Urbane Gesundheit ist von globalem Interesse, da schon jetzt die Mehrheit der Menschen in Städten wohnt und dies zunehmend in Entwicklungsländern. Obwohl mentale Gesundheitsprobleme (z.B. Depressionen) in Entwicklungsländern stark verbreitet sind, wurden diese für die rasant anwachsenden Städte dieser Länder bisher nicht zufriedenstellend untersucht. Mit einem räumlich-epidemiologischen Ansatz werden in der vorliegenden Dissertation Faktoren identifiziert, welche das mentale Wohlbefinden der Slumbewohner Dhakas beeinflussen. Hierfür wurden Baseline Daten einer Kohortenstudie verwendet, welche Anfang 2009 in neun Slums in Dhaka durchgeführt wurde. Es wurden Daten von 1.938 Erwachsenen (≥15 Jahre) erhoben. Der WHO-5 Well-being Index wurde als Instrument zur Selbsteinschätzung des mentalen Wohlbefindens verwendet. Mentales Wohlbefinden war signifikant mit verschiedenen Faktoren der natürlichen Umwelt, der Sicherheit vor Überflutungen, sanitären Verhältnissen, sowie mit qualitativ hochwertiger, zufriedenstellender und beständiger Behausung assoziiert. Weitere mentale Gesundheitsassoziationen wurden in Bezug auf Bevölkerungsdichte, Zufriedenheit mit der Arbeitsstelle und mit der Einkommensgenerierung identifiziert, während für individuelle Faktoren wie Alter, Geschlecht, Krankheiten, Gesundheitswissen und Migrationshintergrund angepasst wurde. Räumliche Konzentrationen von gutem und schlechtem mentalem Wohlbefinden wurden festgestellt, welche auf massive Gesundheitsungleichheiten innerhalb der Slums hindeuten. Verschiedene Nachbarschaftskontexte wirken sich zudem in anderer Weise auf das Wohlbefinden aus als persönliche und Haushaltscharakteristika. In der Annahme, dass mentale Gesundheitsprobleme das Risiko physischer Krankheiten sowie die Unfallgefahr erhöhen, liefert diese Dissertation wichtige Informationen, um sowohl eine bessere Krankheitsversorgung als auch sinnvolle Krankheitspräventionsprogramme für die Slums von Dhaka und vergleichbarer Gebiete zu entwickeln. / Urban health is of global concern because the majority of the world’s population lives in urban areas, mainly in the global south. Although mental health problems (e.g., depression) in developing countries are highly prevalent, such issues are not yet adequately addressed in the rapidly urbanising megacities of these countries, where a growing number of residents live in slums. Little is known about the spectrum and burden of mental disease in urban slums. Using a spatial-epidemiological approach, this thesis identifies factors that contribute to mental well-being in the slums of Dhaka, the capital of Bangladesh, which currently accommodates an estimated population of more than 14 million, including 3.4 million slum dwellers. The baseline data from a cohort study conducted in early 2009 in nine slums of Dhaka were used. Data were collected from 1,938 adults (≥15 years). The WHO-5 Well-being Index was used as a measure of self-rated mental well-being. It was found that mental well-being was significantly associated with various factors such as selected features of the natural environment, flood non-affectedness, sanitation, and housing quality, sufficiency and durability. Further associations with population density, job satisfaction, and income generation were identified while adjusting for individual factors such as age, gender, diseases, health knowledge, and migration. Spatial clusters of poor and good mental well-being among different population groups were detected and point to severe health disparities both within and between the slums. Diverse neighbourhood conditions affected mental well-being differently from personal and household level characteristics. Given that mental health conditions could elevate the risk for physical diseases and contribute to injuries, this thesis may provide crucial information for developing better health care and disease prevention programmes in Dhaka’s slums and other comparable settings.

