Dados composicionais são vetores, chamados de composições, cujos componentes são todos positivos, satisfazem a soma igual a 1 e possuem um espaço amostral próprio chamado Simplex. A restrição da soma induz a correlação entre os componentes. Isso exige que os métodos estatísticos para análise desses conjuntos de dados considerem esse fato. A teoria para dados composicionais foi desenvolvida inicialmente por Aitchison na década de 80. Desde então, várias técnicas e métodos têm sido desenvolvidos para a modelagem dos dados composicionais. Este trabalho apresenta as principais abordagens para a análise estatística de dados composicionais independentes. Sendo, regressão Dirichlet (distribuição natural aos dados composicionais) ou o uso de transformações em razões logarítmicas que saem do espaço simplex para o espaço real. Também descreve os métodos para os casos em que a suposição de independência não pode ser atendida. Por exemplo, dados composionais com dependência espacial. Para esses casos, há na literatura métodos baseados nas teorias desenvolvidas para análise geoestatística de dados univariados; ou, no uso de transformações em razões logarítmicas com a inclusão da dependência espacial. Além de revisitar os métodos já difundidos, propõe-se o uso do método de Equações de Estimação Generalizadas (EEG) como alternativa para a análise de dados composicionais independentes e com dependência espacial. A principal vantagem é que as equações de estimação necessitam apenas da especificação de funções que descrevam a média e a estrutura de covariância. Assim, não é necessário atribuir uma distribuição de probabilidade aos dados ou fazer o uso de transformações. A aplicação do método EEG para dados composicionais independentes apresentou resultados tão eficientes quanto a regressão Dirichlet ou transformação em razões logarítmicas. Para os dados composicionais com dependência espacial, o método baseado em verossimilhança foi o que apresentou valores preditos mais próximos aos valores reais. O método EEG foi mais eficaz do que a abordagem geoestatística dos componentes individuais, porém, comparado com os demais métodos, foi o que apresentou maior valor residual. / C ompositional data are vectors, called compositions, whose components are all positive, it satisfies the sum equal one and has a Simplex space. The sum constraint induces the correlation between the components and this requires that the statistical methods for the analysis of datasets consider this fact. The theory for compositional data was developed mainly by Aitchison in the 1980s, and since then, several techniques and methods have been developed for compositional data modelling. This work presents the main approaches for the statistical analysis of independent compositional data, such as Dirichlet regression (natural distribution to compositional data) or the use of transformations log-ratios that aim to leave the simplex space for to Euclidean space. Also describes the methods for cases where the assumption of independence cannot be satisfied, for example, spatial dependence compositional data. For these cases, there are in the literature methods of analysis based on the theories developed for univariate geostatistics analysis or use of logratios transformations with the inclusion of the spatial dependence generated by the distance between the points. In addition, to revisiting the already diffused methods, this work propose the use of the Generalized Estimation Equation (GEE) method as an alternative for the analysis of independent compositional data and with spatial dependence. The GEE only requires the specification of functions that describe the mean and correlation matrix (covariance structure, therefore, it is not necessary to assign a probability distribution to the data or transformations. The application of the GEE method for independent compositional data presented results as efficient as Dirichlet regression or log-ratios transformation. Compositional data with spatial dependence, log-ratios transformations presented predicted values close to the real values. GEE method was more effective than the traditional geostatistical approach, however, compared with the other methods, It was the one that presented the high residual values.

