Knowledge discovery on spatio-temporal datasets has attracted<br />growing interests. Recent advances on remote sensing technology mean<br />that massive amounts of spatio-temporal data are being collected,<br />and its volume keeps increasing at an ever faster pace. It becomes<br />critical to design efficient algorithms for identifying novel and<br />meaningful patterns from massive spatio-temporal datasets. Different<br />from the other data sources, this data exhibits significant<br />space-time statistical dependence, and the assumption of i.i.d. is<br />no longer valid. The exact modeling of space-time dependence will<br />render the exponential growth of model complexity as the data size<br />increases. This research focuses on the construction of efficient<br />and effective approaches using approximate inference techniques for<br />three main mining tasks, including spatial outlier detection, robust<br />spatio-temporal prediction, and novel applications to real world<br />problems.<br /><br />Spatial novelty patterns, or spatial outliers, are those data points<br />whose characteristics are markedly different from their spatial<br />neighbors. There are two major branches of spatial outlier detection<br />methodologies, which can be either global Kriging based or local<br />Laplacian smoothing based. The former approach requires the exact<br />modeling of spatial dependence, which is time extensive; and the<br />latter approach requires the i.i.d. assumption of the smoothed<br />observations, which is not statistically solid. These two approaches<br />are constrained to numerical data, but in real world applications we<br />are often faced with a variety of non-numerical data types, such as<br />count, binary, nominal, and ordinal. To summarize, the main research<br />challenges are: 1) how much spatial dependence can be eliminated via<br />Laplace smoothing; 2) how to effectively and efficiently detect<br />outliers for large numerical spatial datasets; 3) how to generalize<br />numerical detection methods and develop a unified outlier detection<br />framework suitable for large non-numerical datasets; 4) how to<br />achieve accurate spatial prediction even when the training data has<br />been contaminated by outliers; 5) how to deal with spatio-temporal<br />data for the preceding problems.<br /><br />To address the first and second challenges, we mathematically<br />validated the effectiveness of Laplacian smoothing on the<br />elimination of spatial autocorrelations. This work provides<br />fundamental support for existing Laplacian smoothing based methods.<br />We also discovered a nontrivial side-effect of Laplacian smoothing,<br />which ingests additional spatial variations to the data due to<br />convolution effects. To capture this extra variability, we proposed<br />a generalized local statistical model, and designed two fast forward<br />and backward outlier detection methods that achieve a better balance<br />between computational efficiency and accuracy than most existing<br />methods, and are well suited to large numerical spatial datasets.<br /><br />We addressed the third challenge by mapping non-numerical variables<br />to latent numerical variables via a link function, such as logit<br />function used in logistic regression, and then utilizing<br />error-buffer artificial variables, which follow a Student-t<br />distribution, to capture the large valuations caused by outliers. We<br />proposed a unified statistical framework, which integrates the<br />advantages of spatial generalized linear mixed model, robust spatial<br />linear model, reduced-rank dimension reduction, and Bayesian<br />hierarchical model. A linear-time approximate inference algorithm<br />was designed to infer the posterior distribution of the error-buffer<br />artificial variables conditioned on observations. We demonstrated<br />that traditional numerical outlier detection methods can be directly<br />applied to the estimated artificial variables for outliers<br />detection. To the best of our knowledge, this is the first<br />linear-time outlier detection algorithm that supports a variety of<br />spatial attribute types, such as binary, count, ordinal, and<br />nominal.<br /><br />To address the fourth and fifth challenges, we proposed a robust<br />version of the Spatio-Temporal Random Effects (STRE) model, namely<br />the Robust STRE (R-STRE) model. The regular STRE model is a recently<br />proposed statistical model for large spatio-temporal data that has a<br />linear order time complexity, but is not best suited for<br />non-Gaussian and contaminated datasets. This deficiency can be<br />systemically addressed by increasing the robustness of the model<br />using heavy-tailed distributions, such as the Huber, Laplace, or<br />Student-t distribution to model the measurement error, instead of<br />the traditional Gaussian. However, the resulting R-STRE model<br />becomes analytical intractable, and direct application of<br />approximate inferences techniques still has a cubic order time<br />complexity. To address the computational challenge, we reformulated<br />the prediction problem as a maximum a posterior (MAP) problem with a<br />non-smooth objection function, transformed it to a equivalent<br />quadratic programming problem, and developed an efficient<br />interior-point numerical algorithm with a near linear order<br />complexity. This work presents the first near linear time robust<br />prediction approach for large spatio-temporal datasets in both<br />offline and online cases. / Ph. D.

