Background: Obesity and physical inactivity rates have reached epidemic levels in Canada, but differ based on whether they are self-reported or directly measured. Canadian research examining the combined and independent effects of social and built environments on adult physical activity (PA) and body mass index (BMI) is limited. Furthermore there is a lack of Canadian studies to assess these relationships using directly measured PA and BMI. Objectives: The objectives of this thesis were to systematically compare self-reported and directly measured PA and to examine associations between neighbourhood built and social environmental factors with both self-reported and directly measured PA and overweight/obesity in adults living in Ottawa, Canada. Methods: A systematic review was conducted to identify observational and experimental studies of adult populations that used both self-report and direct measures of PA and to assess the agreement between the measures. Associations between objectively measured neighbourhood-level built recreation and social environmental factors and self-reported individual-level data including total and leisure-time PA (LTPA) and overweight/obesity were examined in the adult population of Ottawa, Canada using multilevel models. Neighbourhood differences in directly measured BMI and PA (using accelerometry) were evaluated in a convenience sample of adults from four City of Ottawa neighbourhoods with contrasting socioeconomic (SES) and built recreation (REC) environments. Results: Results from the review generally indicate a poor level of agreement between self-report and direct measures of PA, with trends differing based on the measures of PA, the level of PA examined and the sex of the participants. Results of the multilevel analyses identified that very few of the built and social environmental variables were ii significantly associated with PA or overweight/obesity. Greater park area was significantly associated with total PA in females. Greater green space was shown to be associated with lower odds of male LTPA. Factors from the social environment were generally more strongly related to male outcomes. Further to the recreation and social environment, factors in the food landscape were significantly associated with male and female PA and overweight/obesity. Results of the directly measured PA and BMI investigation showed significant neighbourhood-group effects for light intensity PA and sedentary time. Post-hoc tests identified that the low REC/high SES neighbourhood had significantly more minutes of light PA than the low REC/low SES. BMI differed between the four neighbourhoods, but the differences were not significant after controlling for age, sex and household income. Conclusions: Results of this dissertation show that the quantity of PA can differ based on its method of measurement (i.e. between self-report and direct methods) with implications for the interpretation of study findings. It also identifies that PA and BMI can differ by neighbourhood and recognizes that the relationships between neighbourhood environments and PA and body composition are complex, may be differ between males and females, and may not always follow intuitive relationships. Furthermore it suggests that other factors in the environment not examined in this dissertation may influence adult PA and BMI and that longitudinal and intervention studies are needed.

The learning process consists of different steps: building a Training Set (TS), training the system, testing its behaviour and finally classifying unknown objects. When using a distance based rule as a classifier, i.e. 1-Nearest Neighbour (1-NN), the first step (building a training set) includes editing and condensing data. The main reason for that is that the rules based on distance need many time to classify each unlabelled sample, x, as each distance from x to each point in the training set should be calculated. So, the more reduced the training set, the shorter the time needed for each new classification process. This thesis is mainly focused on building a training set from some already given data, and specially on condensing it; however different classification techniques are also compared.The aim of any condensing technique is to obtain a reduced training set in order to spend as few time as possible in classification. All that without a significant loss in classification accuracy. Somenew approaches to training set size reduction based on prototypes are presented. These schemes basically consist of defining a small number of prototypes that represent all the original instances. That includes those approaches that select among the already existing examples (selective condensing algorithms), and those which generate new representatives (adaptive condensing algorithms).Those new reduction techniques are experimentally compared to some traditional ones, for data represented in feature spaces. In order to test them, the classical 1-NN rule is here applied. However, other classifiers (fast classifiers) have been considered here, as linear and quadratic ones constructed in dissimilarity spaces based on prototypes, in order to realize how editing and condensing concepts work for this different family of classifiers.Although the goal of the algorithms proposed in this thesis is to obtain a strongly reduced set of representatives, the performance is empirically evaluated over eleven real data sets by comparing not only the reduction rate but also the classification accuracy with those of other condensing techniques. Therefore, the ultimate aim is not only to find a strongly reduced set, but also a balanced one.Several ways to solve the same problem could be found. So, in the case of using a rule based on distance as a classifier, not only the option of reducing the training set can be afford. A different family of approaches consists of applying several searching methods. Therefore, results obtained by the use of the algorithms here presented are compared in terms of classification accuracy and time, to several efficient search techniques.Finally, the main contributions of this PhD report could be briefly summarised in four principal points. Firstly, two selective algorithms based on the idea of surrounding neighbourhood. They obtain better results than other algorithms presented here, as well as better than other traditional schemes. Secondly, a generative approach based on mixtures of Gaussians. It presents better results in classification accuracy and size reduction than traditional adaptive algorithms, and similar to those of the LVQ. Thirdly, it is shown that classification rules other than the 1-NN can be used, even leading to better results. And finally, it is deduced from the experiments carried on, that with some databases (as the ones used here) the approaches here presented execute the classification processes in less time that the efficient search techniques.