Dados composicionais são vetores, chamados de composições, cujos componentes são todos positivos, satisfazem a soma igual a 1 e possuem um espaço amostral próprio chamado Simplex. A restrição da soma induz a correlação entre os componentes. Isso exige que os métodos estatísticos para análise desses conjuntos de dados considerem esse fato. A teoria para dados composicionais foi desenvolvida inicialmente por Aitchison na década de 80. Desde então, várias técnicas e métodos têm sido desenvolvidos para a modelagem dos dados composicionais. Este trabalho apresenta as principais abordagens para a análise estatística de dados composicionais independentes. Sendo, regressão Dirichlet (distribuição natural aos dados composicionais) ou o uso de transformações em razões logarítmicas que saem do espaço simplex para o espaço real. Também descreve os métodos para os casos em que a suposição de independência não pode ser atendida. Por exemplo, dados composionais com dependência espacial. Para esses casos, há na literatura métodos baseados nas teorias desenvolvidas para análise geoestatística de dados univariados; ou, no uso de transformações em razões logarítmicas com a inclusão da dependência espacial. Além de revisitar os métodos já difundidos, propõe-se o uso do método de Equações de Estimação Generalizadas (EEG) como alternativa para a análise de dados composicionais independentes e com dependência espacial. A principal vantagem é que as equações de estimação necessitam apenas da especificação de funções que descrevam a média e a estrutura de covariância. Assim, não é necessário atribuir uma distribuição de probabilidade aos dados ou fazer o uso de transformações. A aplicação do método EEG para dados composicionais independentes apresentou resultados tão eficientes quanto a regressão Dirichlet ou transformação em razões logarítmicas. Para os dados composicionais com dependência espacial, o método baseado em verossimilhança foi o que apresentou valores preditos mais próximos aos valores reais. O método EEG foi mais eficaz do que a abordagem geoestatística dos componentes individuais, porém, comparado com os demais métodos, foi o que apresentou maior valor residual. / C ompositional data are vectors, called compositions, whose components are all positive, it satisfies the sum equal one and has a Simplex space. The sum constraint induces the correlation between the components and this requires that the statistical methods for the analysis of datasets consider this fact. The theory for compositional data was developed mainly by Aitchison in the 1980s, and since then, several techniques and methods have been developed for compositional data modelling. This work presents the main approaches for the statistical analysis of independent compositional data, such as Dirichlet regression (natural distribution to compositional data) or the use of transformations log-ratios that aim to leave the simplex space for to Euclidean space. Also describes the methods for cases where the assumption of independence cannot be satisfied, for example, spatial dependence compositional data. For these cases, there are in the literature methods of analysis based on the theories developed for univariate geostatistics analysis or use of logratios transformations with the inclusion of the spatial dependence generated by the distance between the points. In addition, to revisiting the already diffused methods, this work propose the use of the Generalized Estimation Equation (GEE) method as an alternative for the analysis of independent compositional data and with spatial dependence. The GEE only requires the specification of functions that describe the mean and correlation matrix (covariance structure, therefore, it is not necessary to assign a probability distribution to the data or transformations. The application of the GEE method for independent compositional data presented results as efficient as Dirichlet regression or log-ratios transformation. Compositional data with spatial dependence, log-ratios transformations presented predicted values close to the real values. GEE method was more effective than the traditional geostatistical approach, however, compared with the other methods, It was the one that presented the high residual values.

