Faster than the speed of print : reconciling ‘big data’ social media analysis and academic scholarship

The promise of ‘big data’ has generated a significant deal of interest in the development of new approaches to research in the humanities and social sciences, as well as a range of important critical interventions which warn of an unquestioned rush to ‘big data’. Drawing on the experiences made in developing innovative ‘big data’ approaches to social media research, this paper examines some of the repercussions for the scholarly research and publication practices of those researchers who do pursue the path of ‘big data’–centric investigation in their work. As researchers import the tools and methods of highly quantitative, statistical analysis from the ‘hard’ sciences into computational, digital humanities research, must they also subscribe to the language and assumptions underlying such ‘scientificity’? If so, how does this affect the choices made in gathering, processing, analysing, and disseminating the outcomes of digital humanities research? In particular, is there a need to rethink the forms and formats of publishing scholarly work in order to enable the rigorous scrutiny and replicability of research outcomes?

Young children's statistical reasoning : a tale of two contexts

This thesis explored the knowledge and reasoning of young children in solving novel statistical problems, and the influence of problem context and design on their solutions. It found that young children's statistical competencies are underestimated, and that problem design and context facilitated children's application of a wide range of knowledge and reasoning skills, none of which had been taught. A qualitative design-based research method, informed by the Models and Modeling perspective (Lesh & Doerr, 2003) underpinned the study. Data modelling activities incorporating picture story books were used to contextualise the problems. Children applied real-world understanding to problem solving, including attribute identification, categorisation and classification skills. Intuitive and metarepresentational knowledge together with inductive and probabilistic reasoning was used to make sense of data, and beginning awareness of statistical variation and informal inference was visible.

From science to management : using Bayesian networks to learn about Lyngbya

Toxic blooms of Lyngbya majuscula occur in coastal areas worldwide and have major ecological, health and economic consequences. The exact causes and combinations of factors which lead to these blooms are not clearly understood. Lyngbya experts and stakeholders are a particularly diverse group, including ecologists, scientists, state and local government representatives, community organisations, catchment industry groups and local fishermen. An integrated Bayesian Network approach was developed to better understand and model this complex environmental problem, identify knowledge gaps, prioritise future research and evaluate management options.

Reconceptualizing statistical learning in the early years

This chapter argues for the need to restructure children’s statistical experiences from the beginning years of formal schooling. The ability to understand and apply statistical reasoning is paramount across all walks of life, as seen in the variety of graphs, tables, diagrams, and other data representations requiring interpretation. Young children are immersed in our data-driven society, with early access to computer technology and daily exposure to the mass media. With the rate of data proliferation have come increased calls for advancing children’s statistical reasoning abilities, commencing with the earliest years of schooling (e.g., Langrall et al. 2008; Lehrer and Schauble 2005; Shaughnessy 2010; Whitin and Whitin 2011). Several articles (e.g., Franklin and Garfield 2006; Langrall et al. 2008) and policy documents (e.g., National Council of Teachers ofMathematics 2006) have highlighted the need for a renewed focus on this component of early mathematics learning, with children working mathematically and scientifically in dealing with realworld data. One approach to this component in the beginning school years is through data modelling (English 2010; Lehrer and Romberg 1996; Lehrer and Schauble 2000, 2007)...

Monitoring and benchmarking government policies and actions to improve the healthiness of food environments : a proposed Government Healthy Food Environment Policy Index

Government action is essential to increase the healthiness of food environments and reduce obesity, diet-related non-communicable diseases (NCDs), and their related inequalities. This paper proposes a monitoring framework to assess government policies and actions for creating healthy food environments. Recommendations from relevant authoritative organizations and expert advisory groups for reducing obesity and NCDs were examined, and pertinent components were incorporated into a comprehensive framework for monitoring government policies and actions. A Government Healthy Food Environment Policy Index (Food-EPI) was developed, which comprises a ‘policy’ component with seven domains on specific aspects of food environments, and an ‘infrastructure support’ component with seven domains to strengthen systems to prevent obesity and NCDs. These were revised through a week-long consultation process with international experts. Examples of good practice statements are proposed within each domain, and these will evolve into benchmarks established by governments at the forefront of creating and implementing food policies for good health. A rating process is proposed to assess a government's level of policy implementation towards good practice. The Food-EPI will be pre-tested and piloted in countries of varying size and income levels. The benchmarking of government policy implementation has the potential to catalyse greater action to reduce obesity and NCDs.

