Difference between revisions of "Smartie Mall"
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Profile Data: contains demographic information of the shopping mall members
Transaction Data: contains two months transnational records from January to February 2017
Wi-Fi Sensor Data: contains two months Wi-Fi sensor records captured in the mall, which can be used to analyze the movement of customers
Maps: shopping mall layouts
Data Integration: Combine the three dataset to derive patterns, associations and actionable insights
Interactive Visualization:
R: Hexagonal binning using ggplot2 and Kernel Decimal Estimate using stat_Density2d
R: Chord diagram using chorddiag
R: Shinyapp, shinydashboard, flexdashboard
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<p><font size = 4; color="#FFFFFF"><span style="font-family:Century Gothic;">Team S-MALL</span></font></p> | <p><font size = 4; color="#FFFFFF"><span style="font-family:Century Gothic;">Team S-MALL</span></font></p> | ||
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<font size = 5>Motivation</font> | <font size = 5>Motivation</font> | ||
<br>Brick and Mortar retail malls are facing stiff challenge from online e-commerce shopping and mobile smartphone penetration. How can physical malls continue to survive under such conditions? Also, how can shopping malls transform as part of the Smart Nation initiative in Singapore? Using real data of a mega-mall, this project aims to leverage multiple data sources from typical retail operation, and develop a visual application to assist company reveal customer behavior, uncover patterns that may present opportunities for the mall as well as tenants to better optimise operations, layout, events, maximize sales as well improve the customers' experience. | <br>Brick and Mortar retail malls are facing stiff challenge from online e-commerce shopping and mobile smartphone penetration. How can physical malls continue to survive under such conditions? Also, how can shopping malls transform as part of the Smart Nation initiative in Singapore? Using real data of a mega-mall, this project aims to leverage multiple data sources from typical retail operation, and develop a visual application to assist company reveal customer behavior, uncover patterns that may present opportunities for the mall as well as tenants to better optimise operations, layout, events, maximize sales as well improve the customers' experience. | ||
</div> | </div> | ||
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<font size = 5>Data Description</font> | <font size = 5>Data Description</font> | ||
[[File:T3 Datades.PNG|center|800x300 px]] | [[File:T3 Datades.PNG|center|800x300 px]] | ||
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<font size = 5>Expected Outcome</font> | <font size = 5>Expected Outcome</font> | ||
<li>Data Integration: Combine the three dataset to derive patterns, associations and actionable insights</li> | <li>Data Integration: Combine the three dataset to derive patterns, associations and actionable insights</li> | ||
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<font size = 5>Visualization Tool & Packages</font> | <font size = 5>Visualization Tool & Packages</font> | ||
<li>R: Hexagonal binning using ggplot2 and Kernel Decimal Estimate using stat_Density2d</li> | <li>R: Hexagonal binning using ggplot2 and Kernel Decimal Estimate using stat_Density2d</li> | ||
Revision as of 14:01, 17 June 2017
ISSS608 Visual Analytics and Applications Project
Team S-MALL
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Milestone |
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Motivation
Brick and Mortar retail malls are facing stiff challenge from online e-commerce shopping and mobile smartphone penetration. How can physical malls continue to survive under such conditions? Also, how can shopping malls transform as part of the Smart Nation initiative in Singapore? Using real data of a mega-mall, this project aims to leverage multiple data sources from typical retail operation, and develop a visual application to assist company reveal customer behavior, uncover patterns that may present opportunities for the mall as well as tenants to better optimise operations, layout, events, maximize sales as well improve the customers' experience.
Data Description
Expected Outcome
- Overview of customers movements patterns by weeks, days, hours
- Inter-floor movement
- Customers' profile and transactions integration
Visualization Tool & Packages