No description available.

A raiva é uma zoonose viral que afeta o Sistema Nervoso Central (SNC) causando encefalite e meningoencefalite, de evolução aguda e fatal, que acomete mamíferos carnívoros e morcegos, e periodicamente se manifesta sob a forma de epizootias ou surtos epidêmicos em populações humanas. Neste estudo foram analisadas 16.190 amostras de bovinos, equídeos e morcegos, e menos frequentemente de outros mamíferos durante o período de 1981 a 2012, provenientes do Estado do Paraná. Desse total, 2.766 amostras foram positivas para raiva; 81,74% foram de bovinos, 10,34% de equídeos, 4,05% de morcegos, 2,31% em animais de produção não bovinos, 1,52% em caninos e 0,04%em outros animais. Ao longo da série histórica, há, para os bovinos, uma tendência de aumento das notificações e não foram observadas variação sazonal e cíclica. Na análise espaço-temporal foi detectado um aglomerado mais provável de notificações de raiva em bovinos, envolvendo 20 municípios da região litorânea e metropolitana de Curitiba entre 1981 e 1987. Além dele, foram detectados seis aglomerados secundários sugerindo uma migração da raiva ao longo do tempo no Estado do Paraná. Ao longo da série histórica dos equídeos há uma tendência de diminuição das notificações e não foram observadas variação sazonal e cíclica. Os clusters encontrados na análise espaço-temporal da raiva nos equídeos corroboram com aqueles encontrados na análise dos bovinos localizados nas mesmas regiões durante no mesmo período, sugerindo a migração do vírus da raiva no mesmo sentido da observada na análise dos bovinos. Durante o período de 1981 a 1997, os casos de raiva em morcegos acompanham o trajeto da migração dos aglomerados dos bovinos e dos equídeos, o que demonstra que a raiva ocorre endemicamente no território do Estado do Paraná em herbívoros e morcegos. / Rabies is a viral zoonosis that affects the central nervous system (CNS) causing encephalitis and meningoencephalitis, acute and fatal outcome, which affects mammalian carnivores and bats, and periodically manifests itself in the form of epidemics or outbreaks in human populations. In this study 16,190 samples of cattle, horses and bats, and less frequently other mammals were analyzed during the period 1981 to 2012, from the State of Paraná. Of this total, 2,766 samples were positive for rabies; 81.74 % were bovine, equine 10.34 %, 4.05 % of bats, 2.31 % in livestock no bovine, 1.52 % in canine, and 0.04% in other animals. Throughout the time series, there is, for cattle, a trend of increased reporting and no seasonal or cyclical variations were observed. In spatio-temporal analysis, more likely to notifications of rabies in cattle, a cluster involving 20 municipalities in coastal and metropolitan Curitiba between 1981 and 1987 was detected. Besides this, six sub clusters were detected suggesting a migration of anger over time in the state of Paraná. Throughout the historical series of equine there is a downward trend in notifications and no seasonal and cyclical variations were observed. Clusters found in the spatio-temporal analysis of rabies in horses corroborate those found in the analysis of cattle located in the same regions during the same period, suggesting the migration of rabies virus in the same direction as that observed in the cattle analysis. During the period from 1981 to 1997, cases of rabies in bats follow the migration path of clusters of bovine and equine. This shows that rabies is endemic in the state of Paraná in herbivores and bats.

