Difference between revisions of "SuicideWatch: Proposal v2"

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After further discussion and consultations with Prof. Kam, our team concluded that our initial problem statement and methodology may not be the best approach to analysing suicide data. There was insufficient evidence to draw correlations between socioeconomic factors and suicide rates, as suicide is a complex issue encompassing many factors that cannot be generalised. As such, we decided to pivot our project's direction slightly. Instead of attempting to make conclusions, we will focus on creating a visualisation that allows for exploration and comparison of suicide rates globally.
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<!-- Problem and Motivation -->
 
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==<div style="background: #364558; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 0px;font-size:20px"><font face="Arial" color=#fbfcfd> PROBLEM & MOTIVATION </font></div>==
 
==<div style="background: #364558; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 0px;font-size:20px"><font face="Arial" color=#fbfcfd> PROBLEM & MOTIVATION </font></div>==
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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? <br>
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<p>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 all ages. In Singapore, suicide is the leading cause of death for those aged 10-29. Interestingly in Singapore, 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.  
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<p>Suicide rates vary from continent to continent, country to country. But are there similarities or patterns that we can discern from this data? Often seen as a taboo topic, suicide is a very real problem that is not talked about enough. Through our visualisation, we hope to be able to shed some light on and increase awareness of this issue.
 
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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.
 
  
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==<div style="background: #364558; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 0px;font-size:20px"><font face="Arial" color=#fbfcfd>OBJECTIVES</font></div>==
 
==<div style="background: #364558; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 0px;font-size:20px"><font face="Arial" color=#fbfcfd>OBJECTIVES</font></div>==
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<p>In this project, we are interested to create a visualization that helps analysts perform the following:</p>
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<p>In this project, we are hope to create a visualization that enables the following:</p>
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# Explore suicides rates in each country
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# Identify regions or countries with high/low suicide rates
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# Explore if there is a correlation between the suicide rate and happiness index
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# Visualise the relationship between Happiness Index scores and suicide rates
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# Explore suicide demographic of each country
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# Exploration of suicide demographics by country
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# Explore possible correlations of each country’s suicide with its GDP and social media penetration
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# Comparison of suicide demographics between countries
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# Conduct in-indepth visual analysis for Japan
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# A more in-depth breakdown of Japan's suicide demographics
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As mentioned, the reasons behind suicide is complex and cannot be generalised. Through our visualization we hope to show that generic measures like political stability or GDP per capita do not provide a general explanation for suicide. There are definitely many deep-seated issues or underlying cultural norms that could contribute to suicide rates as well. Ideally a further breakdown of the suicide demographics should be conducted for each country, as suicide rates within different parts of a country could vary as well. However, such data is not readily available. We managed to find detailed data on Japan's suicide rates and thought it would make for a good visualisation of the varying suicide rates within a country. Japan has long been known to have high suicide rates, posited to be an amalgamation of its aging population, overwork and even cultural glorification of suicide ("Seppuku"). As such, we decided to breakdown Japan's suicide figures across different prefectures. 
 
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Revision as of 11:13, 13 October 2019

<--- Back to Project Groups

Team 3 - SuicideWatch Logo.png

ABOUT US

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER


Version 2


After further discussion and consultations with Prof. Kam, our team concluded that our initial problem statement and methodology may not be the best approach to analysing suicide data. There was insufficient evidence to draw correlations between socioeconomic factors and suicide rates, as suicide is a complex issue encompassing many factors that cannot be generalised. As such, we decided to pivot our project's direction slightly. Instead of attempting to make conclusions, we will focus on creating a visualisation that allows for exploration and comparison of suicide rates globally.


PROBLEM & MOTIVATION

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 all ages. In Singapore, suicide is the leading cause of death for those aged 10-29. Interestingly in Singapore, 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.

Suicide rates vary from continent to continent, country to country. But are there similarities or patterns that we can discern from this data? Often seen as a taboo topic, suicide is a very real problem that is not talked about enough. Through our visualisation, we hope to be able to shed some light on and increase awareness of this issue.

OBJECTIVES

In this project, we are hope to create a visualization that enables the following:

  1. Identify regions or countries with high/low suicide rates
  2. Visualise the relationship between Happiness Index scores and suicide rates
  3. Exploration of suicide demographics by country
  4. Comparison of suicide demographics between countries
  5. A more in-depth breakdown of Japan's suicide demographics


As mentioned, the reasons behind suicide is complex and cannot be generalised. Through our visualization we hope to show that generic measures like political stability or GDP per capita do not provide a general explanation for suicide. There are definitely many deep-seated issues or underlying cultural norms that could contribute to suicide rates as well. Ideally a further breakdown of the suicide demographics should be conducted for each country, as suicide rates within different parts of a country could vary as well. However, such data is not readily available. We managed to find detailed data on Japan's suicide rates and thought it would make for a good visualisation of the varying suicide rates within a country. Japan has long been known to have high suicide rates, posited to be an amalgamation of its aging population, overwork and even cultural glorification of suicide ("Seppuku"). As such, we decided to breakdown Japan's suicide figures across different prefectures.


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

Worldmap suicide rate.png

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

Social media and depression.png

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

Two-point-line-graph.png

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

Bubble-chart.png

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
SuicideWatch v2 home.jpg
  • 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
SuicideWatch v2 overview.jpg
  • 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
SuicideWatch v2 demographic.jpg
  • Breakdown of Suicides by Gender/Age


CASE STUDY: JAPAN

The purpose of this page is...

Proposed Storyboard Description
SuicideWatch v2 japan.jpg
  • 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

Team 3 - SuicideWatch Timeline.png
Team 3 - SuicideWatch Gantt.png


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

Feel free to leave us some comments or feedback!

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