Made available in DSpace on 2017-07-10T19:25:04Z (GMT). No. of bitstreams: 1 Grazieli_Suszek.pdf: 8578209 bytes, checksum: 412947ba9d6b66d701a0dfec5060f428 (MD5) Previous issue date: 2012-02-08 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The plant nutritional quality influences directly in juice quality. Therefore it is important the knowledge of its spatial and temporary variation in the orchard for a better application and use of the inputs, larger yield and quality of fruit. This way, input maps can be generated for the best use, providing larger yield and better quality of fruits, reduction of the production cost and environmental impact. The objective of this work was to study the spatial variability of soil and plant chemical properties, fruit quality, and orchard yield in Monte Parnaso citrus orchard, by using nutritional sufficiency range and diagnose recommendation integrated system (DRIS). For that, the experiment was driven in an orange-tree orchard (1 ha) of Monte Parnaso variety, located in Nova Laranjeiras/PR. Twenty trees were georeferenced, to collect samples for accomplishment of soil analysis, leaf analysis and quantification of the parameters of fruit quality, in 2009 and 2010. After parameters determination, the results of leaf analysis were evaluated and compared by using sufficiency range and DRIS. For the spatial variability Investigation, it was used the spatial dependence index (IDE) and global correlation of Moran index. The maps were constructed by using geostatistical techniques and compared by using coefficient of relative deviation (CDR). Thus, the sample collection made in branches without fruit (July) presented the best coefficient of determination and the best fit around the trend line for the method DRIS, compared to the samples collected in branches with fruit (January). The ratio (SST/ATT) has better relationship with IBN (nutrient balance index), compared to the productivity and thus presents a better response in the choice of standard population to calculate the DRIS. For the definition of management zones, it is possible to say that the maps were reclassified by DRIS showed best performance since, and greater sensitivity in the diagnosis when compared to bands of sufficiency, which is desirable when seeking to refine the nutritional management. / A qualidade nutricional da planta influencia diretamente na qualidade do suco. Por isso é importante o conhecimento de sua variação espacial e temporal no pomar, para uma melhor aplicação e aproveitamento dos insumos, maior produtividade e qualidade do fruto. Desta forma, mapas podem ser gerados para o melhor aproveitamento de insumos, proporcionando maior produtividade e melhor qualidade dos frutos e redução do custo de produção e do impacto ambiental. O objetivo deste trabalho foi estudar a variabilidade espacial das propriedades químicas do solo e da folha, qualidade do fruto e produtividade em pomar de laranja Monte Parnaso, utilizando faixas de suficiência nutricional e o sistema integrado de diagnose e recomendação (DRIS). Para isso, o experimento foi conduzido em um pomar de laranjeiras (1 ha) da variedade Monte Parnaso, localizado em Nova Laranjeiras/PR. Vinte árvores foram georreferenciadas, visando coletar amostras para a realização da análise de solo, análise foliar e quantificação dos parâmetros de qualidade dos frutos, nos anos de 2009 e 2010. Após a determinação dos parâmetros, os resultados da análise foliar foram avaliados e comparados, utilizando os critérios de faixa de suficiência e o DRIS. Para investigação da variabilidade espacial, utilizou-se o índice de dependência espacial (IDE) e o índice de correlação global de Moran. Os mapas foram construídos utilizando técnicas de geoestatística e comparados através do coeficiente de desvio relativo (CDR). Assim, a coleta de amostras realizadas em ramos sem fruto (julho) apresentou melhor coeficiente de determinação e melhor ajuste em torno da linha de tendência para o método DRIS, quando comparada com a amostragem realizada em ramos com fruto (janeiro). A relação SST/ATT (sólidos solúveis totais/acidez total titulável) apresentou melhor relação com o IBN (índice de balanço nutricional), quando comparado à produtividade e, desta forma, apresenta melhor resposta na escolha da população padrão para o cálculo do DRIS. Para definição das zonas de manejo é possível afirmar que os mapas reclassificados pelo DRIS apresentaram melhor desempenho, visto que mostraram maior sensibilidade no diagnóstico, quando comparados às faixas de suficiência, o que é desejável quando se busca refinar o manejo nutricional.