Social ecological models are recognized for allowing researchers to examine the influence of multiple factors on behaviour, yet to date relatively little research has simultaneously examined the role of personal, social, and environmental factors on youths’ physical activity, while giving consideration to the role of different types of neighbourhoods. The current study examined the physical activity of 804 youth aged 10 to 16 years residing in urban high density, suburban, or suburban low density neighbourhoods, by exploring relationships between several personal, social, and environmental factors. Findings revealed that all three factors were significant in explaining youths’ physical activity. Differences in participation were found based on gender and neighbourhood; males and youth from suburban low density neighbourhoods were significantly more physically active. Moreover, several variables were found to be important for understanding youth physical activity: sex, age, self-efficacy, encouragement, neighbourhood safety, and the use of a number of neighbourhood facilities.

With increased enrolment in post-secondary education in Canada, communities hosting universities or colleges have witnessed a higher number of students residing in their near-campus neighbourhoods. The concentration of students has negative impacts on these once low-density, family-oriented neighbourhoods. Near-campus neighbourhoods are often associated with images of run-down properties, garbage-strewn front yards, and alcohol fuelled parties. Some Ontario municipalities have responded to these problems with student accommodation strategies and planning initiatives. However, the problems in near-campus neighbourhoods and the effectiveness of the planning strategies have not been well understood due to the novelty of the issue and the scarcity of related research in the Canadian context. This study aims to answer two research questions: 1) what are the impacts of student populations on near-campus neighbourhoods in Ontario? 2) how, and how effectively, have planning authorities responded to the challenges of near-campus neighbourhoods? To answer these research questions, surveys and interviews were conducted among planners and post-secondary institution housing officers in Ontario university/college communities. Evidence was also sought from other resources including other academic studies, planning documents and media output. The study results showed that at least 23 Ontario municipalities faced challenges associated with the concentration of student population in near-campus neighbourhoods. The challenges relate to demographic imbalance, decreased rates of homeownership, a deteriorating neighbourhood environment, and student behavioural issues. However, only around half of the affected communities have developed planning strategies to respond to the challenges. Eleven planning approaches are identified, analyzed, and evaluated in the study. In general, planning policies to encourage high-density development to accommodate students are common approaches and considered effective: purpose-built student housing has significantly increased its market share in some university communities. This type of development relieves housing pressure, but it is not clear if it helps to stabilize low-density neighbourhoods near campus; the attempt to regulate the student private rental market by zoning and licensing regimes has several drawbacks. The study also reveals diverse interests of different stakeholders in near-campus neighbourhoods, and the lack of effective collaboration and partnerships among them in addressing challenges in their neighbourhoods in general.

Social ecological models are recognized for allowing researchers to examine the influence of multiple factors on behaviour, yet to date relatively little research has simultaneously examined the role of personal, social, and environmental factors on youths’ physical activity, while giving consideration to the role of different types of neighbourhoods. The current study examined the physical activity of 804 youth aged 10 to 16 years residing in urban high density, suburban, or suburban low density neighbourhoods, by exploring relationships between several personal, social, and environmental factors. Findings revealed that all three factors were significant in explaining youths’ physical activity. Differences in participation were found based on gender and neighbourhood; males and youth from suburban low density neighbourhoods were significantly more physically active. Moreover, several variables were found to be important for understanding youth physical activity: sex, age, self-efficacy, encouragement, neighbourhood safety, and the use of a number of neighbourhood facilities.