Associations of body mass index and waist circumference with: energy intake and percentage energy from macronutrients, in a cohort of Australian children

Background It is evident from previous research that the role of dietary composition in relation to the development of childhood obesity remains inconclusive. Several studies investigating the relationship between body mass index (BMI), waist circumference (WC) and/or skin fold measurements with energy intake have suggested that the macronutrient composition of the diet (protein, carbohydrate, fat) may play an important contributing role to obesity in childhood as it does in adults. This study investigated the possible relationship between BMI and WC with energy intake and percentage energy intake from macronutrients in Australian children and adolescents. Methods Height, weight and WC measurements, along with 24 h food and drink records (FDR) intake data were collected from 2460 boys and girls aged 5-17 years living in the state of Queensland, Australia. Results Statistically significant, yet weak correlations between BMI z-score and WC with total energy intake were observed in grades 1, 5 and 10, with only 55% of subjects having a physiologically plausible 24 hr FDR. Using Pearson correlations to examine the relationship between BMI and WC with energy intake and percentage macronutrient intake, no significant correlations were observed between BMI z-score or WC and percentage energy intake from protein, carbohydrate or fat. One way ANOVAs showed that although those with a higher BMI z-score or WC consumed significantly more energy than their lean counterparts. Conclusion No evidence of an association between percentage macronutrient intake and BMI or WC was found. Evidently, more robust longitudinal studies are needed to elucidate the relationship linking obesity and dietary intake.

National survey of viruses in pastures of subterranean clover. II. Statistical methodology for large scale quantitative ELISA

Statistical methodology was applied to a survey of time-course incidence of four viruses (alfalfa mosaic virus, clover yellow vein virus, subterranean clover mottle virus and subterranean clover red leaf virus) in improved pastures in southern regions of Australia. -from Authors

Acceptability-based QoE models for mobile video

Quality of experience (QoE) measures the overall perceived quality of mobile video delivery from subjective user experience and objective system performance. Current QoE computing models have two main limitations: 1) insufficient consideration of the factors influencing QoE, and; 2) limited studies on QoE models for acceptability prediction. In this paper, a set of novel acceptability-based QoE models, denoted as A-QoE, is proposed based on the results of comprehensive user studies on subjective quality acceptance assessments. The models are able to predict users’ acceptability and pleasantness in various mobile video usage scenarios. Statistical regression analysis has been used to build the models with a group of influencing factors as independent predictors, including encoding parameters and bitrate, video content characteristics, and mobile device display resolution. The performance of the proposed A-QoE models has been compared with three well-known objective Video Quality Assessment metrics: PSNR, SSIM and VQM. The proposed A-QoE models have high prediction accuracy and usage flexibility. Future user-centred mobile video delivery systems can benefit from applying the proposed QoE-based management to optimize video coding and quality delivery decisions.

Reported consumption of takeaway food and its contribution to socioeconomic inequalities in body mass index

The aim of this study was to examine whether takeaway food consumption mediated (explained) the association between socioeconomic position and body mass index (BMI). A postal-survey was conducted among 1500 randomly selected adults aged between 25 and 64 years in Brisbane, Australia during 2009 (response rate 63.7%, N=903). BMI was calculated using self-reported weight and height. Participants reported usual takeaway food consumption, and these takeaway items were categorised into "healthy" and "less healthy" choices. Socioeconomic position was ascertained by education, household income, and occupation. The mean BMI was 27.1kg/m(2) for men and 25.7kg/m(2) for women. Among men, none of the socioeconomic measures were associated with BMI. In contrast, women with diploma/vocational education (β=2.12) and high school only (β=2.60), and those who were white-collar (β=1.55) and blue-collar employees (β=2.83) had significantly greater BMI compared with their more advantaged counterparts. However, household income was not associated with BMI. Among women, the consumption of "less healthy" takeaway food mediated BMI differences between the least and most educated, and between those employed in blue collar occupations and their higher status counterparts. Decreasing the consumption of "less healthy" takeaway options may reduce socioeconomic inequalities in overweight and obesity among women but not men.