A raiva é uma zoonose viral que afeta o Sistema Nervoso Central (SNC) causando encefalite e meningoencefalite, de evolução aguda e fatal, que acomete mamíferos carnívoros e morcegos, e periodicamente se manifesta sob a forma de epizootias ou surtos epidêmicos em populações humanas. Neste estudo foram analisadas 16.190 amostras de bovinos, equídeos e morcegos, e menos frequentemente de outros mamíferos durante o período de 1981 a 2012, provenientes do Estado do Paraná. Desse total, 2.766 amostras foram positivas para raiva; 81,74% foram de bovinos, 10,34% de equídeos, 4,05% de morcegos, 2,31% em animais de produção não bovinos, 1,52% em caninos e 0,04%em outros animais. Ao longo da série histórica, há, para os bovinos, uma tendência de aumento das notificações e não foram observadas variação sazonal e cíclica. Na análise espaço-temporal foi detectado um aglomerado mais provável de notificações de raiva em bovinos, envolvendo 20 municípios da região litorânea e metropolitana de Curitiba entre 1981 e 1987. Além dele, foram detectados seis aglomerados secundários sugerindo uma migração da raiva ao longo do tempo no Estado do Paraná. Ao longo da série histórica dos equídeos há uma tendência de diminuição das notificações e não foram observadas variação sazonal e cíclica. Os clusters encontrados na análise espaço-temporal da raiva nos equídeos corroboram com aqueles encontrados na análise dos bovinos localizados nas mesmas regiões durante no mesmo período, sugerindo a migração do vírus da raiva no mesmo sentido da observada na análise dos bovinos. Durante o período de 1981 a 1997, os casos de raiva em morcegos acompanham o trajeto da migração dos aglomerados dos bovinos e dos equídeos, o que demonstra que a raiva ocorre endemicamente no território do Estado do Paraná em herbívoros e morcegos. / Rabies is a viral zoonosis that affects the central nervous system (CNS) causing encephalitis and meningoencephalitis, acute and fatal outcome, which affects mammalian carnivores and bats, and periodically manifests itself in the form of epidemics or outbreaks in human populations. In this study 16,190 samples of cattle, horses and bats, and less frequently other mammals were analyzed during the period 1981 to 2012, from the State of Paraná. Of this total, 2,766 samples were positive for rabies; 81.74 % were bovine, equine 10.34 %, 4.05 % of bats, 2.31 % in livestock no bovine, 1.52 % in canine, and 0.04% in other animals. Throughout the time series, there is, for cattle, a trend of increased reporting and no seasonal or cyclical variations were observed. In spatio-temporal analysis, more likely to notifications of rabies in cattle, a cluster involving 20 municipalities in coastal and metropolitan Curitiba between 1981 and 1987 was detected. Besides this, six sub clusters were detected suggesting a migration of anger over time in the state of Paraná. Throughout the historical series of equine there is a downward trend in notifications and no seasonal and cyclical variations were observed. Clusters found in the spatio-temporal analysis of rabies in horses corroborate those found in the analysis of cattle located in the same regions during the same period, suggesting the migration of rabies virus in the same direction as that observed in the cattle analysis. During the period from 1981 to 1997, cases of rabies in bats follow the migration path of clusters of bovine and equine. This shows that rabies is endemic in the state of Paraná in herbivores and bats.

