SuicideWatch: Proposal v2

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ABOUT US

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

Version 1

Version 2

PROBLEM & MOTIVATION

Suicide rates are at their highest since WWII. Close to 800 000 people die due to suicide globally every year, which is one person every 40 seconds. Suicide is a global phenomenon and occurs throughout the lifespan. In Singapore, suicide is the leading cause of death for those aged 10-29. Females are more likely to be diagnosed with depression and attempt suicide, but males accounted for more than 71% of all suicides in Singapore in 2018. The steady suicide figures signal the presence of an unseen epidemic, one that silently tips people over the edge. While most push the blame to depression, there is no one size fits all explanation when it comes to suicide. Can suicide rates be simply mapped to the Happiness Index or GDP per Capita? Or is there more than meets the eye?

With primary investigation, we found out that the suicide rate varies from country to country. To further study the suicide rate breakdown and the behind, we looked into Japan as it is a Asia country known for high suicide rate, and the Japan government has published more detailed statistics for annual suicide rate. This gives us a good starting point for visualization.

OBJECTIVES

In this project, we are interested to create a visualization that helps analysts perform the following:

1. Explore suicides rates in each country
2. Explore if there is a correlation between the suicide rate and happiness index
3. Explore suicide demographic of each country
4. Explore possible correlations of each country’s suicide with its GDP and social media penetration
5. Conduct in-indepth visual analysis for Japan

SELECTED DATASETS

Dataset/Source	Data Attributes	Why this Dataset?
Suicide Rates Overview (1985 to 2016) (https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016)	Suicide rate by country Suicide demography (Age/Gender) Economic data (GDP)	This dataset will be our main source of global suicide data.
World Happiness index 2019 (https://worldhappiness.report/ed/2019/)	Overall Happiness Rankings of Countries Worldwide Individual segment scores for each country (Freedom of speech, social support, etc)	This dataset will be used for comparison to each country's suicide rates.
Worldwide GDP (https://data.worldbank.org/indicator/ny.gdp.mktp.cd)	Annual GDP by country	This dataset will be used for comparison to each country's suicide rates.
Worldwide Social Media Penetration Data (https://www.statista.com/statistics/282846/regular-social-networking-usage-penetration-worldwide-by-country/) (https://ourworldindata.org/internet)	Social Media Penetration by country Worldwide Internet access figures	This dataset will be used for comparison to each country's suicide rates.
Japan Suicide Statistics(Yearly) (https://www.npa.go.jp/publications/statistics/safetylife/jisatsu.html/)	Suicide rate by prefecture Suicide rate breakdown by gender and age group Suicide rate breakdown by profession and reason of suicide	This dataset will be used for in-depth visual analysis for Japan.

RELATED WORKS

Example	Takeaways
An interactive dashboard for worldwide suicide data 1985-2015 Source: https://www.kaggle.com/tavoosi/suicide-data-full-interactive-dashboard/#data	We get inspiration from this dashboard, which utilises the same set of data for world suicide statistics. This dashboard used a world map and bar chart for worldwide suicide rates, which we found informational and easy to understand. Breakdown by country,gender and age group are diplayed in diffierent charts accordingly.
Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5904786/	This paper supports our hypothesis on use of social media leaders to depression and suicide. The research results shows that membership in 2 clusters – “Wired” and “Connected” – increased the odds of elevated depression and anxiety symptoms (AOR = 2.7, 95% CI = 1.5–4.7; AOR = 3.7, 95% CI = 2.1–6.5, respectively, and AOR = 2.0, 95% CI = 1.3–3.2; AOR = 2.0, 95% CI = 1.3–3.1, respectively). We will further look into this issue by plotting the correlation line graph for suicide rate against social media penetration by country.

DESIGN INSPIRATIONS

Example	Takeaways
The New Zealand Labour Market Dashboard Source: https://mbienz.shinyapps.io/labour-market-dashboard_prod/	Pros: This chart is suitable for showing figure change between two timestamps It is clear and easy to understand Cons: The value fluctuation within the duration is excluded from the graph
Bubble Chart - Obama’s 2013 Budget Proposal Source: https://archive.nytimes.com/www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html	Pros: This chart is eye-appealing for two-dimension analysis Extreme data points can be easily identified Cons: Comparison for similar values is hard to be done, as no axis is given and all figures are displayed in color or size

PROPOSED STORYBOARD

Our proposed application will consist of four pages:

LANDING PAGE

Proposed Storyboard	Description
	This page will serve as an introduction to our problem and objectives, to give the viewer an overview of our project.

OVERVIEW

This page will provide the viewer with an overview of global suicide rates and overall happiness scores.

Proposed Storyboard	Description
	Allows visualization of Worldwide Suicide Rate/Happiness Index Scores by year, sorted in ascending/descending order. Color of the bars and map will correspond to the Suicide Rate/Happiness Index Score of each country. This makes it easy to identify clusters or regions where suicide rates or happiness index scores tend to be high or low.

DEMOGRAPHICS

Proposed Storyboard	Description
	Breakdown of Suicides by Gender/Age

CASE STUDY: JAPAN

The purpose of this page is...

Proposed Storyboard	Description
	Represents the correlation between Suicide Rate/GDP and Suicide Rate/Social Network Penetration. Suicide Rates are represented by the size of the bubble GDP/Social Network Penetration Rates are represented by the color of the bubble

PROJECT TIMELINE

KEY CHALLENGES

Challenge	Mitigation
The team is new to data visualization and R Shiny	Engage in hands-on practice during class time and after class Complete R Shiny training courses on Datacamp
Suicide data is not very accessible as it is a sensitive social issue	Acquire data from various sources and conduct data cleaning to organize the data
Tight timeline for the semester	The team should come up with a reasonable project timeline based on everyone's capability Set milestones and adjust the timeline accordingly based on the team's progress

REFERENCES

• link 1
• link 2

COMMENTS

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