O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras- / The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.

La croissance rapide et la complexité de nombreuses villes contemporaines offrent de nombreux défis de recherche pour les scientifiques à la recherche d'une meilleure compréhension des mobilités qui se produisent dans l'espace et dans le temps. A l’heure où de très grandes séries de données de trajectoires en milieu urbain sont disponibles grâce à profusion de nombreux capteurs de positionnement et de services de nombreuses et nouvelles opportunités de recherche et d’application nous sont offertes. Cependant, une bonne intégration de ces données de mobilité nécessite encore l'élaboration de cadres méthodologiques et conceptuels tout comme la mise en oeuvre de bases de données spatio-temporelles qui offriront les capacités appropriées de représentation et de manipulation des données. La recherche développée dans cette thèse introduit une modélisation conceptuelle et une approche de gestion de base de données spatio-temporelles pour représenter et analyser des trajectoires humaines dans des espaces urbains. Le modèle considère les dimensions spatiales, temporelles et sémantiques afin de tenir compte de l’ensemble des propriétés issues des informations de mobilité. Plusieurs abstractions de données de mobilité et des outils de manipulation de données sont développés et expérimentés à partir d’une large base de données de trajectoires disponibles dans la ville de Pékin. L'intérêt de l'approche est double: il montre d’une part que de larges ensembles de données de mobilité peuvent être intégrés au sein de SGBD spatiotemporels extensibles; d’autre part des outils de manipulation et d’interrogation spécifiques peuvent être dérivés à partir de fonctions intégrées au sein d’un langage d’interrogation. Le potentiel de l’approche est illustré par une série d’interrogations qui montrent comment à partir d’une large base de données de trajectoires quelques patrons de déplacements peuvent être obtenus. / Massive trajectory datasets generated in modern cities generate not only novel research opportunities but also important methodological challenges for academics and decision-makers searching for a better understanding of travel patterns in space and time. This PhD research is oriented towards the conceptual and GIS-based modeling of human displacements derived from large sets of urban trajectories. The motivation behind this study originates from the necessity to search for and explore travel patterns that emerge from citizens acting in the city. Our research introduces a conceptual modelling framework whose objective is to integrate and analyze human displacements within a GIS-based practical solution. The framework combines conceptual and logical models that represent travel trajectories of citizens moving in a given city. The whole approach has been implemented in a geographical database system, experimented in the context of transportation data, and enriched by a series of query interface manipulations and specific functions that illustrate the potential of our whole framework for urban studies. The whole framework has been experimented on top of the Geolife project and large trajectories datasets available in the city of Beijing. Overall, the findings are twofold: first, it appears that our modelling framework can appropriately act as an extensible geographical database support for the integration of large trajectory datasets; second the approach shows that several emerging human displacements can be explored from the manipulation of large urban trajectories.

The aim of this master's thesis is to get familiar with problems of data mining and classification. This thesis also continues with application SUNAR, which is upgraded in practical part with SVM classification of persons passing between cameras. In the conclusion, we discuss ways to improve classification and person recognition in application SUNAR.

This master's thesis is devoted to the studies of possibilities, which can be used for representation of moving objects data and for querying such spatio-temporal data. It also shows results of the master's thesis created by Ing. Jaroslav Vališ, that should be used for the solution of this master's thesis. But based on the theoretical grounds defined at the beginning of this work was designed and implemented new database extension for saving and querying spatio-temporal data. Special usage of this extension is demonstrated in an example application. This application uses the database extension for the implementation of its own database functions that are domain specific. At the conclusion, there are presented ways of the farther development of this database extension and the results of this master's thesis are there set into the context of the following project, doctoral thesis "Moving objects database".

No description available.

There is currently no set standard for evaluating visualization environments. Even though the number of visualizations has increased, there is a tendency to overlook the evaluation of their usability. This thesis investigates how a visualization tool for crowd trajectory data can be made using the visualization technique of animated maps and the JavaScript library D3.js. Furthermore it explores how such a visualization tool can be evaluated according to a suggested framework for spatio-temporal data.     The developed tool uses data taken from the UCY Graphics Lab, consisting of 415 trajectories collected from a video recorded at a campus area. User evaluation was performed through a user test with a total of six participants, measuring effectiveness as completed tasks, and satisfaction as ease of use for three different amounts of trajectories. Qualitative data was recorded through using the think aloud protocol to gather feedback to further improve the implementation. The evaluation shows that the visualization tool is usable and effective, and that the technique of animated maps in combination with a heatmap can aid users when exploring and formulating ideas about data of this kind. It is also concluded that the framework is a possible tool to utilize when validating visualization systems for crowd trajectory data. / Det finns i dagsläget ingen etablerad standard för att utvärdera visualiseringssystem. Även om antalet visualiseringar har ökat finns det en tendens att förbise utvärderandet av deras användbarhet. I det här arbetet undersöker vi hur ett visualiseringsverktyg för data av gångbanor hos folksamlingar kan skapas, med hjälp utav visualiseringsmetoden animated maps och JavaScript-biblioteket D3.js. Vidare undersöker vi hur det är möjligt att evaluera ett visualiseringsverktyg utefter ett givet ramverk.  Visualiseringsverktyget använder data från UCY Graphics Lab. Datan består av 415 gångbanor som är insamlade från en videoinspelning av ett campusområde. En utvärdering genomfördes sedan med sex deltagare, där visualiseringens effektivitet och användarvänlighet mättes. Frågorna ställdes för tre olika mängder av gångbanor. Kvalitativa data dokumenterades genom en så kallad ''think aloud'', för att ge återkoppling och förslag på möjliga förbättringar av visualiseringen. Evalueringen visar på att animated maps i kombination med en heatmap kan hjälpa användare att utforska data av gångbanor hos folksamlingar, samt att verktyget är effektivt och användbart. Det är också visat att det ramverk som användes vid evalueringen är ett möjligt verktyg för att validera visualiseringsverktyg av den typ som gjorts i det här projektet.

