Difference between revisions of "S-MALL Application"

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R Packages for Analytics & Visualization: [https://cran.r-project.org/web/packages/ggplot2/index.html ggplot2], [https://cran.r-project.org/web/packages/plotly/index.html plotly], [https://cran.r-project.org/web/packages/sunburstR/index.html sunburstR], [https://cran.r-project.org/web/packages/hexbin/index.html hexbin], [https://github.com/mtennekes/treemap treemap], [https://github.com/jbkunst/highcharter highCharter], [https://cran.r-project.org/web/packages/arules/index.html arules], [https://github.com/datastorm-open/visNetwork visNetwork]
  
 
== References ==
 
== References ==

Latest revision as of 16:04, 6 August 2017

Turning Concrete Malls into Smart Malls (S-MALL):
A web-based analytics application for visualizing and mapping in-mall customer journeys and shopping behaviours

Team S-MALL: Chen Yun-Chen | Chiam Zhan Peng | Zheng Bijun

Overview

Application

User Guide

Poster

 

APPLICATION OVERVIEW

Movement Analysis Member Analysis Association Analysis
T3 Appoverview.png



APPLICATION DESCRIPTION

Part1: Movement Analysis

Visualization Methodology & Technique Usage
T3 Trellis.JPG
  • Chart type: Line chart & trellis plot
  • R Package: ggplot2, plotly
  • Interactivity: Use selectInput to control plot and segment by different timelevels, eg. Date, day of week, and hour
  • Line chart without trellis (segment=None) can be used to analyze the daily/weekly/hourly pattern of footfalls.
  • Trellis plot can be used to detect the cycling pattern over time.
T3 Chord.png
  • Chart type: Chord diagram
  • R Package: chorddiag
  • Interactivity: Set specific datetime using selectIput and sliderInput to view the traffic transfer across floors.
    Hover to each floor to see the destination of its outflow traffics.
  • Analyze traffic flow across floor for specific datetime selection
T3 HexbinMap.jpg
  • Chart type: Hexagonal binning map
  • R Package: hexbin, ggplot2, plotly
  • Key parameter setting: number of bins is set to 50
  • Interactivity: Set specific datetime using selectIput and sliderInput to view the traffic density on each floor.
    Click on specific hexbin to investigate members identity.
  • Analyze traffic density of floors for specific datetime selection
  • Drill down to shoppers’ identity based on interested density area

Part2: Member Analysis

Visualization Methodology & Technique Usage
T3 sunburst.JPG
  • Chart type: Sunburst diagram
  • R Package: sunburstR
  • Interactivity: Set radio button to view the journey of different member segments.
    Set minimum dwell time using sliderInput to exclude passing-by floors.
    Hover to see the path and relevant statistics.
  • Investigate popular shopping path of members based on floors.
T3 treemap.JPG
  • Chart type: Treemap
  • R Package: treemap, highCharter
  • Interactivity: Set radio button to view the journey of different member segments.
    Click on floor level to drill down to store level.
  • Analyze members’ average dwell time on each floor and store.
T3 Boxplot.JPG
  • Chart type: Boxplot
  • R Package: plotly
  • Key parameter setting: number of bins is set to 50
  • Interactivity: Click on the treemap to get the relevant boxplot for selected floor.
  • Analyze dwell time distribution of each store based on floor selection on treemap.
T3 demobar.JPG
  • Chart type: Bar chart
  • R Package: plotly
  • Interactivity: Select on the boxplot distribution to view the demographic of interested members.
    Set the ‘profile count by’ parameter to decide the y-axis of bar plots.
  • Analyze members’ profile based on selection of their dwell time distribution. For example, we may analyze the high time spender of a specific store to see if they are of similar demographic.

Part3: Association Anlaysis

Visualization Methodology & Technique Usage
T3 Transbar.JPG
  • Chart type: Bar chart
  • R Package: ggplot2, plotly
  • Interactivity: Set date range using radio button to see the plot of different months.
  • Investigate popular shopping path of members based on floors.
T3 Quadrant.JPG
  • Chart type: Scatter plot
  • R Package: arules, ggplot2, plotly
  • Interactivity: Set date range using radio button to generate association rules based on different month’s transactions.
    Set parameters (support/confidence/min items) to generate valid associations rules and render plot.
    Hover over the bar to fade out non-relevant rules in the quadrant.
    Hover over the network to fade out non-relevant rules in the quadrant.
  • Analyze the competitive position of different rules based on set parameters.
T3 network.JPG
  • Chart type: Network
  • R Package: visNetwork
  • Interactivity: Set date range using radio button to plot rules of different months.
    Set parameters (support/confidence/min items) to generate valid associations rules and render plot.
  • Visualize the associations among departments.

R Packages for Analytics & Visualization: ggplot2, plotly, sunburstR, hexbin, treemap, highCharter, arules, visNetwork

References