The unavailability of crash-related data has been a long lasting challenge in Brazil. In addition to the poor implementation and follow-up of road safety strategies, this drawback has hampered the development of studies that could contribute to national goals toward road safety. In contrast, developed countries have built their effective strategies on solid data basis, therefore, investing a considerable time and money in obtaining and creating pertinent information. In this research, we aim to assess the potential impacts of supplementary data on spatial model performance and the suitability of different spatial modeling approaches on crash prediction. The intention is to notify the authorities in Brazil and other developing countries, about the importance of having appropriate data. In this thesis, we set two specific objectives: (I) to investigate the spatial model prediction accuracy at unsampled subzones; (II) to evaluate the performance of spatial data analysis approaches on crash prediction. Firstly, we carry out a benchmarking based on Geographically Weighted Regression (GWR) models developed for Flanders, Belgium, and São Paulo, Brazil. Models are developed for two modes of transport: active (i.e. pedestrians and cyclists) and motorized transport (i.e. motorized vehicles occupants). Subsequently, we apply the repeated holdout method on the Flemish models, introducing two GWR validation approaches, named GWR holdout1 and GWR holdout2. While the former is based on the local coefficient estimates derived from the neighboring subzones and measures of the explanatory variables for the validation subzones, the latter uses the casualty estimates of the neighboring subzones directly to estimate outcomes for the missing subzones. Lastly, we compare the performance of GWR models with Mean Imputation (MEI), K-Nearest Neighbor (KNN) and Kriging with External Drift (KED). Findings showed that by adding the supplementary data, reductions of 20% and 25% for motorized transport, and 25% and 35% for active transport resulted in corrected Akaike Information Criterion (AICc) and Mean Squared Prediction Errors (MSPE), respectively. From a practical perspective, the results could help us identify hotspots and prioritize data collection strategies besides identify, implement and enforce appropriate countermeasures. Concerning the spatial approaches, GWR holdout2 out performed all other techniques and proved that GWR is an appropriate spatial technique for both prediction and impact analyses. Especially in countries where data availability has been an issue, this validation framework allows casualties or crash frequencies to be estimated while effectively capturing the spatial variation of the data. / A indisponibilidade de variáveis explicativas de acidentes de trânsito tem sido um desafio duradouro no Brasil. Além da má implementação e acompanhamento de estratégias de segurança viária, esse inconveniente tem dificultado o desenvolvimento de estudos que poderiam contribuir com as metas nacionais de segurança no trânsito. Em contraste, países desenvolvidos tem construído suas estratégias efetivas com base em dados sólidos, e portanto, investindo tempo e dinheiro consideráveis na obtenção e criação de informações pertinentes. O objetivo dessa pesquisa é avaliar os possíveis impactos de dados suplementares sobre o desempenho de modelos espaciais, e a adequação de diferentes abordagens de modelagem espacial na previsão de acidentes. A intenção é notificar as autoridades brasileiras e de outros países em desenvolvimento sobre a importância de dados adequados. Nesta tese, foram definidos dois objetivos específicos: (I) investigar a acurácia do modelo espacial em subzonas sem amostragem; (II) avaliar o desempenho de técnicas de análise espacial de dados na previsão de acidentes. Primeiramente, foi realizado um estudo comparativo, baseado em modelos desenvolvidos para Flandres (Bélgica) e São Paulo (Brasil), através do método de Regressão Geograficamente Ponderada (RGP). Os modelos foram desenvolvidos para dois modos de transporte: ativos (pedestres e ciclistas) e motorizados (ocupantes de veículos motorizados). Subsequentemente, foi aplicado o método de holdout repetido nos modelos Flamengos, introduzindo duas abordagens de validação para GWR, denominados RGP holdout1 e RGP holdout2. Enquanto o primeiro é baseado nas estimativas de coeficientes locais derivados das subzonas vizinhas e medidas das variáveis explicativas para as subzonas de validação, o último usa as estimativas de acidentes das subzonas vizinhas, diretamente, para estimar os resultados para as subzonas ausentes. Por fim, foi comparado o desempenho de modelos RGP e outras abordagens, tais como Imputação pela Média de dados faltantes (IM), K-vizinhos mais próximos (KNN) e Krigagem com Deriva Externa (KDE). Os resultados