Global ETD Search

151	Contributions à la modélisation de données spatiales et fonctionnelles : applications / Contributions to modeling spatial and functional data : applications Ternynck, Camille 28 November 2014 (has links) Dans ce mémoire de thèse, nous nous intéressons à la modélisation non paramétrique de données spatiales et/ou fonctionnelles, plus particulièrement basée sur la méthode à noyau. En général, les échantillons que nous avons considérés pour établir les propriétés asymptotiques des estimateurs proposés sont constitués de variables dépendantes. La spécificité des méthodes étudiées réside dans le fait que les estimateurs prennent en compte la structure de dépendance des données considérées.Dans une première partie, nous appréhendons l’étude de variables réelles spatialement dépendantes. Nous proposons une nouvelle approche à noyau pour estimer les fonctions de densité de probabilité et de régression spatiales ainsi que le mode. La particularité de cette approche est qu’elle permet de tenir compte à la fois de la proximité entre les observations et de celle entre les sites. Nous étudions les comportements asymptotiques des estimateurs proposés ainsi que leurs applications à des données simulées et réelles.Dans une seconde partie, nous nous intéressons à la modélisation de données à valeurs dans un espace de dimension infinie ou dites "données fonctionnelles". Dans un premier temps, nous adaptons le modèle de régression non paramétrique introduit en première partie au cadre de données fonctionnelles spatialement dépendantes. Nous donnons des résultats asymptotiques ainsi que numériques. Puis, dans un second temps, nous étudions un modèle de régression de séries temporelles dont les variables explicatives sont fonctionnelles et le processus des innovations est autorégressif. Nous proposons une procédure permettant de tenir compte de l’information contenue dans le processus des erreurs. Après avoir étudié le comportement asymptotique de l’estimateur à noyau proposé, nous analysons ses performances sur des données simulées puis réelles.La troisième partie est consacrée aux applications. Tout d’abord, nous présentons des résultats de classification non supervisée de données spatiales (multivariées), simulées et réelles. La méthode de classification considérée est basée sur l’estimation du mode spatial, obtenu à partir de l’estimateur de la fonction de densité spatiale introduit dans le cadre de la première partie de cette thèse. Puis, nous appliquons cette méthode de classification basée sur le mode ainsi que d’autres méthodes de classification non supervisée de la littérature sur des données hydrologiques de nature fonctionnelle. Enfin, cette classification des données hydrologiques nous a amené à appliquer des outils de détection de rupture sur ces données fonctionnelles. / In this dissertation, we are interested in nonparametric modeling of spatial and/or functional data, more specifically based on kernel method. Generally, the samples we have considered for establishing asymptotic properties of the proposed estimators are constituted of dependent variables. The specificity of the studied methods lies in the fact that the estimators take into account the structure of the dependence of the considered data.In a first part, we study real variables spatially dependent. We propose a new kernel approach to estimating spatial probability density of the mode and regression functions. The distinctive feature of this approach is that it allows taking into account both the proximity between observations and that between sites. We study the asymptotic behaviors of the proposed estimates as well as their applications to simulated and real data. In a second part, we are interested in modeling data valued in a space of infinite dimension or so-called "functional data". As a first step, we adapt the nonparametric regression model, introduced in the first part, to spatially functional dependent data framework. We get convergence results as well as numerical results. Then, later, we study time series regression model in which explanatory variables are functional and the innovation process is autoregressive. We propose a procedure which allows us to take into account information contained in the error process. After showing asymptotic behavior of the proposed kernel estimate, we study its performance on simulated and real data.The third part is devoted to applications. First of all, we present unsupervised classificationresults of