3. 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
4. Potential Social Issues Increased Online Number of Gamblers Increased Online Number of High Risk Gamblers High Risk Online Gamblers Addiction Disordered/Pathological Gambling Public exposed to more detrimental social effects
6. 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.
7. 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
8. Dataset 3 Datasets 1 Analytic Dataset (For reference) 1 Raw Demographic Dataset 1 Raw Daily Aggregation Dataset Text Format Codebook
24. 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.
25. Visualization Topic Header TreeMap Shows summary of metrics for filtered subgroup Filters Line Chart
description of the problem and motivation, explaining why it is worth addressing.a background survey of related work and a list of references. Include the 2-3 most relevant pieces of prior work in your presentation.post your full list of references to your wiki page.a list of the key technical challenges your group expects to face and a description (or storyboard) of the approach you plan to use to address the challenge.a list of milestones breaking the project into smaller chunks and a description of what each person in the group will work on.
(i)frequency—total number of active days (i.e. days on whicha participant placed at least one live-action bet) during thefirst 30 days, starting from the first day of live-actiongambling; (ii) intensity—total number of live-action bets divided by frequency; (iii) variability—standard deviation ofwagers; and (iv) trajectory—the trajectory of first monthwagers. To calculate trajectory, we coded the active bettingdays sequentially (i.e. the first betting day was coded 1; thenext betting day was coded 2, and so on). We then computeda linear regression model with wager as a dependent variableand a sequence number as a predictor. We used the slopecoefficient of the regression model to describe the trajectoryof wager. A positive slope value indicated increasing wager size;a negative slope value reflected a decreasing pattern ofwager size