mostraram que, adicionando os dados suplementares, reduções de 20% e 25% para o transporte motorizado, e 25% e 35% para o transporte ativo, foram resultantes em termos de Critério de Informação de Akaike corrigido (AICc) e Erro Quadrático Médio da Predição (EQMP), respectivamente. Do ponto de vista prático, os resultados poderiam ajudar a identificar hotspots e priorizar estratégias de coleta de dados, além de identificar, implementar e aplicar contramedidas adequadas. No que diz respeito às abordagens espaciais, RGP holdout2 teve melhor desempenho em relação a todas as outras técnicas e, provou que a RGP é uma técnica espacial apropriada para ambas as análises de previsão e impactos. Especialmente em países onde a disponibilidade de dados tem sido um problema, essa estrutura de validação permite que as acidentes sejam estimados enquanto, capturando efetivamente a variação espacial dos dados.

Airborne discrete-return lidar is an active remote sensing technology capable of obtaining accurate, fine-resolution three-dimensional measurements over large areas. Discrete-return lidar data produce three-dimensional object characterizations in the form of point clouds defined by precise x, y and z coordinates. The data also provide intensity values for each point that help quantify the reflectance and surface properties of intersected objects. These data features have proven to be useful for the characterization of many important forest attributes, such as standing tree biomass, height, density, and canopy cover, with new applications for the data currently accelerating. This dissertation explores three new applications for airborne discrete-return lidar data. The first application uses lidar-derived metrics to predict understory vegetation cover, which has been a difficult metric to predict using traditional explanatory variables. A new airborne lidar-derived metric, understory lidar cover density, created by filtering understory lidar points using intensity values, increased the coefficient of variation (R²) from non-lidar understory vegetation cover estimation models from 0.2-0.45 to 0.7-0.8. The method presented in this chapter provides the ability to accurately quantify understory vegetation cover (± 22%) at fine spatial resolutions over entire landscapes within the interior ponderosa pine forest type. In the second application, a new method for quantifying and locating snags using airborne discrete-return lidar is presented. The importance of snags in forest ecosystems and the inherent difficulties associated with their quantification has been well documented. A new semi-automated method using both 2D and 3D local-area lidar point filters focused on individual point spatial location and intensity information is used to identify points associated with snags and eliminate points associated with live trees. The end result is a stem map of individual snags across the landscape with height estimates for each snag. The overall detection rate for snags DBH ≥ 38 cm was 70.6% (standard error: ± 2.7%), with low commission error rates. This information can be used to: analyze the spatial distribution of snags over entire landscapes, provide a better understanding of wildlife snag use dynamics, create accurate snag density estimates, and assess achievement and usefulness of snag stocking standard requirements. In the third application, live above-ground biomass prediction models are created using three separate sets of lidar-derived metrics. Models are then compared using both model selection statistics and cross-validation. The three sets of lidar-derived metrics used in the study were: 1) a 'traditional' set created using the entire plot point cloud, 2) a 'live-tree' set created using a plot point cloud where points associated with dead trees were removed, and 3) a 'vegetation-intensity' set created using a plot point cloud containing points meeting predetermined intensity value criteria. The models using live-tree lidar-derived metrics produced the best results, reducing prediction variability by 4.3% over the traditional set in plots containing filtered dead tree points. The methods developed and presented for all three applications displayed promise in prediction or identification of unique forest attributes, improving our ability to quantify and characterize understory vegetation cover, snags, and live above ground biomass. This information can be used to provide useful information for forest management decisions and improve our understanding of forest ecosystem dynamics. Intensity information was useful for filtering point clouds and identifying lidar points associated with unique forest attributes (e.g., understory components, live and dead trees). These intensity filtering methods provide an enhanced framework for analyzing airborne lidar data in forest ecosystem applications. / Graduation date: 2013