simulated and real spatial data (multivariate). The considered classification method is based on the estimation of spatial mode, obtained from the spatial density function introduced in the first part of this thesis. Then, we apply this classification method based on the mode as well as other unsupervised classification methods of the literature on hydrological data of functional nature. Lastly, this classification of hydrological data has led us to apply change point detection tools on these functional data. Estimation non paramétrique Estimateur à noyau Densité de probabilité Mode Régression Statistique spatiale Données fonctionnelles Séries temporelles Classification non supervisée Détection de rupture Nonparametric estimation Kernel estimate Probability density Mode Regression Spatial statistics Functional data Time series Unsupervised classification Change point detection
152	A Comparison of Models and Methods for Spatial Interpolation in Statistics and Numerical Analysis / Eine Gegenüberstellung von Modellen und Methoden zur räumlichen Interpolation in der Statistik und der Numerischen Analysis Scheuerer, Michael 28 October 2009 (has links) No description available. 510 Mathematik Mathematics and Computer Science Räumliche Interpolation Geostatistik Kerninterpolation Zufallsfelder spatial interpolation geostatistics kernel interpolation random fields reproducing kernel Hilbert spaces 31.70 31.73 31.76 EGCH 110: Directional data
153	Un'Analisi della Variazione Lessicale Regionale Nell’Inglese di California Attraverso le Ricerche in Rete Limitate per Sito / AN ANALYSIS OF REGIONAL LEXICAL VARIATION IN CALIFORNIA ENGLISH USING SITE-RESTRICTED WEB SEARCHES ASNAGHI, COSTANZA 12 March 2013 (has links) Lo studio esamina la variazione lessicale regionale in forma scritta nell’inglese standard in California. Attraverso ricerche in rete limitate a 336 siti di giornali online con sede in 270 città in California, vengono raccolti i valori di 45 variabili continue di alternanze lessicali e quindi calcolati come proporzioni. Tecniche statistiche di autocorrelazione spaziale globale e locale analizzano i valori. I risultati delle analisi, riportati in 90 mappe, confermano la distribuzione regionale delle variabili in California. Le 45 variabili lessicali sono poi esaminate con tecniche statistiche multivariate per individuare le relazioni linguistiche tra le città della California esaminate. L’analisi fattoriale, che rappresenta il 50,5% della variazione nei dati, evidenzia tre aree nella distribuzione regionale lessicale: nord/sud, urbano/rurale, e aree centrali e basso meridionali/aree alto meridionali e del nord. L’analisi dei cluster gerarchica distingue inoltre sei regioni dialettali principali in California: quella del Nord, quella di Sacramento-Santa Cruz, quella della San Francisco Bay Area, quella centrale, quella alto meridionale, e quella basso meridionale. Cinque mappe multivariate sono fornite nella tesi. La spiegazione dei risultati si basa sia su modelli di insediamento storico che su una spiegazione socio-culturale, che si riflettono nel linguaggio in California. / The study examines regional lexical variation in written Standard California English. The valuesof 45 continuous lexical alternation variables are gathered through site-restricted web searches in 336 online newspaper websites based in 270 locations in California and then calculated as proportions. Statistical techniques analyze global and local spatial autocorrelation values. The results of the analysis, reported in 90 maps, confirm the regional distribution of the variables in California. The 45 lexical variables are then analyzed with multivariate techniques to identify the linguistic relations between the surveyed California cities. Factor analysis, which accounts for 50.5% of the variation in the data, highlights three areas in the regional lexical distribution: north/south, urban/rural, central and lower southern/upper southern and northern areas. The hierarchical cluster analysis also distinguishes six major dialect regions in California: the North dialect region, the Sacramento-Santa Cruz dialect region, the San Francisco Bay Area dialect region, the Central dialect region, the Upper Southerns dialect region, and the Lower Southern dialect region. Five multivariate maps are provided in the thesis. The explanation of the results is based both on historical settlement patterns and on a socio-cultural explanation, which are reflected in the language in California.
