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Dissertations / Theses on the topic 'Spatial analysis (Statistics)'

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Published: 4 June 2021
Last updated: 30 July 2024

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1

White, Gentry. "Bayesian semiparametric spatial and joint spatio-temporal modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4450.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.
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2

Zhang, Jun. "Nearest neighbor queries in spatial and spatio-temporal databases /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20ZHANG.

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3

Yue, Yu. "Spatially adaptive priors for regression and spatial modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6059.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.
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4

Butler, Thomas W. "Spatial statistics and analysis of earth's ionosphere." Thesis, Boston University, 2013. https://hdl.handle.net/2144/10950.

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Thesis (Ph.D.)--Boston University
The ionosphere, a layer of Earths upper atmosphere characterized by energetic charged particles, serves as a natural plasma laboratory and supplies proxy diagnostics of space weather drivers in the magnetosphere and the solar wind. The ionosphere is a highly dynamic medium, and the spatial structure of observed features (such as auroral light emissions, charge density, temperature, etc.) is rich with information when analyzed in the context of fluid, electromagnetic, and chemical models. Obtaining measurements with higher spatial and temporal resolution is clearly advantageous. For instance, measurements obtained with a new electronically-steerable incoherent scatter radar (ISR) present a unique space-time perspective compared to those of a dish-based ISR. However, there are unique ambiguities for this modality which must be carefully considered. The ISR target is stochastic, and the fidelity of fitted parameters (ionospheric densities and temperatures) requires integrated sampling, creating a tradeoff between measurement uncertainty and spatio-temporal resolution. Spatial statistics formalizes the relationship between spatially dispersed observations and the underlying process(es) they represent. A spatial process is regarded as a random field with its distribution structured (e.g., through a correlation function) such that data, sampled over a spatial domain, support inference or prediction of the process. Quantification of uncertainty, an important component of scientific data analysis, is a core value of spatial statistics. This research applies the formalism of spatial statistics to the analysis of Earth's ionosphere using remote sensing diagnostics. In the first part, we consider the problem of volumetric imaging using phased-array ISR based on optimal spatial prediction ("kriging"). In the second part, we develop a technique for reconstructing two-dimensional ion flow fields from line-of-sight projections using Tikhonov regularization. In the third part, we adapt our spatial statistical approach to global ionospheric imaging using total electron content (TEC) measurements derived from navigation satellite signals.
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5

Ho, Lai Ping. "Complete spatial randomness tests, intensity-dependent marking and neighbourhood competition of spatial point processes with applications to ecology." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/770.

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6

Maimon, Geva. "A Bayesian spatial analysis of glass data /." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82284.

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In criminal investigations involving glass evidence, refractive index (RI) is the property of glass most commonly used by forensic examiners to determine the association between control samples of glass obtained at the crime scene, and samples of glass found on a suspect. Previous studies have shown that an intrinsic variability of RI exists within a pane of float glass. In this thesis, we attempt to determine whether this variability is spatially determined or random in nature, the conclusion of which plays an important role in the statistical interpretation of glass evidence. We take a Bayesian approach in fitting a spatial model to our data, and utilize the WinBUGS software to perform Gibbs sampling. To test for spatial variability, we propose two test quantities, and employ Bayesian Monte Carlo significance tests to test our data, as well as nine other specifically formulated data-sets.
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Oleson, Jacob J. "Bayesian spatial models for small area estimation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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8

Assefa, Yared. "Time series and spatial analysis of crop yield." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15142.

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Master of Science
Department of Statistics
Juan Du
Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.
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9

Wilson, Helen Elizabeth. "Statistical analysis of replicated spatial point patterns." Thesis, Lancaster University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268009.