<p>Ontario’s Regional Cancer Programs provide an organized system of adjuvant treatment and follow-up care in its 13 Regional Cancer Centres. For breast cancer patients in particular, these centres become a part of daily activities, as appointments over five years of cancer care result in patients repeatedly accessing these centres at varying frequencies over their treatment periods. The experience of seeking out and receiving care has grown to incorporate cancer care services that exist on a variety of spatial scales, in both formal and informal environments (e.g. support groups, workshops). This study focuses on the role of geography in health behaviors and care experiences of breast cancer patients. Individual interviews were conducted with patients (<em>n=</em>23) attending the Juravinski Cancer Centre in Hamilton, Ontario about their care experiences in their community, and the location and organization of the centre. Rosenstock’s (1966) health belief model and Bandura’s (1986) social cognitive theory informed the analysis of patient satisfaction and produced attitudes that impact the likelihood of health action. Results demonstrated that patients were generally satisfied with their interactions with health care providers and the design and location of the centre. Parking and perceived disconnect between the centre and community health care providers (e.g. family doctors) were identified as being sources of patient dissatisfaction. Patients made sense of their care experience through ‘routinization’ – fixed times and predictable intervals – of travel and appointment schedules. Satisfaction with accessibility to health care providers when at home (e.g. call-in services) appeared to impact at-home adherence to medication and suggested lifestyle changes. Uptake of community support services depended on patient perceptions of need, suitability and proximity, but went largely unused. Patients’ understanding of their care experiences highlight the need to give greater consideration to geography and the physical care environment in the future planning of breast cancer care services.</p> / Master of Arts (MA)

L'estimation d'indices d'étiage est d'une grande importance pour une meilleure connaissance de la ressource en eau disponible et en déduire des règles de gestion de cette ressource et des risques associés. Idéalement, ces indices sont calculés en sites jaugés à partir de longues chroniques de débits mesurés. En sites non jaugés, ces indices doivent être estimés. Cette thèse s'inscrit dans le projet d'amélioration de l'outil informatique " LoiEau " utilisé par les DREAL et les Agences de l'eau. Le principal objectif est de développer un modèle hydrologique simple et flexible pour estimer tous les indices d'étiage souhaités à des intervalles de temps journaliers ou supérieurs en sites non jaugés. La simplicité du modèle repose sur l'hypothèse forte que deux paramètres plus facilement régionalisables suffisent à estimer les indices avec une précision voulue. Cette précision dépend de la fonction objectif qu'il a fallu adapter aux faibles débits pour caler convenablement les paramètres du modèle sur des sites jaugés. Le modèle se veut flexible dans le sens où il est applicable à une large variété de bassins versants. Il a été comparé au modèle journalier à quatre paramètres GR4J et au modèle mensuel à deux paramètres LoiEau en s'intéressant aussi à la façon de régionaliser les paramètres. Des comparaisons ont également été réalisées avec une méthode d'estimation dite stochastique d'un indice d'étiage classique. Ces travaux montrent le potentiel du modèle à deux paramètres et l'avantage du pas de temps journalier, la variabilité temporelle des précipitations n'étant pas sans conséquence sur l'estimation de débits moyens en raison de la non-linéarité des modèles pluies-débits. / Estimating low-flow indices is of paramount importance to understand low flows and to manage water resources and risk assessments. These indices are derived from time-series of river discharges that are measured at gauged sites over long periods. At ungauged sites, the indices must be estimated. This research work is part of a project to improve “LoiEau”, a software package that is used by French regional environmental or water agencies. The main objective is to develop a simple and flexible rainfall-runoff model to simulate low-flow indices of ungauged sites at daily or longer time intervals. The