154	Indicadores de renda baseados em consumo de energia elétrica: abordagens domiciliar e regional na perspectiva da estatística espacial Francisco, Eduardo de Rezende 29 April 2010 (has links) Submitted by Cristiane Oliveira (cristiane.oliveira@fgv.br) on 2011-05-24T13:38:06Z No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) / Approved for entry into archive by Gisele Isaura Hannickel(gisele.hannickel@fgv.br) on 2011-05-24T13:40:59Z (GMT) No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) / Approved for entry into archive by Gisele Isaura Hannickel(gisele.hannickel@fgv.br) on 2011-05-24T13:43:37Z (GMT) No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) / Made available in DSpace on 2011-05-24T13:47:00Z (GMT). No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) Previous issue date: 2010-04-29 / In order to evaluate the use of Electricity Consumption as a Socioeconomic Status, this research analyzes information in two levels of geographical aggregation. At the first level, under a territorial perspective, it investigates indicators of Income and Electric Energy Consumption aggregated by weighted areas (set of census sectors) in the city of São Paulo and uses the microdata of Demographic Census 2000 jointly with residential consumers’ database of AES Eletropaulo. It applies Spatial Auto-Regressive (SAR) models, Geographically Weighted Regression (GWR), and an unprecedented combined model (GWR+SAR), developed in this study. Several neighborhood matrices were used to assess the influence of space (with Downtown-Suburbs pattern) of the variables under study. The variables showed strong spatial autocorrelation (Moran's I greater than 58% for the Energy Consumption and more than 75% for the Household Income). Relations between Income and Electricity Consumption were very strong (coefficients of determination of Income reached values from 0.93 to 0.98). At the second level, the household one, it uses data collected in the Annual Satisfaction Survey of Residential Customer, coordinated by the Brazilian Electricity Distributors Association (ABRADEE) for the years 2004, 2006, 2007, 2008 and 2009. Weighted Linear Model (WLM), GWR and SAR were applied to survey data with interviews allocated on the centroid and the seat of the districts. For the year 2009, we obtained the actual locations of the households interviewed. Additionally, 6 algorithms of points distribution within the polygons of the districts have been developed. The results from models based on centroids and seats obtained a coefficient of determination R 2 of around 0.45 for the GWR technique, while the models based on scattering points within the polygons of the districts have reduced this account to about 0.40. These results suggest that the algorithms of allocation of points in polygons allow the observation of a more realistic association between the constructs analyzed. The combined use of the findings shows that the billing information of the electricity distributors has great potential to support strategic decisions. Because they are current, available and monthly updated, socioeconomic indicators based on energy consumption can be very useful as an aid to processes of classification, concentration and estimation of household income. / Com o objetivo de avaliar o uso do consumo de energia elétrica como indicador socioeconômico, esta pesquisa analisa informações em dois níveis de agregação geográfica. No primeiro, sob perspectiva territorial, investiga indicadores de Renda e Consumo de Energia Elétrica agregados por áreas de ponderação (conjunto de setores censitários) do município de São Paulo e utiliza os microdados do Censo Demográfico 2000 em conjunto com a base de domicílios da AES Eletropaulo. Aplica modelos de Spatial Auto-Regression (SAR), Geographically Weighted Regression (GWR), e um modelo inédito combinado (GWR+SAR), desenvolvido neste estudo. Diversas matrizes de vizinhança foram utilizadas na avaliação da influência espacial (com padrão Centro-Periferia) das variáveis em estudo. As variáveis mostraram forte auto-correlação espacial (I de Moran superior a 58% para o Consumo de Energia Elétrica e superior a 75% para a Renda Domiciliar). As relações entre Renda e Consumo de Energia Elétrica mostraram-se muito fortes (os coeficientes de explicação da Renda atingiram valores de 0,93 a 0,98). No segundo nível, domiciliar, utiliza dados coletados na Pesquisa Anual de Satisfação do Cliente Residencial, coordenada pela Associação Brasileira dos Distribuidores de Energia Elétrica (ABRADEE), para os anos de 2004, 2006, 2007, 2008 e 2009. Foram aplicados os modelos Weighted Linear Model (WLM), GWR e SAR para os dados das