Informações que caracterizam o risco quebra de produção agrícola são necessárias para a precificação de prêmio do seguro agrícola de produção e de renda. A distribuição de probabilidade da variável rendimento agrícola é uma dessas informações, em especial aquela que descreve a variável aleatória rendimento agrícola condicionada aos fatores de risco climáticos. Este trabalho objetiva aplicar redes Bayesianas (grafo acíclico direcionado, ou modelo hierárquico Bayesiano) a estimação da distribuição de probabilidade de rendimento da soja em alguns municípios do Paraná, com foco na analise comparativa de riscos. Dados meteorológicos (ANA e INMET, período de 1970 a 2011) e de sensoriamento remoto (MODIS, período de 2000 a 2011) são usados conjuntamente para descrever espacialmente o risco climático de quebra de produção. Os dados de rendimento usados no estudo (COAMO, período de 2001 a 2011) requerem agrupamento de todos os dados ao nível municipal e, para tanto, a seleção de dados foi realizada nas dimensões espacial e temporal por meio de um mapa da cultura da soja (estimado por SVM - support vector machine) e os resultados de um algoritmo de identificação de ciclo de culturas. A interpolação requerida para os dados de temperatura utilizou uma componente de tendência estimada por dados de sensoriamento remoto, para descrever variações espaciais da variável que são ofuscadas pelos métodos tradicionais de interpolação. Como resultados, identificou-se relação significativa entre a temperatura observada por estações meteorológicas e os dados de sensoriamento remoto, apoiando seu uso conjunto nas estimativas. O classificador que estima o mapa da cultura da soja apresenta sobre-ajuste para safras das quais as amostras usadas no treinamento foram coletadas. Além da seleção de dados, a identificação de ciclo também permitiu obtenção de distribuições de datas de plantio da cultura da soja para o estado do Paraná. As redes bayesianas apresentam grande potencial e algumas vantagens quando aplicadas na modelagem de risco agrícola. A representação da distribuição de probabilidade por um grafo facilita o entendimento de problemas complexos, por suposições de causalidade, e facilita o ajuste, estruturação e aplicação do modelo probabilístico. A distribuição log-normal demonstrou-se a mais adequada para a modelagem das variáveis de ambiente (soma térmica, chuva acumulada e maior período sem chuva), e a distribuição beta para produtividade relativa e índices de estado (amplitude de NDVI e de EVI). No caso da regressão beta, o parâmetro de precisão também foi modelado com dependência das variáveis explicativas melhorando o ajuste da distribuição. O modelo probabilístico se demonstrou pouco representativo subestimando bastante as taxas de prêmio de seguro em relação a taxas praticadas no mercado, mas ainda assim apresenta contribui para o entendimento comparativo de situações de risco de quebra de produção da cultura da soja. / Information that characterize the risk of crop losses are necessary to crop and revenue insurance underwriting. The probability distribution of yield is one of this information. This research applies Bayesian networks (direct acyclic graph, or hierarchical Bayesian model) to estimate the probability distribution of soybean yield for some counties in Paraná state (Brazil) with focus on risk comparative analysis. Meteorological data (ANA and INMET, from 1970 to 2011) and remote sensing data (MODIS, from 2001 to 2011) were used to describe spatially the climate risk of production loss. The yield data used in this study (COAMO, from 2001 to 2011) required grouping to county level and, for that, a process of data selection was performed on spatial and temporal dimensions by a crop map (estimated by SVM - support vector machine) and by the results of a crop cycle identification algorithm. The interpolation required to spatialize temperature required a trend component which was estimated by remote sensing data, to describe the spatial variations of the variable obfuscated by traditional interpolation methods. As results, a significant relation between temperature from meteorological stations and remote sensing data was found, sustaining the use of the supposed relation between the two variables. The soybean map classifier shown over-fitting for the crop seasons for which the training samples were collected. Besides the data collection, a seeding dates distribution of soybean in Paraná state was obtained from the crop cycle identification process. The Bayesian networks showed big potential and some advantages when applied to agronomic risk modeling. The representation of the probability distribution by graphs helps the understanding of complex problems, with causality suppositions, and also helps the fitting, structuring and application of the probabilistic model. The log-normal probability distribution showed to be the best to model environment variables (thermal sum, accumulated precipitation and biggest period without rain), and the beta distribution to be the best to model relative yield and state indexes (NDVI and EVI ranges). In the case of beta regression, the precision parameter was also modeled with explanation variables as dependencies increasing the quality of the distribution fitting. In the overall, the probabilistic model had low representativity underestimating the premium rates, however it contributes to understand scenarios with risk of yield loss for the soybean crop.

