School of Geography - Research Publications
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ItemWhich theoretical distribution function best fits measured within day rainfall distributions across Australia?(Conference Organising Committee, 2006)Rainfall data at high temporal resolutions is required to accurately model the dynamics of surface runoff processes, in particular sediment entrainment. These processes respond to rainfall intensity variations over short intervals, yet measurement of rainfall intensity at sufficient resolution is available only at a limited number of locations across Australia. On the other hand there is good coverage of rainfall data registered at a daily time step, thus it is desirable to establish a means to estimate within-day distributions of rainfall intensity given the daily rainfall depth and other readily available hydrometeorological data (e.g. temperature, pressure). As a first step towards such a method, an investigation was conducted into the shape of the temporal distribution of high-resolution (6 minute) rainfall intensity within the wet part of rainy days (total rainfall depth > 10mm). This paper quantifies the skill of nine different theoretical distribution functions (TDFs) in fitting characteristics of measured rainfall that are most likely to drive sediment entrainment and transport on hillslopes. Skill is reported by two goodness-of-fit statistics: the Root Mean Square Error (RMSE) between the fitted and observed within-day distribution; and the efficiency of prediction of the 30 minutes of highest rainfall intensity (average intensity of the 5 highest intensity intervals). Four TDFs provided relatively poor fits to higher intensity rainfall (two and three parameter lognormal, two parameter Generalized Pareto and Gumbel), and also showed higher RMSE values. The remaining five TDFs performed equally well for both goodness-of-fit measures. Two of these TDFs are extreme value distributions (Generalized Extreme Value and Weibull) and in a strict statistical sense should not be applied to within-day rainfall intensity data. On this basis, the remaining three TDFs (gamma, exponential and the three parameter Generalized Pareto) were selected as suitable candidates to represent within-day rainfall distributions in Australia, in particular for hydrological models seeking to estimate runoff and erosion.
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ItemFast food purchasing and access to fast food restaurants: a multilevel analysis of VicLANES(BMC, 2009-05-27)BACKGROUND: While previous research on fast food access and purchasing has not found evidence of an association, these studies have had methodological problems including aggregation error, lack of specificity between the exposures and outcomes, and lack of adjustment for potential confounding. In this paper we attempt to address these methodological problems using data from the Victorian Lifestyle and Neighbourhood Environments Study (VicLANES) - a cross-sectional multilevel study conducted within metropolitan Melbourne, Australia in 2003. METHODS: The VicLANES data used in this analysis included 2547 participants from 49 census collector districts in metropolitan Melbourne, Australia. The outcome of interest was the total frequency of fast food purchased for consumption at home within the previous month (never, monthly and weekly) from five major fast food chains (Red Rooster, McDonalds, Kentucky Fried Chicken, Hungry Jacks and Pizza Hut). Three measures of fast food access were created: density and variety, defined as the number of fast food restaurants and the number of different fast food chains within 3 kilometres of road network distance respectively, and proximity defined as the road network distance to the closest fast food restaurant.Multilevel multinomial models were used to estimate the associations between fast food restaurant access and purchasing with never purchased as the reference category. Models were adjusted for confounders including determinants of demand (attitudes and tastes that influence food purchasing decisions) as well as individual and area socio-economic characteristics. RESULTS: Purchasing fast food on a monthly basis was related to the variety of fast food restaurants (odds ratio 1.13; 95% confidence interval 1.02 - 1.25) after adjusting for individual and area characteristics. Density and proximity were not found to be significant predictors of fast food purchasing after adjustment for individual socio-economic predictors. CONCLUSION: Although we found an independent association between fast food purchasing and access to a wider variety of fast food restaurant, density and proximity were not significant predictors. The methods used in our study are an advance on previous analyses.
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ItemOf texts and practices: Empowerment and organisational cultures in world bank-funded rural development programmes(ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, 2007)