An investigation of the distribution of porcine reproduction and respiratory syndrome virus (PRRSV) and factors associated with the presence of PRRSV in Ontario from 2004 – 2007 was conducted. Surveys on the presence of clinical signs associated with PRRS, management practices, animal suppliers, and herd location were administered to the managers of 458 PRRSV positive herds and 61 PRRSV negative herds. Open reading frame (ORF) 5 of the PRRSV genome was sequenced from herds with PRRSV. PRRSV positive herds were compared to PRRSV negative herds. Management practices associated with being PRRSV positive were: not washing animal- and feed-delivery vehicles, feed-delivery and animal-transport vehicles visiting multiple herds at one time, allowing a truck driver to enter the barn, not requiring visitors to shower prior to farm entry, and not utilizing all-in all-out flow in gilt and finisher barns. Specific PRRSV restriction fragment length polymorphism (RFLP) genotypes of the ORF5 gene were compared with clinical signs. Herds with RFLP type ‘1-undetermined-4’, ‘1-undetermined-2’ and 1-3-4 were associated with clinical signs in sows and 2-6-2 was associated with finisher mortality compared to herds with vaccine virus. Additionally, genotypes 1-3-4 and 1-8-4 increased in frequency during this study. The between-herd PRRSV similarity of genome and clinical signs were compared. Abortions and stillbirths were associated with similarity in genetic sequences between herds. This relationship did not extend to those herds where vaccine virus was identified. Patterns in space and time of herds with different RFLP types of PRRSV were investigated after accounting for ownership. There was weak evidence to suggest local spread the genotype 1-3-4. The association between genetic similarity and proximity in space, time, ownership, animal, and semen suppliers was tested. Significant correlation was detected for distances up to 30 km. After controlling for ownership, only small associations between breeding stock and semen suppliers and genetic similarity of PRRSV were found. The spread of PRRSV among herds in Ontario cannot be attributed to any one factor. However, similarity in ownership between herds was a key variable indicating that movement of animals, personnel, and vehicles among herds must be measured in future investigations of PRRSV dynamics. / Ontario Pork and the Canada-Ontario Research and Development (CORD) Program and the OMAFRA/University of Guelph agreement

Spatial epidemiological studies which assume perfect health status information can be biased if imperfect diagnostic tests have been used to obtain the health status of individuals in a population. This study investigates the effect of diagnostic misclassification on the spatial statistical methods commonly used to analyze regional health status data in spatial epidemiology. The methods considered here are: Moran's I to assess clustering in the data, a Gaussian random field model to estimate prevalence and the range and sill parameters of the semivariogram, and Kulldorff's spatial scan test to identify clusters. Various scenarios of diagnostic misclassification were simulated from a West Nile virus dead-bird surveillance program, and the results were evaluated. It was found that non-differential misclassification added random noise to the spatial pattern in observed data which created bias in the statistical results. However, when regional sample sizes were doubled, the effect from misclassification bias on the spatial statistics decreased.

No description available.

Spatial analysis methods used for detecting, interpolating or predicting local patterns require a delineation of a neighbourhood defining the extent of spatial interaction in geographic data. The most common neighbourhood delineation techniques include fixed distance bands, k-nearest neighbours, or spatial adjacency (contiguity) matrices optimized to represent spatial dependency in data. However, these standard approaches do not take into consideration the geographic or environmental constraints such as impassable mountain ranges, road networks or coastline barriers. Specifically, complex marine landscapes and coastlines present common problematic neighbourhood definitions for standard neighbourhood matrices used in the spatial analysis of marine environments. Therefore, the goal of our research is to present a new approach to constraining spatial neighbourhoods when conducting geographical analysis in marine environments. To meet this goal, we developed methods and software (COnstraining SpatIal NEighbourhoods - COSINE) for modifying spatial neighbourhoods, and demonstrate their utility in two case studies. Our method enables delineation of neighbourhoods that are constrained by coastlines and the direction of marine currents. Our software calculates and evaluates whether neighbouring features are separated by land, or are within a user defined angle that excludes interaction based on directional processes. Using decision rules a modified spatial weight matrix is created, either in binary or row-standardized format. Within open source software (R), a graphical user interface enables users to modify the standard spatial neighbourhood definition distance, inverse distance and k-nearest neighbour. Two case studies are presented to demonstrate the usefulness of the new approach for detecting spatial patterns: the first case study observes marine mammals’ abundance and the second, oil spill observation. Our results indicate that constraining spatial neighbourhoods in marine environments is particularly important at larger spatial scales. The COSINE tool has many applications for modelling both environmental and human processes. / Graduate / 0463 / 0366 / suarezc@uvic.ca

