Change-detection analysis showed the success of biodiversity conservation practices of GNPC since establishment of the park and suggests that its management strategies are effective in maintaining vegetation heterogeneity in the region. Overall, my research has advanced the understanding of RS of the northern mixed prairie vegetation, especially in the context of effects of scale and scaling. From an eco-management perspective, this research has provided cost- and time-effective methods for vegetation mapping and monitoring. Data and techniques tested in this study will be even more useful with hyperspectral imagery should they become available for the northern mixed prairie

    Explainable deep learning for insights in El Ni\~no and river flows

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    The El Ni\~no Southern Oscillation (ENSO) is a semi-periodic fluctuation in sea surface temperature (SST) over the tropical central and eastern Pacific Ocean that influences interannual variability in regional hydrology across the world through long-range dependence or teleconnections. Recent research has demonstrated the value of Deep Learning (DL) methods for improving ENSO prediction as well as Complex Networks (CN) for understanding teleconnections. However, gaps in predictive understanding of ENSO-driven river flows include the black box nature of DL, the use of simple ENSO indices to describe a complex phenomenon and translating DL-based ENSO predictions to river flow predictions. Here we show that eXplainable DL (XDL) methods, based on saliency maps, can extract interpretable predictive information contained in global SST and discover SST information regions and dependence structures relevant for river flows which, in tandem with climate network constructions, enable improved predictive understanding. Our results reveal additional information content in global SST beyond ENSO indices, develop understanding of how SSTs influence river flows, and generate improved river flow prediction, including uncertainty estimation. Observations, reanalysis data, and earth system model simulations are used to demonstrate the value of the XDL-CN based methods for future interannual and decadal scale climate projections
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