Orientador: Antonio Padilha Feltrin / Resumo: Neste trabalho é apresentado um método que permite determinar o crescimento espaço-temporal da demanda de energia elétrica devido às mudanças nos hábitos de consumo no setor residencial. A proposta é baseada em uma regressão ponderada geograficamente que permite determinar a localização espacial dos setores com maior proporção de residências candidatas para comprar um novo eletrodoméstico, e uma regressão de distribuição logística que permite simular em cada setor, como vai ser o crescimento ao longo do tempo dessa proporção de residências candidatas para comprar o aparelho. Finalmente, o método determina o impacto nas curvas de carga dos transformadores de distribuição, considerando: o número de residências candidatas em cada setor, e informações do eletrodoméstico em estudo, tais como: curva de carga em p.u, potência nominal, fator de utilização, fator de coincidência e fator de potência. A região em estudo é dividida em pequenas subáreas, com o objetivo de melhorar a resolução espacial do prognóstico, e também considerar interrelações de proximidade entre as subáreas, para determinar como as decisões tomadas em um local influenciam nas preferências de seus vizinhos. O método proposto usa como dados de entrada variáveis socioeconômicas do censo da população que são de fácil acesso para as empresas do setor elétrico e que caracterizam a economia e as preferências da população da cidade em estudo. O método proposto foi aplicado em uma cidade de médio porte da República do Equ... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

Submitted by MARIO ANDRES MEJIA ALZATE (marioandretty_17@hotmail.com) on 2017-01-15T06:04:36Z No. of bitstreams: 1 Dissertação Mestrado_final_1 (2).pdf: 10151663 bytes, checksum: 19b32f17aadb3d9188da99327b13cc74 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-01-19T17:23:34Z (GMT) No. of bitstreams: 1 mejiaalzate_ma_me_ilha.pdf: 10151663 bytes, checksum: 19b32f17aadb3d9188da99327b13cc74 (MD5) / Made available in DSpace on 2017-01-19T17:23:34Z (GMT). No. of bitstreams: 1 mejiaalzate_ma_me_ilha.pdf: 10151663 bytes, checksum: 19b32f17aadb3d9188da99327b13cc74 (MD5) Previous issue date: 2016-12-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho é apresentado um método que permite determinar o crescimento espaço-temporal da demanda de energia elétrica devido às mudanças nos hábitos de consumo no setor residencial. A proposta é baseada em uma regressão ponderada geograficamente que permite determinar a localização espacial dos setores com maior proporção de residências candidatas para comprar um novo eletrodoméstico, e uma regressão de distribuição logística que permite simular em cada setor, como vai ser o crescimento ao longo do tempo dessa proporção de residências candidatas para comprar o aparelho. Finalmente, o método determina o impacto nas curvas de carga dos transformadores de distribuição, considerando: o número de residências candidatas em cada setor, e informações do eletrodoméstico em estudo, tais como: curva de carga em p.u, potência nominal, fator de utilização, fator de coincidência e fator de potência. A região em estudo é dividida em pequenas subáreas, com o objetivo de melhorar a resolução espacial do prognóstico, e também considerar interrelações de proximidade entre as subáreas, para determinar como as decisões tomadas em um local influenciam nas preferências de seus vizinhos. O método proposto usa como dados de entrada variáveis socioeconômicas do censo da população que são de fácil acesso para as empresas do setor elétrico e que caracterizam a economia e as preferências da população da cidade em estudo. O método proposto foi aplicado em uma cidade de médio porte da República do Equador a fim de determinar o crescimento espaço-temporal da demanda de energia devido à compra de fogões de indução. Os resultados obtidos são mapas que permitem identificar os setores mais vulneráveis para apresentar crescimento da demanda devido à compra do eletrodoméstico. Também são apresentados gráficos que mostram o impacto nas curvas de carga dos transformadores durante o período de estudo estabelecido. Esses resultados fornecem informações importantes que servem de referência no planejamento do sistema de distribuição e do mercado de energia elétrica. / This work presents a method to determine the spatial-temporal growth of electric energy demand due to changes in consumption habits in the residential sector. The proposal is based on a geographically weighted regression that allows us to determine the spatial location of the sectors with the highest proportion of candidate households to buy a new appliance, and a logistic distribution regression that allows us to simulate in each of these sectors, the growth over time, the proportion of households