This dissertation describes spatio-temporal properties of rainfall. Rainfall in space was modeled by a precipitation areal reduction factor (ARF) using a NEXRAD image. The storms are represented as ellipses, which are determined by maximizing the volume of rainfall. The study investigated 18,531 storms of different durations that took place in different seasons and regions of Texas. Statistical analysis was carried out to find a relationship between ARFs and predictor variables (storm duration, area, season, region, and precipitation depth). The stochastic model for temporal disaggregation of rainfall data was evaluated across Texas. The hourly historic data from the selected 531 hourly gauges in Texas were used to evaluate the model’s performance to reproduce hourly rainfall statistics. Spatial trends in performance statistics or spatial patterns among gauge characteristics (e.g. period of record, precipitation statistics) were examined by cluster analysis. Since no spatial trends or patterns were identified, the state database is used and verified for a selection of gauges. The method was further applied to estimate intensity-duration curves for hydrologic applications. To obtain basic information on the spatial and dynamic patterns of rainfall over an area, it is necessary to identify and track a storm objectively. Automated algorithms are needed to process a large amount of radar images. A methodology was presented to overcome the identification and tracking difficulties of one-hour accumulated distributed rainfall data and to extract the characteristics of moving storms (e.g., size, intensity, orientation, propagation speed and direction, etc.). The method presented in this dissertation allows the user to better understand the precipitation patterns in any given area of the United States, and yields parameters that describe storm dynamic characteristics. These parameters can then be used in the definition of synthetic dynamic storms for hydrologic modeling.

No description available.

Over half of the world's population are at risk of infection from dengue fever (Guha-Sapir 2005). This viral disease is transmitted by the female Aedes aegypti mosquito and is the major source of human death in the world when compared with any other vector borne disease (Gubler1998a). The first important epidemic of dengue haemorrhagic fever (DHF) in America was reported in Cuba in 1981 and subsequently in Venezuela during 1989 and 1990 (Oletta2006, Brightmer1998). There has been a trend of increased incidence in many Central and South American countries since 1990 - Brazil, Venezuela, Honduras and Mexico (SanMartin2010) with Venezuela having the highest number of cases of DHF. The urgent need for more effective public health measures to combat this disease in Venezuela drove the decision to undertake the work described in this dissertation. Spatio-Temporal modelling has been developed for the prediction of the occurrence of dengue fever in Zulia state, Venezuela. A systematic approach has been adopted to validate this tool. At the first stage of the analysis an exploratory study was performed to underline the most significant features of the dynamics of incidence rates of dengue fever from 2002 to 2008. In the second stage a Generalized Linear Model (GLM) approach was used in the form of Negative Binomial Generalized Linear Mixed model (GLMM) to compare Relative Risk (RR) across exposure groups by age and sex, using an epidemiological dataset covering the whole of Zulia State, Venezuela. This approach used both a frequentist and a Bayesian perspective for comparative purposes of both outcomes and methodologies. Finally a Spatio-Temporal model was constructed based on Generalized Additive Mixed model (GAMM) framework because the earlier analysis identified a complex association between covariates and response variables. This GAMM structure was further developed so that it could be used to help predict future outbreaks of the disease in Zulia state with a good degree of accuracy.

This thesis makes a contribution to the spatio-analysis of contemporary cultures that creatively and reflexively experiment with the surface of the body. Drawing upon the philosophical work of Henri Lefebvre, The Savage Body provides an exploration of representational space. Lefebvre considered this element of social relations to be clandestine and transgressive, relating to the aesthetic sphere of symbols and codes. For Lefebvre, these spaces were lived through associated imagery, embracing passion, pleasure and dismay. This thesis provides an exploration of two distinct, yet related representational spaces. The Fetish Scene is a culture that has a more bounded sense of symbolic community and dwells within the night-time sexual economy of club land. In comparison, the Pierced Body is a more fragmented sphere, scattered and located within a diverse cultural landscape. Though separate cultures, these two spaces overlap in the Torture Garden, a night-club on the periphery and cutting edge of the fetish scene. It is within this realm that the fetish people and pierced bodies meet to celebrate the pleasures of the flesh. This work explores the way these cultural sites are produced through the creativity of those involved and the subjective relation to representational space. The body is considered as actively weaving its way through landscapes which both mark and leave their mark on those engaging in these spheres. Beyond mapping out the cultures of the Fetish Scene and the Pierced Body on a descriptive and broad level, I also consider identity as reflexively situated within representational space and the way that memory plays an active part in constructing narratives that operate on an individual and broader cultural level. Developing the idea of a 'haptic ethnography', drawing upon a variety of methods, I have used and documented my own body as a means of exploring these rhythmic realms, speaking with the Other instead of for the Other.

