Sixes: Proposal Version 1

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Proposal

Poster

Application

Research Paper

PROBLEM & MOTIVATION

Every year, more than 480,000 people die due to tobacco-related diseases. That is around 1 in 5 of all deaths annually. It is estimated that 1 in 2 smokers will die from a smoking-related disease. This interactive data visualization tool shows modeled trends in smoking prevalence worldwide. Our aim is to reveal perspectives of smoking and correlates, determinants and consequences about smoking with visual analytics.

OBJECTIVES

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

1. View the geographical distribution of drug use in 2016
2. Identify which drug types that people are most addicted
3. The distribution of drug users' age groups

SELECTED DATASET

Datasets are retrieved from https://data.unodc.org/#state:1
The data set describes the annual prevalence of use of drug in 2016:

BACKGROUND SURVEY OF RELATED WORKS

There are many charts and visualisations available which illustrates the various trends of house prices and index. We have selected a few of these to study and learn before we begin developing our own visualizations.

Related Works	What We Can Learn
https://dataunodc.un.org/drugs/prevalence_map	Heatmap is an explicit way to show the goegraphical distribution of data. The breakdowns shown upon hover give further details without distracting the audience at the first glance.
Map of annual seizures https://dataunodc.un.org/drugs/seizures_map%7C	Filter is essential to hide unnecessary data when only a group of data is focused.

PROPOSED STORYBOARD

WIP

Proposed Layout	How Analyst Can Conduct Analysis

KEY TECHNICAL CHALLENGES

The following are some of the key technical challenges that we may face throughout the course of the project:

Key Technical Challenges	How We Propose To Resolve
Unfamiliarity of Visualization Tool Usage	Independent Learning on Visualization Tools Peer Learning
New to R and Javascript	Attend R Workshop Self explore on R & Technical Tools Peer Learning
Data Cleaning & Transformation	Work together to clean, transform and analyze the data Documentation to keep track of changes

PROJECT TIMELINE

The following shows our project timeline for the completion of this project:

TOOLS/TECHNOLOGIES

The following are some of the tools/technologies that we will be utilizing during the project:

• Excel
• Github
• Tableau
• R

WIP

REFERENCES

• https://data.unodc.org/

WIP

COMMENTS

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