that are candidates to buy this appliance. Finally, the method determines the impact on the load curves of the distribution transformers, considering: the number of candidate households in each sector, and information of the home appliance, such as: load curve in pu, nominal power, utilization factor, Coincidence factor and power factor. The study area is divided into small subareas with the aim of improving the spatial resolution of the prognosis and also considers the interrelation of proximity between the subareas to determine how decisions made in one place can influence the preferences of its neighbors. The input data of the proposed method are socioeconomic variables of the population census, which are easily accessible to companies in the electricity sector, and which characterize the economy and the preferences of the population of the studied city. The method was applied in a medium-sized city of the Republic of Ecuador in order to determine the spatial-temporal growth of energy demand due to the purchase of induction stoves. The results obtained are maps that allow identifying the most vulnerable sectors to show increased demand due to the purchase of the appliance. Also, graphs were obtained that show the impact on the load curves of the transformers during the established study period. These results provide important information that serve as a reference in planning the distribution system and the electricity market.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2018-06-18T13:56:01Z No. of bitstreams: 1 marciodeoliveira.pdf: 54067664 bytes, checksum: a5c84df2cc84ff15fc4881a06fd4f6ae (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-09-03T16:07:04Z (GMT) No. of bitstreams: 1 marciodeoliveira.pdf: 54067664 bytes, checksum: a5c84df2cc84ff15fc4881a06fd4f6ae (MD5) / Made available in DSpace on 2018-09-03T16:07:04Z (GMT). No. of bitstreams: 1 marciodeoliveira.pdf: 54067664 bytes, checksum: a5c84df2cc84ff15fc4881a06fd4f6ae (MD5) Previous issue date: 2018-02-28 / A represa de Chapéu D’Uvas é um importante manancial de abastecimento público para o município de Juiz de Fora, MG, sendo a expectativa de garantia de água para as próximas décadas em quantidade e qualidade. Ao longo dos anos a bacia de contribuição da represa vem passando por um processo de ocupação antrópica, sendo preocupante a possibilidade de impactos sobre o manancial. A pesquisa aqui apresentada teve por objetivo identificar as relações espaciais entre o uso e cobertura da terra na bacia de contribuição da represa de Chapéu D’Uvas e a qualidade da água do reservatório. A metodologia passou por um estudo com sensoriamento remoto e geoprocessamento para conhecimento das condições de uso e cobertura da terra, e da manutenção das áreas de preservação permanente. Foram analisados dados históricos de qualidade da água no ponto de captação fornecidos pela Companhia de Saneamento Municipal de Juiz de Fora, Cesama, relativos aos anos de 2005 a 2017. Também foram realizadas campanhas para amostragem na represa e nos principais tributários, nos anos de 2016 e 2017. Os dados de uso e cobertura da terra e de qualidade da água foram analisados a partir dos princípios da limnologia da paisagem e com a aplicação da regressão geograficamente ponderada. Os resultados mostraram como as variáveis densidade de cianobactérias, E. coli, condutividade elétrica, oxigênio consumido, ortofosfato, nitrato e demanda química de oxigênio foram influenciadas, principalmente, pelo percentual de áreas de mata, pastagens, silvicultura e área urbanizada. A declividade média das subbacias também influenciou nos resultados. A ponderação geográfica diferenciou as influências das sub bacias conforme sua posição mais próxima ou mais afastada da barragem. Desse modo, por meio da análise das correlações entre as variáveis, foi possível identificar os efeitos da ocupação da bacia de contribuição sobre a qualidade da água, e, assim, relacionar as principais questões que merecem atenção dos gestores do manancial. / The Chapeu D'Uvas dam is an important source of public water supply for the municipality of Juiz de Fora, MG, with the expectation of guaranteeing water for the coming decades in quantity and quality. Over the years the watershed of the dam has undergone a process of anthropic occupation, being worrying the possibility of impacts on the source. The research presented here aimed to identify the spatial relationships between land use and land cover in the watershed of the Chapeu D'Uvas dam and the water quality of the reservoir. The methodology underwent a study with remote sensing and geoprocessing to know the conditions of use and land cover, and the maintenance of permanent preservation areas. Historical data on water quality at the catchment point provided by the Companhia de Saneamento Municipal de Juiz de