Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet -- to the best of our knowledge -- no working solution exists. To this end, in the present paper we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at http://data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

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.

Les COV oxygénés sont des espèces peu mesurées dans l’air ambiant en raison de difficultés analytiques liées à la présence d’eau dans l’échantillon. Nos connaissances sur les teneurs, les sources et les puits de ces composés restent incomplètes. Afin de caractériser les COV (notamment les COVO) sur la zone littorale multi-influencée de Dunkerque, deux méthodes de mesures complémentaires ont été développées : l’échantillonnage passif (révélant la répartition spatiale des espèces) et l’analyse en mode on-line (informant l’évolution temporelle des teneurs).Deux campagnes de mesures préliminaires réalisées sur la zone d’étude pendant des périodes hivernale et estivale à l’aide des tubes passifs Radiello ont permis d’identifier les COV présents sur la zone d’étude et d’examiner les origines des BTEX mesurés. Concernant la mesure en mode on-line, une méthode chromatographique pour l’analyse de 14 composés oxygénés, 4 nitriles a été développée et validée. Un procédé de réduction de l’humidité avant l’étape de préconcentration a permis d’utiliser une colonne polaire spécifique aux composés oxygénés. Des premières mesures effectuées à Douai et à Dunkerque ont mis en évidence les origines urbaines et industrielles de certains composés. Quant à l’échantillonnage passif, la mesure de 2 composés oxygénés (éthanol acétone) et des BTEX a été développée pour une durée de prélèvement de 8 h. Les caractéristiques métrologiques de l’échantillonneur ont été déterminées : débits d’échantillonnage dans les conditions standard et limites de détection et l’influence des facteurs environnementaux sur l’échantillonnage. Une campagne de mesures à l’aide d’échantillonneurs passifs a ensuite été réalisée pendant un épisode de brise de mer. Des cartographies des COV et de l’ozone ont été établies et ont montré les « points chauds » en COV à proximité des émetteurs industriels et une zone de formation locale d’ozone sur le site d’étude. / Only few studies were dedicated to Oxygenated Volatile Organic Compounds (OVOC) in ambient air because of the analytical difficulties encountered during the measurements of these compounds caused by the presence of water in the sample. Consequently, there is a lack of knowledge of OVOC concentration, sources and sinks. In order to characterize the VOC (especially OVOC) in the urban industrialized area of Dunkerque two methods have been developed: passive sampling (information about spatial distribution) automated monitoring analyzer (information about temporal evolution). Two preliminary measurement campaigns have been carried out in the studied zone and have led to the identification of VOC present on the studied zone and to examine the BTEX origins. Concerning the online analyzer, a chromatographic method for the measurement of 14 OVOC and 4 nitriles was developed and validated. Reduction of humidity before the preconcentration step allowed the use of a polar column specific for oxygenated compounds. Measurement in two urban sites: Dunkerque and Douai highlighted the urban and industrial origins for some compounds. As for the passive sampling, the measurement of 2 oxygenated compounds (ethanol acetone) and BTEX was developed for 8 hours exposure time. The metrological characteristics have been evaluated: sampling rate, detection limits and environmental factors on the sampling rate. A measurement campaign has been established during a sea breeze event. Mapping of VOC and ozone have shown the hot spot of VOC pollution in the industrial area and a local ozone formation

