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 20 of 41 for “"Spatio-temporal data."”.
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Algorithms for Analyzing Spatio-Temporal Data
<p>In today's age, huge data sets are becoming ubiquitous. In addition to their size, most of these data sets are often noisy, have outliers, and are incomplete. Hence, analyzing such data is challenging. We look at applying geometric techniques to tackle some of these challenges, with an emphasis …
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A Spatio-Temporal Data Model for Zoning
Planning departments are besieged with temporal/historical information. While for many institutions historical information can be relegated to archives, planning departments have a constant need to access and query their historical information, particularly their historical spatial information such …
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Spatio-temporal data fusion in cerebral angiography
… presented on both clinical and simulated phantom data sets. The 3D time series results are visualized using the following tools: time series of intensity slices, synthetic X-rays from an arbitrary view, time series of isosurfaces, and 3D surfaces that show arrival times of contrast using color. …
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Efficient Algorithms for Mining Large Spatio-Temporal Data
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 …
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Visualization of spatio-temporal data in two dimensional space
Spatio-temporal data is becoming very popular in the recent times, as there are large number of datasets that collect both location and temporal information in the real time. The main challenge is that extracting useful insights from such large data set is extremely complex and laborious. In this …
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Spatio-temporal data modeling with applications to weather and disease
Meteorological and epidemiological data are oftentimes collected over many years at various locations. In such cases, it is beneficial to use spatio-temporal modeling to account for trends and the correlation of nearby observations. This thesis explores applications to spatio-temporal modeling. …
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Automated safety analysis of construction site activities using spatio-temporal data
… remote sensing technology provides critical spatio-temporal data that has the potential to automate and advance the safety monitoring of construction processes. This doctoral research focuses on pro-active safety utilizing radio-frequency location tracking (Ultra Wideband) and real-time …
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Using genetic programming to learn predictive models from spatio-temporal data
… learning predictive models from nondeterministic spatio-temporal data. The prediction models are represented as a production system, which requires two parts: a set of production rules, and a conflict resolver. The production rules model different, typically independent, aspects of the …
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Calculation, utilization, and inference of spatial statistics in practical spatio-temporal data
… irregular, rotated, missing or degenerate data, with complex or non-probabilistic state definitions. Memory efficient personal computer oriented implementations are discussed for the extended framework. A universal microstructure generation framework with the ability to efficiently address …
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Comparing Different Levels of Interactivity in the Visualization of Spatio-Temporal Data
… static paper maps in allowing users to visualize spatio-temporal patterns? How important is a higher level of interactivity in visualizing data? Which format is preferred? To examine these questions, human subject tests were conducted to evaluate different levels of interactivity as represented by …
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Models and methods for computationally efficient analysis of large spatial and spatio-temporal data
… development of technology, massive amounts of data are often observed at a large number of spatial locations (n). However, statistical analysis is usually not feasible or not computationally efficient for such large dataset. This is the so-called "big n problem".</p><p>The goal of this …
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From spatio-temporal data to a weighted and lagged network between functional domains: Applications in climate and neuroscience
Spatio-temporal data have become increasingly prevalent and important for both science and enterprises. Such data are typically embedded in a grid with a resolution larger than the true dimensionality of the underlying system. One major task is to identify the distinct semi-autonomous functional …
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Satpura: A Novel Framework for Density Estimation, Hotspot Discovery, Change Analysis, and Change-based Alerts
… sensors and sensor networks, different types of spatio-temporal data are increasingly available. Spatio-temporal data analysis has applications in many fields, including criminology, epidemiology, and traffic analysis. The main focus of this research is to develop a generic analysis framework …
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Simulation and visualization of malaria transmission In West Africa
… consists of a simulation-based study and a data visualization framework development focusing on malaria transmission in West Africa. The simulation-based study introduces the concept of hysteresis in malaria transmission, which is defined as the dependence of malaria transmission on initial …
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Globally -Coordinated Locally-Linear Modeling of Multi-Dimensional Data
… over existing approaches to analyzing complex spatio-temporal data. Experiments show that the new modeling features of our approach improve the performance of existing approaches in many applications. In object tracking, our approach is the first one to track nonlinear appearance variations by …
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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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Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario
Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, …
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DENSITY BASED FRAMEWORKS FOR SPATIO-TEMPORAL CHANGE ANALYSIS
Analyzing change in spatial data is important for many different domains such as biology, ecology, meteorology, medicine, transportation, and forestry. On the other hand, density functions have served as a valuable tool in data mining. However, the development of spatiotemporal data mining …
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The Effects of Spatial Aggregation on Spatial Time Series Modeling and Forecasting
Spatio-temporal data analysis involves modeling a variable observed at different locations over time. A key component of space-time modeling is determining the spatial scale of the data. This dissertation addresses the following three questions: 1) How does spatial aggregation impact the properties …
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Bayesian Analysis of Temporal and Spatio-temporal Multivariate Environmental Data
High dimensional space-time datasets are available nowadays in various aspects of life such as economy, agriculture, health, environment, etc. Meanwhile, it is challenging to reveal possible connections between climate change and weather extreme events such as hurricanes or tornadoes. In …
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