Science 10 – 3D Data Visualization Project
In stumbling across a data set concerning the suicide rates in 10-year age groups throughout many different countries, I realized that the suicide rates in those aged 65 years and older had risen significantly compared to other age groups. After comparing it to another data set on the number of nursing personnel in each country, I discovered a correlation between a lack of nursing personnel and the mental health of older adults. A shortage in nurses leads to the inaccessibility of long-term care programs which can cause seniors to feel isolated and helpless in times of worsening frailty and chronic sickness, ultimately leading them to commit suicide. This is especially prevalent in lower-income countries due to their lack of resources to provide sufficient healthcare to older adults. And so, I selected 20 countries, 5 countries from each income class, and began the visualization process.
3D Visualization Explanation
The 63 origami beds I folded crammed into my art drawer…
To represent the suicide rate in seniors, I used empty origami beds, since they would not only portray the idea of a healthcare setting, but also symbolize the absence of a person while conveying a feeling of uneasiness due to its vastness, similar to a cemetery. I figured it would be disorderly to clump all 20 countries together into one group, so I created quadrants for each income class instead. This way, the contrast between the richer quadrants’ and the poorer quadrants’ suicide and nursing personnel rates are immediately noticeable.
Base decorations, featuring the scrap cardboard I used for testing.
To visually represent the income classes, I ripped and discoloured the walls of the lower-income quadrants with coffee, and chose specific patterned papers as flooring. The richer quadrants got hardwood flooring or a clean, light coloured carpet, and the poorer quadrants got old-fashioned, dirtier carpeting.
The pom-poms represent the rate of nursing personnel. They were purposefully colour-coded to have the lower values be warmer colours (orange, yellow) since they typically carry negative connotations in data, while the higher values’ colours (purple, blue, green) often represent royalty/nobility and good. Printed flags were cut up and used to specify the rates in each country to allow better comparisons.
The finished project!