828 resultados para visual data analysis
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Mode of access: Internet.
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Mode of access: Internet.
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Vol. 7: second ed., 1975.
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"NIIC-0600-75-H006."
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I present results of my evaluation to identify topographic lineaments that are potentially related to post-glacial faulting using bare-earth LiDAR topographic data near Ridley Island, British Columbia. The purpose of this evaluation has been to review bare-earth LiDAR data for evidence of post-glacial faulting in the area surrounding Ridley Island and provide a map of the potential faults to review and possibly field check. My work consisted of an extensive literature review to understand the tectonic, geologic, glacial and sea level history of the area and analysis of bare-earth LiDAR data for Ridley Island and the surrounding region. Ridley Island and the surrounding north coast of British Columbia have a long and complex tectonic and geologic history. The north coast of British Columbia consists of a series of accreted terranes and some post-accretionary deposits. The accreted terranes were attached to the North American continent during subduction of the Pacific Plate between approximately 200 Ma and 10 Ma. The terrane and post-accretionary deposits are metamorphosed sedimentary, volcanic and intrusive rocks. The rocks have experienced significant deformation and been intruded by plutonic bodies. Approximately 10 Ma subduction of the Pacific Plate beneath the North America Plate ceased along the central and north coast of British Columbia and the Queen Charlotte Fault Zone was formed. The Queen Charlotte Fault Zone is a transform-type fault that separates the Pacific Plate from the North America Plate. Within the past 1 million years, the area has experienced multiple glacial/interglacial cycles. The most recent glacial cycle occurred approximately 23,000 to 13,500 years ago. Few Quaternary deposits have been mapped in the area. The vast majority of seismicity around the northwest coast of British Columbia occurs along the Queen Charlotte Fault Zone. Numerous faults have been mapped in the area, but there is currently no evidence to suggest these faults are active (i.e. have evidence for post-glacial surface displacement or deformation). No earthquakes have been recorded within 50 km of Ridley Island. Several small earthquakes (less than magnitude 6) have been recorded within 100 km of the island. These earthquakes have not been correlated to active faults. GPS data suggests there is ongoing strain in the vicinity of Ridley Island. The strain has the potential to be released along faults, but the calculated strain may be a result of erroneous data or accommodated aseismically. Currently, the greatest known seismic hazard to Ridley Island is the Queen Charlotte Fault Zone. LiDAR data for Ridley Island, Digby Island, Lelu Island and portions of Kaien Island, Smith Island and the British Columbia mainland were reviewed and analyzed for evidence of postglacial faulting. The data showed a strong fabric across the landscape with a northwest-southeast trend that appears to mirror the observed foliation in the area. A total of 80 potential post-glacial faults were identified. Three lineaments are categorized as high, forty-one lineaments are categorized as medium and thirty-six lineaments are categorized as low. The identified features should be examined in the field to further assess potential activity. My analysis did not include areas outside of the LiDAR coverage; however faulting may be present there. LiDAR data analysis is only useful for detecting faults with surficial expressions. Faulting without obvious surficial expressions may be present in the study area.
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Thesis (Ph.D.)--University of Washington, 2016-06
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Quantile computation has many applications including data mining and financial data analysis. It has been shown that an is an element of-approximate summary can be maintained so that, given a quantile query d (phi, is an element of), the data item at rank [phi N] may be approximately obtained within the rank error precision is an element of N over all N data items in a data stream or in a sliding window. However, scalable online processing of massive continuous quantile queries with different phi and is an element of poses a new challenge because the summary is continuously updated with new arrivals of data items. In this paper, first we aim to dramatically reduce the number of distinct query results by grouping a set of different queries into a cluster so that they can be processed virtually as a single query while the precision requirements from users can be retained. Second, we aim to minimize the total query processing costs. Efficient algorithms are developed to minimize the total number of times for reprocessing clusters and to produce the minimum number of clusters, respectively. The techniques are extended to maintain near-optimal clustering when queries are registered and removed in an arbitrary fashion against whole data streams or sliding windows. In addition to theoretical analysis, our performance study indicates that the proposed techniques are indeed scalable with respect to the number of input queries as well as the number of items and the item arrival rate in a data stream.