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The field of pathology provides us with many opportunities for collecting replicated spatial data. Using an ordinary microscope, for example, we can digitise cell positions within windows imposed on pieces of tissue. Suppose now that we have some such replicated spatial data from several groups of individuals, where each point in each window represents a cell position. We seek to determine whether the spatial arrangement of cells differs between the groups. We propose and develop a new method which allows us to answer such questions, and apply it to some spatial neuro-anatomical data. We introduce point process theory, and extend the existing second order methods to deal with replicated spatial data. We conclude the first part of the thesis by defining Sudden Infant Death Syndrome (S.LD.S.) and Intra-Uterine Growth Retardation (LU.G.R.), and stating why these conditions are neuro-anato,mically interesting. We develop and validate a method for comparing groups of spatial data, which is motivated by analysis of variance, and uses a Monte Carlo procedure to attach significance to between-group differences. Having carried out our initial investigative work looking exclusively at the one-way set up, we extend the new methods to cope with two and higher way set ups, and again carry out some validation. We turn our attention to practical issues which arise in the collection of spatial neuroanatomical data. How, for example, should we collect the data to ensure the unbiasedness of any inference we may draw from it? We introduce the field of stereology which facilitates the unbiased sampling of tissue. We note a recent proposal to assess spatial distribution of cells using a stereological approach, and compare it with an existing second order method. We also note the level of structural heterogeneity within the brain, and consider the best way to design a sampling protocol. We conclude with a spatial analysis of cell position data, collected using our specified design, from normal birth-weight non S.LD.S., normal birth-weight S.I.D.S and low birth-weight S.LD.S cases.
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AvRuskin, Gillian. "Towards A Spatial Model of Rurality." Fogler Library, University of Maine, 2000. http://www.library.umaine.edu/theses/pdf/AvRuskinG2000.pdf.

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Kim, Hyon-Jung. "Nonparametric Spatial analysis in spectral and space domains." NCSU, 2000. http://www.lib.ncsu.edu/theses/available/etd-20000822-235839.

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KIM, HYON-JUNG. Variance Estimation in Spatial Regression Using a NonparametricSemivariogram Based on Residuals. (Under the direction of Professor Dennis D. Boos.)The empirical semivariogram of residuals from a regression model withstationary errors may be used to estimate the covariance structure of the underlyingprocess.For prediction (Kriging) the bias of the semivariogram estimate induced byusing residuals instead of errors has only a minor effect because thebias is small for small lags. However, for estimating the variance of estimatedregression coefficients and of predictions,the bias due to using residuals can be quite substantial. Thus wepropose a method for reducing the bias in empirical semivariogram estimatesbased on residuals. The adjusted empirical semivariogram is then isotonizedand made positive definite and used to estimate the variance of estimatedregression coefficients in a general estimating equations setup.Simulation results for least squares and robust regression show that theproposed method works well in linear models withstationary correlated errors. Spectral Analysis with Spatial Periodogram and Data Tapers.(Under the direction of Professor Montserrat Fuentes.)The spatial periodogram is a nonparametric estimate of the spectral density, which is the Fourier Transform of the covariance function. The periodogram is a useful tool to explain the dependence structure of aspatial process.Tapering (data filtering) is an effective technique to remove the edge effects even inhigh dimensional problemsand can be applied to the spatial data in order to reduce the bias of the periodogram.However, the variance of the periodogram increases as the bias is reduced.We present a method to choose an appropriate smoothing parameter for datatapers and obtain better estimates of the spectral densityby improving the properties of the periodogram.The smoothing parameter is selected taking intoaccount the trade-off between bias and variance of the taperedperiodogram. We introduce a new asymptotic approach for spatial datacalled `shrinking asymptotics', which combines theincreasing-domain and the fixed-domain asymptotics.With this approach, the tapered spatial periodogram can be usedto determine uniquely the spectral density of the stationary process,avoiding the aliasing problem.

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12

Slack, Marc G. "Spatial and temporal path planning." Thesis, This resource online, 1987. http://scholar.lib.vt.edu/theses/available/etd-04272010-020255/.

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13

Li, Hongfei. "Approximate profile likelihood estimation for spatial-dependence parameters." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1191267954.

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14

Wheeler, David C. "Diagnostic tools and remedial methods for collinearity in linear regression models with spatially varying coefficients." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155413322.

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15

Pereira, Sandra M. C. "Analysis of spatial point patterns using hierarchical clustering algorithms." University of Western Australia. School of Mathematics and Statistics, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0056.

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[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.
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16

McBride, John Jacob Bratcher Thomas L. "Conjugate hierarchical models for spatial data an application on an optimal selection procedure /." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/3955.

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17

Donkor, Faustina Fosua. "Spatial Analysis of Teen Births in North Central Texas." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc3056/.