Les chalcogénures sont des matériaux très prometteurs pour des applications dans divers domaines, comme l’enregistrement et la transmission des données, l’optique intégrée, les capteurs ou la médecine. Ceci est dû à leurs propriétés exceptionnelles, en termes de transparence dans l’infrarouge, effets photo-induits, grande solubilité pour les terres rares, ou contraste élevé des paramètres électriques et optiques lors d’un changement de phase. Pour beaucoup de ces applications, les chalcogénures doivent se présenter sous forme de couches minces. Une méthode efficace et flexible pour obtenir de telles couches est le dépôt par ablation laser (PLD).Dans cette thèse nous avons entrepris une étude systématique sur l’influence de divers paramètres, comme la longueur d’onde, la durée de l’impulsion ou la fluence du laser, la pression de dépôt et la distance cible-substrat, sur les propriétés des couches minces déposées. Deux familles de composés ont été étudiées : (GeTe)x(Sb2Te3)1-x (avec x = 0, 1/3, 1/2, 2/3, 1) et GaLaS (pur ou dopé avec des terres rares – Er et Pr). Une étude en spectroscopie optique d’émission a été également effectuée afin d’explorer la dynamique et l’énergétique du plasma créé par ablation laser. Les propriétés des couches minces ont été caractérisées par microscopie optique et électronique, profilométrie, diffraction de rayons X, spectroscopie Raman, ellipsométrie etc. Les principaux résultats indiquent de meilleures propriétés pour les couches déposées à faible longueur d’onde, courte durée d’impulsion et fluence modérée. Ils ouvrent la voie vers l’optimisation du processus PLD pour la croissance de couches minces de chalcogénures de manière contrôlée. / Chalcogenides are among the most promising materials for applications in various fields, like data storage and transmission, integrated optics, bio- and chemical-sensing, or medicine. This is due to their outstanding properties, in terms of wide infrared transparency, photo-induced effects, high rare-earth solubility or high contrast in electrical and optical parameters upon phase transformation. For many of these applications, processing of chalcogenides in form of thin films with required chemical composition and appropriate physical properties is necessary. Pulsed Laser Deposition (PLD) is one of the most efficient and flexible methods for the preparation of such multicomponent layers.In this thesis we performed a systematic study on the influence of various PLD parameters, like laser wavelength, pulse duration and fluence, background pressure and target-substrate distance, on the properties of deposited thin films. Two families of chalcogenide compounds were explored: (GeTe)x(Sb2Te3)1-x (with x = 0, 1/3, 1/2, 2/3, 1) and GaLaS (pure or doped with Er and Pr rare earths). Additionally, a time- and space-resolved optical emission spectroscopy study was performed in order to characterize the dynamics and energetics of the laser ablation plasma plume. The properties of the thin films were investigated by specific methods, as optical and electronic microscopy, profilometry, X-ray diffraction, Raman spectroscopy, ellipsometry etc. The main results indicate better properties for samples deposited at lower wavelength, short pulse duration and moderate fluence. They open the way for the optimization of the PLD process for chalcogenide thin film growth in a controlled manner.

No description available.

