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PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

Introduction

According to Forbes, the automobile industry has grown by a massive 68% since hitting a trough during the 2009 global financial crises according to a report published by car auction company Manheim earlier this year.

Q3 2016 closed with 9.8M vehicles sold in the used car market -an increase of 3.3% over the previous year. Also the average retail used vehicle sold for $19.232 in Q3 2016, an increase of 4.3% over last year. Changes in car buying behavior are beginning to alter the landscape of franchised used vehicles.

So both franchised used car firms and other giant online marketplaces like E-Bay are leveraging the growth rate of used car industy. As a result, we tried to understand this market and its dynamics with the help of ‘Used Car Database’ from E-Bay.

Problem and Motivation

With the change in consumer car buying behaviour and a rising market for used cars, our aim is to understand this growing used car market. When consumers look at used cars, price is the most important factor that influences opinions, but there are also few other facts that affect their purchasing decision. So we will try to find out which variables affect the price most and how they are correlated.

Objective

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

1. Visualise resale car prices against other factors such as:

• Type of cars
• Car Brands
• Car age
• Kilometers traveled
• Time period
• Location/ state

2. Identify relationships and correlations across different factors affecting resale prices

3. Uncover the top 10 most common brands for car resale

• Difference in prices & quantity sold across different brands

Data

Data used is from “OASIS Boss Bidding History Results”. There are 21 excel files from 6 academic years. Every excel file contains 17 columns: term, session, bidding window, course code, description, section, vacancy, opening vacancy, before process vacancy, D.I.C.E, after process vacancy, enrolled students, max bid, median bid, min bid, instructor and school.

Research Visualisation

Visualizations	Explaination
	Parallel Coordinates Parallel coordinates is a great way to visualize high-dimensional data. In our case, we would like to use opening vacancy, before process vacancy, instructor, school, term, etc. as dimensions and to see how these variables affect the bid price. https://bl.ocks.org/jasondavies/1341281
	Scatter Plot Scatter plot can help readers to see the relationship between two variables. In our case, we would like to see which variables are highly correlated to bidding price so that we could have a better understanding of the bidding behaviors. http://bl.ocks.org/bunkat/2595950
	Stock price movement It gives an overview of how the stock price changes over the time. In our case, we would like to see how the bidding price change in each window in each round. It would help us understand popular trends in bidding activities. http://active-analytics.com/blog/plottinglivechartswithyahoofinancedataandggplot2inr/

Tools

 -Excel

 -D3

 -Javascript

 -Github

 -Tableau

Technical Challenges

Technical Challenges	Action Plan
Data Preparation	Data collection: work together to export data from BOSS. Data cleaning: work together to clean and analyse the data.
Unfamiliarity in Programming Language like Javascript & Libraries like D3	Initial hands-on experience during D3.js workshop. Independent learning on Javascript & D3.js. Peer learning and sharing of skills.
Unfamiliarity in Implementing Interactive Visualisation App	Self-learning and view online tutorials.

Roles & Milestones

• Project Roles

Dong Ruiyan: Visualisation Analyst
Zhang Qian: Visualisation Designer
Jeremy LEE Ting Kok: Project Manager

• Project Timeline

References

• Boss Bidding: https://oasis.smu.edu.sg/Pages/RO/All-About-BOSS.aspx
• Parallel Coordinates: https://bl.ocks.org/jasondavies/1341281
• Simple Scatter Chart Example: http://bl.ocks.org/bunkat/2595950
• Stock Price: http://active-analytics.com/blog/plottinglivechartswithyahoofinancedataandggplot2inr/
• D3.js: https://d3js.org/
• Chart.js: http://www.chartjs.org/