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The United States has the highest teen birth rate among western industrialized countries and the highest levels of pregnancy among adolescents (Alan Guttmacher Institute, 1994). While the rate of teen births is high throughout the country, considerable variations exist between and within regions. Texas is one of the 5 leading states with the highest teen birth rates to mothers less than 18 years of age. This research provides a detailed analysis of births to mothers aged between 10 and 19 years in North Central Texas counties. Due to the modifiable area unit problem and to provide a finer geographical scale of analysis, teen births in Dallas County zip codes were examined as a special case study. Statistical and Geographic Information System (GIS) analysis reveal that race/ethnicity, education and income are significant factors in teen births in the region. Single parent households and receipt of public assistance were not statistically significant. Suggestions for reducing vulnerability to teen births are presented.
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18

Yiu, Man-lung. "Advanced query processing on spatial networks." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36279365.

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Yiu, Man-lung, and 姚文龍. "Advanced query processing on spatial networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36279365.

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Li, Jie Zimmerman Dale L. "Spatial multivariate design in the plane and on stream networks." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/395.

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21

Keefe, Matthew James. "Statistical Monitoring and Modeling for Spatial Processes." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/76664.

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Statistical process monitoring and hierarchical Bayesian modeling are two ways to learn more about processes of interest. In this work, we consider two main components: risk-adjusted monitoring and Bayesian hierarchical models for spatial data. Usually, if prior information about a process is known, it is important to incorporate this into the monitoring scheme. For example, when monitoring 30-day mortality rates after surgery, the pre-operative risk of patients based on health characteristics is often an indicator of how likely the surgery is to succeed. In these cases, risk-adjusted monitoring techniques are used. In this work, the practical limitations of the traditional implementation of risk-adjusted monitoring methods are discussed and an improved implementation is proposed. A method to perform spatial risk-adjustment based on exact locations of concurrent observations to account for spatial dependence is also described. Furthermore, the development of objective priors for fully Bayesian hierarchical models for areal data is explored for Gaussian responses. Collectively, these statistical methods serve as analytic tools to better monitor and model spatial processes.
Ph. D.
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22

Yin, Jiangyong. "Bayesian Analysis of Non-Gaussian Stochastic Processes for Temporal and Spatial Data." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406928537.

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23

Ghobarah, Hazem. "A statistical assessment of the spatial model of ideology /." Digital version accessible at:, 2000. http://wwwlib.umi.com/cr/utexas/main.

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24

Jensen, Daniel. "Spatial analysis and visualization in the NBA using GIS applications." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527009.

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Basketball is a unique sport in which the use of space and time is greatly important for a team’s success. Furthermore, the National Basketball Association (NBA) is undergoing drastic change in terms of the way teams approach spatial issues as well as the spatio-temporal technologies and analytics. Given these facts, Geographic Information Systems (GIS) provide the opportunity to develop new analytic and visual methodologies to perform spatial analysis for team performances and meet the league’s changing needs. This project thus develops new approaches, methods, and toolsets using GIS to demonstrate its efficacy and potential for professional application in the NBA. The first application uses GIS to adapt Relative Motion analysis techniques to an existing play, seeking to represent the average motion characteristics entailed therein. The other application uses a tool developed to map, glean spatial statistics, and model the use and importance of floor spacing for teams in the NBA.

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Luna, Ronaldo. "Liquefaction evaluation using a spatial analysis system." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/19413.

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Rau, Christian. "Curve estimation and signal discrimination in spatial problems /." View thesis entry in Australian Digital Theses Program, 2003. http://thesis.anu.edu.au/public/adt-ANU20031215.163519/index.html.

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Vohra, Neeru Rani. "Three dimensional statistical graphs, visual cues and clustering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ56213.pdf.

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Southey, Richard. "Bayesian hierarchical modelling with application in spatial epidemiology." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/59489.

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Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.
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Porter, Erica May. "Applying an Intrinsic Conditional Autoregressive Reference Prior for Areal Data." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91385.

