Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 322 for “"spatial data"”.
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Spatial Data Analysis
… of five chapters with a focus on modeling spatial and temporal data. In chapter 1, we explained different terminology and principles that appear frequently in the analysis of spatial and temporal data. These concepts were explained in detail to form a basis and motivation for the research …
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Responsible Spatial Data Science
The goal of responsible spatial data science is to encourage the design and development of spatial methods, processes, algorithms, and systems to discover spatial patterns (e.g., hotspots, colocations) that reduce adverse impacts on the communities that use them. Related work on fairness issues (F) …
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GPS augmentation using digital spatial data
… used to augment GPS using a variety of digital spatial data. It is well known that the use of GPS can be severely compromised by various error sources such as signal obstructions, multipath and poor satellite geometry etc., especially in highly built-up areas. In order to improve the accuracy …
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Abnormal Pattern Recognition in Spatial Data
In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different …
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A Unified Spatial Data Structure for GIS
… thematic "layers" to store different types of spatial data. Each of them contains specific characteristics of the area, so there are separate layers for the distribution of buildings, the road network or the relief of the terrain. The spatial information used in GIS can be grouped into four …
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Towards Efficient Processing of Big Spatial Data
… location-based services in which huge amounts of spatial data need to be efficiently processed. To cope with such proliferation of spatial data, this dissertation addresses two key issues that are overlooked by existing spatial-query processing platforms: i) the multiplicity of predicates in …
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Using Sound to Represent Uncertainty in Spatial Data
There is a limit to the amount of spatial data that can be shown visually in an effective manner, particularly when the data sets are extensive or complex. Using sound to represent some of these data (sonification) is a way of avoiding visual overload. This thesis creates a conceptual model showing …
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Topics in interpolation and smoothing of spatial data
… thesis addresses a number of special topics in spatial interpolation and smoothing. The motivation for the thesis comes from two projects, one being to extend the availability of a daily rainfall model for southern Africa to sites at which little or no rainfall data is available, using data from …
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Prediction and Anomaly Detection Techniques for Spatial Data
… concern on environmental issues, huge amounts of spatial data have been collected from location based social network applications to scientific data. This has encouraged formation of large spatial data set and generated considerable interests for identifying novel and meaningful patterns. Allowing …
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Management of spatial data for visualization on mobile devices
… to enable the e�cient transmission of vector data over the internet by delivering various incremental levels of detail(LoD). However, it is still challenging to apply this technique in a mobile context due to many inherent limitations of mobile devices, such as small screen size, slow …
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Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics
<p>In this thesis, temporal and spatial dependence are considered within nonparametric priors to help infer patterns, clusters or segments in data. In traditional nonparametric mixture models, observations are usually assumed exchangeable, even though dependence often exists associated with the …
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Estimation in random field models for noisy spatial data
"The random field model has been applied to model spatial heterogeneity for spatial data in many applications. The purpose of this dissertation is to explore statistical properties of noisy spatial data through estimation of the Gaussian random field. Large sample properties of the Maximum …
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Exploratory Spatial Data Analysis for Origin and Destination Flow Data
… the existence of geographical attributes in the datasets, interactive visualization tools and dynamic graphics. In this study, I propose a new set of techniques for exploring O-D flow data and integrate it into an operational ESDA environment. These objectives are decomposed into the following …
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Bayesian Model Selection for Spatial Data and Cost-constrained Applications
… in the case of hierarchical models for spatial data, which can have complex dependence structures. We develop an approach using trained priors via fractional Bayes factors where standard Bayesian model selection methods fail to produce valid probabilities under improper reference priors. …
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Spatial data science: from aerial data to discrete spatio-temporal event analysis
L'abstract è presente nell'allegato / the abstract is in the attachment
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