What Google Maps can do for Biomedical Data Dissemination: Examples and a Design Study

Background: Biologists often need to assess whether unfamiliar datasets warrant the time investment required for more detailed exploration. Basing such assessments on brief descriptions provided by data publishers is unwieldy for large datasets that contain insights dependent on specific scientific questions. Alternatively, using complex software systems for a preliminary analysis may be deemed as too time consuming in itself, especially for unfamiliar data types and formats. This may lead to wasted analysis time and discarding of potentially useful data. Results: We present an exploration of design opportunities that the Google Maps interface offers to biomedical data visualization. In particular, we focus on synergies between visualization techniques and Google Maps that facilitate the development of biological visualizations which have both low-overhead and sufficient expressivity to support the exploration of data at multiple scales. The methods we explore rely on displaying pre-rendered visualizations of biological data in browsers, with sparse yet powerful interactions, by using the Google Maps API. We structure our discussion around five visualizations: a gene co-regulation visualization, a heatmap viewer, a genome browser, a protein interaction network, and a planar visualization of white matter in the brain. Feedback from collaborative work with domain experts suggests that our Google Maps visualizations offer multiple, scale-dependent perspectives and can be particularly helpful for unfamiliar datasets due to their accessibility. We also find that users, particularly those less experienced with computer use, are attracted by the familiarity of the Google Maps API. Our five implementations introduce design elements that can benefit visualization developers. Conclusions: We describe a low-overhead approach that lets biologists access readily analyzed views of unfamiliar scientific datasets. We rely on pre-computed visualizations prepared by data experts, accompanied by sparse and intuitive interactions, and distributed via the familiar Google Maps framework. Our contributions are an evaluation demonstrating the validity and opportunities of this approach, a set of design guidelines benefiting those wanting to create such visualizations, and five concrete example visualizations.

An Evaluation of Three Nutrition Labeling Formats for Restaurant Menus

This study evaluated three menu nutrition labeling formats: calorie only information, a healthy symbol, and a nutrient list. Daily sales data for a table-service restaurant located on a university campus were recorded during a four-week period from January to February 2013 to examine changes in average nutritional content of the entrees purchased by customers when different nutrition labels were provided. A survey was conducted to assess the customers’ use of nutrition labels, their preferences among the three labeling formats, their entree selections, their cognitive beliefs with regard to healthy eating, and their demographic characteristics. A total of 173 questionnaires were returned and included in data analysis. Analysis of Variance (ANOVA) and regression analyses were performed using SAS. The results showed that favorable attitudes toward healthy eating and the use of nutrition labels were both significantly associated with healthier entrée selections. Age and diet status had some effects on the respondent’s use of nutrition labels. The calorie only information format was the most effective in reducing calories contained in the entrees sold, and the nutrient list was most effective in reducing fat and saturated fat content of the entrees sold. The healthy symbol was the least effective format, but interestingly enough, was most preferred by respondents. The findings provide support for future research and offer implications for policy makers, public health professionals, and foodservice operations.