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The paper investigates a Bayesian hierarchical model for the analysis of categorical longitudinal data from a large social survey of immigrants to Australia. Data for each subject are observed on three separate occasions, or waves, of the survey. One of the features of the data set is that observations for some variables are missing for at least one wave. A model for the employment status of immigrants is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response and then subsequent terms are introduced to explain wave and subject effects. To estimate the model, we use the Gibbs sampler, which allows missing data for both the response and the explanatory variables to be imputed at each iteration of the algorithm, given some appropriate prior distributions. After accounting for significant covariate effects in the model, results show that the relative probability of remaining unemployed diminished with time following arrival in Australia.
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The importance of availability of comparable real income aggregates and their components to applied economic research is highlighted by the popularity of the Penn World Tables. Any methodology designed to achieve such a task requires the combination of data from several sources. The first is purchasing power parities (PPP) data available from the International Comparisons Project roughly every five years since the 1970s. The second is national level data on a range of variables that explain the behaviour of the ratio of PPP to market exchange rates. The final source of data is the national accounts publications of different countries which include estimates of gross domestic product and various price deflators. In this paper we present a method to construct a consistent panel of comparable real incomes by specifying the problem in state-space form. We present our completed work as well as briefly indicate our work in progress.
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This paper describes how the statistical technique of cluster analysis and the machine learning technique of rule induction can be combined to explore a database. The ways in which such an approach alleviates the problems associated with other techniques for data analysis are discussed. We report the results of experiments carried out on a database from the medical diagnosis domain. Finally we describe the future developments which we plan to carry out to build on our current work.
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Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.
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Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of parameters in a latent variable model closely related to factor analysis. We consider the properties of the associated likelihood function, giving an EM algorithm for estimating the principal subspace iteratively, and discuss the advantages conveyed by the definition of a probability density function for PCA.
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This paper examines the source country determinants of FDI into Japan. The paper highlights certain methodological and theoretical weaknesses in the previous literature and offers some explanations for hitherto ambiguous results. Specifically, the paper highlights the importance of panel data analysis, and the identification of fixed effects in the analysis rather than simply pooling the data. Indeed, we argue that many of the results reported elsewhere are a feature of this mis-specification. To this end, pooled, fixed effects and random effects estimates are compared. The results suggest that FDI into Japan is inversely related to trade flows, such that trade and FDI are substitutes. Moreover, the results also suggest that FDI increases with home country political and economic stability. The paper also shows that previously reported results, regarding the importance of exchange rates, relative borrowing costs and labour costs in explaining FDI flows, are sensitive to the econometric specification and estimation approach. The paper also discusses the importance of these results within a policy context. In recent years Japan has sought to attract FDI, though many firms still complain of barriers to inward investment penetration in Japan. The results show that cultural and geographic distance are only of marginal importance in explaining FDI, and that the results are consistent with the market-seeking explanation of FDI. As such, the attitude to risk in the source country is strongly related to the size of FDI flows to Japan. © 2007 The Authors Journal compilation © 2007 Blackwell Publishing Ltd.
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Objective: It is investigated to which extent measures of nonlinearity derived from surrogate data analysis are capable to quantify the changes of epileptic activity related to varying vigilance levels. Methods: Surface and intracranial EEG from foramen ovale (FO-)electrodes was recorded from a patient with temporal lobe epilepsy under presurgical evaluation over one night. Different measures of nonlinearity were estimated for non-overlapping 30-s segments for selected channels from surface and intracranial EEG. Additionally spectral measures were calculated. Sleep stages were scored according to Rechtschaffen/Kales and epileptic transients were counted and classified by visual inspection. Results: In the intracranial recordings stronger nonlinearity was found ipsilateral to the epileptogenic focus, more pronounced in NREM sleep, weaker in REM sleep. The dynamics within the NREM episodes varied with the different nonlinearity measures. Some nonlinearity measures showed variations with the sleep cycle also in the intracranial recordings contralateral to the epileptic focus and in the surface EEG. It is shown that the nonlinearity is correlated with short-term fluctuations of the delta power. The higher frequency of occurrence of clinical relevant epileptic spikes in the first NREM episode was not clearly reflected in the nonlinearity measures. Conclusions: It was confirmed that epileptic activity renders the EEG nonlinear. However, it was shown that the sleep dynamics itself also effects the nonlinearity measures. Therefore, at the present stage it is not possible to establish a unique connection between the studied nonlinearity measures and specific types of epileptic activity in sleep EEG recordings.