In order to account for spatial correlation in residuals in regression models for areal and lattice data, different disciplines have developed distinct approaches. Bayesian spatial statistics typically has used a Gaussian conditional autoregressive (CAR) prior on random effects, while geographers utilize Moran's I statistic as a measure of spatial autocorrelation and the basis for creating spatial models. Recent work in both fields has recognized and built on a common feature of the two approaches, specifically the implicit or explicit incorporation into the linear predictor of eigenvectors of a matrix representing the spatial neighborhood structure. The inclusion of appropriate choices of these vectors effectively reduces the spatial autocorrelation found in the residuals. We begin with extensive simulation studies to compare Bayesian CAR models, Restricted Spatial Regression (RSR), Bayesian Spatial Filtering (BSF), and Eigenvector Spatial Filtering (ESF) with respect to estimation of fixed-effect coefficients, prediction, and reduction of residual spatial autocorrelation. The latter three models incorporate the neighborhood structure of the data through the eigenvectors of a Moran operator. We propose an alternative selection algorithm for all candidate predictors that avoids the ad hoc approach of RSR and selects on both model fit and reduction of autocorrelation in the residuals. The algorithm depends on the marginal posterior density a quantity that measures what proportion of the total variance can be explained by the measurement error. The algorithm selects candidate predictors that lead to a high probability that this quantity is large in addition to having large marginal posterior inclusion probabilities (PIP) according to model fit. Two methods were constructed. The first is based on orthogonalizing all of the candidate predictors while the second can be applied to the design matrix of candidate predictors without orthogonalization. Our algorithm was applied to the same simulated data that compared the RSR, BSF and ESF models. Although our algorithm performs similarly to the established methods, the first of our selection methods shows an improvement in execution time. In addition, our approach is a statistically sound, fully Bayesian method.

This article summarizes the ideas behind a few programs we developed for spatial data analysis in EViews and MATLAB. They allow the user to check for spatial autocorrelation using Moran's I and provide a spatial filtering procedure based on the Gi statistic by Getis and Ord (1992). We have also implemented graphical tools like Moran Scatterplots for the detection of outliers or local spatial clusters.

The 2009 H1N1 pandemic caused serious concerns worldwide due to the novel biological feature of the virus strain, and the high morbidity rate for youth. The urban scale is crucial for analyzing the pandemic in metropolitan areas such as the Greater Toronto Area (GTA) of Canada because of its large population. The challenge of exploring the spatial dynamics of H1N1 is exaggerated by data scarcity and the absence of an immediately applicable methodology at such a scale. In this study, a stepwise methodology is developed, and a retrospective spatial statistical analysis is conducted using the methodology to estimate the spatial dynamics of the 2009 H1N1 pandemic in the GTA when the data scarcity exists. The global and local spatial autocorrelation analyses are carried out through the use of multiple spatial analysis tools to confirm the existence and significance of spatial clustering effects. A Generalized Linear Mixed Model (GLMM) implemented in Statistical Analysis System (SAS) is used to estimate the area-specific spatial dynamics. The GLMM is configured to a spatial model that incorporates an Intrinsic Gaussian Conditionally Autoregressive (ICAR) model, and a non-spatial model respectively. Comparing the results of spatial and non-spatial configurations of the GLMM suggests that the spatial GLMM, which incorporates the ICAR model, proves a better predictability. This indicates that the methodology developed in this study can be applied to epidemiology studies to analyze the spatial dynamics in similar scenarios. / Thesis (Master, Geography) -- Queen's University, 2012-10-30 17:41:28.445

Various environmental standards have been established for the sake of public health and ecosystem diversity since environmental awareness was awakened in the late 1960s. However, the results were often unsatisfactory. Either environmental goals achieved were far from desired, or regional development was hampered due to some unpractical high environmental standards. The failure of these environmental standards resulted in innovations of environmental policy instruments to find practical environmental goals and methods approaching them scientifically. Another class of environmental policy instruments, so called economic incentive policies, is established based on environmental economics theory. A neo-classical economics framework is founded for setting appropriate environmental goals and assessing efficiency of environmental policies in reaching these goals. This thesis summarizes rationales and factors affecting the performance for environmental policy instruments under the neo-classical economic framework. Since the acid rain program, the first large-scale implementation of the emissions trading policy, has achieved great success in reducing SO₂ emissions from the electricity generators in the United States, the emission trading policy attracted many interests in this kind of environmental policy instrument. Many countries, such as China, plan to adopt the emissions trading policy to address various environmental problems. Hence, factors leading to the success of this program should be identified. Potential risks and problems must be addressed as well lest the emissions trading policy causes some problem during implementation. Feasibility of implementing an emissions trading policy will be discussed based on these results. Three kinds of geographic analyses, change detection, network analysis, and hot spots identification, are conducted in this thesis to study the effectiveness and efficiency of the acid rain program. It is found that the acid rain program is successful in improving the sustainability of the economic development in the United States. But the effectiveness is not as great as the high emissions cutting rate achieved in this program. In addition, the acid rain program lowers the compliance costs of achieving the environmental goal since the radius of the high quality coal service area doubles. Lastly, hot spots are found around the Ohio River valley and Los Angeles. Suggestions on integrating geographic factors into the economic framework are presented in order to eliminate the risk of causing severe environmental problems. Finally, the feasibility of migrating the emissions trading policy to China is discussed. Further work can be conducted in this direction to realize sustainable development quicker with lower costs.

