Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 150-155). Also available on the World Wide Web.

Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an exploration of the most effective methods of visualising results. A human being is capable of processing a vast amount of data as long as it is effectively displayed. However, the perceptual load will at some point exceed the cognitive processing ability and therefore the ability to comprehend data. Although increases in data scale did increase the cognitive load and reduce processing, prior knowledge of geographical information systems did not result in an overall processing advantage. The empirical work in the thesis is divided into two parts. The first part aims to gain insight into visualisations which would be effective for interpretation and analysis of Geographically Weighted Regression (GWR), a popular Spatial Statistical Method. Three different visualisation techniques; two dimensional, three dimensional and interactive, are evaluated through an experiment comprising two data set sizes. Interactive visualisations perform best overall, despite the apparent lack of researcher familiarity. The increase in data volume can present additional complexity for researchers. Although the evaluation of the first experiment augments understanding of effective visualisation display, the scale at which data can be adequately presented within these visualisations is unclear. Therefore, the second empirical investigation seeks to provide insight into data scalability, and human cognitive limitations associated with data comprehension. The general discussion concludes that there is a need to better inform researchers of the potential of interactive visualisations. People do need to be properly trained to use these systems, but the limits of human perceptual processing also need to be considered in order to permit more efficient and insightful analysis.

Ho, Siu Tung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 43-44). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 2 --- Methodology and Algorithm --- p.6 / Chapter 2.1 --- Testing one. homogeneous cluster --- p.8 / Chapter 3 --- Simulation Study --- p.17 / Chapter 3.1 --- Simulation Plan --- p.19 / Chapter 3.1.1 --- One single cluster --- p.19 / Chapter 3.1.2 --- Two separated clusters --- p.20 / Chapter 3.2 --- Measure of Performance --- p.26 / Chapter 3.3 --- Simulation Results --- p.27 / Chapter 3.3.1 --- One single cluster --- p.27 / Chapter 3.3.2 --- Two separated clusters --- p.30 / Chapter 4 --- Conclusion and further research --- p.36 / Chapter 4.1 --- Constructing Data depth --- p.38 / Bibliography --- p.43

Geostatistical linear interpolation procedures such as kriging require knowledge of the covariance structure of the spatial process under investigation. In practice, the covariance of the process is unknown, and must be estimated from the available data. As the quality of the resulting predictions, and associated mean square prediction errors, depends on adequate specification of the covariance structure, it is important that the analyst be able to detect inadequacies in the specified covariance model. Case-deletion diagnostics are currently used by geostatisticians to evaluate spatial models. The second chapter of the thesis describes a particular case-deletion diagnostic based on standardized PRESS residuals, and its use in assessing the predictive capacity of spatial covariance models. Distributional properties of this statistic, denoted T [subscript PR], are discussed, and a saddlepoint approximation to its distribution is derived. Guidelines for calculating approximate p-values for the statistic under an hypothesized covariance model are also given. A simulation study demonstrates that the distributional and p-value approximations are accurate. The proposed method is illustrated through an example, and recommendations for calculation of T [subscript PR], and associated approximate p-values on a regional basis are given. The third chapter investigates the behavior of the standardized PRESS residuals under various misspecifications of the covariance matrix, V. A series of simulation studies show consistent patterns in the standardized PRESS residuals under particular types of misspecifications of V. It is observed that misspecification of V may lead to variability among the standardized PRESS residuals greater or less than would be expected if V was correctly specified, depending on the nature of the misspecification. Based on this observation, an adjustment to normal probability plots of the standardized PRESS residuals is proposed. The adjusted normal probability plots may be used to identify potential improvements to covariance models, without requiring extensive further calculations. / Graduation date: 1996

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Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 173-183). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.

Nearest Neighbor (NN in short) queries are important in emerging applications, such as wireless networks, location-based services, and data stream applications, where the data obtained are often imprecise. The imprecision or imperfection of the data sources is modeled by uncertain data in recent research works. Handling uncertainty is important because this issue affects the quality of query answers. Although queries on uncertain data are useful, evaluating the queries on them can be costly, in terms of I/O or computational efficiency. In this thesis, we study how to efficiently evaluate NN queries on uncertain data. Given a query point q and a set of uncertain objects O, the possible nearest neighbor query returns a set of candidates which have non-zero probabilities to be the query answer. It is also interesting to ask \which region has the same set of possible nearest neighbors", and \which region has one specific object as its possible nearest neighbor". To reveal the relationship between the query space and nearest neighbor answers, we propose the UV-diagram, where the query space is split into disjoint partitions, such that each partition is associated with a set of objects. If a query point is located inside the partition, its possible nearest neighbors could be directly retrieved. However, the number of such partitions is exponential and the construction effort can be expensive. To tackle this problem, we propose an alternative concept, called UV-cell, and efficient algorithms for constructing it. The UV-cell has an irregular shape, which incurs difficulties in storage, maintenance, and query evaluation. We design an index structure, called UV-index, which is an approximated version of the UV-diagram. Extensive experiments show that the UV-index could efficiently answer different variants of NN queries, such as Probabilistic Nearest Neighbor Queries, Continuous Probabilistic Nearest Neighbor Queries. Another problem studied in this thesis is the trajectory nearest neighbor query. Here the query point is restricted to a pre-known trajectory. In applications (e.g. monitoring potential threats along a flight/vessel's trajectory), it is useful to derive nearest neighbors for all points on the query trajectory. Simple solutions, such as sampling or approximating the locations of uncertain objects as points, fails to achieve a good query quality. To handle this problem, we design efficient algorithms and optimization methods for this query. Experiments show that our solution can efficiently and accurately answer this query. Our solution is also scalable to large datasets and long trajectories. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 134-137).