pesquisas com as entrevistas alocadas no centróide e na sede dos distritos. Para o ano de 2009, foram obtidas as localizações reais dos domicílios entrevistados. Adicionalmente, foram desenvolvidos 6 algoritmos de distribuição de pontos no interior dos polígonos dos distritos. Os resultados dos modelos baseados em centróides e sedes obtiveram um coeficiente de determinação R2 em torno de 0,45 para a técnica GWR, enquanto os modelos baseados no espalhamento de pontos no interior dos polígonos dos distritos reduziram essa explicação para cerca de 0,40. Esses resultados sugerem que os algoritmos de alocação de pontos em polígonos permitem a observação de uma associação mais realística entre os construtos analisados. O uso combinado dos achados demonstra que as informações de faturamento das distribuidoras de energia elétrica têm grande potencial para apoiar decisões estratégicas. Por serem atuais, disponíveis e de atualização mensal, os indicadores socioeconômicos baseados em consumo de energia elétrica podem ser de grande utilidade como subsídio a processos de classificação, concentração e previsão da renda domiciliar. Renda domiciliar Consumo de energia elétrica Classificação econômica Estatística espacial Pesquisa domiciliar Household income Electricity consumption Economic status Spatial statistics Residential survey Administração de empresas Análise espacial (Estatística) Pesquisa - Métodos estatísticos
155	Data-driven prediction of saltmarsh morphodynamics Evans, Ben Richard January 2018 (has links) Saltmarshes provide a diverse range of ecosystem services and are protected under a number of international designations. Nevertheless they are generally declining in extent in the United Kingdom and North West Europe. The drivers of this decline are complex and poorly understood. When considering mitigation and management for future ecosystem service provision it will be important to understand why, where, and to what extent decline is likely to occur. Few studies have attempted to forecast saltmarsh morphodynamics at a system level over decadal time scales. There is no synthesis of existing knowledge available for specific site predictions nor is there a formalised framework for individual site assessment and management. This project evaluates the extent to which machine learning model approaches (boosted regression trees, neural networks and Bayesian networks) can facilitate synthesis of information and prediction of decadal-scale morphological tendencies of saltmarshes. Importantly, data-driven predictions are independent of the assumptions underlying physically-based models, and therefore offer an additional opportunity to crossvalidate between two paradigms. Marsh margins and interiors are both considered but are treated separately since they are regarded as being sensitive to different process suites. The study therefore identifies factors likely to control morphological trajectories and develops geospatial methodologies to derive proxy measures relating to controls or processes. These metrics are developed at a high spatial density in the order of tens of metres allowing for the resolution of fine-scale behavioural differences. Conventional statistical approaches, as have been previously adopted, are applied to the dataset to assess consistency with previous findings, with some agreement being found. The data are subsequently used to train and compare three types of machine learning model. Boosted regression trees outperform the other two methods in this context. The resulting models are able to explain more than 95% of the variance in marginal changes and 91% for internal dynamics. Models are selected based on validation performance and are then queried with realistic future scenarios which represent altered input conditions that may arise as a consequence of future environmental change. Responses to these scenarios are evaluated, suggesting system sensitivity to all scenarios tested and offering a high degree of spatial detail in responses. While mechanistic interpretation of some responses is challenging, process-based justifications are offered for many of the observed behaviours, providing confidence that the results are realistic. The work demonstrates a potentially powerful alternative (and complement) to current morphodynamic models that can be applied over large areas with relative ease, compared to numerical implementations. Powerful analyses with broad scope are now available to the field of coastal geomorphology through the combination of spatial data streams and machine learning. Such methods are shown to be of great potential value in support of applied management and monitoring interventions.