Fora, Cesama, were analyzed for the years 2005 to 2017. Campaigns were also conducted for sampling at the reservoir and at the main tributaries, in the years 2016 and 2017. Data on land use and land cover and water quality were analyzed from the principles of landscape limnology and the application of geographically weighted regression. The results showed that the variables density of cyanobacteria, E. coil, electrical conductivity, oxygen consumed, orthophosphate, nitrate and chemical oxygen demand were influenced mainly by the percentage of forest, silviculture, pasture, and urbanized areas. The longitudinal profile of the sub basins also influenced the results. The geographic weighting differentiated the influences of the sub basins according to their position closest to or farther from the dam. Thus, through the analysis of the correlations between the variables, it was possible to identify the effects of the occupation of the basin of contribution on water quality, and, thus, to relate the main issues that deserve attention of the managers of the source.

Since the introduction of economic reforms in the late 1970s, China has experienced phenomenal economic growth along with rapid urbanization. The accelerated urbanization coincides with remarkable social-economic transformations and urban landscape changes. A city, as an urban system, is composed of social and physical subsystems that interact with each other. Equally assessing each component is necessary for a comprehensive understanding of the urbanization process. The goal of this thesis research is to deconstruct the urbanization process through a social-ecological perspective. More specifically, this study examines social transformations, physical evolutions, and their relationships. Four research questions are proposed as (1) How does urban social landscape transform in time and space? (2) What trends are apparent in the urban land growth process and spatial heterogeneity? (3) How does social transformation relate to urban land growth, within a spatio-temporal perspective? and (4) How do social-demographic features relate to residents’ use and perception of urban green open spaces, focusing on the ecological services provided by and the need to improve those spaces? Given the lack of research on second-tier cities’ growth processes, this study selects Wuhan, a megacity in central China, as a case study, with a focus on its urban development zone (UDZ). A social-ecological approach is applied to study the multi-dimensional features of an urban system. The thesis is in paper format, containing five chapters. Besides the Introduction (Chapter 1) and Conclusion (Chapter 5), the main body consists of three articles. These three articles correspond to the four research questions proposed. Chapter 2 responds to the first research question by addressing how the urban social landscape transforms. Chapter 3 seeks to answer the second and third questions by evaluating urban land growth and its links with social factors from a spatio-temporal perspective. Chapter 4 matches the fourth question by seeking to understand residents’ preferences and feelings toward the urban green open space. Chapter 1 introduces the research context, reviews the urban ecology theory and relevant empirical research, as well as assesses the social-ecological approach related to studying the urban system. In this chapter, we also propose an improved social-ecological system (ISES) framework which guides the equally weighted study of both social and physical subsystems in an urban area. Chapter 2 (the first paper) seeks to investigate progressive transformations in the social dimensions of Wuhan UDZ while also focusing on their spatial transformations, using national census data in 1990, 2000, and 2010. We used varimax rotated principal component analysis (PCA) for the extraction of social dimensions and ArcMap for spatial visualization. This allows us to further analyze the spatial distribution of social clusters. The results suggest that industrial relocation, educational attainment increase, population aging, and migration are the main characteristics of social transformation during 1990 and 2010. Industrial relocation along with the spatial separation appeared as principal social dimensions in the 1990s but became more prominent in the 2000s, accompanied by the improvement of workers’ education levels. Aging population presented spatial movement outward from the city center. Population mobility increased significantly, and immigration became an important social dimension and presented spatial expansion in the 2000s. The socio-spatial patterns transform with a combination of concentric rings and sectoral clusters in different stages. These transformations are formed by the regional push-pull forces and the centripetal-centrifugal forces inside