Made available in DSpace on 2017-05-12T14:48:26Z (GMT). No. of bitstreams: 1 Grazieli_Suszek.pdf: 8578209 bytes, checksum: 412947ba9d6b66d701a0dfec5060f428 (MD5) Previous issue date: 2012-02-08 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The plant nutritional quality influences directly in juice quality. Therefore it is important the knowledge of its spatial and temporary variation in the orchard for a better application and use of the inputs, larger yield and quality of fruit. This way, input maps can be generated for the best use, providing larger yield and better quality of fruits, reduction of the production cost and environmental impact. The objective of this work was to study the spatial variability of soil and plant chemical properties, fruit quality, and orchard yield in Monte Parnaso citrus orchard, by using nutritional sufficiency range and diagnose recommendation integrated system (DRIS). For that, the experiment was driven in an orange-tree orchard (1 ha) of Monte Parnaso variety, located in Nova Laranjeiras/PR. Twenty trees were georeferenced, to collect samples for accomplishment of soil analysis, leaf analysis and quantification of the parameters of fruit quality, in 2009 and 2010. After parameters determination, the results of leaf analysis were evaluated and compared by using sufficiency range and DRIS. For the spatial variability Investigation, it was used the spatial dependence index (IDE) and global correlation of Moran index. The maps were constructed by using geostatistical techniques and compared by using coefficient of relative deviation (CDR). Thus, the sample collection made in branches without fruit (July) presented the best coefficient of determination and the best fit around the trend line for the method DRIS, compared to the samples collected in branches with fruit (January). The ratio (SST/ATT) has better relationship with IBN (nutrient balance index), compared to the productivity and thus presents a better response in the choice of standard population to calculate the DRIS. For the definition of management zones, it is possible to say that the maps were reclassified by DRIS showed best performance since, and greater sensitivity in the diagnosis when compared to bands of sufficiency, which is desirable when seeking to refine the nutritional management. / A qualidade nutricional da planta influencia diretamente na qualidade do suco. Por isso é importante o conhecimento de sua variação espacial e temporal no pomar, para uma melhor aplicação e aproveitamento dos insumos, maior produtividade e qualidade do fruto. Desta forma, mapas podem ser gerados para o melhor aproveitamento de insumos, proporcionando maior produtividade e melhor qualidade dos frutos e redução do custo de produção e do impacto ambiental. O objetivo deste trabalho foi estudar a variabilidade espacial das propriedades químicas do solo e da folha, qualidade do fruto e produtividade em pomar de laranja Monte Parnaso, utilizando faixas de suficiência nutricional e o sistema integrado de diagnose e recomendação (DRIS). Para isso, o experimento foi conduzido em um pomar de laranjeiras (1 ha) da variedade Monte Parnaso, localizado em Nova Laranjeiras/PR. Vinte árvores foram georreferenciadas, visando coletar amostras para a realização da análise de solo, análise foliar e quantificação dos parâmetros de qualidade dos frutos, nos anos de 2009 e 2010. Após a determinação dos parâmetros, os resultados da análise foliar foram avaliados e comparados, utilizando os critérios de faixa de suficiência e o DRIS. Para investigação da variabilidade espacial, utilizou-se o índice de dependência espacial (IDE) e o índice de correlação global de Moran. Os mapas foram construídos utilizando técnicas de geoestatística e comparados através do coeficiente de desvio relativo (CDR). Assim, a coleta de amostras realizadas em ramos sem fruto (julho) apresentou melhor coeficiente de determinação e melhor ajuste em torno da linha de tendência para o método DRIS, quando comparada com a amostragem realizada em ramos com fruto (janeiro). A relação SST/ATT (sólidos solúveis totais/acidez total titulável) apresentou melhor relação com o IBN (índice de balanço nutricional), quando comparado à produtividade e, desta forma, apresenta melhor resposta na escolha da população padrão para o cálculo do DRIS. Para definição das zonas de manejo é possível afirmar que os mapas reclassificados pelo DRIS apresentaram melhor desempenho, visto que mostraram maior sensibilidade no diagnóstico, quando comparados às faixas de suficiência, o que é desejável quando se busca refinar o manejo nutricional.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Abstract: Road traffic accidents claim more than 1,500 lives each year in Canada and affect society adversely, so transport authorities must reduce their impact. This is a major concern in Quebec, where the traffic-accident risks increase year by year proportionally to provincial population growth. In reality, the occurrence of traffic crashes is rarely random in space-time; they tend to cluster in specific areas such as intersections, ramps, and work zones. Moreover, weather stands out as an environmental risk factor that affects the crash rate. Therefore, traffic-safety engineers need to accurately identify the location and time of traffic accidents. The occurrence of such accidents actually is determined by some important factors, including traffic volume, weather conditions, and geometric design. This study aimed at identifying hotspot locations based on a historical crash data set and spatiotemporal patterns of traffic accidents with a view to improving road safety. This thesis proposes two new methods for identifying hotspot locations on a road network. The first method could be used to identify and rank hotspot locations in cases in which the value of traffic volume is available, while the second method is useful in cases in which the value of traffic volume is not. These methods were examined with three years of traffic-accident data (2011–2013) in Sherbrooke. The first