model simplicity relies on the strong assumption that two free parameters are sufficient to provide accurate enough estimates of low-flow indices, yet making easier the regionalisation of models. The model accuracy depends on the objective function that is used to calibrate model parameters on gauged sites and had to be adapted to low-flow simulations. The model is flexible in the sense that it is designed to fit to a wide variety of catchments and hydro-meteorological behaviours. This model was compared with GR4J, a daily rainfall-runoff model which involves four parameters, and LoiEau, a monthly model. Comparisons were also carried out with a stochastic estimation method applied to a specific low-flow index. This research work shows the potential of the two-parameter model, but also the advantage of a daily time step to account for the temporal variability of precipitations, which is not without consequence on the assessment of average discharges due to the nonlinearity of rainfall-runoff models.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The rice (Oryza sativa L.) is one of the cereals most produced and consumed in the world. In it the insect species, Tibraca limbativentris, Oebalus poecilus and Oebalus ypsilongriseus (Hemiptera: Pentatomidae), generate significant damage. Insect populations in crops can be estimated through the use of interpolation procedures, which can generate continuous surfaces by single point sampling units. The study aimed to compare the methods of ordinary kriging and inverse distance weighted interpolation applied on the spatial distribution of population density T. limbativentris, O. poecilus and O. ypsilongriseus in rice cultivation in Santa Maria, Rio Grande do Sul, Brazil. In the locality of Parada Link, Santa Maria, we generated in six fields, a grid of 30m x 30m. From seeding have been made to seven evaluations for each tillage in area 1m², corresponding to 200 rice plants. In these areas, the adults of the species were quantified and the sum used for the analysis. The values were submitted to the ordinary kriging interpolation in different semivariogram models and inverse distance weighted in different weights being the best for each evaluation compared by cross-validation. The ordinary kriging interpolation method showed better performance than the method of inverse distance weighted applied in the spatial distribution of population density of T. limbativentris and O. poecilus in rice cultivation in Santa Maria, Rio Grande do Sul, Brazil. O. ypsilongriseus could not generate estimates for comparison. / O arroz (Oryza sativa L.) é um dos cereais mais produzidos e consumidos no mundo. Nele as espécies de insetos, Tibraca limbativentris, Oebalus poecilus e Oebalus ypsilongriseus (Hemiptera: Pentatomidae), geram perdas significativas. As populações de insetos em lavouras podem ser estimadas através do uso de procedimentos de interpolação, que permitem gerar superfícies contínuas através de unidades amostrais pontuais. O trabalho teve por objetivo comparar os métodos de interpolação krigagem ordinária e inverso da distância ponderada aplicados na espacialização da densidade populacional de T. limbativentris, O. poecilus e O. ypsilongriseus no cultivo do arroz irrigado em Santa Maria, Rio Grande do Sul, Brasil. Na localidade de Parada Link, Santa Maria gerou-se em seis lavouras um grid de amostragem de 30m x 30m. A partir da semeadura foram procedidas sete avaliações para cada lavoura em área de 1m2, correspondendo a 200 plantas de arroz. Nestas áreas os adultos das espécies foram quantificados e o somatório utilizado para a análise. Os valores foram submetidos ao interpoladores da krigagem ordinária em diferentes modelos de semivariogramas e do inverso da distância ponderada em diferentes ponderadores sendo os melhores, para cada avaliação, comparados por validação cruzada. O método de interpolação krigagem ordinária apresentou melhor desempenho que o método do inverso da distância ponderada aplicados na espacialização da densidade populacional de T. limbativentris e O. poecilus no cultivo do arroz irrigado em Santa Maria, Rio Grande do Sul, Brasil. Para O. ypsilongriseus não foi possível gerar as estimativas para comparação.

