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.
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Showing 1 to 11 of 11 for “"Spatial-temporal data"”.
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Spatial-Temporal Data Modeling with Graph Neural Networks
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. It aims to model the dynamic node-level inputs by assuming inter-dependency between connected nodes. A basic assumption behind spatial-temporal graph modeling is that …
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Machine learning models on geographic spatial-temporal data predictions
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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Spatial-temporal data modelling and processing for personalised decision support
… is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of …
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Exploring link, text and spatial-temporal data in social media
… of Web 2.0, a huge amount of user generated data in social media sites is attracting the attentions from different research areas. Social media data has heterogenous data types including link, text and spatial-temporal information, which poses many interesting and challenging tasks for data …
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Predictive modeling of spatial-temporal data: A graph-centric approach
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Semantics orientated spatial temporal data mining for water resource decision support
… heavily on computer software processing to help data queries for common and rare patterns for analyzing critical water events. For example, it is vital for decision makers to know if certain types of water quality problems are isolated (e.g. rare) or ubiquitous (e.g. common) and whether the …
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Weibull mixture model for grouped data and pattern identification in spatial and spatial-temporal data.
… patterns that exist in different types of data. In the first project, we propose the Weibull mixture model to fit the distribution of grain size in continental sediments in geological studies. We use an EM algorithm to fit the model and a bootstrap likelihood ratio test (LRT) to compare …
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Effectiveness of Spatial-temporal Data Using GIS in America’s Professional Sports Leagues (MLS, MLB, and NFL)
… a relationship exists between game results and spatial, temporal, and stadium attributes. These stadium attributes include field surface type, roof type (i.e., open, fixed, and retractable), time zone, and field orientation (e.g., N/S, E/W, NE/SW) for U.S. professional sports such as soccer, …
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Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature Study
… region, it is important to accommodate the spatial and temporal information of the steam temperature. In this dissertation, I devote effort to several statistical modeling techniques for analyzing bivariate spatial-temporal data in a stream temperature study. In the first part, I focus our …
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Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies
… modeling for matched case-crossover data. In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression …
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New progress in hot-spots detection, partial-differential-equation-based model identification and statistical computation
… the new progress in (1) hot-spots detection in spatial-temporal data, (2) partial-differential-equation-based (PDE-based) model identification, and (3) optimization in the Least Absolute Shrinkage and Selection Operator (Lasso) type problem. In this thesis, we have four main works. Chapter 1 and …