the city. We conclude that the social landscape transforms in a way with diversity and inclusion. Government dominates socio-spatial transformations in the initial stages, while market plays an increasing role in the later stages. To build a more inclusive society requires continuous and systematic improvement of relevant policies. Chapter 3 (the second paper) discusses urban land growth patterns and answers how social factors are associated with the evolution patterns between 1990 and 2010. We extract land cover information based on Landsat images with the vegetation area – impervious surface –water area (V-I-W) model and examine the urban growth patterns during various stages using landscape metrics of the area, aggregation, and shape. Then, we apply geographically weighted regression (GWR) to depict the link between urban land metrics and social factors. The results show that urban land coalescence and diffusion simultaneously exist; the city center is dominated by redevelopment, infilling, edge expansion; and the peripheral areas by outlying expansion. GWR coefficient surfaces show little differences in the models between social factors and urban land area metrics PLAND while remarkable differences are present in the coefficients of GWR models for the urban land patch shape irregularities and social factors. Urban land growth patterns relate to the government-led land supply system, the functional zoning of urban space planning, and the agglomeration and dispersion of social space under the market orientation. The authors conclude that urban management should consider the coexistence of different spatial growth modes and introduce factors such as social preferences in the urban land layout. This may apply to rapidly urbanizing areas. Chapter 4 (the third paper) aims to understand social-natural relationships, with a focus on how socio-demographic features can shape residents’ preference toward green open spaces and their perceptions of ecological services and improvements. Data is collected through online questionnaire surveys and interviews. The results indicate that preferences toward green open spaces vary among different social groups. Demands for improvement to green open spaces are rooted in residents’ appreciation for daily relaxation and health benefits, and link with their preference for visiting. However, how residents perceive green open spaces’ benefits does not rely only on an in-person visit. Interaction experience with nature and knowledge of natural development affect perception of daily use and health-related services. Residents’ perceptions of green open space’s ecological functions are associated with the changes in nature reported by those respondents. Responses to improving green open space reflect the residents’ pursuit of the aesthetics and practicality of such spaces. Though respondents are commonly aware of the ecological importance of green open space, there are differences in their willingness to voluntarily participate in its management. We conclude that to encourage the public to participate in configuration and improvement of green open spaces through a variety of ways, including considering residents’ opinions, is an efficient way in order to better social-ecological relationships. Chapter 5 reviews the main findings and conclusions, research limitations as well as future possibilities. This study establishes a dialogue between urban social and physical subsystems, with an integrated quantitative study of the urbanization process, emphasizing the relationships between two urban subsystems. It provides a comprehensive social-ecological view on a second-tier city based on the social and physical transformations that occurred in Wuhan during a transitional period of a socialist market economy. We conclude that the development of China's second-tier cities between 1990 and 2010 is characterized by the transformations of social dimensions and landscape, the coexistence of multiple urban spatial development modes, and the spatial differentiation between the center and the periphery of the city. The GWR models present spatial non-stationary relationships between social factors and the urban patch shape regularities. The further examination of social-natural relationships finds that residents’ social-demographic features and environmental experience affect their perceptions toward green open space, especially ecological services and improvement necessity. The evolution of urban social and physical systems and their relationships has brought increased attention to inclusive urban social management, public participatory planning, and people-centered social and ecological interactions. This research provides a constructive rethinking of second-tier cities’ growth in China and may serve as a reference for other rapidly urbanizing areas.