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Bayesian hierarchical models are useful for modeling spatial data because they have flexibility to accommodate complicated dependencies that are common to spatial data. In particular, intrinsic conditional autoregressive (ICAR) models are commonly assigned as priors for spatial random effects in hierarchical models for areal data corresponding to spatial partitions of a region. However, selection of prior distributions for these spatial parameters presents a challenge to researchers. We present and describe ref.ICAR, an R package that implements an objective Bayes intrinsic conditional autoregressive prior on a vector of spatial random effects. This model provides an objective Bayesian approach for modeling spatially correlated areal data. ref.ICAR enables analysis of spatial areal data for a specified region, given user-provided data and information about the structure of the study region. The ref.ICAR package performs Markov Chain Monte Carlo (MCMC) sampling and outputs posterior medians, intervals, and trace plots for fixed effect and spatial parameters. Finally, the functions provide regional summaries, including medians and credible intervals for fitted values by subregion.
Master of Science
Spatial data is increasingly relevant in a wide variety of research areas. Economists, medical researchers, ecologists, and policymakers all make critical decisions about populations using data that naturally display spatial dependence. One such data type is areal data; data collected at county, habitat, or tract levels are often spatially related. Most convenient software platforms provide analyses for independent data, as the introduction of spatial dependence increases the complexity of corresponding models and computation. Use of analyses with an independent data assumption can lead researchers and policymakers to make incorrect, simplistic decisions. Bayesian hierarchical models can be used to effectively model areal data because they have flexibility to accommodate complicated dependencies that are common to spatial data. However, use of hierarchical models increases the number of model parameters and requires specification of prior distributions. We present and describe ref.ICAR, an R package available to researchers that automatically implements an objective Bayesian analysis that is appropriate for areal data.
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Woodard, Roger. "Bayesian hierarchical models for hunting success rates /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9951135.

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Sun, Xiaoqian. "Bayesian spatial data analysis with application to the Missouri Ozark forest ecosystem project." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4477.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 1, 2007) Vita. Includes bibliographical references.
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Keil, Mitchel J. "Automatic generation of interference-free geometric models of spatial mechanisms." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-08252008-162631/.

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Sha, Zhe. "Estimation of conditional auto-regressive models." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6cc56943-2b4d-4931-895a-f3ab67e48e3a.

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Conditional auto-regressive (CAR) models are frequently used with spatial data. However, the likelihood of such a model is expensive to compute even for a moderately sized data set of around 1000 sites. For models involving latent variables, the likelihood is not usually available in closed form. In this thesis we use a Monte Carlo approximation to the likelihood (extending the approach of Geyer and Thompson (1992)), and develop two strategies for maximising this. One strategy is to limit the step size by defining an experimental region using a Monte Carlo approximation to the variance of the estimates. The other is to use response surface methodology. The iterative procedures are fully automatic, with user-specified options to control the simulation and convergence criteria. Both strategies are implemented in our R package mclcar. We demonstrate aspects of the algorithms on simulated data on a torus, and achieve similar results to others in a short computational time on two datasets from the literature. We then use the methods on a challenging problem concerning forest restoration with data from around 7000 trees arranged in transects within study plots. We modelled the growth rate of the trees by a linear mixed effects model with CAR spatial error and CAR random e ects for study plots in an acceptable computational time. Our proposed methods can be used for similar models to provide a clearly defined framework for maximising Monte Carlo approximations to likelihoods and reconstructing likelihood surfaces near the maximum.
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Kim, Kamyoung. "Spatial analytical approaches for supporting security monitoring." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1186593136.

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Amarasinghe, Anura Kumara. "A socioeconomic and spatial analysis of obesity in West Virginia policy implications /." Morgantown, W. Va. : [West Virginia University Libraries], 2006. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4832.

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Thesis (Ph. D.)--West Virginia University, 2006.
Title from document title page. Document formatted into pages; contains ix, 145 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 129-141).
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Stromberg, David A. "Performance of AIC-Selected Spatial Covariance Structures for fMRI Data." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd981.pdf.

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Ma, Tingting. "Isotropy test and variance estimation for high order statistics of spatial point process." HKBU Institutional Repository, 2011. https://repository.hkbu.edu.hk/etd_ra/1297.

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Naskar, Susmita. "Spatial variability characterisation of laminated composites." Thesis, University of Aberdeen, 2018. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=239036.