Cette thèse est consacrée à une analyse de l'activité d'éclairs des systèmes orageux en Afrique équatoriale (10°E - 35°E ; 15°S - 10°N) sur la période de temps 2005-2013. Tout d'abord, les données fournies par le réseau global de détection d'éclairs WWLLN (World Wide Lightning Location Network) ont été comparées à celles obtenues par le capteur optique spatial LIS (Lightning Imaging Sensor) afin d'estimer l'efficacité de détection relative du WWLLN. Ensuite, elles ont permis d'établir une climatologie régionale à haute résolution de l'activité d'éclairs. Enfin, elles ont été associées à des données sur les caractéristiques nuageuses et météorologiques pour des études de cas d'orages dans différentes situations, afin d'examiner les corrélations entre activité d'éclairs, activité orageuse, caractéristiques nuageuses et conditions météorologiques. La méthode adaptée pour estimer l'efficacité de détection du WWLLN dans la zone d'étude a permis d'obtenir des valeurs compatibles avec celles trouvées dans d'autres régions du monde, et de mettre en évidence une variabilité spatio-temporelle qui aide à l'interprétation des changements affectant plusieurs paramètres de l'activité d'éclairs. La climatologie réalisée dévoile des caractéristiques originales de l'évolution temporelle et de la distribution spatiale de l'activité d'éclairs, notamment celles d'un maximum très aigu dans l'Est de la République Démocratique du Congo. Ainsi, la localisation, les dimensions, la forme, la persistance saisonnière et l'environnement de ce maximum ont été précisés. La distribution zonale des éclairs montre une forte proportion dans la bande tropicale sud, liée au maximum principal mais aussi à une forte activité étalée longitudinalement et constituant un large maximum secondaire où l'activité orageuse est plus variable spatialement d'une année à l'autre, temporellement d'une saison à l'autre, et où le cycle diurne est moins marqué.[...] / This thesis is devoted to an analysis of the lightning activity of storm systems in Equatorial Africa (10°E-35°E; 15°S-10°N) over the period 2005-2013. Firstly, data from the World Wide Lightning Location Network (WWLLN) were compared with those from the Lightning Imaging Sensor (LIS) to estimate the relative detection efficiency of the WWLLN. Then, they established a high-resolution regional climatology of lightning activity. Finally, they were combined with data on cloud and meteorological characteristics to carry out thunderstorm case studies in different situations in order to examine the correlations between lightning activity, storm activity, cloud characteristics and meteorological conditions. The appropriate method introduced for estimating the WWLLN detection efficiency in the study area provides values ??consistent with those found in other regions of the world. Its spatial and temporal variability helps to interpret changes affecting several parameters of lightning activity. The climatology realized reveals original characteristics of the temporal evolution and the spatial distribution of the lightning activity, in particular those of a very sharp maximum in the Eastern Democratic Republic of Congo. Thus, the location, the dimensions, the shape, the seasonal persistence and the environment of this maximum have been specified. The zonal distribution of lightning shows a high proportion in the southern tropical band, linked to the principal maximum but also to a high activity spread out longitudinally and constituting a large secondary maximum where the storm activity is more spatially variable from one year to another, temporally from one season to another, and where the diurnal cycle is less marked. [...]

Land degradation is one of the most important drivers of landscape change around the globe. This dissertation examines land use-land cover change within a mosaic landscape in Eastern Terai, India, and shows evidence of anthropogenic factors contributing to landscape change. Land use and land cover change were examined within the Alipurduar Subdivision, a representative of the Eastern Terai landscape and the Jaldapara Wildlife Sanctuary, a protected area nested within Alipurduar through the use of multi-temporal satellite data over the past 28 years (1978 – 2006). This study establishes the potential of remote sensing technology to identify the drivers of landscape change; it provides an assessment of how regional drivers of landscape change influence the change within smaller local study extents and provides a methodology to map different types of grassland and monitor their loss within the region. The Normalized Difference Vegetation Index (NDVI) and a Normalized Difference Dry Index (NDDI) were found instrumental in change detection and the classification of different grasslands found inside the park based on their location, structure, and composition. Successful spectral segregation of different types of grasslands and their direct association with different grassland specialist species (e.g., hispid hare, hog deer, Bengal florican) clearly showed the potential of remote sensing technology to efficiently monitor these grasslands and assist in species conservation. Temporal analysis provided evidence of the loss of dense forest and grasslands within both study areas with a considerably higher rate of loss outside the protected area than inside. Results show a decline of forest from 40% in 1978 to 25% in 2006 across Alipurduar. Future trends project forest cover and grassland within Alipurduar to reduce to 15% and 5%, respectively. Within the Alipurduar, deforestation due to growth of tea industry was the primary driver of change. Flooding changed the landscape, but more intensely inside the wildlife preserve. Change of the river course inside Jaldapara during the flood of 1968 significantly altered the distribution of grassland inside the park. Unless, the direction of landscape change is altered, future trends predict growth of the tea industry within the region, increased forest loss, and homogenization of the landscape.