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No. of bitstreams: 1 Dissertação-Esther M O de Souza.pdf: 828862 bytes, checksum: f1d29e16cda95236926164567c1bee77 (MD5) Previous issue date: 2014-09-29 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Brazil has made great strides in the field of food and nutrition security by adopting public policies to improve the social conditions and power of the most vulnerable social groups. However, rates of underweight and stunting in children under five, in the northern region are 3.3% and 14.7%, respectively. According to OMS, for both indices should be below 2.3%. In past decades, food science, was concerned with developing food for human survival, a goal that was replaced by the concept of producing it with quality and as vehicles for promoting wellness, while reducing the risk of diseases. Multiple flour is an alternative food supplement intended to combat child malnutrition. However, there are questions about its quality especially on hydrogen cyanide present in cassava leaves. Bread is a staple food consumed worldwide, and its composition is mainly of wheat flour, water, salt and yeast.The aim of this research was to develop an enriched bread with a flour composed of multiple regional ingredients - pupunha flour, amaranth powder and pumpkin seed caboclo powder. The microbiological, physicochemical and sensory properties of the final product were evaluated. Sensory acceptability test was applied, using a nine-point hedonic scale, with one hundred tasters who evaluated the sample bout color, flavor, texture and overall acceptability. The moisture content found in enriched bread was 35.5%, ash 1.6%, 3.0 fiber, 5.4% protein, 2.3% lipid and carbohydrate 52.2%. The microbiological characteristics were in accordance with the standards set out in legislation. There was a slight decrease in pH of enriched bread after three days and slight increase after seven days. The enriched bread formulation had good acceptability and purchase intent, obtaining scores between the terms "liked" and "liked it very much". The use of multiple flour made of Amazonian ingredients changed the chemical composition of the bread, especially increased levels of dietary fiber; having a concentration of 3% fiber, this value characterize the product is a fiber source. / O Brasil tem realizado grandes avanços no campo da segurança alimentar e nutricional, mediante adoção de políticas públicas para a melhoria das condições sociais e de alimentação dos grupos sociais mais vulneráveis. Contudo, os índices de baixo peso e de baixa estatura em crianças menores de cinco anos, na região Norte são 3,3% e 14,7%, respectivamente. Segundo a OMS, para ambos os índices devem estar abaixo de 2,3%. Nas décadas passadas, a ciência de alimentos, preocupava-se em desenvolver alimentos para a sobrevivência humana, objetivo que foi substituído pelo conceito de produzi-lo com qualidade e como veículos de promoção de bem-estar, ao mesmo tempo reduzindo o risco de doenças. A farinha múltipla é um suplemento alimentar alternativo destinado ao combate à desnutrição infantil. No entanto, existem questionamentos sobre a sua qualidade notadamente sobre o ácido cianídrico presente nas folhas de mandioca. O pão é um alimento básico, consumido em todo o mundo, e sua composição é principalmente de farinha de trigo, água, sal e fermento. Na presente pesquisa o objetivo foi desenvolver um pão enriquecido com uma farinha múltipla composta por ingredientes regionais – farinha de pupunha, pó de caruru e pó de semente de jerimum caboclo. O produto obtido teve suas características microbiológicas, físico-químicas e sensoriais avaliadas. Foi aplicado um teste de aceitação sensorial , utilizando-se uma escala hedônica de nove pontos, com cem provadores, que avaliaram a amostra quanto aos atributos cor, aroma, sabor, textura e aceitação geral. O teor de umidade encontrado no pão enriquecido foi de 35,5%, de cinzas 1,6%, fibras 3,0%, proteínas 5,4%, lipídios 2,3% e carboidratos 52,2%. As características microbiológicas estavam de acordo com os padrões estabelecidos na legislação. Houve discreta diminuição no pH do pão enriquecido após três dias e discreto aumento após sete dias. A formulação do pão enriquecido teve boa aceitação sensorial e intenção de compra, obtendo escores localizados entre os termos “gostei muito” e “gostei muitíssimo”. A utilização da farinha múltipla composta por ingredientes amazônicos alterou a composição química do pão, com destaque para o aumento nos teores de fibra alimentar; apresentando concentração de 3% de fibra, esse valor caracteriza que o produto é uma fonte de fibra.