Impact of demographic changes on municipal technical infrastructure in the Czech Republic - case study of waste management Abstract The aim of this study is to quantify the impact of demographic development on the current household production of municipal waste in the Czech Republic and at the same time to compare their influence with the impact of other factors. Based on a literature review and statistical data available for the Czech Republic, a set of 22 explanatory variables concerning structure of population by age, gender, highest education and employment, as well as the size of households, unemployment, purchasing power, population density and basic characteristics of housing was identified in order to explain the production of municipal solid waste, mixed municipal waste and separately collected waste components (glass and plastics). It has turned out that demographic variables, especially the average household size, gender, age, level of education or sector of employment have a statistically significant but rather weak impact on the production of these waste streams. Given that the detailed analysis of the variables indicated that there is a significant spatial variability of these characteristics and that they exhibit spatial non- stationarity, the next step was to analyze the relationship of the...

Contexte: La réduction de l'utilisation de l’automobile a déjà été identifiée comme étant une intervention populationnelle pouvant promouvoir l’activité physique. Les recherches portant sur la relation entre l’utilisation de l’automobile et l’activité physique utilisent typiquement une variable catégorielle pour décrire le mode de transport, tiennent peu compte du motif de déplacement et n’explorent pas la variation de l’association selon le lieu de résidence. Cette étude utilise à la fois l'activité physique auto-rapportée et objectivement mesurée pour tester l'association entre l’utilisation de l’automobile et l’activité physique totale, tout en tenant compte de ces limitations antérieures. Méthode: Les données de 780 participants provenant de la branche montréalaise de l’étude INTERACT (2018-2019) ont été analysées. Des modèles de régression linéaire ont été construits afin d’examiner la relation entre l’utilisation de l’automobile et l'activité physique modérée-vigoureuse (APMV) totale (auto-rapportée avec transformation logarithmique et objectivement mesurée). Ensuite, le motif de déplacement a été inclus dans ces modèles. Enfin, une régression pondérée géographiquement a permis d’explorer la variation spatiale de l'association entre l’usage de l’automobile et l’APMV. Résultats: La proportion des déplacements effectués en automobile est associée négativement avec l'APMV totale auto-rapportée (coefficient : -0,009, intervalle de confiance à 95% : -0,012 à -0,006) et objectivement mesurée (Coefficient : -0,29 minute par jour, intervalle de confiance à 95% : -0,55 à -0,03). La régression pondérée géographiquement indique une faible variation spatiale de l'association entre l'utilisation de l’automobile et l’APMV totale auto-rapportée. Le nombre de déplacements pour un motif tel que commerce et services est associé à l'APMV totale. Conclusion: Cette étude transversale a démontré une association négative entre l’utilisation de l’automobile et l’APMV totale sur le territoire montréalais. Certains motifs de déplacement sont associés à l’activité physique totale. / Background: Car usage reduction has previously been pointed out to be a population-based intervention promoting physical activity. Previous literature on the car usage-physical activity relation typically uses categorical variables for transport modes, rarely accounts for trip purpose and does not explore the influence of home location on this association. This study uses both self-reported and objectively measured physical activity to test the association between car usage and total daily physical activity, while accounting for previous limitations. Methods: INTERACT data collected in 2018-2019 among 780 participants from the Montreal metropolitan area site were analysed. Linear regression models of self-reported (log-transformed) and objectively measured total moderate-vigorous physical activity (MVPA) were built separately, as a function of car usage. Trip purpose was then included in these models. Finally, a geographically weighted regression (GWR) model was built to explore the spatial variation of the car usage-MVPA association. Results: The proportion of trips made by car showed a negative association with both self-reported (coefficient: -0.009, 95% CI [-0.012, -0.006]) and objectively measured (coefficient: -0.29 minutes per day, 95% CI [-0.55, -0.03]) total MVPA. GWR showed little spatial variation in the car usage-total self-reported MVPA. The number of trips toward certain purposes (e.g. “shops and services”) is associated with total MVPA. Conclusion: This cross-sectional study showed a negative association between car usage and total MVPA in the Montreal metropolitan area. Some trip purpose is associated with total physical activity.