Les données permettant de décrire des phénomènes spatio-temporels sont de plus en plus nombreuses. Ces nouvelles données peuvent alors être éloignées de celles habituellement observées pour l'étude de certains phénomènes. Leur analyse, selon une approche hypothético-déductive telle qu'elle est majoritairement effectuée en statistique et dans les SIG, peut ainsi passer sous silence certaines informations insoupçonnées, mais pertinentes, sur les dynamiques de ces phénomènes spatio-temporels.Il peut alors être intéressant de simplement donner à voir les données, pour observer ce qu'elles ont à montrer, avant de les analyser. Ce principe est celui de l'analyse exploratoire: le procédé est de permettre à un utilisateur d'effectuer une exploration libre des données, au moyen de représentations visuelles, afin de mettre en lumière des structures ou des relations insoupçonnées. Aujourd'hui, l'analyse exploratoire est notamment possible au moyen d'environnements de visualisation, intégrant différentes représentations graphiques et cartographiques interactives.Les environnements de visualisation sont majoritairement développés de manière ad hoc, dans le cadre d'une thématique particulière. Or l'émergence constante de nouvelles données incite à promouvoir des méthodes d'analyse applicables à des phénomènes de différentes natures. En fonction de la problématique dans laquelle s'insèrent ces derniers, les dynamiques sur lesquelles va se focaliser l'analyse diffèrent. Analyser un phénomène météorologique dans un but de prévision implique de s’intéresser aux récurrences cycliques du phénomène. Analyser l'évolution d'une population pour la mise en place de politiques publiques implique d’analyser ce phénomène sur le temps long et selon différentes zones de l’espace.Notre objectif est de proposer une méthode d'analyse exploratoire des phénomènes spatio-temporels et de leurs dynamiques, indépendante du thème traité. Pour cela, nous proposons un environnement de géovisualisation, GrAPHiST (Géovisualisation pour l'Analyse des PHenomenes Spatio-Temporels), permettant l'analyse de différentes dynamiques, selon différentes échelles spatiales et temporelles (linéaires ou cycliques). Développer cet environnement implique de s’interroger sur la modélisation du changement dans l’espace, la nature des dynamiques spatio-temporelles à étudier, et les outils visuels et interactifs permettant de les identifier.Ainsi, les contributions de notre recherche se situent à plusieurs niveaux :- une modélisation générique des phénomènes spatio-temporels, sous la forme de séries événementielles;- de nouvelles méthodes de représentations graphiques et interactives, autorisant la recherche et l'identification des dynamiques spatio-temporelles, notamment: l'introduction de diagrammes temporels interactifs permettant la recherche visuelle de récurrences cycliques dans les données spatio-temporelles; l'utilisation de règles de symbologie permettant la visualisation des relations entre les composantes temporelle et spatiale des phénomènes; de nouvelles méthodes de représentations des agrégats d'événements proches, permettant d'identifier des structures dans leur distribution spatio-temporelle;- la formalisation d’une approche d'analyse exploratoire des dynamiques spatio-temporelles, déclinée en plusieurs scénarios selon l’objectif poursuivi.Nous validons notre approche en l'appliquant à l'analyse de différents jeux de données. L'objectif est de vérifier la possibilité d'identifier des dynamiques, relatives au temps linéaire ou cyclique, au moyen de GrAPHiST, et d'illustrer le caractère générique de l'approche, ainsi que les opportunités d'analyse offertes par l'environnement. / Datasets allowing the description of spatio-temporal phenomena are becoming ever more numerous. These new data can be very different from those usually observed for studying spatio-temporal phenomena. An analysis through a hypothetico-deductive approach, like is mainly done in statistic and GIS domains, can ignore some unsuspected, but relevant, information about the dynamics of these spatio-temporal phenomena.It can be interesting then, to just present the data, to observe what they have to show, before analysing them. This is the principle of the exploratory data analysis: the process is to allow a user to freely explore data, through visual representations, in order to highlight unsuspected structures or relationships. Today, exploratory analysis is possible through visualization environments, which integrate different graphic or cartographic interactive representations.Visualization environments are mainly developed in an ad hoc manner, in the context of a particular thematic field. However, the constant appearance of new data encourages promoting analysis methods, which could be applied to several types of phenomena. According to the domain related to these phenomena, the analysis will be focused on different dynamics. Analysing a meteorological phenomenon, in a forecasting purpose, implies a focus on the cyclic recurrences of the phenomenon. Analysing the increase of a population, for the purpose of deciding public policies, implies an analysis of the phenomenon on a long-term, through different spatial areas.Our objective is to propose a method for the exploratory analysis of spatio-temporal phenomena and their dynamics, which would be independent of the topic. In order to achieve this, we propose a geovisualization environment, GrAPHiST (Géovisualisation pour l'Analyse des PHenomenes Spatio-Temporels; Geovisualization for spatio-temporal phenomena analysis), allowing the analysis of several dynamics, through different spatial and temporal (linear or cyclic) scales. Developing this environment implies to focus on how spatial changes are modelled, on the nature of the spatio-temporal dynamics we have to study, and on the visual and interactive tools, which allow the identification of these dynamics.So, the contributions of our research can be found at several levels:a generic modelling approach of spatio-temporal phenomena, in the form of event series;new graphical and interactive representation methods, which allow the searching and the identification of spatio-temporal dynamics, including: the introduction of interactive temporal diagrams, which allow the visual searching of cyclic recurrences in spatio-temporal data; the use of symbology rules, which allow the visualization of relationships between the spatial and temporal components of phenomena; new methods to represent aggregated closed events, which allow to identify structures in their spatio-temporal distribution;the formalization of an exploratory approach for the spatio-temporal dynamics analysis, divided into several scenarios, according to the purpose of the analysis.We validate our proposition by applying it to the analysis of several datasets. The objective is to verify the possibility to identify dynamics, related to linear or cyclic time, through the use of GrAPHiST, and to illustrate the generic aspect of the approach, as well as the analysis opportunities given by the environment.

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use di ff erent schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union's classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/ .

Intuitively, most datasets found in Open Data are organised by spatio-temporal scope, that is, single datasets provide data for a certain region, valid for a certain time period. 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. Therefore, we argue that spatio-temporal search is a crucial need for Open Data portals and across Open Data portals, yet - to the best of our knowledge - no working solution exists. We argue that - just like for for regular Web search - knowledge graphs can be helpful to significantly improve search: in fact, the ingredients for a public knowledge graph of geographic entities as well as time periods and events exist already on the Web of Data, although they have not yet been integrated and applied - in a principled manner - to the use case of Open Data search. In the present paper we aim at doing just that: 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, (iii) enable structured, spatio-temporal search over Open Data catalogs through our spatio-temporal knowledge graph, both via a search interface as well as via a SPARQL endpoint, available at data.wu.ac.at/odgraphsearch/ / Series: Working Papers on Information Systems, Information Business and Operations

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use different schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union¿s classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/.