[pt] Esta dissertação visa contribuir entre múltiplas leituras geográficas sobre a Região Metropolitana de Manaus (RMM), em meio a delimitação para essa pesquisa, a presente dissertação tem como objeto a realização da política de alimentação escolar na distribuição e circulação do Programa de Regionalização da Merenda Escolar (Preme). Propõe-se assim, a partir da identificação na área de estudo, os municípios de Itacoatiara e Manacapuru compreender o Preme, enquanto política pública de alimentação escolar, que capta tanto a produção agrícola local de alimento quanto o hábito alimentar de estudantes em escolas públicas. Desse modo, essa pesquisa qualitativa tem como objetivo geral analisar a distribuição e circulação do Preme, uma política de alimentação escolar em Itacoatiara e Manacapuru, municípios da Região Metropolitana de Manaus. E os objetivos específicos são: 1. Analisar o Preme, enquanto uma política pública de Alimentação Escolar; 2. Caracterizar o Preme nos municípios de Itacoatiara e Manacapuru; 3. Analisar a distribuição e circulação, a partir das estruturas do Preme nos municípios de Itacoatiara e Manacapuru, inseridos na RMM. Com levantamentos de dados primários por meio de trabalho de campo e entrevistas. / [en] This dissertation aims to contribute between multiple geographic readings about the Metropolitan Region of Manaus (RMM), amid the delimitation for this research, this dissertation aims to carry out the school feeding policy in the distribution and circulation of the School Meal Regionalization Program (Preme). Thus, based on the identification in the study area, the municipalities of Itacoatiara and Manacapuru are proposed to understand Preme, as a public policy for school feeding, which captures both the local agricultural production of food and the eating habits of students in public schools. Thus, this qualitative research aims to analyze the distribution and circulation of Preme, a school feeding policy in Itacoatiara and Manacapuru, municipalities in the Metropolitan Region of Manaus. And the specific objectives are: 1. To analyze Preme as a public policy for School Feeding; 2. Characterize Preme in the municipalities of Itacoatiara and Manacapuru; 3. Analyze the distribution and circulation, from the structures of Preme in the municipalities of Itacoatiara and Manacapuru, inserted in the RMM. With primary data surveys through field work and interviews.

As an expression of current challenges faced by contemporary societies, past decades have witnessed heavy demands for higher education to change and transform. One key question here has been the increased digitalization of higher education. Within this wider setting, this thesis deals with an attempt to handle the increasing shortage of physicians in Sweden by way of digitalizing medical education. The aim of this explorative and longitudinal thesis is to describe and analyze structural and educational transformation work in medical education during the digitalization of the program and the transition from face-to-face to distance education. This thesis focuses on teachers, students and management, who are all heavily involved in this transition of the medical program. Two questions guide the research: (1) what are teachers’ and students’ expectations pending the transition, and what are the influences of already established tools and activities on the program and (2) in what ways do conflicts and changes occur over time, and how do teachers, students, and management deal with these as part of the transition? Cultural-historical activity theory (CHAT) serves as the theoretical framework of the thesis. In particular, the concepts of dominant and non-dominant activities, conflicts, transitional actions, and levels of learning inform the analysis. The data are generated by surveys (N = 108), logging of actors’ activity patterns (N = 100 teachers and 100 students), field studies (65 hours), and interviews (N = 62). The data cover teachers’, students’ and management’s roles in the transition. The analysis shows that the way of theoretically understanding the transition – from a dominant face-to-face activity to a new and unproven non-dominant distance activity – have proved to contribute to deeper understanding of the process of digitalizing medical education. The analysis further displays how the transition from face-to-face to distance education creates considerable conflicts that over time force teachers, students and management into structural and educational transformation work. This type of work successively renders new educational design solutions and new flexible ways of organizing distance medical education. This thesis discusses how the structural and educational transformation work forces actors to collectively engage in the transition by experimenting with new suitable methods and designs, as digital technologies and technology-enhanced learning (TEL) could make sense to teachers and students when they are at a distance.