156	Tessellations à base de champs aléatoires gaussiens. Application à la modélisation spatiale et temporelle de l'endothélium cornéen humain. / Tessellations based on Gaussian random fields. Application to the spatial and temporal modelling of the human corneal endothelium. Rannou, Klervi 12 December 2016 (has links) Les tessellations, aussi appelées mosaïques, permettent de modéliser de nombreuses structures, comme des assemblages de cellules en biologie ou de grains en science des matériaux. La tessellation aléatoire la plus connue est le diagramme de Voronoï qui à partir d'un ensemble de points, appelés germes, partitionne le plan. L'approche innovante de cette thèse est d'utiliser des champs aléatoires gaussiens pour générer des germes et des distances aléatoires, qui vont permettre de simuler une grande variété de tessellations en termes de formes et de tailles des cellules.Pour connaître les propriétés des tessellations simulées à partir de champs aléatoires gaussiens, celles-ci vont être caractérisées et comparées à d'autres tessellations. Tout d'abord par une approche ponctuelle en étudiant les germes, dont leur distribution spatiale. Puis par une approche par région, en étudiant la géométrie et la morphométrie des cellules.L'endothélium cornéen humain est une monocouche de cellules formant un pavage hexagonal régulier à la naissance, et perdant de sa régularité ensuite. La qualité du greffon cornéen est donnée par certaines observations, comme la densité, l'homogénéité de la forme et des tailles des cellules endothéliales.L'évolution avec l'âge de cette mosaïque cornéenne va être caractérisée à partir d’une base d’images de l’endothélium. L'originalité est ensuite d'effectuer une estimation de l'âge d’un endothélium à partir des différentes mesures permettant de caractériser les tessellations, et enfin de mettre en place une méthode prometteuse afin de savoir si une cornée a une évolution normale. / Tessellations, also called mosaics, are used to model many structures, for example cellular arrangements in biology or grains in material science. The most known tessellation is the Voronoï diagram which partitions the space from a set of points, called germs. The innovative approach of this thesis is to use Gaussian random fields to generate germs and random distances. The use of random fields allows to simulate a great variety of tessellations in terms of cells forms and sizes.To study the properties of each type of tessellation, they are characterized: first, by studying the germs, including their spatial distribution, and then by analyzing the cells geometry and morphometry. These tessellations are also compared to other known tessellations.The human corneal endothelium is a mono-layer of cells forming a regular hexagonal mosaic at birth, and losing his regularity later. The corneal graft quality is given by some observations made on the endothelial mosaic (cells density, the homogeneity of cells sizes and shapes).A database of endothelium images allows to characterize the evolution with age of the corneal mosaic. The originality is to estimate the age of an endothelium based on the measures computed to characterize the tessellations, and finally to set up a promising method to evaluate if a corneal evolution is normal. Tessellations Modèles d’arrangement cellulaire Champs aléatoires gaussiens Processus ponctuel Diagramme de Voronoï Morphologie mathématique Statistiques spatiales Endothélium cornéen humain Fonction de ripley Fonctionnelles réduites de Minkowski Diagrammes de formes Tessalations Model of cellular arrangement Gaussian random fields Point processes Voronoï diagram Mathematical morphology Spatial statistics Ripley's function Reduced Minkowski functionals Human corneal endothelium Shape diagrams
157	Geospatial Approaches to Social Determinants of Cancer Outcomes Dong, Weichuan 19 November 2021 (has links) No description available. Epidemiology Geography Geographic Information Science Health Health Care Health Care Management Oncology Public Health Public Policy Statistics geography geographic information science geographic information systems social determinants of health machine learning spatial statistics spatial epidemiology breast cancer late-stage cancer classification and regression tree random forest geographic random forest space-time scan statistic SaTScan Medicaid expansion health disparity
158	Contribution à la modélisation spatiale des événements extrêmes / Contributions to modeling spatial extremal events and applications Bassene, Aladji 06 May 2016 (has links) Dans cette de thèse, nous nous intéressons à la modélisation non paramétrique de données extrêmes spatiales. Nos résultats sont basés sur un cadre principal de la théorie des valeurs extrêmes, permettant ainsi d’englober les lois de type Pareto. Ce cadre permet aujourd’hui d’étendre l’étude des événements extrêmes au cas spatial à condition que les propriétés asymptotiques des estimateurs étudiés vérifient les conditions classiques de la Théorie des Valeurs Extrêmes (TVE) en plus des conditions locales sur la structure des données proprement dites. Dans la littérature, il existe un vaste panorama de modèles d’estimation d’événements extrêmes adaptés aux structures des données pour lesquelles on s’intéresse. Néanmoins, dans le cas de données extrêmes spatiales, hormis les modèles max stables,il n’en existe que peu ou presque pas de modèles qui s’intéressent à l’estimation fonctionnelle de l’indice de queue ou de quantiles extrêmes. Par conséquent, nous étendons les travaux existants sur l’estimation de l’indice de queue et des quantiles dans le cadre de données indépendantes ou temporellement dépendantes. La spécificité des méthodes étudiées réside sur le fait que les résultats asymptotiques des estimateurs prennent en compte la structure de dépendance spatiale des données considérées, ce