Abdominal aortic aneurysms are the third leading cause of sudden death in men 60 years and over. AAAneurysm Outreach provides free screenings to residents of Louisiana and beyond. Service areas were calculated for each AAAneurysm Outreach screening event location and stroke center. Data provided by the 2010 U.S. Census, the American Community Survey, and the Behavioral Risk Factor Surveillance System were used to describe demographics of the underserved populations and to identify high-risk areas for targeted interventions. Twenty-five percent of age-eligible Louisianans reside outside optimal drive-time-to-screening-event zones but within spatially clustered areas of increased prevalence rates. The maximum drive-time-to-treatment zones excluded 1,395 residents 65 years and over from timely access to emergency medical care. Results revealed limitations in the geographic breadth of the screening program and small disparities in accessibility to emergency healthcare.

No description available.

No description available.

Bien que les interactions biotiques soient connues pour être déterminantes dans l’établissement des espèces, il est encore difficile de savoir quels facteurs sont impliqués dans l’équilibre entre interaction positive et interaction négative. Il est de fait difficile de savoir sous quelles conditions les interactions biotiques peuvent favoriser ou empêcher la régénération des espèces. Cette thèse vise à étudier le rôle des interactions biotiques d'arbustes avec des semis de chêne sur la régénération de trois espèces de chênes sur les forêts des dunes côtières d'Aquitaine en testant comment l’effet net de ces interactions varie le long d’un gradient d’aridité, sur deux étages de canopée et en fonction des stratégies fonctionnelles de trois espèces de Chêne dans un contexte de changement global. Ce travail a été effectué en utilisant deux approches, une approche descriptive en utilisant un patron de points répartis dans l’espace et une approche expérimentale en transplantant les semis cibles. Les résultats montrent que la variation spatiale, en terme d’interactions biotiques, est fortement corrélée avec la sévérité environnementale, avec des interactions entre jeunes pousses de chêne très sensibles aux sécheresses estivales et aux trouées dans les canopées. Les interactions testées étaient de nature facilitatrice dans les plots découverts dans les dunes sèches du nord de Soulac et tournaient à la compétition sous le couvert forestier dans les dunes plus humides du sud, à Seignosse. La nature des interactions était constant entre les stratégies fonctionnelles des espèces cibles de chêne. Les résultats de cette thèse montrent de manière générale une confirmation de la formulation originale du SGH qui prédit une augmentation de la facilitation en lien avec une augmentation de la sévérité environnementale et souligne le fait que la réduction du stress hydrique atmosphérique par des arbustes est nécessaire à la régénération des semis de chêne. Dans cette perspective, le sylviculteur doit conserver les arbustes du sous-étage, en particulier dans les trouées, afin de permettre une meilleure régénération des plants de chêne. Cette thèse met en évidence la nécessaire considération des interactions biotiques dans la régénération du chêne dans les actuelles sévères conditions climatiques et le rôle prépondérant de ces interactions dans la réponse aux changements climatiques futurs probables dans cette région Aquitaine. / Although biotic interactions are known to be important determinants of species establishment, it is uncertain what factors determine the net balance between positive and negative interactions thus, under what conditions biotic interactions could enhance or impede species regeneration. Bien que les interactions biotiques soient connues pour être This thesis aims to study the role of biotic interactions of shrubs with oak seedlings for regeneration of three oak species on the Aquitaine coastal dune forests, by testing how the net effect of these interactions vary along aridity gradient, between two overstory canopies and in respect to the functional strategies of three oak species in the context of climate change. This was done by using two approaches, descriptive approach using spatial point pattern data and experimental approach by transplanting the target seedlings. The results show that the spatial variation in the nature of biotic interactions is strongly relate to environmental severity conditions, where the shrub-oak seedling interactions were very sensitive to increasing summer drought and canopy opening, the interactions strength was facilitative under gap plots in the dry northern dunes in Soulac and switch on competitive under forest plots in the wet southern dunes in Seignosse. The nature of the interactions was constant across the functional strategies of the targets species of oak. For the most part, results of this thesis show general support to the original formulation of SGH which predicts increasing facilitation with increasing severity and underscore the fact that atmospheric water stress reduction by shrubs is required for oak seedling regeneration. In this perspective, silviculturist should conserve understory shrubs, in particular in gaps, in order to allow a better regeneration niche of oak seedlings. This thesis highlights the importance of considering biotic interactions in oak regeneration under current harshness climatic conditions and with expectation to have an ambitious role in alleviation future climatic change consequence in this region.

The thesis investigates the concepts of archaeological spatial association within the context of both site and regional data sets. The techniques of geophysical surveying, surface distribution collection and aerial photography are described and discussed. Several new developments of technique are presented as well as a detailed discussion of the problems of data presentation and analysis. The quantitative relationships between these data sets are explored by modelling them as operands and describing association in terms of operators. Both local and global measures of association are considered with a discussion as to their relative merits. Methods for the spatial association of regional lattice and point processes are developed. A detailed discussion of distance based spatial analysis techniques is presented.