Aquesta tesi estudia com estimar la distribució de les variables regionalitzades l'espai mostral i l'escala de les quals admeten una estructura d'espai Euclidià. Apliquem el principi del treball en coordenades: triem una base ortonormal, fem estadística sobre les coordenades de les dades, i apliquem els output a la base per tal de recuperar un resultat en el mateix espai original. Aplicant-ho a les variables regionalitzades, obtenim una aproximació única consistent, que generalitza les conegudes propietats de les tècniques de kriging a diversos espais mostrals: dades reals, positives o composicionals (vectors de components positives amb suma constant) són tractades com casos particulars. D'aquesta manera, es generalitza la geostadística lineal, i s'ofereix solucions a coneguts problemes de la no-lineal, tot adaptant la mesura i els criteris de representativitat (i.e., mitjanes) a les dades tractades. L'estimador per a dades positives coincideix amb una mitjana geomètrica ponderada, equivalent a l'estimació de la mediana, sense cap dels problemes del clàssic kriging lognormal. El cas composicional ofereix solucions equivalents, però a més permet estimar vectors de probabilitat multinomial. Amb una aproximació bayesiana preliminar, el kriging de composicions esdevé també una alternativa consistent al kriging indicador. Aquesta tècnica s'empra per estimar funcions de probabilitat de variables qualsevol, malgrat que sovint ofereix estimacions negatives, cosa que s'evita amb l'alternativa proposada. La utilitat d'aquest conjunt de tècniques es comprova estudiant la contaminació per amoníac a una estació de control automàtic de la qualitat de l'aigua de la conca de la Tordera, i es conclou que només fent servir les tècniques proposades hom pot detectar en quins instants l'amoni es transforma en amoníac en una concentració superior a la legalment permesa. / This Thesis presents an estimation procedure for the distribution of regionalized variables with sample space and scale admitting an Euclidean structure. We apply the principle of working on coordinates: choose an orthonormal basis; do statistics on the coordinates of your observations on that basis; and, by applying the output to the basis, you will recover a result within the original space. Applying this procedure to regionalized variables, we obtain a unified, consistent method, with the same properties of classical linear kriging techniques, but valid for several sample spaces: real data, positive data and compositions (vectors of positive components summing up to a constant) are regarded as particular cases. In this way we generalize the linear kriging techniques, and offer a solution to several well-known problems of the non-linear ones, by adapting the measure of the space and the averaging criterion (the way means are computed) to the data. The obtained estimator for positive variables is a weighted geometric mean, equivalent to estimate the median, which has none of the drawback of classical lognormal kriging. For compositional data, equivalent results are obtained, but which also serve to treat multinomial probability vectors. By combining this with a preliminary Bayesian estimation, our kriging for compositions become also a valid alternative to indicator kriging, without its order-relation problems (i.e. the rather-usual negative estimates of some probabilities). These techniques are validated by studying the ammonia pollution hazard in an automatic water quality control station placed in a small Mediterranean river. Only the proposed techniques allow us to assess when the secondary pollution by ammonia exceeds the existing legal threshold.

Se propusieron innovaciones en la predicción espacio y espacio-temporal, a partir de métodos geoestadísticos y de funciones de base radial (RBF), considerando métodos basados en distancias. En este sentido, por medio de las distancias entre las variables explicativas, incorporadas específicamente en la regresión basada en distancias, se propusieron modificaciones en: el método kriging universal y en la interpolación con splines espacial y espacio-temporal usando las RBF. El método basado en la distancia se utiliza en un modelo Geoestadístico para estimar la tendencia y la estructura de covarianza. Esta estrategia aprovecha al máximo la información existente, debido a la relación entre las observaciones, mediante el uso de una descomposición espectral de una distancia seleccionada y las coordenadas principales correspondientes. Para el método propuesto kriging universal basado en distancias (DBUK), se realizó un estudio de simulación que permitió comparar la capacidad predictiva del método tradicional kriging universal con respecto a kriging universal basado en distancias; mientras que en la interpolación con Splines espacial y espacio-temporal, los estudios de simulación permitieron comparar el funcionamiento de las funciones de base radial espaciales y espaciotemporales, considerando en la tendencia las coordenadas principales generadas a partir de las variables explicativas mixtas mediante el uso del método basado en distancias. El método propuesto DBUK muestra, tanto en las simulaciones como en las aplicaciones, ventajas