This research examines strategies aimed at fostering empathy through data visualizations. Loneliness experienced during the COVID-19 pandemic (during May, July and September 2020) is used as a case study to explore alternate ways of representing data. Along with ways to humanize data representation and curb Statistical numbing, this research uses metaphors to encode sensitive data to help visually represent people suffering loneliness in Ontario during the first wave of the COVID-19 pandemic. The research amalgamates ‘affect theory’ with concepts of ‘arithmetic of emotions’ and ‘compassion fade’ to try and create a solution for issues related to the ways in which we respond to sensitive issues. By employing the mixed methods of research for creation and iterative design development, this thesis comprises 1) a written document stating the research process; 2) a series of iterations leading to a data visualization for the first wave of the COVID-19 pandemic and 3) an interactive web-based data story.

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