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

This study aims to develop a method to investigate the relationship between socio-economic status of village settlements and some topographic variables using geographical information systems (GIS) and spatial statistical methods. The study area is &Ccedil / ankiri province, a mountainous region that lays at the northeast of Ankara. 331 villages represented by areal units are used in this study. 195 variables are used to extract a single socio-economic status indicator. First, all the variables are divided under three groups, namely economic, social and service. Principal Components Analysis (PCA) is used to construct an index indicating socio-economic status. The parameters that represent natural environment are / mean elevation, mean slope, mean aspect and the ratio of high quality soil in the total area, for each settlement unit. The data is visualized by choropleth, cartogram and 3D techniques. Then it is explored by using correlograms, spatial moving averages and geographically weighted regression (GWR). Finally linear non-spatial regression and spatial regression methods are utilized in order to establish a relation between the socio-economic status and environmental parameters.

This thesis is developed upon inquires on urban spatial structure of Southeast Asian extended metropolitan region (EMR) and its impacts on travel. Literature suggests that while efforts in promoting transport sustainability in the developed world have included policy measures involving urban spatial structure and its physical features as a consequence of the understanding on strong link between land use and transport, there has been lack of understandings on the spatial structure in major cities in Southeast Asia. Exploratory spatial data analysis (ESDA) is adopted for identification of important components of the spatial structure of employment in the Jakarta Metropolitan Area (JMA). The approach has been specifically designed in order to extract clusters as suggested in the Southeast Asian EMR concept. It is found that the spatial structure of employment in the JMA consists of the following major components: the urban core of Jakarta; the single dominant and expanded regional CBD within the urban core of Jakarta; manufacturing corridors that are largely follow toll roads radiating out of the urban core; local government regions that in general have not been developed into substantial sub-centres; desakota areas overlapping the manufacturing corridors and the agricultural areas; and portions of agricultural areas in the outer parts of Bekasi, Bogor and Tangerang regencies. The result shows that spatial structure of JMA conforms to the Southeast Asian EMR concept rather than the monocentric, polycentric or sprawl patterns debated for the case of developed cities. Commuting impacts of the identified spatial structure of employment and its physical features are investigated using the desireline analysis, home-to-work trip pattern comparisons (ANOVA) by the employment clusters, and ordinary linear regression and logistic regression models. It is found that the spatial structure identified and its physical features have significant associations to variations in the pattern of commuting across the region. The physical features of the employment spatial structure identified include important policy sensitive variables such as job density, job to household ratio, land use diversity and job accessibility. Policy implications of the findings are developed and centred on recommending both the spatial structure of employment and its physical characteristics that promote more sustainable transport in the JMA.

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.

The aim of this thesis is technical evaluation of existing approaches to defining rural areas in our country, in Europe and in the world. The main result of the work will be a comprehensive proposal for a new definition of rural areas of the Czech Republic by means of spatial data analysis in a geographic information system. The main method of the spatial analysis will be the map algebra, using it to define a new criterion for rural areas. A new proposal for the definition of rural areas of the Czech Republic will be based on a combination of commonly used criterias for defining rural (population, population density, unemployment rate, etc.) along with their processing in GIS (spatial models, overlay operations, spatial statistics and others). The result of this work should serve to further evaluate the proposals and rural areas of sustainable development.