en la reducción del error con respecto al método clásico de krigeado universal. Esta reducción de los errores se asocia a una mejor modelización de la tendencia y a un menor error en el ajuste y modelado del variograma, al considerar las coordenadas principales obtenidas a partir de las variables explicativas mixtas. Entre muchas otras posibles causas, el error es generado por omisión de variables y por considerar formas funcionales incorrectas. El estudio de simulación muestra que el método propuesto DBUK es mejor que el método de krigeado universal tradicional ya que se encontró una notoria reducción del error, asociada a un RMSPE más pequeño, esta reducción en general fue superior al 10%. El método DBUK podrá producir una mejor estimación de la variable regionalizada si el número de coordenadas principales se incrementa. Esto es posible, incluyendo las coordenadas principales más significativas tanto en modelo de tendencia como en el variograma; se presenta una aplicación que ilustra este hecho. Los métodos propuestos de interpolación espacial basada en distancias con RBF (DBSIRBF) e interpolación espacio-temporal basada en distancias con RBF (DBSTIRBF) analizados mediante una estructura de krigeado considerando en la tendencia las coordenadas principales, presentan un buen funcionamiento al trabajar con vecindarios grandes, indicando en general que se tendrá un menor error asociado a un RMSPE más pequeño En diversos estudios, la detección de variabilidad entre zonas es una tarea muy difícil, y por lo cual los métodos propuestos DBUK, DBSIRBF y DBSTIRBF son útiles de acuerdo a los resultados obtenidos en la tesis, ya que aprovechan al máximo la información existente asociada a las variables explicativas. Aunque la correlación de las variables explicativas puede ser baja con respecto a la variable respuesta, el punto clave en los métodos propuestos es la correlación entre las coordenadas principales (construida con las variables explicativas) y la variable respuesta. Los métodos propuestos se aplicaron a datos agronómicos (Concentración de calcio medido a una profundidad de 0-20 cm de Brasil) y climatológicos (Temperaturas medias diarias de la Tierra en Croacia en el año 2008). Los resultados de validación cruzada “leave-one-out” mostraron un buen rendimiento de los predictores propuestos, lo cual indica que se pueden utilizar como métodos alternos y validos a los tradicionales para el modelado de variables correlacionadas espacialmente y espacio-temporalmente, considerando siempre covariables en la remoción de la tendencia. / Space-time geostatistical analysis based on distances and splines with applications. Innovations were proposed in the space and space-time prediction, based on geostatistical methods and radial basis function (RBF), considering distance-based methods. In this sense, through the distances between the explanatory variables, specifically incorporated in the regression based on distances, changes were proposed in: the universal kriging and interpolation with space and space-time splines using RBF. The distance-based method is used in a geostatistical model to estimate the trend and the covariance structure. This strategy takes full advantage of existing information, because of the relationship between the observations, using a spectral decomposition of a selected distance and the corresponding principal coordinates. For the universal kriging method proposed based on distances (DBUK), we performed a simulation study, which allowed to compare the predictive capacity of traditional universal kriging over universal kriging based on distances. The simulation study shows that the proposed method DBUK, is better than the traditional universal kriging method and was found a marked reduction of error associated with a smaller RMSPE, this reduction was generally greater than 10%. Spatial and spatio-temporal spline interpolation in simulation studies possible to compare the performance of space and spatio-temporal radial basis functions, considering the trend in the principal coordinates generated from the mixed explanatory variables using the method based distances. The proposed spatial interpolation methods based on distances with RBF (DBSIRBF) and spatio-temporal interpolation based on distances RBF (DBSTIRBF) analyzed through kriging structure whereas in the trend the principal coordinates, show good performance when working with large neighborhoods, indicating that in general will have less error associated with a smaller RMSPE. The key point in the proposed methods is the correlation between the principal coordinates (constructed with the explanatory variables) and the response variable. The proposed methods were applied to agronomic data (concentration of calcium measured at a depth of 0-20 cm from Brazil) and climatological (average daily temperature of the Earth in Croatia in 2008). The results of cross-validation "leave-one-out" showed a good performance of the proposed predictors, indicating that can be used as alternative methods to traditional and valid for the modeling of spatially correlated variables in space and time, always considering covariates in the removal of the trend.