Addressing addiction 3 final

Addressing AddictionIS 428

Contents

• Background

• Issues

• Research

• Findings

• Objective

• Dataset

• Visualizations

• Technical Challenges

• Project Timeline

• Feedback

Background

• US/Global Internet Poker Sites expanding

• Eg. Full Tilt, Absolute Poker and PokerStars

• Big Casinos moving in as well

May lead to:

• Legalized Online Gambling in the near future, currently in debate in US (2011)

• Likely since: Billion dollar market if legalized, more tax revenue for Government

Potential Social Issues

• Increased Online Number of Gamblers

• Increased Online Number of High Risk Gamblers

• High Risk Online Gamblers

1. Addiction

2. Disordered/Pathological Gambling

• Public exposed to more detrimental social effects

Initial Research

• Transparency Project

• Statistical Analysis Approach

4 Variables:

• Gambling Frequency,

• Gambling Intensity,

• Gambling Variability,

• Gambling Trajectory

Research Findings

• Research papers on the subject were:

• text heavy

• required statistics background to understand

• Difficult to visualize data

• Unable to sort data using different categories like age, country of origin, gender, etc.

Objective

• By utilizing Visual Analytics, help to identify potentially online High Risk Gamblers in a more visually appealing and interactive manner whilst using metrics found in the paper

Which is to:

• Assist in early intervention to prevent addiction/gambling related problems

Dataset

• 3 Datasets

• 1 Analytic Dataset (For reference)

• 1 Raw Demographic Dataset

• 1 Raw Daily Aggregation Dataset

• Text Format

• Codebook

Analytic Dataset

• Contains derived values from Raw Data• Used for reference purposes only

Raw Dataset 1

Demographics

Contains basic User Details

• User ID, • CountryID, • Language,• Gender, • Registration Date,• Age,• Date_First_Poker _Session• Date_Last_Poker_Session

Raw Dataset 2

Daily Aggregation

Contains Data on each session played by a particular user

• User ID, • TimeDATE• Stake• Winnings• Bets

Data Transformation

- Combined tables- Combine Session Data for each User- Change Country, Gender and Language ID to text

Visualization 1 - Tree Map• Gives a Hierarchical view on Metrics

• Intend Hierarchies Filters:1. Regions

2. Age Groups • 20 – 30s

• 30 – 40s

• 40 – 50s

• 50+

3. Player Lifetime: 1mth, 2-3mths, 4-6mths, 6mth+

• Clustered Players, 4 ClustersI. High Variability + High Intensity

II. Low First Month Activity

III. High Intensity Low Variability

IV. Moderate Betting

Visualization 2

• Line Graph

• Time series with variables : no of bets and stakes

• 3 lines are determined by reasons for quitting (Sereason)

• Relationship between reasons for quitting and cluster group.

0

1

2

3

4

5

6

Jan Feb Mar April

No. Of BetsCat 1

Cat 2

Cat 3

Visualization

Line Chart

TreeMap

Shows summary of metrics for filtered subgroup

Filters

Topic Header

Technical ChallengesTechnical Challenge Description How to Address:

Not familiar with d3.js framework

The team is new to d3.js Spend time reading up forums,Pair Programming

Research Paper Jargon

Paper has some psychological terms which need to be made understandable

Not a big effect on project, just have to ensureterminology used is consistent

Statistics – Cluster Analysis

From what we have read it is quite advanced

Read up on subject,however it is mostly setting up formulas and plugging in values.

Project Timeline

Week Notable Event Milestones Contribution

9 Initial Presentation Hands on with d3.jsData Finalization

M & LeeM & Lee

10 Prototype Designs M & Lee

11 Implement PrototypeUser testingReview/Amend Prototype

M & Lee

12 User testingReview/Amend PrototypeDevelop Poster IdeasReport

M & LeeM & LeeM & Lee

13 Poster Presentation (Friday)

Finalize Poster M & LeeM & Lee

14 Final Deliverable(Friday)

Prepare for submission M & LeeM & Lee

Feedback!