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Gill, Ivan Sohrab

University of British Columbia

Visualization designers sometimes divide subsets of public health data across multiple static diagrams, as more consolidated figures encoding the entire dataset can be limited by space and encoding considerations. However, the division of related items and attributes into discrete diagrams can decrease the efficiency of visual analyses due to inconsistent encodings and navigational concerns. Therefore, in this thesis, I investigated whether utilizing channels of interactivity could facilitate the development of more consolidated diagrams relative to existing public health visualizations. During my investigation, I developed two interactive web visualizations for distinct public health domain problems. The first application is COVID-MVP, which utilizes an interactive heatmap with panning and hover interactions to encode a variety of attributes across SARS-CoV-2 mutations, lineages, and user-uploaded sequences. The second application is AMR-TV, which utilizes an interactive faceted time series with panning and zooming interactions to encode a variety of user-specified attributes across antimicrobial resistant bacteria. Specifically, I tested AMR-TV by visualizing a variety of carbapenemase producing Klebsiella pneumoniae bacteria sampled across British Columbia. In both applications, I utilized interactivity to avoid dividing unique items and attributes into discrete diagrams, which then facilitated efficient visual analyses of the aggregated dataset. I thus concluded that interactive applications could be a viable strategy for developing less disjointed representations of public health data.

Text

2023-05-01

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/84515

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/