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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.
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This work is licensed under a Creative Commons Attribution 2.0 License.
Jianu, Radu and Laidlaw, David H., "What Google Maps can do for Biomedical Data Dissemination: Examples and a Design Study" (2013). School of Computing and Information Sciences. 2.
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