SuicideWatch: Proposal v2

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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

Death by suicide is an extremely complex issue that causes pain to hundreds of thousands of people every year around the world. Close to 800 000 people die due to suicide globally every year, which is one person every 40 seconds. In Singapore, suicide is the leading cause of death for those aged 10-29.

The mortality data from the WHO suggests that the prevalence and characteristics of suicidal behavior vary widely between different communities, in different demographic groups and over time. One important source of heterogeneity, both globally and within countries, is gender: suicide rates are much higher for males, particularly in high-income countries. 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. But are there other similarities or patterns that we can discern from the available 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 spur conversations on 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, to visualise the relationship between "Happiness" and suicides. Although it may not be the best measure of happiness, we will make do with it as it is the most comprehensive dataset available for happiness scores worldwide.
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. Compiled and released by Japan's National Police Agency, it contains a detailed breakdown of the suicide demographics across prefectures in 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

  • This dashboard used a combination of a choropleth map and bar chart, which aids in recognition of regions with high/low suicide rates and identifying the rank of a particular country.
  • The coordinated color scale makes it easy for users to understand data from both charts.

Animated time-series bar chart

SW related2.png

Source: https://ourworldindata.org/suicide

  • Visualises the changes in countries with the highest suicide rate over the years
  • Allows user to select specific countries that they wish to include in the comparison

Animated time-lapse of suicide rates between developed and developing countries

SW related3.png

Source: https://medium.com/@garytse_91587/world-suicide-rates-a-visualization-636c6f2f1e15

  • The visualization allows users to filter the data based on the type of countries(developed/developing) over time.
  • By breaking down the data the user can target specific countries of interest without getting overwhelmed by the large amount of data.



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

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