The collection of crop yield data has become much easier with the introduction of technologies such as the Global Positioning System (GPS), ground-based yield sensors, and Geographic Information Systems (GIS). This explosive growth and widespread use of spatial data has challenged the ability to derive useful spatial knowledge. In addition, outlier detection as one important pre-processing step remains a challenge because the technique and the definition of spatial neighbourhood remain non-trivial, and the quantitative assessments of false positives, false negatives, and the concept of region outlier remain unexplored. The overall aim of this study is to evaluate different spatial outlier detection techniques in terms of their accuracy and computational efficiency, and examine the performance of these outlier removal techniques in a site-specific management context. In a simulation study, unconditional sequential Gaussian simulation is performed to generate crop yield as the response variable along with two explanatory variables. Point and region spatial outliers are added to the simulated datasets by randomly selecting observations and adding or subtracting a Gaussian error term. With simulated data which contains known spatial outliers in advance, the assessment of spatial outlier techniques can be conducted as a binary classification exercise, treating each spatial outlier detection technique as a classifier. Algorithm performance is evaluated with the area and partial area under the ROC curve up to different true positive and false positive rates. Outlier effects in on-farm research are assessed in terms of the influence of each spatial outlier technique on coefficient estimates from a spatial regression model that accounts for autocorrelation. Results indicate that for point outliers, spatial outlier techniques that account for spatial autocorrelation tend to be better than standard spatial outlier techniques in terms of higher sensitivity, lower false positive detection rate, and consistency in performance. They are also more resistant to changes in the neighbourhood definition. In terms of region outliers, standard techniques tend to be better than spatial autocorrelation techniques in all performance aspects because they are less affected by masking and swamping effects. In particular, one spatial autocorrelation technique, Averaged Difference, is superior to all other techniques in terms of both point and region outlier scenario because of its ability to incorporate spatial autocorrelation while at the same time, revealing the variation between nearest neighbours. In terms of decision-making, all algorithms led to slightly different coefficient estimates, and therefore, may result in distinct decisions for site-specific management. The results outlined here will allow an improved removal of crop yield data points that are potentially problematic. What has been determined here is the recommendation of using Averaged Difference algorithm for cleaning spatial outliers in yield dataset. Identifying the optimal nearest neighbour parameter for the neighbourhood aggregation function is still non-trivial. The recommendation is to specify a large number of nearest neighbours, large enough to capture the region size. Lastly, the unbiased coefficient estimates obtained with Average Difference suggest it is the better method for pre-processing spatial outliers in crop yield data, which underlines its suitability for detecting spatial outlier in the context of on-farm research.

This thesis attempts to analyze the European Neighbourhood Policy from a Neo-Gramscian perspective, mapping transnational power relations in Europe and identifying the historical-specific articulations between economic, political and (civil) societal processes in the specific case of Ukraine. Thus the thesis attempts to show how the EU&rsquo / s hegemonic project is formed and applied, it also explores whether there are redefinitions of the EU hegemonic project and ask whether it is sustainable or not. It is contended that the European Commission&rsquo / s neo-liberal strategy is designed to transform the region into a space in which the free flow of capital, goods and services is secured, but the free movement of people is heavily restricted, and no commitment is made towards full membership for its partners. In fact, this study explores whether or not the ENP as a hegemonic project is likely to establish an historic bloc / whether the ENP is sustainable or not with the consent of its partners. This study focuses on social power relations and their organisation and articulation within the structures of the state/civil-societal complex at the national and transnational levels in order to analyse the degree and manner of both coercion and consent given to the EU&rsquo / s hegemonic project, which in turn will allow for an assessment of the project&rsquo / s likelihood of success. The EU&rsquo / s general strategy towards its neighbours is shown to be a hegemonic project spearheaded by an intellectual and moral leadership directed by conflicting political and cultural agents and organisations.

<p>Abstract</p><p>The thesis investigates how the European Union promotes stability and security in the Mediterranean region. The aim is to analyse the European Union’s security ambitions with Euro-Mediterranean Partnership, the Barcelona Process. An analytical framework with five sectors (military, political, economic, environmental and societal), based on the Copenhagen School’s theories about security sectors and securitization, is used for the analysis. Within the framework threats, objectives and methods are categorized into each security sector. Then it is possible to distinguish if any sector is more prioritised by the EU. Qualitative text analysis is used to examine relevant EU-documents. The result of the analysis shows that the European Union prioritizes the economical and societal sector in promoting peace and security within the Euro-Mediterranean Partnership. Through economical integration and cultural dialogue, the EU tries to enhance security and stability. The EU emphasises the importance of global governance and international law for a stable peace. The study concludes that the widened concept of security, within the Copenhagen School, can help to explain the European Union’s work inside the Euro-Mediterranean Partnership.</p>

Thesis (Ph. D.)--Open University. BLDSC no. DX215644.

Diss. Uppsala : Univ., 2008.