Group04 proposal

Proposal

Poster

Research Paper

Team

Problem & Motivation

Close to 800 000 people die due to suicide every year, which comes up to one every 40 seconds according to the World Health Organisation (WHO). Suicide is a global phenomenon, present in many countries and is pervasive to people from all walks of life. For example, South Korea, despite being one of the fastest growing developed countries in the world, has the highest suicide rate in the world. What is more worrying is that the rate of suicides is increasing. In the last 45 years, suicide rates have increased by 60% worldwide and is now the second leading cause of death among young people after road injury.

Every life is precious. The numbers in the dataset was once a person who took their own lives, and had a ripple effect affecting their loved ones, family and friends. Our group would like to analyse factors such as gender, age, generation and GDP per capita to better understand the demographic of those who are likely to commit suicides, so that organisations such as Samaritans of Singapore or even schools and companies can identify those that are of higher risk and take measures to mitigate the likelihood of suicides for those people.

Objectives

• To analyse suicide rates over the years, from 1987 to 2016.
• To analyse and compare which between regions, to identify which are the most and least impacted.
• To understand how significant each factor (age, gender, etc.) affects suicide rates.
• To discover which demographic are more likely to commit suicide.

Selected Dataset

Dataset/Source	Data Description
Suicide Rates Overview (1985 to 2016) (https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016)	This compiled dataset pulled from four other datasets linked by time and place, and was built to find signals correlated to increased suicide rates among different cohorts globally, across the socio-economic spectrum.

Background Survey of Related Works

Example	Takeaways
	This is a visualisation showing suicide rates per 100,000 people by country (1978-2009). It is good practice to use the world map to show each country's suicide rate. The visualisation's colour usage is pretty messy. It is hard for the user to capture insightful information at first glance. Our further improvement to this visualisation will be:     1. Change the colour usage. We will use analogous colors instead of complementary colors     2. We will add interactive tooltips to provide detailed information about this suicide rate.
	This visualisation reveals the changes in suicide rate from 1965 to 2007. It is a good practice to insert Geometric figures inline chart to differentiate different countries. However, this visualisation looks very messy. This is because the author put too many countries on the chart at the time. It would be a good practice if we can add a filter which can let the user select the country they want.
	This visualisation is neat and clean. I like the way the author put the tooltip which makes the line chart very easy to read. There is no further improvement need to make for this visualisation.
	This visualisation provides Global suicide rate by age and income level of the country and regional distribution of global suicides. One further improvement that can be made to this visualisation is to change the abbreviation "LMIC" to plain text. It takes some time for the reader to find the reference for this abbreviation below. When making a comparison between high-income country and low-income country, we should use each country's suicide rate as the Y-Xias instead of exact suicide number.

References

References for Dataset

1. United Nations Development Program. (2018). Human development index (HDI). Retrieved from http://hdr.undp.org/en/indicators/137506
2. World Bank. (2018). World development indicators: GDP (current US$) by country:1985 to 2016. Retrieved from http://databank.worldbank.org/data/source/world-development- indicators#
3. [Szamil]. (2017). Suicide in the Twenty-First Century [dataset]. Retrieved from https://www.kaggle.com/szamil/suicide-in-the-twenty-first-century/notebook
4. World Health Organization. (2018). Suicide prevention. Retrieved from http://www.who.int/mental_health/suicide-prevention/en/

References for Visualisation

1. https://en.wikipedia.org/wiki/List_of_countries_by_suicide_rate#/media/File:Sucide_rate.PNG
2. https://en.wikipedia.org/wiki/Epidemiology_of_suicide#/media/File:Suicide-deaths-per-100000-trend.jpg
3. https://en.wikipedia.org/wiki/Epidemiology_of_suicide#/media/File:Suicides_by_race_hispanic_gender_and_age_1999-2005.png
4. https://www.dailymail.co.uk/news/article-2743457/WHO-calls-action-reduce-global-suicide-rate-800-000-year.html

Key Technical Challenges

No.	Challenges	Mitigation Plan
1.	Inexperience with R	Self-learn R by completing available courses online such as in DataCamp Consult Stackoverflow and GitHub when facing difficulties Learning from available tutorials Seek help from peers experienced with R
2.	Limited experience with Storyboard in Tableau	Refer to relevant examples of how Storyboards are utilised in Tableau Familiarise ourselves by hands-on practice using Storyboards Refer to Tableau documentation on Storyboards
3.	Workload constraint since our group consists of 2 members	Reasonable project timeline and scope Time management and coordination between us Achievable goals that are not too ambitious

Milestones

Comments

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