A simplistic view of a dataset is that it is collection of numbers. In fact data are much more than that and all data are collected at a set place and time. Often either the location, or the time, is fixed within the dataset and one or both are disregarded. When the place and time of the collection are incorporated into the analysis, the result is a spatio-temporal model. Spatio-temporal data are the focus of this thesis. The majority of the datasets used are radio tracking studies of animals where the objective is to measure the habitat use. Observations are made over a long period of time and a large area. The largest dataset analysed tracks over a hundred animals, in an area larger than 40 square miles, for multiple years. In this context understanding the spatio-temporal relationships between observations is essential. Even data that do not have an obvious spatial component can benefit from spatio-temporal analysis. For example, the data presented on volatility in the stock market do not have an obvious spatial component. The spatial component is the location in the market, not a physical location. Two different methods for measuring and correcting bias are presented. One method relies on direct modelling of the underlying process being observed. The underlying process is animal movement. A model for animal movement is constructed and used to estimate the missing observations that are thought to be the cause of the bias. The second method does not model the animal movement, but instead relies on a Bayesian Hierarchical Model with some simple assumptions. A long running estimation is used to calculate the most likely result without ever directly estimating the underlying equations. In the second section of the thesis two methods for measuring change from shifts in both spatial and temporal location are presented. The methods, Large Diffeomorphic Deformation Metric Mapping (LDDMM) and Diffeomorphic Demons (DD), were originally developed for anatomical data and are adapted here for nonparametric regression surfaces. These are the first applications of LDDMM and DD outside of computational anatomy.

Les relations spatiales entre les différentes régions dans une image sont utiles pour la compréhension et l'interprétation de la scène représentée. L'analyse Spatio-temporelle d'une scène implique l'intégration du temps dans des relations spatiales entre les objets en mouvement. Les relations spatio-temporelles sont définies dans un intervalle de temps utilisant la géométrie 3D ou l'extension de la géométrie 2D à la dimension temporelle. La modélisation des relations spatiales dynamiques prend en compte la position relative des objets et leurs relations directionnelles, ceci implique les relations topologiques, directionnelles et de distance. Ces relations sont étendues au domaine temporel. Dans notre travail, on décrit une méthode de combinaison d’information topologique et directionnelle où les relations d'Allen floues 1D sont appliquées au domaine spatial. La méthode proposée intègre le flou au niveau des relations. La méthode très gourmande initialement en temps de calcul en raison de l’approximation des objets ainsi qu'à l'algorithme de fuzzification des segments des sections longitudinales est améliorée en utilisant une approximation polygonale adaptée sur les objets considérés. L'algorithme du fuzzification des segments d'une section longitudinale inclut des opérateurs d'agrégation floue. Dans la méthode proposée, Les relations topologiques 2Dsont représentées par un histogramme. Les relations floues n'étant pas exhaustives, un algorithme de défuzzification des relations spatiales a été proposé pour réaliser un ensemble JEPD de relations spatiales. Cet ensemble de relations spatiales est représenté par un graphe de voisinage où chaque nœud du graphe représente la relation topologique et directionnelle. Cette méthode définit des relations spatio-temporelles en utilisant le modèle de données Espace-Temps. Un ensemble de relations spatio-temporelles est également fourni à l'aide de la stabilité topologique. Afin de valider le modèle, nous avons développé des applications fondées sur le raisonnement spatio-temporel proposé. Celui-ci a permit la création de tables de composition pour les relations spatiales topologiques structurées en sous-tables. Les entités de ces sous-tables sont liées les unes aux autres par des relations spatiales. Dans une seconde application, nous avons proposé une méthode de prédiction des évènements entre objets en mouvement fondée sur le même raisonnement spatio-temporel. Les objets en mouvement changeant de position à chaque instant, la prédiction de la nouvelle position spatiale d'un objet tient compte des états de relations spatiales calculées précédemment. / Spatial relations between different image regions are helpful in image understanding, interpretation and computer vision applications. Spatio-temporal analysis involves the integration of spatial relations changing over time between moving objects of a dynamic scene. Spatio-temporal relations are defined for a selected time interval using 3D geometry or extension of 2D object geometry to the time dimension with sequence occurrence of primitive events for each snapshot. Modeling dynamic spatial relations takes into account the relative object position and their directional relations; this involves the topological, directional and distance relations and their logical extension to the temporal domain. In this thesis, a method for combining topological and directional relations information is discussed where 1D temporal fuzzy Allen relations are applied in spatial domain. Initially, the method has a high computational cost. This computing cost is due to the object approximation and the fuzzification algorithm of segments. The computing time has been using polygonal object approximation. Fuzzification algorithm is replaced with fuzzy aggregation operators for segments of a longitudinal section. In this method, two dimensional topological relations are represented in a histogram. The representation method for two dimensional spatial relations has been changed. These fuzzy relations are not Jointly Exhaustive and Pairwise Disjoint (JEPD). An algorithm for defuzzification of spatial relations is proposed to realize JEPD set of spatial relations, these JEPD spatial relations are represented in a neighborhood graph. In this neighborhood graph, each node represents the topological and directional relation. This method is further extended for defining spatio-temporal relations using space and time data model, a set of spatio-temporal relations are also elaborated using the stability property in topology. In an application, a method for spatio-temporal reasoning based on this new model is developed. Spatio-temporal reasoning consists of developing the composition tables for spatial relations. Composition table for topological relations are rearranged into sub-tables. Entities in these sub-tables are related to each other and mathematical rules are defined for composition of spatial relations which elaborate the relation between entities of sub-tables. In another application, we propose a method for motion event predictions between moving objects. It is a similar process to the spatio-temporal reasoning. Dynamic objects occupy different places at different time points, these objects have multiple choices for subsequent positions and a unique history. Prediction about motion events take into account the history of a moving object and predict about the semantics of a motion event.