Face à l'explosion des nouvelles technologies (mobiles, capteurs, etc.), de grandes quantités de données localisées dans l'espace et dans le temps sont désormais disponibles. Les bases de données associées peuvent être qualifiées de bases de données spatio-temporelles car chaque donnée est décrite par une information spatiale (e.g. une ville, un quartier, une rivière, etc.) et temporelle (p. ex. la date d'un événement). Cette masse de données souvent hétérogènes et complexes génère ainsi de nouveaux besoins auxquels les méthodes d'extraction de connaissances doivent pouvoir répondre (e.g. suivre des phénomènes dans le temps et l'espace). De nombreux phénomènes avec des dynamiques complexes sont ainsi associés à des données spatio-temporelles. Par exemple, la dynamique d'une maladie infectieuse peut être décrite par les interactions entre les humains et le vecteur de transmission associé ainsi que par certains mécanismes spatio-temporels qui participent à son évolution. La modification de l'un des composants de ce système peut déclencher des variations dans les interactions entre les composants et finalement, faire évoluer le comportement global du système.Pour faire face à ces nouveaux enjeux, de nouveaux processus et méthodes doivent être développés afin d'exploiter au mieux l'ensemble des données disponibles. Tel est l'objectif de la fouille de données spatio-temporelles qui correspond à l'ensemble de techniques et méthodes qui permettent d'obtenir des connaissances utiles à partir de gros volumes de données spatio-temporelles. Cette thèse s'inscrit dans le cadre général de la fouille de données spatio-temporelles et l'extraction de motifs séquentiels. Plus précisément, deux méthodes génériques d'extraction de motifs sont proposées. La première permet d'extraire des motifs séquentiels incluant des caractéristiques spatiales. Dans la deuxième, nous proposons un nouveau type de motifs appelé "motifs spatio-séquentiels". Ce type de motifs permet d'étudier l'évolution d'un ensemble d'événements décrivant une zone et son entourage proche. Ces deux approches ont été testées sur deux jeux de données associées à des phénomènes spatio-temporels : la pollution des rivières en France et le suivi épidémiologique de la dengue en Nouvelle Calédonie. Par ailleurs, deux mesures de qualité ainsi qu'un prototype de visualisation de motifs sont été également proposés pour accompagner les experts dans la sélection des motifs d'intérêts. / Thanks to the new technologies (smartphones, sensors, etc.), large amounts of spatiotemporal data are now available. The associated database can be called spatiotemporal databases because each row is described by a spatial information (e.g. a city, a neighborhood, a river, etc.) and temporal information (e.g. the date of an event). This huge data is often complex and heterogeneous and generates new needs in knowledge extraction methods to deal with these constraints (e.g. follow phenomena in time and space).Many phenomena with complex dynamics are thus associated with spatiotemporal data. For instance, the dynamics of an infectious disease can be described as the interactions between humans and the transmission vector as well as some spatiotemporal mechanisms involved in its development. The modification of one of these components can trigger changes in the interactions between the components and finally develop the overall system behavior.To deal with these new challenges, new processes and methods must be developed to manage all available data. In this context, the spatiotemporal data mining is define as a set of techniques and methods used to obtain useful information from large volumes of spatiotemporal data. This thesis follows the general framework of spatiotemporal data mining and sequential pattern mining. More specifically, two generic methods of pattern mining are proposed. The first one allows us to extract sequential patterns including spatial characteristics of data. In the second one, we propose a new type of patterns called spatio-sequential patterns. This kind of patterns is used to study the evolution of a set of events describing an area and its near environment.Both approaches were tested on real datasets associated to two spatiotemporal phenomena: the pollution of rivers in France and the epidemiological monitoring of dengue in New Caledonia. In addition, two measures of quality and a patterns visualization prototype are also available to assist the experts in the selection of interesting patters.

Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.

隨著地籍管理工作在廣度深度上的增加，使得地籍、土地利用等歷史資料的查詢與檢索日益頻繁，但現階段地籍管理系統之建置方法對於歷史資料查詢之效率較差。本文在探討含時間維度資料庫之建置方法，並利用異動時間與異動註記等欄位及異動紀錄表之建立，令目前地籍管理系統可達到以時間維度，或指定地號，進行地籍資料查詢與檢索。 / The more cadastral management is developed, the more historic data query of cadastre and land use are important. The primary purpose of this study is concentrated on the recoveries of parcel linage and areas on any time profile. The targets of this research are placed on design of spatio-temporal data model, and the analysis of efficiency of data recovery and query.