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Public transport plays a vital role in society as the economy, the degree of sustainability and the qualityof life of a city is directly affected by transportation. A shift in modal share towards public transport isassociated with many benefits such as increased air quality and improved space allocation within thecity. To further promote public transport, an appropriate measure of competitiveness is required toevaluate the impact of past and future transport policies. This study introduces the journeys per capitaratio as a new way of measuring public transport competitiveness. Firstly, the key factors affecting thepublic transportation usage rate expressed as the journeys per capita ratio are identified to evaluatethe impact of public transport provider efforts. For this purpose, data for a total of 32 explanatoryvariables and a scope of 218 regions for seven consecutive time frames are collected. Secondly,geographically weighted regression (GWR) – a local regression-based spatial analysis technique – isperformed to test if the journeys per capita ratio is a suitable target variable to predict the impact ofcertain transport supply changes. A traditional global ordinary least square (OLS) model is conductedas well to compare if a local model could be more beneficial. The GWR and the OLS model are trainedwith the data of previous years and tested with data from the consecutive following years. Thirdly,further temporal and socio-economic based cluster analyses are performed to assess the validity andthe explanatory power of the journeys per capita ratio. The conducted analyses reveal that thejourneys per capita ratio is a superior measure for assessing public transport competitiveness.Goodness of fit statistics and estimation results demonstrate that the GWR model has betterprediction accuracy and is more capable of retrospectively predicting the impact of previous transportpolicies. / Kollektivtrafiken har en avgörande roll i samhället då ekonomin, graden av hållbarhet och städerslivskvalité är direkt påverkad av transport. En förändring av transportanvändning från bil motkollektivtrafik är förknippad med flera fördelar, såsom ökad luftkvalitet och förbättrad rumsligallokering inom staden. För att ytterligare främja kollektivtrafik krävs ett lämpligt mått påkonkurrenskraft för att utvärdera effekterna av tidigare och framtida transportpolitik. Den här studienintroducerar resor per capita-förhållanden som ett nytt sätt att mäta kollektivtransportenskonkurrenskraft. För det första identifieras nyckelfaktorerna som påverkar användningsgraden förkollektivtrafik, uttryckt som förhållandet resor per capita för att utvärdera effekten avkollektivtrafikleverantörens insatser. För det här syftet har data för totalt 32 variabler och ett omfångav 218 regioner under sju, på varandra, följande tidsramar har samlats in. För det andra har Geografisktviktad regression (GWR), vilket är en lokal regressionsbaserad rumslig analysteknik, använts för atttesta om resor per capita-förhållanden är en lämplig målvariabel för att förutsäga effekterna av vissatransportförändringar. En traditionell Global ordinary least square model (OLS) har också använts föratt jämföra om en lokal modell är mer fördelaktig. GWR och OLS modellerna har tränats med data fråntidigare år och testats med data från följande år. För det tredje har ytterligare tidsmässigsocioekonomisk baserad klusteranalys utförts för att bedöma validiteten och förklaringsförmågan förresornas förhållande per capita. De genomförda analyserna pekar på att förhållandet resor per capitaär ett fördelaktigt mått för att bedöma kollektivtrafikens konkurrenskraft. Goodness of fit statistics ochde uppskattade resultaten visar att GWR-modellen har en bättre förmåga att göra noggrannaförutsägelser och är mer kapabel att i efterhand förutsäga effekterna av tidigare transportpolitik.