By improving the precision and accuracy of public health surveillance tools, we can improve cost-efficacy and obtain meaningful information to act upon. In this dissertation, we propose statistical methods for improving public health surveillance research. In Chapter 1, we introduce a pooled testing option for HIV prevalence estimation surveys to increase testing consent rates and subsequently decrease non-response bias. Pooled testing is less certain than individual testing, but, if more people to submit to testing, then it should reduce the potential for non-response bias. In Chapter 2, we illustrate technical issues in the design of neonatal tetanus elimination surveys. We address identifying the target population; using binary classification via lot quality assurance sampling (LQAS); and adjusting the design for the sensitivity of the survey instrument. In Chapter 3, we extend LQAS survey designs for monitoring malnutrition for longitudinal surveillance programs. By combining historical information with data from previous surveys, we detect spikes in malnutrition rates. Using this framework, we detect rises in malnutrition prevalence in longitudinal programs in Kenya and the Sudan. In Chapter 4, we develop a computationally efficient geostatistical disease mapping model that naturally handles model fitting issues due to temporal boundary misalignment by assuming that an underlying continuous risk surface induces spatial correlation between areas. We apply our method to assess socioeconomic trends in breast cancer incidence in Los Angeles between 1990 and 2000. In Chapter 5, we develop a statistical framework for addressing statistical uncertainty associated with denominator interpolation and with temporal misalignment in disease mapping studies. We propose methods for assessing the impact of the uncertainty in these predictions on health effects analyses. Then, we construct a general framework for spatial misalignment in regression.

Pagrindinis šio darbo tikslas yra išnagrinėti judančių objektų indeksavimo duomenų bazėse problemas, siūlomus sprendimus bei palyginti keleto iš jų veiksmingumą. Įvairiais pjūviais buvo lyginami praeities duomenis indeksuojantys R ir iš jo išvesti STR bei TB medžiai. Eksperimentai atlikti naudojant sugeneruotus judančių objektų duomenis. Gauti rezultatai parodė, kad indeksų veiksmingas priklauso nuo tam tikrų sąlygų ir aplinkybių, kuriomis jie naudojami. / Over the past few years, there has been a continuous improvement in the wireless communications and the positioning technologies. As a result, tracking the changing positions of continuously moving objects is becoming increasingly feasible and necessary. Databases that deal with objects that change their location and/or shape over time are called spatio-temporal databases. Traditional database approaches for effective information retrieval cannot be used as the moving objects database is highly dynamic. A need for so called spatio-temporal indexing techniques comes to scene. Mainly, by the problem they are addressed to, indices are divided into two groups: a) indexing the past and b) indexing the current and predicted future positions. Also the have been proposed techniques covering both problems. This work is a survey for well known and used indices. Also there is a performance comparison between several past indexing methods. STR Tree, TB Tree and the predecessor of many indices, the R Tree are compared in various aspects using generated datasets of simulated objects movement.