Models of land use change fall into two broad categories: pattern based and process based. This thesis focuses on pattern based land use change models, expanding our understanding of these models in three important ways. First, it is demonstrated that some driving variables do not have a smooth impact on the land use transition process. Our example variable is access to water. Land managers with access to piped water do not have any need for surface or groundwater. For variables like this, a model needs to change the way that driving variables are represented. The second important result is that including a variable which captures spatial correlation between land use types significantly increases the explanatory power of the prediction model. A major weakness of pattern based land use models is their inability to model interactions between neighbouring land parcels; the method proposed in this study can be an alternative to account for spatial neighbourhood association. These innovations are applied using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) system to the Deep Creek watershed in the Southern Interior of British Columbia. The results highlight the challenge of balancing the protection of agricultural land and conserving forest and natural areas when population and economic growth are inevitable. The results also demonstrate the implications of land use change on existing land use policies. The calibrated model was validated using remote sensing data. A series of discriminant functions were estimated for each land use type in the recent period and these functions were then used to classify. The calibrated model was run in reverse, back to the generated land use classification, and results compared. Fit was reasonable with error rates falling below ten percent when radii beyond 2.5 km were considered. Another important contribution is demonstrating the importance of modelling dynamic variables. Some important drivers are changing continuously and others depend on land use change itself. Failure to update these variables will bias model forecasts. Spatial neighbourhood association, an endogenous variable governed by land use change itself, is again used as the example dynamic variable. The study demonstrates the importance of updating all associated information. / Graduate Studies, College of (Okanagan) / Graduate

The thesis investigates the concepts of archaeological spatial association within the context of both site and regional data sets. The techniques of geophysical surveying, surface distribution collection and aerial photography are described and discussed. Several new developments of technique are presented as well as a detailed discussion of the problems of data presentation and analysis. The quantitative relationships between these data sets are explored by modelling them as operands and describing association in terms of operators. Both local and global measures of association are considered with a discussion as to their relative merits. Methods for the spatial association of regional lattice and point processes are developed. A detailed discussion of distance based spatial analysis techniques is presented.

A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial. / The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.

A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial. / The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.

A técnica de mineração de regras de associação surgiu com o objetivo de encontrar conhecimento novo, útil e previamente desconhecido em bancos de dados transacionais, e uma grande quantidade de algoritmos de mineração de regras de associação tem sido proposta na última década. O maior e mais bem conhecido problema destes algoritmos é a geração de grandes quantidades de conjuntos freqüentes e regras de associação. Em bancos de dados geográficos o problema de mineração de regras de associação espacial aumenta significativamente. Além da grande quantidade de regras e padrões gerados a maioria são associações do domínio geográfico, e são bem conhecidas, normalmente explicitamente representadas no esquema do banco de dados. A maioria dos algoritmos de mineração de regras de associação não garantem a eliminação de dependências geográficas conhecidas a priori. O resultado é que as mesmas associações representadas nos esquemas do banco de dados são extraídas pelos algoritmos de mineração de regras de associação e apresentadas ao usuário. O problema de mineração de regras de associação espacial pode ser dividido em três etapas principais: extração dos relacionamentos espaciais, geração dos conjuntos freqüentes e geração das regras de associação. A primeira etapa é a mais custosa tanto em tempo de processamento quanto pelo esforço requerido do usuário. A segunda e terceira etapas têm sido consideradas o maior problema na mineração de regras de associação em bancos de dados transacionais e tem sido abordadas como dois problemas diferentes: “frequent pattern mining” e “association rule mining”. Dependências geográficas bem conhecidas aparecem nas três etapas do processo. Tendo como objetivo a eliminação dessas dependências na mineração de regras de associação espacial essa tese apresenta um framework com três novos métodos para mineração de regras de associação utilizando restrições semânticas como conhecimento a priori. O primeiro método reduz os dados de entrada do algoritmo, e dependências geográficas são eliminadas parcialmente sem que haja perda de informação. O segundo método elimina combinações de pares de objetos geográficos com dependências durante a geração dos conjuntos freqüentes. O terceiro método é uma nova abordagem para gerar conjuntos freqüentes não redundantes e sem dependências, gerando conjuntos freqüentes máximos. Esse método reduz consideravelmente o número final de conjuntos freqüentes, e como conseqüência, reduz o número de regras de associação espacial. / The association rule mining technique emerged with the objective to find novel, useful, and previously unknown associations from transactional databases, and a large amount of association rule mining algorithms have been proposed in the last decade. Their main drawback, which is a well known problem, is the generation of large amounts of frequent patterns and association rules. In geographic databases the problem of mining spatial association rules increases significantly. Besides the large amount of generated patterns and rules, many patterns are well known geographic domain associations, normally explicitly represented in geographic database schemas. The majority of existing algorithms do not warrant the elimination of all well known geographic dependences. The result is that the same associations represented in geographic database schemas are extracted by spatial association rule mining algorithms and presented to the user. The problem of mining spatial association rules from geographic databases requires at least three main steps: compute spatial relationships, generate frequent patterns, and extract association rules. The first step is the most effort demanding and time consuming task in the rule mining process, but has received little attention in the literature. The second and third steps have been considered the main problem in transactional association rule mining and have been addressed as two different problems: frequent pattern mining and association rule mining. Well known geographic dependences which generate well known patterns may appear in the three main steps of the spatial association rule mining process. Aiming to eliminate well known dependences and generate more interesting patterns, this thesis presents a framework with three main methods for mining frequent geographic patterns using knowledge constraints. Semantic knowledge is used to avoid the generation of patterns that are previously known as non-interesting. The first method reduces the input problem, and all well known dependences that can be eliminated without loosing information are removed in data preprocessing. The second method eliminates combinations of pairs of geographic objects with dependences, during the frequent set generation. A third method presents a new approach to generate non-redundant frequent sets, the maximal generalized frequent sets without dependences. This method reduces the number of frequent patterns very significantly, and by consequence, the number of association rules.