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Advanced lightweight structural materials like composites are being increasingly utilized in various engineering applications due to high specific strength and stiffness with tailorable properties. Even though composites have the advantage of modulating a large number of design parameters to achieve various application-specific requirements, this concurrently brings the challenge of dealing with inevitable uncertainties during manufacturing and service-life conditions. This dissertation focuses on practically relevant modelling of random spatial variability coupled with the influence of damage to quantify the effect of source-uncertainties following an efficient surrogate based framework. Layer-wise random variable based approach and the random field based approaches of uncertainty modelling are investigated to quantify the stochastic dynamics and stability characteristics of in a probabilistic multi-scale framework. A novel concept of stochastic representative volume element is proposed to consider the spatially varying structural attributes effectively. A physically relevant random field based modelling approach with correlated material properties is adopted based on the Karhunen-Loève expansion. To understand the relative influences, sensitivity of the stochastic input parameters are analyzed for the global structural responses of composite laminates considering micro and macro mechanical properties separately. Besides the conventional sources of uncertainty in material and structural properties, another source of uncertainty is considered in the form of noise. Besides probabilistic analysis, this dissertation proposes a fuzzy representative volume element based approach for modelling spatial variability in non-probabilistic analysis for the cases where statistical distributions of the stochastic input parameters are not available. The results reveal that stochasticity affects the system performance significantly. A notable difference in the global stochastic behaviour is identified depending upon the adopted uncertainty modeling approach. Thus, it is imperative to appropriately model the sourceuncertainties during the analysis and design process. The dissertation provides comprehensive insights on the effect of source-uncertainties on composites following an efficient, yet practically relevant modelling approach.
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Smith, Alison B. "Multiplicative mixed models for the analysis of multi-environment trial data /." Title page, contents and abstract only, 1999. http://web4.library.adelaide.edu.au/theses/09PH/09phs64221.pdf.

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Moores, Matthew T. "Bayesian computational methods for spatial analysis of images." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/84728/12/84728%28thesis%29.pdf.

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This thesis introduces a new way of using prior information in a spatial model and develops scalable algorithms for fitting this model to large imaging datasets. These methods are employed for image-guided radiation therapy and satellite based classification of land use and water quality. This study has utilized a pre-computation step to achieve a hundredfold improvement in the elapsed runtime for model fitting. This makes it much more feasible to apply these models to real-world problems, and enables full Bayesian inference for images with a million or more pixels.
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Rafferty, Paula S. "Spatial Analysis of North Central Texas Traffic Fatalities 2001-2006." Thesis, University of North Texas, 2010. https://digital.library.unt.edu/ark:/67531/metadc33195/.

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A traditional two dimensional (planar) statistical analysis was used to identify the clustering types of North Central Texas traffic fatalities occurring in 2001-2006. Over 3,700 crash locations clustered in ways that were unlike other researched regions. A two dimensional (x and y coordinates) space was manipulated to mimic a one dimensional network to identify the tightest clustering of fatalities in the nearly 400,000 crashes reported from state agencies from 2003-2006. The roadway design was found to significantly affect crash location. A one dimensional (linear) network analysis was then used to measure the statistically significant clustering of flow variables of after dark crashes and daylight crashes. Flow variables were determined to significantly affect crash location after dark. The linear and planar results were compared and the one dimensional, linear analysis was found to be more accurate because it did not over detect the clustering of events on a network.
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42

Higdon, David. "Spatial applications of Markov chain Monte Carlo for Bayesian inference /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/8942.

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43

黎寶欣 and Po-yan Lai. "Effect of visual item arrangement on search performance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B3124189X.

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Lai, Po-yan. "Effect of visual item arrangement on search performance." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23530212.

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45

Hällmark, Kristin, and Baldesi Angelo Ljungquist. "Political views as neighbourhood effects : A study of Swedish voting behaviour using spatial analysis and socio-economic factors." Thesis, Uppsala universitet, Statistiska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-356145.

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46

Yu, Jihai. "Essays on spatial dynamic panel data model theories and applications /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1179767430.

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47

Sikdar, Khokan Chandra. "Application of geographically weighted regression for assessing spatial non-stationarity /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,172881.

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48

Arab, Ali. "Hierarchical spatio-temporal models for environmental processes." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4698.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed Nov. 21, 2007). Vita. Includes bibliographical references.
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Canessa, Rosaline Regan. "Towards a coastal spatial decision support system for multiple-use management." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ32737.pdf.

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50

Sharma, Jayant. "Integrated Spatial Reasoning in Geographic Information Systems: Combining Topology and Direction." Fogler Library, University of Maine, 1996. http://www.library.umaine.edu/theses/pdf/Sharma.pdf.

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