Various environmental standards have been established for the sake of public health and ecosystem diversity since environmental awareness was awakened in the late 1960s. However, the results were often unsatisfactory. Either environmental goals achieved were far from desired, or regional development was hampered due to some unpractical high environmental standards. The failure of these environmental standards resulted in innovations of environmental policy instruments to find practical environmental goals and methods approaching them scientifically. Another class of environmental policy instruments, so called economic incentive policies, is established based on environmental economics theory. A neo-classical economics framework is founded for setting appropriate environmental goals and assessing efficiency of environmental policies in reaching these goals. This thesis summarizes rationales and factors affecting the performance for environmental policy instruments under the neo-classical economic framework. Since the acid rain program, the first large-scale implementation of the emissions trading policy, has achieved great success in reducing SO₂ emissions from the electricity generators in the United States, the emission trading policy attracted many interests in this kind of environmental policy instrument. Many countries, such as China, plan to adopt the emissions trading policy to address various environmental problems. Hence, factors leading to the success of this program should be identified. Potential risks and problems must be addressed as well lest the emissions trading policy causes some problem during implementation. Feasibility of implementing an emissions trading policy will be discussed based on these results. Three kinds of geographic analyses, change detection, network analysis, and hot spots identification, are conducted in this thesis to study the effectiveness and efficiency of the acid rain program. It is found that the acid rain program is successful in improving the sustainability of the economic development in the United States. But the effectiveness is not as great as the high emissions cutting rate achieved in this program. In addition, the acid rain program lowers the compliance costs of achieving the environmental goal since the radius of the high quality coal service area doubles. Lastly, hot spots are found around the Ohio River valley and Los Angeles. Suggestions on integrating geographic factors into the economic framework are presented in order to eliminate the risk of causing severe environmental problems. Finally, the feasibility of migrating the emissions trading policy to China is discussed. Further work can be conducted in this direction to realize sustainable development quicker with lower costs.

Aim It is commonly asserted that climate presents the primary constraint on species’ geographic distributions, and therefore, that species' ranges shift in response to changing climate given their specific climatic tolerances. However, supporting evidence is surprisingly inconsistent. Alternatively, spatially structured processes (e.g., dispersal) could more strongly determine species’ geographic distributions. Is climate the primary determinant of species’ geographic distributions, or might non-climatic, spatial processes constitute a stronger influence, such that the effect of climate is indirect? This study tests a number of predictions made by each of these hypotheses, during a single period of time. Location Contiguous United States and southern Canada. Methods We used 19 species of passerine birds whose distributions fall entirely within the area sampled by the North American Breeding Bird Survey from 1990-2000. We related these distributions to the mean breeding season climate, geographic locations and neighbourhood effects. Two spatial scales were addressed to assess the geographic location of species’ ranges and species' distributions within ranges. Results On average, geographic coordinates and a model representing neighbourhood occupancy outperform a simple climatic model. After controlling for geographic coordinates, species occupancy is poorly related to climate. A neighbourhood model on average accounts for the majority of variance captured by geographic coordinates within ranges, and more for the continental placement of ranges. Spatially explicit variables are more important than macroclimatic variables in a predictive model of species occupancy on average. Main Conclusions The geographic distributions of wide-spread North American passerine birds appear not to be primarily determined by climate. Our results are consistent with the hypothesis that localized spatial processes such as dispersal are stronger determinants of both continental range placement and within-range distributions of North American birds.

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Spatial interaction models of the gravity type are widely used to model origindestination flows. They draw attention to three types of variables to explain variation in spatial interactions across geographic space: variables that characterise an origin region of a flow, variables that characterise a destination region of a flow, and finally variables that measure the separation between origin and destination regions. This paper outlines and compares two approaches, the spatial econometric and the eigenfunction-based spatial filtering approach, to deal with the issue of spatial autocorrelation among flow residuals. An example using patent citation data that capture knowledge flows across 112 European regions serves to illustrate the application and the comparison of the two approaches.(authors' abstract)

