Cupid Minions

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

Problem and Motivation

Low marriage rate and increasing divorce rate has been prominent issues in the developed world. One of the reasons for causing these issues is the wrong selection of marriage partner. Some couples only know that they don’t suit each other after marriage. This creates social issues like increaing divorce rate and domestic violence. Therefore, choosing the correct marriage partner is a prompt need for the young adults nowadays. In order to increase the successful matching rate for couples and make sure people can get the most satisfied partners, there is a need to understand these partner-seeking people’s characterstics, demographics, habits and lifestyle informations, etc. From the insight brought by the data, we could suggest the factors which contribute to successful and rapid matching and as a result help the partner-seekers.

Objective

The objective of the project is to:

• Understand the demographics of drivers
  1. Distribution of age of drivers.
• Understand the demographics of casualties
  1. Distribution of age of casualties.
  2. Distribution of severity of casualties.
  3. Distribution of type of casualties.
• Examine the underlying factors which contributes to accidents. The following are some factors, but not limited to:
  1. Temporal patterns: Accident records based on time.
  2. Weather conditions: Which type of weather conditions would cause more accidents?
  3. Road conditions: Which type of road conditions would cause more accidents?
  4. Location: Which city has the most accidents?
• Develop appropriate interactive visualisation to allow discovery of insights from multiple dimensions from the dataset.

Data

Data used in this project was compiled by Columbia Business School professors Ray Fisman and Sheena Iyengar for their paper Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment.

This dataset was originally collected from experimental speed dating events participants during the period of 2002-2004. Participants were given four minutes 'speed dating' time with another participant of opposite sex. After their 'speed dating', they were requested to rate their corresponding 'dating partners' on six aspects:

• Attractiveness
• Sincerity
• Intelligence
• Fun
• Ambition
• Shared Interests

On top of which, questionnaire data was collected from the participants and recorded in the dataset throughout the process. Questionnaire data that we may find relevant and useful include:

• Demographics
• Dating habits
• Self-perception across key attributes
• Beliefs on what others find valuable in a mate
• Lifestyle information

Research Visualisation

Visualisations	Comments
	Interactive Visualisation to Track Fatal Accidents (http://news.bbc.co.uk/2/hi/in_depth/uk/2009/crash/8414354.stm) The pie-chart on the left allows you to select the desired category of data to display. The radial bar chart will then allow you to look at the distinctive pattern of each age group over a time-period. The radial bar chart will be beneficial for high number of bins, where we will be able to look at all the bars or columns from one view without scrolling back and forth.
	Interactive Visualization to Rush Hour Danger (http://www.bbc.co.uk/news/uk-15975564) Visualization of the pattern of pedestrian casualties across the week Map which shows the deaths involving pedestrians and buses on London’s world-famous Oxford Street between 1999 and 2010 Although this visualization is interesting with the data points of casualties over the years, it lacks the interactivity that our team envision our storyboard to be. We will use this visualization as a reference when we implement an interactive map in our storyboard.

Tools

Following tools are expected to be utilized through this project:

• Tableau DeskTop 10
• JMP Pro
• Javascript
• D3.js
• JQuery
• Brackets
• Github
• Excel

Technical Challenges

Technical Challenges	Action Plan
Data Preprocessing - 195 Variables & 8378 Datasets	Collaborative team work in data cleaning, selection and transformation.
Lack of technical background in programming languages like, javascript and libraries like, D3.js and JQuery.	Initial hands-on experience during D3.js workshop Peer learning and sharing of skills developed during IS 480 Arranging frequent consultation with Instructor Prakash regarding technical difficulties encountered along the way
Unfamiliar with implementing interactive visual analytics application	Self-learning and practicing via online tutorials Continuous exploration of readily available alternative tools

Roles & Milestones

References

1. Reasearch paper: Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment.
   (Raymond Fisman Sheena S. Iyengar Emir Kamenica Itamar Simonson)
2. Racial Preferences in Dating
   (Raymond Fisman Sheena S. Iyengar Emir Kamenica Itamar Simonson)
3. Speed Dating Experiment Data
4. Build a Demographic Data Visualization Tool Based On D3.js
5. The Data Visualization Catalog
6. D3 Gallery
   Chart.js
   JavaScript Chart Comparison