Prevalence of overweight and obesity in children from urban and rural Niagara

Purpose: The influence of environment in the development of overweight and obesity is an ongoing concern. This investigation examined the influence of urbanization on the rates of childhood overweight and obesity. Method: 2167 (1090M, 1077F) grade four children from 75 schools in Ontario's Niagara Region were sampled. A sophisticated algorithm overlaying electoral boundaries, population densities, and the knowledge of community members was used to classify schools into one of three location categories: urban {N= 1588), urban fringe {N= 379), and rural (A^= 234). Each subject was measured for: height, weight, and aerobic performance (Leger). Physical activity was evaluated with the self-report Participation Questionnaire (free-time and organized sport activities), and teacher's evaluations of student activity. Overweight (overweight and obesity combined) was measured both as a continuous (BMI) and categorical variable (BMI category), to evaluate the prevalence by location. A multivariate analysis was used to test for a suppression effect. Results: BMI and BMI category did not differ significantly by location or gender, and no evidence of a gender interaction existed. According to both a linear and logistic regression, physical activity or fitness levels did not suppress the influence of location on BMI and BMI category. Age, gender, free-time activity, organized sports, fitness level, and number of siblings, were all found to significantly influence overweight. Conclusions: It is plausible that the prevalence of overweight does not differ in urban and rural children from the Niagara Region. Further investigation is recommended, examining subjects by individual location of residence, in multiple regions throughout Ontario.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).