Made available in DSpace on 2017-05-12T14:46:51Z (GMT). No. of bitstreams: 1 Everton.pdf: 4714138 bytes, checksum: a59b9d4eb09d8201b1cddd3c78f52e24 (MD5) Previous issue date: 2012-12-01 / This paper aimed to present methods to be applied in the area of spatial statistics on soybean yield and agrometeorological factors in Western Paraná state. The data used, related to crop years from 2000/2001 to 2007/2008, are the following variables: soybean yield (t ha-1) and agrometeorological factors, such as rainfall (mm), average temperature (oC) and solar global radiation average (W m-2). In the first phase,it was used indices of spatial autocorrelation (Moran Global and Local) and presented multiple spatial regression models, with performance evaluations. The estimation of parameters occurred when using the Maximum Likelihood method and the performance evaluation of the models was based on the coefficient of determination (R2), the maximum value of the function of the logarithm of the maximum value of the likelihood function logarithm and the Bayesian information criterion of Schwarz. In a second step, cluster analysis was performed using spatial statistical multivariate associations, seeking to identify the same set of variables, but with a larger number of crop years. Finally, the data from one crop year were utilized in an approach based on fuzzy clustering, through the Fuzzy C-Means algorithm and the similarity measure by defining an index for this purpose. The first phase of the study showed the correlation between spatial autocorrelation and soybean yield and agrometeorological elements, through the analysis of spatial area, using techniques such as index Global Moran's I and Local univariate and bivariate and significance tests. It was possible to demonstrate, through the performance indicators used, that the SAR and CAR models offered better results than the classical multiple regression model. In the second phase, it was possible to present the formation of groups of cities using the similarities of the variables under analysis. Cluster analysis is a useful tool for better management of production activities in agriculture, since, with the grouping, it was possible to establish similarities parameters that provide better management of production processes that bring quantitative and qualitatively better, results sought by the farmer. In the final step, through the use of Fuzzy C-Means algorithm, it was possible to form groups of cities of similar soybean yield using the method of decision by the Higher Degree of Relevance (MDMGP) and Method of Decision Threshold by β (β CDM). Subsequently, identification of the adequate number of clusters was obtained using modified partition entropy. To measure the degree of similarity of each cluster, a Cluster Similarity Index (ISCl) was designed and used, which considers the degree of relevance of each city within the group to which it belongs. Within the perspective of this study, the method used was adequate, allowing to identify clusters of cities with degrees of similarities in the order of 60 to 78%. / Este trabalho apresenta métodos para serem aplicados na estatística espacial de área na produtividade da soja e fatores agrometeorológicos na região oeste do estado do Paraná. Os dados utilizados estão relacionados aos anos-safra de 2000/2001 a 2007/2008, sendo as variáveis: produtividade da soja (t ha-1) e agrometeorológicas, tais como precipitação pluvial (mm), temperatura média (oC) e radiação solar global média (W m-2). Em uma primeira fase foram utilizados índices de autocorrelação espacial (Moran Global e Local) e apresentados modelos de regressão espacial múltipla, com avaliações de desempenho. A estimativa dos parâmetros dos modelos ajustados se deu pelo uso do método de Máxima Verossimilhança e a avaliação do desempenho dos modelos foi realizada com base no coeficiente de determinação (R2), no máximo valor do logaritmo da função do máximo valor do logaritmo da função verossimilhança e no critério de informação bayesiano de Schwarz. Em uma segunda etapa foram realizadas análises de agrupamento espacial por meio da estatística multivariada, buscando identificar associações no mesmo conjunto de variáveis, porém com um número maior de anos-safra. Finalmente, os dados de um ano-safra foram aplicados em uma abordagem baseada em agrupamento difuso, por meio do algoritmo Fuzzy c-Means, tendo a similaridade medida pela definição de um índice com este objetivo. O estudo da primeira fase permitiu verificar a correlação e a autocorrelação espacial entre a produtividade da soja e os elementos agrometeorológicos, por meio da análise espacial de área, usando técnicas como o índice I de Moran Global e Local uni e bivariado e os testes de significância. Foi possível demonstrar que, por meio dos indicadores de desempenho utilizados, os modelos SAR e CAR ofereceram melhores resultados em relação ao modelo de regressão múltipla clássica. Na segunda fase, foi possível apresentar a formação de grupos de municípios utilizando as similaridades das variáveis em análise. A análise de agrupamento foi um instrumento útil para uma melhor gestão das atividades de produção da agricultura, em função de que, com o agrupamento, foi possível se estabelecer similaridades que proporcionem parâmetros para uma melhor gestão dos processos de produção que traga, quantitativa e qualitativamente, resultados almejados pelo agricultor. Na etapa final, por meio do algoritmo Fuzzy c-Means, foi possível a formação de grupos de municípios similares à produtividade de soja, utilizando o Método de Decisão pelo Maior Grau de Pertinência (MDMGP) e o Método de Decisão pelo Limiar β (MDL β). Posteriormente, a identificação do número adequado de agrupamentos foi obtida utilizando a Entropia de Partição Modificada. Para mensurar o nível de similaridade de cada agrupamento, foi criado e utilizado um Índice de Similaridade de Clusters (ISCl), que considera o grau de pertinência de cada município dentro do agrupamento a que pertence. Dentro das perspectivas deste estudo, o método empregado se mostrou adequado, permitindo identificar agrupamentos de municípios com graus de similaridades da ordem de 60 a 78%. espacial