The politics of Twitter data

Talk of Big Data seems to be everywhere. Indeed, the apparently value-free concept of ‘data’ has seen a spectacular broadening of popular interest, shifting from the dry terminology of labcoat-wearing scientists to the buzzword du jour of marketers. In the business world, data is increasingly framed as an economic asset of critical importance, a commodity on a par with scarce natural resources (Backaitis, 2012; Rotella, 2012). It is social media that has most visibly brought the Big Data moment to media and communication studies, and beyond it, to the social sciences and humanities. Social media data is one of the most important areas of the rapidly growing data market (Manovich, 2012; Steele, 2011). Massive valuations are attached to companies that directly collect and profit from social media data, such as Facebook and Twitter, as well as to resellers and analytics companies like Gnip and DataSift. The expectation attached to the business models of these companies is that their privileged access to data and the resulting valuable insights into the minds of consumers and voters will make them irreplaceable in the future. Analysts and consultants argue that advanced statistical techniques will allow the detection of ongoing communicative events (natural disasters, political uprisings) and the reliable prediction of future ones (electoral choices, consumption)...

Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field.

Did socio-ecological factors drive the spatiotemporal patterns of pandemic influenza A (H1N1)?

BACKGROUND Pandemic influenza A (H1N1) has a significant public health impact. This study aimed to examine the effect of socio-ecological factors on the transmission of H1N1 in Brisbane, Australia. METHODOLOGY We obtained data from Queensland Health on numbers of laboratory-confirmed daily H1N1 in Brisbane by statistical local areas (SLA) in 2009. Data on weather and socio-economic index were obtained from the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. A Bayesian spatial conditional autoregressive (CAR) model was used to quantify the relationship between variation of H1N1 and independent factors and to determine its spatiotemporal patterns. RESULTS Our results show that average increase in weekly H1N1 cases were 45.04% (95% credible interval (CrI): 42.63-47.43%) and 23.20% (95% CrI: 16.10-32.67%), for a 1 °C decrease in average weekly maximum temperature at a lag of one week and a 10mm decrease in average weekly rainfall at a lag of one week, respectively. An interactive effect between temperature and rainfall on H1N1 incidence was found (changes: 0.71%; 95% CrI: 0.48-0.98%). The auto-regression term was significantly associated with H1N1 transmission (changes: 2.5%; 95% CrI: 1.39-3.72). No significant association between socio-economic indexes for areas (SEIFA) and H1N1 was observed at SLA level. CONCLUSIONS Our results demonstrate that average weekly temperature at lag of one week and rainfall at lag of one week were substantially associated with H1N1 incidence at a SLA level. The ecological factors seemed to have played an important role in H1N1 transmission cycles in Brisbane, Australia.

ZnO nanocones with high-index {101̅1} facets for enhanced energy conversion efficiency of dye-sensitized solar cells

ZnO is a promising photoanode material for dye-sensitized solar cells (DSCs) due to its high bulk electron mobility and because different geometrical structures can easily be tailored. Although various strategies have been taken to improve ZnO-based DSC efficiencies, their performances are still far lower than TiO2 counterparts, mainly because low conductivity Zn2+–dye complexes form on the ZnO surfaces. Here, cone-shaped ZnO nanocrystals with exposed reactive O-terminated {101̅1} facets were synthesized and applied in DSC devices. The devices were compared with DSCs made from more commonly used rod-shaped ZnO nanocrystals where {101̅0} facets are predominantly exposed. When cone-shaped ZnO nanocrystals were used, DSCs sensitized with C218, N719, and D205 dyes universally displayed better power conversion efficiency, with the highest photoconversion efficiency of 4.36% observed with the C218 dye. First-principles calculations indicated that the enhanced DSCs performance with ZnO nanocone photoanodes could be attributed to the strength of binding between the dye molecules and reactive O-terminated {101̅1} ZnO facets and that more effective use of dye molecules occurred due to a significantly less dye aggregation on these ZnO surfaces compared to other ZnO facets.

The 2013 creative industries statistical analysis for Western Australia

The Department of Culture and the Arts undertook the first mapping of Perth’s creative industries in 2007 in partnership with the City of Perth and the Departments of Industry and Resources and the Premier and Cabinet. The 2013 Creative Industries Statistical Analysis for Western Australia report has updated the mapping with the 2011 Census employment data to provide invaluable information for the State’s creative industries, their peak associations and potential investors. The report maps sector employment numbers and growth between the 2006 and 2011 Census in the areas of music, visual and performing arts, film, TV and radio, advertising and marketing, software and digital content, publishing, and architecture and design, which includes designer fashion.

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of -1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required.