qui est loin d’être trivial. Cette thèse s’inscrit donc dans le contexte de la statistique spatiale des valeurs extrêmes. Elle y apporte trois contributions principales. • Dans la première contribution de cette thèse permettant d’appréhender l’étude de variables réelles spatiales au cadre des valeurs extrêmes, nous proposons une estimation de l’indice de queue d’une distribution à queue lourde. Notre approche repose sur l’estimateur de Hill (1975). Les propriétés asymptotiques de l’estimateur introduit sont établies lorsque le processus spatial est adéquatement approximé par un processus M−dépendant, linéaire causal ou lorsqu'il satisfait une condition de mélange fort (a-mélange). • Dans la pratique, il est souvent utile de lier la variable d’intérêt Y avec une co-variable X. Dans cette situation, l’indice de queue dépend de la valeur observée x de la co-variable X et sera appelé indice de queue conditionnelle. Dans la plupart des applications, l’indice de queue des valeurs extrêmes n’est pas l’intérêt principal et est utilisé pour estimer par exemple des quantiles extrêmes. La contribution de ce chapitre consiste à adapter l’estimateur de l’indice de queue introduit dans la première partie au cadre conditionnel et d’utiliser ce dernier afin de proposer un estimateur des quantiles conditionnels extrêmes. Nous examinons les modèles dits "à plan fixe" ou "fixed design" qui correspondent à la situation où la variable explicative est déterministe et nous utlisons l’approche de la fenêtre mobile ou "window moving approach" pour capter la co-variable. Nous étudions le comportement asymptotique des estimateurs proposés et donnons des résultats numériques basés sur des données simulées avec le logiciel "R". • Dans la troisième partie de cette thèse, nous étendons les travaux de la deuxième partie au cadre des modèles dits "à plan aléatoire" ou "random design" pour lesquels les données sont des observations spatiales d’un couple (Y,X) de variables aléatoires réelles. Pour ce dernier modèle, nous proposons un estimateur de l’indice de queue lourde en utilisant la méthode des noyaux pour capter la co-variable. Nous utilisons un estimateur de l’indice de queue conditionnelle appartenant à la famille de l’estimateur introduit par Goegebeur et al. (2014b). / In this thesis, we investigate nonparametric modeling of spatial extremes. Our resultsare based on the main result of the theory of extreme values, thereby encompass Paretolaws. This framework allows today to extend the study of extreme events in the spatialcase provided if the asymptotic properties of the proposed estimators satisfy the standardconditions of the Extreme Value Theory (EVT) in addition to the local conditions on thedata structure themselves. In the literature, there exists a vast panorama of extreme events models, which are adapted to the structures of the data of interest. However, in the case ofextreme spatial data, except max-stables models, little or almost no models are interestedin non-parametric estimation of the tail index and/or extreme quantiles. Therefore, weextend existing works on estimating the tail index and quantile under independent ortime-dependent data. The specificity of the methods studied resides in the fact that theasymptotic results of the proposed estimators take into account the spatial dependence structure of the relevant data, which is far from trivial. This thesis is then written in thecontext of spatial statistics of extremes. She makes three main contributions.• In the first contribution of this thesis, we propose a new approach of the estimatorof the tail index of a heavy-tailed distribution within the framework of spatial data. This approach relies on the estimator of Hill (1975). The asymptotic properties of the estimator introduced are established when the spatial process is adequately approximated by aspatial M−dependent process, spatial linear causal process or when the process satisfies a strong mixing condition.• In practice, it is often useful to link the variable of interest Y with covariate X. Inthis situation, the tail index depends on the observed value x of the covariate X and theunknown fonction (.) will be called conditional tail index. In most applications, the tailindexof an extreme value is not the main attraction, but it is used to estimate for instance extreme quantiles. The contribution of this chapter is to adapt the estimator of the tail index introduced in the first part in the conditional framework and use it to propose an estimator of conditional extreme quantiles. We examine the models called "fixed design"which corresponds to the situation where the explanatory variable is deterministic. To tackle the covariate, since it is deterministic, we use the window moving approach. Westudy the asymptotic behavior of the estimators proposed and some numerical resultsusing simulated data with the software "R".• In the third part of this thesis, we extend the work of the second part of the framemodels called "random design" for which the data are spatial observations of a pair (Y,X) of real random variables . In this last model, we propose an estimator of heavy tail-indexusing the kernel method to tackle the covariate. We use an estimator of the conditional tail index belonging to the family of the estimators introduced by Goegebeur et al. (2014b). Statistique spatiale Données extrêmes Données M-dépendantes Processus linéaire causal Processus a−mélangeant Estimation non-paramétrique Estimateur à noyau Estimation de l'indice de queue Estimation de quantiles extrêmes Estimateur de Hill Consistance Normalité asymptotique Spatial statistics Extreme values Spatial M−dependent processes Spatial linear causal processes A−mixing processes Nonparametric estimation Kernel estimator Heavy tail index estimate Extreme quantiles estimate Hill’s estimator Consistency Asymptotic normality.
