Visualizing Air Pollution: Communication of Environmental Health Information in a Chinese Immigrant Community.

This study developed and evaluated a visual approach to promoting environmental health literacy about highway pollution. The Interactive Map of Chinatown Traffic Pollution was the centerpiece of a communication approach designed to make complex scientific information about traffic-related air pollution comprehensible to Chinese immigrants with limited English proficiency. The map enabled visualization of the spatial distribution of ultrafine particles (less than 100 nanometers in diameter), a toxic and invisible form of air pollution, in Boston Chinatown. A university-community partnership enabled design of intergenerational training sessions aimed toward empowering community members to take health-promoting actions that reduce exposure to ultrafine particulate pollution. A mixed methods approach was taken to evaluation. Nine high school youth learned to use the map and then tutored adults recruited from English as a Second Language (ESL) classes and from a community workshop. Seventy-three of these adults completed a pre-post survey measuring change in three domains: pollution knowledge, attitudes toward environmental issues, and self-efficacy in using maps. Adult participants demonstrated statistically significant improvements in all three domains (Wilcoxon signed-rank test, all p < 0.01). Seventeen adults and nine youth participated in interviews. Interview participants reported adjusting daily routines to reduce exposure to pollution.

Identification and management of women at high risk for hereditary breast/ovarian cancer syndrome.

Despite advances in identifying genetic markers of high risk patients and the availability of genetic testing, it remains challenging to efficiently identify women who are at hereditary risk and to manage their care appropriately. HughesRiskApps, an open-source family history collection, risk assessment, and Clinical Decision Support (CDS) software package, was developed to address the shortcomings in our ability to identify and treat the high risk population. This system is designed for use in primary care clinics, breast centers, and cancer risk clinics to collect family history and risk information and provide the necessary CDS to increase quality of care and efficiency. This paper reports on the first implementation of HughesRiskApps in the community hospital setting. HughesRiskApps was implemented at the Newton-Wellesley Hospital. Between April 1, 2007 and March 31, 2008, 32,966 analyses were performed on 25,763 individuals. Within this population, 915 (3.6%) individuals were found to be eligible for risk assessment and possible genetic testing based on the 10% risk of mutation threshold. During the first year of implementation, physicians and patients have fully accepted the system, and 3.6% of patients assessed have been referred to risk assessment and consideration of genetic testing. These early results indicate that the number of patients identified for risk assessment has increased dramatically and that the care of these patients is more efficient and likely more effective.

Promoting insight-based evaluation of visualizations: from contest to benchmark repository.

Information Visualization (InfoVis) is now an accepted and growing field but questions remain about the best uses for and the maturity of novel visualizations. Usability studies and controlled experiments are helpful but generalization is difficult. We believe that the systematic development of benchmarks will facilitate the comparison of techniques and help identify their strengths under different conditions. We were involved in the organization and management of three information visualization contests for the 2003, 2004 and 2005 IEEE InfoVis Symposia, which requested teams to report on insights gained while exploring data. We give a summary of the state of the art of evaluation in information visualization, describe the three contests, summarize their results, discuss outcomes and lessons learned, and conjecture the future of visualization contests. All materials produced by the contests are archived in the InfoVis Benchmark Repository.

Vectorized Radviz and its application to multiple cluster datasets.

Radviz is a radial visualization with dimensions assigned to points called dimensional anchors (DAs) placed on the circumference of a circle. Records are assigned locations within the circle as a function of its relative attraction to each of the DAs. The DAs can be moved either interactively or algorithmically to reveal different meaningful patterns in the dataset. In this paper we describe Vectorized Radviz (VRV) which extends the number of dimensions through data flattening. We show how VRV increases the power of Radviz through these extra dimensions by enhancing the flexibility in the layout of the DAs. We apply VRV to the problem of analyzing the results of multiple clusterings of the same data set, called multiple cluster sets or cluster ensembles. We show how features of VRV help discern patterns across the multiple cluster sets. We use the Iris data set to explain VRV and a newt gene microarray data set used in studying limb regeneration to show its utility. We then discuss further applications of VRV.

Association of Low-Level Ozone with Cognitive Decline in Older Adults.

Increasing evidence points to an association of airborne pollutant exposure with respiratory, cardiovascular, and neurological pathology. We examined whether or not ground-level ozone or fine particulate matter ≤ 2.5 µm in diameter (PM2.5) was associated with accelerated cognitive decline. Using repeated measures mixed regression modeling, we analyzed cognitive performance of a geographically diverse sampling of individuals from the National Alzheimer's Coordinating Center between 2004-2008. Ambient air concentrations of ozone and PM2.5 were established using a space-time Hierarchical Bayesian Model that statistically merged air monitor data and modeled air quality estimates. We then compared the ambient regional concentrations of ozone and PM2.5 with the rate of cognitive decline in residents within those regions. Increased levels of ozone correlated with an increased rate of cognitive decline, following adjustment for key individual and community-level risk factors. Furthermore, individuals harboring one or more APOE4 alleles exhibited a faster rate of cognitive decline. The deleterious association of ozone was confined to individuals with normal cognition who eventually became cognitively impaired as opposed to those who entered the study with baseline impairment. In contrast to ozone, we did not observe any correlation between ambient PM2.5 and cognitive decline at regulatory limits set by the Environmental Protection Agency. Our findings suggest that prolonged exposure to ground-level ozone may accelerate cognitive decline during the initial stages of dementia development.

Pathways for Theoretical Advances in Visualization.