No description available.

No description available.

With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / Master of Science / Because of the ubiquity of the Internet and smart devices, a tremendous amount of data has been collected from multiple sources like vehicles, purchasing details, online searches etc., which is being used to develop innovative applications. These applications aim to improve economic, social and personal lives of people through new start-of-the-art techniques like machine learning and data analytics. With this motivation in mind, we present three applications leveraging the data collected from urban cities to improve the life of people living in such cities. First, we start by using taxi trip data, collected around a given location, and use it to develop a model that can predict taxi demand for next half hour. This model can be used to schedule advertisements or dispatch taxis depending upon the demand. Second, using a similar mathematical approach, we propose a strategy to predict the number of crimes that can happen at a given location on the next day. This helps in maintaining law and order in the city. As our third and last application, we use the traffic and historical charging data to predict electric vehicle charging demand for the next day. Electricity generating power plants can use this model to prepare themselves for the higher demand emerged because of the increasing use of electric vehicles.

Fluxos de dados são usualmente caracterizados por grandes quantidades de dados gerados continuamente em processos síncronos ou assíncronos potencialmente infinitos, em aplicações como: sistemas meteorológicos, processos industriais, tráfego de veículos, transações financeiras, redes de sensores, entre outras. Além disso, o comportamento dos dados tende a sofrer alterações significativas ao longo do tempo, definindo data streams evolutivos. Estas alterações podem significar eventos temporários (como anomalias ou eventos extremos) ou mudanças relevantes no processo de geração da stream (que resultam em alterações na distribuição dos dados). Além disso, esses conjuntos de dados podem possuir características espaciais, como a localização geográfica de sensores, que podem ser úteis no processo de análise. A detecção dessas variações de comportamento que considere os aspectos da evolução temporal, assim como as características espaciais dos dados, é relevante em alguns tipos de aplicação, como o monitoramento de eventos climáticos extremos em pesquisas na área de Agrometeorologia. Nesse contexto, esse projeto de mestrado propõe uma técnica para auxiliar a análise espaço-temporal em data streams multidimensionais que contenham informações espaciais e não espaciais. A abordagem adotada é baseada em conceitos da Teoria de Fractais, utilizados para análise de comportamento temporal, assim como técnicas para manipulação de data streams e estruturas de dados hierárquicas, visando permitir uma análise que leve em consideração os aspectos espaciais e não espaciais simultaneamente. A técnica desenvolvida foi aplicada a dados agrometeorológicos, visando identificar comportamentos distintos considerando diferentes sub-regiões definidas pelas características espaciais dos dados. Portanto, os resultados deste trabalho incluem contribuições para a área de mineração de dados e de apoio a pesquisas em Agrometeorologia. / Data streams are usually characterized by large amounts of data generated continuously in synchronous or asynchronous potentially infinite processes, in applications such as: meteorological systems, industrial processes, vehicle traffic, financial transactions, sensor networks, among others. In addition, the behavior of the data tends to change significantly over time, defining evolutionary data streams. These changes may mean temporary events (such as anomalies or extreme events) or relevant changes in the process of generating the stream (that result in changes in the distribution of the data). Furthermore, these data sets can have spatial characteristics such as geographic location of sensors, which can be useful in the analysis process. The detection of these behavioral changes considering aspects of evolution, as well as the spatial characteristics of the data, is relevant for some types of applications, such as monitoring of extreme weather events in Agrometeorology researches. In this context, this project proposes a technique to help spatio-temporal analysis in multidimensional data streams containing spatial and non-spatial information. The adopted approach is based on concepts of the Fractal Theory, used for temporal behavior analysis, as well as techniques for data streams handling also hierarchical data structures, allowing analysis tasks that take into account the spatial and non-spatial aspects simultaneously. The developed technique has been applied to agro-meteorological data to identify different behaviors considering different sub-regions defined by the spatial characteristics of the data. Therefore, results from this work include contribution to data mining area and support research in Agrometeorology.