159	GIS-based Episode Reconstruction Using GPS Data for Activity Analysis and Route Choice Modeling / GIS-based Episode Reconstruction Using GPS Data Dalumpines, Ron 26 September 2014 (has links) Most transportation problems arise from individual travel decisions. In response, transportation researchers had been studying individual travel behavior – a growing trend that requires activity data at individual level. Global positioning systems (GPS) and geographical information systems (GIS) have been used to capture and process individual activity data, from determining activity locations to mapping routes to these locations. Potential applications of GPS data seem limitless but our tools and methods to make these data usable lags behind. In response to this need, this dissertation presents a GIS-based toolkit to automatically extract activity episodes from GPS data and derive information related to these episodes from additional data (e.g., road network, land use). The major emphasis of this dissertation is the development of a toolkit for extracting information associated with movements of individuals from GPS data. To be effective, the toolkit has been developed around three design principles: transferability, modularity, and scalability. Two substantive chapters focus on selected components of the toolkit (map-matching, mode detection); another for the entire toolkit. Final substantive chapter demonstrates the toolkit’s potential by comparing route choice models of work and shop trips using inputs generated by the toolkit. There are several tools and methods that capitalize on GPS data, developed within different problem domains. This dissertation contributes to that repository of tools and methods by presenting a suite of tools that can extract all possible information that can be derived from GPS data. Unlike existing tools cited in the transportation literature, the toolkit has been designed to be complete (covers preprocessing up to extracting route attributes), and can work with GPS data alone or in combination with additional data. Moreover, this dissertation contributes to our understanding of route choice decisions for work and shop trips by looking into the combined effects of route attributes and individual characteristics. / Dissertation / Doctor of Philosophy (PhD) GPS time use diary episode extraction multinomial logit travel behavior mode detection episode reconstruction GIS map-matching route choice path size logit potential path area scale estimation Python ArcGIS activity analysis trip reconstruction smartphone global positioning system geographic information system toolkit work trip shop trip potential activity location land use activity episode travel episode stop episode transferability scalability modularity scripting big data travel survey respondent burden preprocessing multipath error tracking purpose detection segmentation data filter data smoothing fuzzy logic neural network decision tree rule-based algorithm trajectory point road network network dataset gateway shortest path horizontal dilution of precision HDOP commonality factor mode transfer point Halifax Nova Scotia Space-Time Activity Research variance inflation factor shapefile comma-separated values traveling salesman problem data mining branch-and-bound algorithm pedestrian network alternative route observed route route efficiency route attributes distance time heading bearing duration acceleration latitude longitude coordinate overlay analysis intersect shopping module data logger walk likelihood ratio test classification table path generation kappa statistic degrees data collection transportation research automate framework ArcToolbox spatio-temporal navigation positioning trace path spatial data topology horizontal accuracy SPSS Stata spatial resolution temporal resolution household survey trip reporting proximity analysis DMTI Desktop Mapping Technologies Inc. road intersection route overlap left turn right turn location analysis time geography spatial statistics buffer analysis cycling bus transit public transportation GEOIDE AGILE data need raw data urban canyon endpoint data cleaning elevation satellite outliers automatic processing nearest node classifier classification method short trip multi-point

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