method proposes a two-step integrated approach for identifying traffic-accident hotspots on a road network. The first step included a spatial-analysis method called network kernel-density estimation. The second step involved a network-screening method using the critical crash rate, which is described in the Highway Safety Manual. Once the traffic-accident density had been estimated using the network kernel-density estimation method, the selected potential hotspot locations were then tested with the critical-crash-rate method. The second method offers an integrated approach to analyzing spatial and temporal (spatiotemporal) patterns of traffic accidents and organizes them according to their level of significance. The spatiotemporal seasonal patterns of traffic accidents were analyzed using the kernel-density estimation; it was then applied as the attribute for a significance test using the local Moran’s I index value. The results of the first method demonstrated that over 90% of hotspot locations in Sherbrooke were located at intersections and in a downtown area with significant conflicts between road users. It also showed that signalized intersections were more dangerous than unsignalized ones; over half (58%) of the hotspot locations were located at four-leg signalized intersections. The results of the second method show that crash patterns varied according to season and during certain time periods. Total seasonal patterns revealed denser trends and patterns during the summer, fall, and winter, then a steady trend and pattern during the spring. Our findings also illustrated that crash patterns that applied accident severity were denser than the results that only involved the observed crash counts. The results clearly show that the proposed methods could assist transport authorities in quickly identifying the most hazardous sites in a road network, prioritizing hotspot locations in a decreasing order more efficiently, and assessing the relationship between traffic accidents and seasons. / Les accidents de la route sont responsables de plus de 1500 décès par année au Canada et ont des effets néfastes sur la société. Aux yeux des autorités en transport, il devient impératif d’en réduire les impacts. Il s’agit d’une préoccupation majeure au Québec depuis que les risques d’accidents augmentent chaque année au rythme de la population. En réalité, les accidents routiers se produisent rarement de façon aléatoire dans l’espace-temps. Ils surviennent généralement à des endroits spécifiques notamment aux intersections, dans les bretelles d’accès, sur les chantiers routiers, etc. De plus, les conditions climatiques associées aux saisons constituent l’un des facteurs environnementaux à risque affectant les taux d’accidents. Par conséquent, il devient impératif pour les ingénieurs en sécurité routière de localiser ces accidents de façon plus précise dans le temps (moment) et dans l’espace (endroit). Cependant, les accidents routiers sont influencés par d’importants facteurs comme le volume de circulation, les conditions climatiques, la géométrie de la route, etc. Le but de cette étude consiste donc à identifier les points chauds au moyen d’un historique des données d’accidents et de leurs répartitions spatiotemporelles en vue d’améliorer la sécurité routière. Cette thèse propose deux nouvelles méthodes permettant d’identifier les points chauds à l’intérieur d’un réseau routier. La première méthode peut être utilisée afin d’identifier et de prioriser les points chauds dans les cas où les données sur le volume de circulation sont disponibles alors que la deuxième méthode est utile dans les cas où ces informations sont absentes. Ces méthodes ont été conçues en utilisant des données d’accidents sur trois ans (2011-2013) survenus à Sherbrooke. La première méthode propose une approche intégrée en deux étapes afin d’identifier les points chauds au sein du réseau routier. La première étape s’appuie sur une méthode d’analyse spatiale connue sous le nom d’estimation par noyau. La deuxième étape repose sur une méthode de balayage du réseau routier en utilisant les taux critiques d’accidents, une démarche éprouvée et décrite dans le manuel de sécurité routière. Lorsque la densité des accidents routiers a été calculée au moyen de l’estimation par noyau, les points chauds potentiels sont ensuite testés à l’aide des taux critiques. La seconde méthode propose une approche intégrée destinée à analyser les distributions spatiales et temporelles des accidents et à les classer selon leur niveau de signification. La répartition des accidents selon les saisons a été analysée à l’aide de l’estimation par noyau, puis ces valeurs ont été assignées comme attributs dans le test de signification de Moran. Les résultats de la première méthode démontrent que plus de 90 % des points chauds à Sherbrooke sont concentrés aux intersections et au centre-ville où les conflits entre les usagers de la route sont élevés. Ils révèlent aussi que les intersections contrôlées sont plus à risque par comparaison aux intersections non contrôlées et que plus de la moitié des points chauds (58 %) sont situés aux intersections à quatre branches (en croix). Les résultats de la deuxième méthode montrent que les distributions d’accidents varient selon les saisons et à certains moments de l’année. Les répartitions saisonnières montrent des tendances à la densification durant l’été, l’automne et l’hiver alors que les distributions sont plus dispersées au cours du printemps. Nos observations indiquent aussi que les répartitions ayant considéré la sévérité des accidents sont plus denses que les résultats ayant recours au simple cumul des accidents. Les résultats démontrent clairement que les méthodes proposées peuvent: premièrement, aider les autorités en transport en identifiant rapidement les sites les plus à risque à l’intérieur du réseau routier; deuxièmement, prioriser les points chauds en ordre décroissant plus efficacement et de manière significative; troisièmement, estimer l’interrelation entre les accidents routiers et les saisons.