Thesis advisor: Dale Zimmerman. Includes bibliographic references (p. 81-82).

Thesis (Ph.D.) in Spatial Information Science and Engineering--University of Maine, 2002. / Includes vita. Includes bibliographical references (leaves 151-161 ).

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 114-119).

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 108-118).

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.

No description available.

prepared by: Bernard C. Levy, Alan S. Willsky. / Prepared for Systems Theory and Operations Research Program, Division of Electrical, Computer, and Systems Engineering, National Science Foundation under NSF Grant ECS-8012668. / Description based on: Nov.1, 1980/June 30, 1981.

With an increasing number of large area data sets, many study areas exhibit spatial non-stationarity or spatial variation in mean and variance of observed phenomena. This poses issues for a number of spatial analysis methods which assume data are stationary. The Getis and Ord’s Gi* statistic is a popular measure that, like many others, is impacted by non stationarity. The Gi* is used for locating hot and cold spots in marked data through the detection of spatial autocorrelation in values that are extreme relative to the global mean value, or the mean entire study area. This thesis describes modifications of the Getis and Ord’s Gi* local measure of spatial association, in part to account for regional differences (spatial non-stationarity) in a dataset. Instead of using data from the entire study area to calculate the mean parameter, as is done for the standard Gi*, I capture points for calculation of the mean using a circular distance band centred on the pivot location, which I call the local region (similar to the Ord and Getis Oi statistic). This approach can be applied to a single instance of a local region or to multiple spatial scales of the local region. I explore both in this paper using simulated datasets and a case study on mountain pine beetle infestation data. I find that the local region, when of a similar size to a true region (homogeneous section of the study area where the mean is approximately the same across locations), obtains similar results to the standard Gi* calculated separately on distinct regions (simulated to be distinct), but has the advantage of not needing explicit delineation of regional boundaries or partitioning into separate subareas. The results of a probability score for a multi-scale approach include high and low scores that are more evenly distributed across the study area and that are thus able to pick out more subtle variations within different regions. Through the case study I demonstrate how the multi-scale approach may be applied to a real dataset.

Made available in DSpace on 2015-05-14T12:47:18Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 5985376 bytes, checksum: cf77baf5e807e55caad0d6c10a8b173d (MD5) Previous issue date: 2014-02-20 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The research aimed to propose a model of decision making that can assist managers in health to take actions for tuberculosis control in João Pessoa. The study was quantitative, ecological, exploratory and cross-sectional, using secondary data from cases of tuberculosis and climate of João Pessoa during the period of 2000-2012. The spatial and spatio-temporal analysis was performed by creating maps of relative risk, spatial and space-time scan. The Spearman correlation was used to measure the degree of association between the incidence rate of tuberculosis and climate variables. The spatial and spatio-temporal analysis was performed by creating maps of relative risk, spatial and space-time scan. The Spearman correlation was used to measure the degree of association between the incidence rate of tuberculosis and climate variables. With the results obtained, a decision model based on fuzzy logic was developed. No trend or seasonality in incidence rates was observed. The spatial analysis detected spatial clusters of high risk in the northern region in almost all months, indicating the existence of a geographical barrier in the region. In spatio-temporal analysis, was detected 12 significant spatio-temporal clusters. With the correlation analysis, found that climatic variables did not exert influence on tuberculosis. The decision model based on fuzzy logic was able to classify accurately the degree of priority for tuberculosis in João Pessoa, supporting health managers to direct their actions to control tuberculosis according to the actual situation of the municipality, in addition the model was constructed to also be used for other places and time periods. / O trabalho teve como objetivo propor um modelo de tomada de decisão que auxilie os gestores de saúde nas ações relacionadas ao controle da Tuberculose no município de João Pessoa-PB. Trata-se de um estudo quantitativo, ecológico, exploratório e transversal, fazendo uso de dados secundários dos casos de Tuberculose e do clima do município de João Pessoa durante o período de 2000 a 2012. A análise espacial e espaço-temporal foi realizada mediante a criação de mapas de risco relativo, Scan espacial e espaço-temporal. A correlação de Spearman foi usada para medir o grau de associação entre a taxa de incidência de Tuberculose e as variáveis climáticas. Com os resultados obtidos, foi elaborado um modelo de decisão baseado em lógica fuzzy. Não foi observado tendência ou sazonalidade nas taxas de incidência. A análise espacial detectou conglomerados espaciais de alto risco na região norte em praticamente todos os meses, indicando a existência de uma barreira geográfica na região. Na análise espaço-temporal foram detectados 12 conglomerados significativos. Com a análise de correlação, verificou-se que as variáveis climáticas não exerciam influência sobre a Tuberculose em João Pessoa-PB. O modelo de decisão baseado em lógica fuzzy foi capaz de classificar os graus de prioridade para tuberculose no município de João Pessoa-PB. Espera-se que esses resultados auxiliem os gestores de saúde a direcionar suas ações para o controle da Tuberculose de acordo com a realidade encontrada no município, além disso, o modelo foi construído de forma a ser utilizado também, para outras localidades e períodos de tempo.

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.