There is little doubt that having a theoretic foundation will benefit the field of visualization, including its main subfields. Because there has been a substantial amount of work on taxonomies and conceptual models in the visualization literature and some recent work on theoretic frameworks, such a theoretic foundation is not a foolish or impractical ambition. This article asks, "How can we build a theoretic foundation for visualization collectively as a community?" The authors envision the pathways for four different aspects of a theoretic foundation: taxonomies and ontologies, principles and guidelines, conceptual models and theoretic frameworks, and quantitative laws and theoretic systems.

Making Air Pollution Visible: A Tool for Promoting Environmental Health Literacy.

BACKGROUND: Digital maps are instrumental in conveying information about environmental hazards geographically. For laypersons, computer-based maps can serve as tools to promote environmental health literacy about invisible traffic-related air pollution and ultrafine particles. Concentrations of these pollutants are higher near major roadways and increasingly linked to adverse health effects. Interactive computer maps provide visualizations that can allow users to build mental models of the spatial distribution of ultrafine particles in a community and learn about the risk of exposure in a geographic context. OBJECTIVE: The objective of this work was to develop a new software tool appropriate for educating members of the Boston Chinatown community (Boston, MA, USA) about the nature and potential health risks of traffic-related air pollution. The tool, the Interactive Map of Chinatown Traffic Pollution ("Air Pollution Map" hereafter), is a prototype that can be adapted for the purpose of educating community members across a range of socioeconomic contexts. METHODS: We built the educational visualization tool on the open source Weave software platform. We designed the tool as the centerpiece of a multimodal and intergenerational educational intervention about the health risk of traffic-related air pollution. We used a previously published fine resolution (20 m) hourly land-use regression model of ultrafine particles as the algorithm for predicting pollution levels and applied it to one neighborhood, Boston Chinatown. In designing the map, we consulted community experts to help customize the user interface to communication styles prevalent in the target community. RESULTS: The product is a map that displays ultrafine particulate concentrations averaged across census blocks using a color gradation from white to dark red. The interactive features allow users to explore and learn how changing meteorological conditions and traffic volume influence ultrafine particle concentrations. Users can also select from multiple map layers, such as a street map or satellite view. The map legends and labels are available in both Chinese and English, and are thus accessible to immigrants and residents with proficiency in either language. The map can be either Web or desktop based. CONCLUSIONS: The Air Pollution Map incorporates relevant language and landmarks to make complex scientific information about ultrafine particles accessible to members of the Boston Chinatown community. In future work, we will test the map in an educational intervention that features intergenerational colearning and the use of supplementary multimedia presentations.

Reference Correction to: Making Air Pollution Visible: A Tool for Promoting Environmental Health Literacy.

[This corrects the article DOI: 10.2196/publichealth.7492.].

Dynamic Data Visualization with Weave and Brain Choropleths.

This article introduces the neuroimaging community to the dynamic visualization workbench, Weave (https://www.oicweave.org/), and a set of enhancements to allow the visualization of brain maps. The enhancements comprise a set of brain choropleths and the ability to display these as stacked slices, accessible with a slider. For the first time, this allows the neuroimaging community to take advantage of the advanced tools already available for exploring geographic data. Our brain choropleths are modeled after widely used geographic maps but this mashup of brain choropleths with extant visualization software fills an important neuroinformatic niche. To date, most neuroinformatic tools have provided online databases and atlases of the brain, but not good ways to display the related data (e.g., behavioral, genetic, medical, etc). The extension of the choropleth to brain maps allows us to leverage general-purpose visualization tools for concurrent exploration of brain images and related data. Related data can be represented as a variety of tables, charts and graphs that are dynamically linked to each other and to the brain choropleths. We demonstrate that the simplified region-based analyses that underlay choropleths can provide insights into neuroimaging data comparable to those achieved by using more conventional methods. In addition, the interactive interface facilitates additional insights by allowing the user to filter, compare, and drill down into the visual representations of the data. This enhanced data visualization capability is useful during the initial phases of data analysis and the resulting visualizations provide a compelling way to publish data as an online supplement to journal articles.

A heuristic for gene selection and visual prediction of sample type.

In this paper, we introduce a heuristic method for gene selection. We target this method, coupled with RadViz visualisation, to the visual prediction of tissue samples which may exist in normal and disease states. As a result of this coupling, the gene selection process, predictive model training and evaluation as well as the model's application for tissue sample prediction can all be intuitively visualised. Such integrated visual analytics enhance the insight provided by classical statistics and machine learning methods. The case study shows our proposed method is cost effective and achieves competitive performance when compared with several widely used techniques.

Accuracy of self-reported personal history of cancer in an outpatient breast center.

The self-reporting of cancer history is becoming increasingly important, as it frequently guides medical decision-making. We studied the accuracy of personal cancer history using a self-administered questionnaire, comparing the results with the Tumor Registry at our institution. Among 39,662 records, we identified 3614 women with a single cancer in the Tumor Registry who reported none or one cancer on their questionnaire. The sensitivity in self-reporting cancers was 85.7%, ranging from 92.1% for breast cancer to 42.9% for leukemia. The accuracy for breast cancer and Hodgkin's Lymphoma was significantly better than other cancers (p=0.00027, CI: 1.4-3.88). Analysis of patient's characteristics showed that Caucasians reported breast cancer more accurately than Asian/Pacific Islanders (p=0.008), and those with Jewish ancestry more accurately than non-Jewish (p=0.0435). These results will help us to improve data collection and thus improve medical decision-making.