addressing addiction 3 final
TRANSCRIPT
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!