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The Motivation

Being one of those people who understood and felt loneliness before, I was driven to help make people aware of the silent illness. With the lockdown in action due to the COVID-19 pandemic, the idea was to help them know about the normality of loneliness, and what can be done to help those affected by it. 

The significance of the research lies in its ability to explore abstract elements, the things that cannot be seen, i.e., the feelings and emotions of humans with the care and integrity that is required by humanizing the data. 

The significance of the research lies in its ability to explore abstract elements, the things that cannot be seen, i.e., the feelings and emotions of humans with the care and integrity that is required by humanizing the data. 

Data Visualizations

The idea to create data visualizations evolved from the way sensitive issues were represented, especially during the COVID-19 pandemic. To bring the problem of loneliness into the public eye, and letting people know about the commonality of it, I decided to create these visualizations for the month of May, July, and September 2020.

Each data visualization has been carefully designed to represent the people from Ontario province who were kind enough to let the world know about how they were feeling during the COVID-19 pandemic. The data has been procured from the Centre of Addiction and Mental Health (CAMH), Canada's largest mental health teaching hospital and one of the world's leading research centres in its field.

The creation of the final data visualization which was used in the narrative storytelling had gone through careful evaluation, user testing and improvements. With the use of design principles, visualization techniques, humanized data and visual metaphors, the combination of these led to the final creation.

Below are the legends that will help and guide you to understand the demographic of people being represented in the data visualization. Kindly scroll with regard, and be mindful of the people being represented.

Working with Sensitive data about Loneliness.


Sensitive data, when put in simple words, is –data that is sensitive in nature. This does not include concern privacy-related data, as that is counted under private data. But instead, sensitive data deals with sensitive personal issues or delicate topics.


In the context of my research, the focus of sensitive data lies in the emotional well-being of an individual – specifically those who suffered loneliness. I am representing those who felt lonely during the COVID-19 pandemic lockdown, with the broader picture enlightening the viewers about the way it affects those who are suffering. To help reduce the feeling of loneliness, we can be present to help and empathize with those suffering.

The data used to create the visualizations was procured from the survey conducted by the Centre for Addiction and Mental Health for the period of May to September 2020.

Humanizing the data; using conceptual metaphors and sensorial affect.

Untitled_Artwork 2.jpg

Affect is a broader, more inclusive psychological construct that refers to mental states involving evaluative feelings, states in which a person feels good or bad or likes or dislikes what is happening. The sound in addition to the visuals, will help bring an intimate experience that will personalize the affect that needs to be associated with the data. I work this collaboration of visuals in metaphors and sound in the form of voice to create a narrative about loneliness.

Data visualization, as a practice, combines the disciplines of statistical analysis and design. 

One of the key aspects that can be tapped into these is ‘personalization’. The human touch to elements makes them much more relatable and less robotic in nature. With this purpose in mind, I decided to choose the method of hand-made visuals.


In the context of my thesis, data visualizations work better as they hold the ability to represent numbers and data in a manner that is easier to understand and draw conclusions from.

Trying to curb Statistical numbing, learning about Compassion Fade and Arithmetic of Emotions.


Statistical Numbing is the phenomenon of being indifferent towards statistics and numbers about sensitive issues, tragedies and stories. Paul Slovic, a Statistician and Researcher demonstrated how it leads to 'Compassion Fade' as the number of cases or people affected increases. Human brains can fathom the compassion and empathy needed for a single person.

Some of the ways to curb these issues are by (Bertini et al. 5):

- avoiding excessive usage of text in the data visualization

- making the content relatable to the people who are going to view it

- introducing unit grouping, expressiveness, realism and granularity into the visualization

- representing people as a single unit, rather than in an aggregated format

- the visuals being used should be able to express the emotional quality of the issue

References used on this page:

​Bertini, Enrico. “Can Visualization Elicit Empathy? Our Experiments with ‘Anthropographics.’” Medium, 15 June 2017.,


“COVID-19 National Survey Dashboard.” CAMH, Accessed 11 Feb. 2021.

Frank, David A., et al. “‘Statistics Don’t Bleed’: Rhetorical Psychology, Presence, and Psychic Numbing in Genocide Pedagogy.” JAC, vol. 31, no. 3/4, JAC, 2011, pp. 609–24. JSTOR.

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