Difference between revisions of "SuicideWatch: Proposal v2"
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* Engage in hands-on practice during class time and after class | * Engage in hands-on practice during class time and after class | ||
* Complete R Shiny training courses on Datacamp | * Complete R Shiny training courses on Datacamp | ||
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| + | Suicide data is not very accessible as it is a sensitive social issue | ||
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| + | * Acquire data from various sources and conduct data cleaning to organize the data | ||
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Revision as of 14:54, 7 October 2019
Version 2
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Contents
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:
- Explore suicides rates in each country
- Explore if there is a correlation between the suicide rate and happiness index
- Explore suicide demographic of each country
- Explore possible correlations of each country’s suicide with its GDP and social media penetration
- Conduct in-indepth visual analysis for Japan
SELECTED DATASET
Primary data sets:
Suicide statistics: https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016
This data set consists of the suicide statistics for worldwide countries from 1985 to 2016. Further breakdown for gender, age group is provided.
https://www.who.int/mental_health/prevention/suicide/countrydata/en/
Secondary data sets:
World Happiness index 2019: https://worldhappiness.report/ed/2019/
World GDP report for correlation analysis: https://data.worldbank.org/indicator/ny.gdp.mktp.cd
Social media penetration for correlation analysis:
Source a: https://www.statista.com/statistics/282846/regular-social-networking-usage-penetration-worldwide-by-country/
Source b: https://ourworldindata.org/internet
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RELATED WORKS
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We get inspiration from this dashboard, which utilises the same set of data for world suicide statistics. |
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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). |
DESIGN INSPIRATIONS
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PROPOSED STORYBOARD
Our proposed application will consist of three pages... as can be seen below
LANDING PAGE
The purpose of this page is...
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OVERVIEW
The purpose of this page is...
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DEMOGRAPHICS
The purpose of this page is...
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SOCIOECONOMIC ANALYSIS
The purpose of this page is...
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PROJECT TIMELINE
KEY CHALLENGES
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The team is new to data visualization and R Shiny |
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Suicide data is not very accessible as it is a sensitive social issue |
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Tight timeline for the semester |
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REFERENCES
- link 1
- link 2
COMMENTS
Feel free to leave us some comments or feedback!
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