C-Flow: Visualizing Foot Traffic and Profit Data to Make Informative Decisions

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Contents

Team

Project Advisor

  • George Zaimes - Program Manager at MicroStrategy

Students

Figure 0: The C-Flow Team, photo taken by Fan Du
  • Tiffany Chao (tchao at cs dot umd dot edu)
  • Ruofei Du (ruofei at cs dot umd dot edu)
  • Jonathan Gluck (jdg at cs dot umd dot edu)
  • Hitesh Maidasani (hitmai at cs.umd.edu)
  • Kent Wills (rkw14 at umd dot edu)

Additional Collaboration

  • Mahajan Suhas - UMD CS Student Intern at MicroStrategy

Project Proposal

Users

Businesses looking to understand how product placement affects sales.

Data

  • Temporal Foot Traffic: Age, Sex, GPS, TimeStamp…
    • {Customer {Customer Demographics {Gender: Male}}{Customer Path {ARRAY}}}
  • Temporal Sales, Profits, No. of customers (from credit card records).
  • Map data and Hierarchy Structure of Shops
  • Additional: ADs data, location, price (we can analyze with foot traffic)

Goal

MicroStrategy is a company that specializes in Business Intelligence. A space that aims to educate businesses by transforming and understanding the vast collections of data that the companies collect in their daily interactions with consumers. Recently MicroStrategy has been exploring the topic of indoor visualization. To satisfy customer needs, a tool needs to be created that can help managers analyze patterns in temporal foot-traffic and profit data in order to make intelligent business decisions. To motivate this problem we provide a simple example of a mall owner.

A group of mall owners have tasked their head of marketing to fill the advertising space throughout the mall. Their head of marketing decides to charge the same amount for all advertisements throughout the mall no matter what time of day it is. If their head of marketing had a tool that could quickly give the head of marketing an idea of hot spots in the mall during certain times of the day, he could instead charge for ads based off of those statistics. He may have been giving his advertising space away for less than he should have, effecting his bottom line. Furthermore, he may want to delve deeper and provide targeted ads to certain demographics or answer questions about whether high profit stores result in high foot-traffic, etc.

Our focus through the semester will be to create a tool and conduct a usability study with the help of the employees at MicroStrategy in order to create an interactive prototype HTML5 web visualization that they can adapt to company needs.


Mockup

Figure 4: Overview if map doesn't fit.
Figure 5: 2nd Tier with profit and foot-traffic data.
Figure 6: Zoom, showing the detailed paths for further inspection.

Code

C-Flow Bitbucket

C-Flow Backend Github

Prototype C-Flow

12/12/2013

Prototype-IndoorViz4.png

12/5/2013

Prototype-IndoorViz3.png

11/22/2013

Prototype-IndoorViz2.png

11/17/2013

Prototype-IndoorViz.png

Usability Test Plan PDF

Ethics

All persons involved with the usability test are required to adhere to the following ethical guidelines:

  • The performance of any test participant must not be individually attributable. Individual participant's name should not be used in reference

outside the testing session.

Usability Study 1 Usability Study 2

Timeline In-Depth Timeline

  • October 3, 2013: Draft proposal
  • October 9, 2013: Revised proposal.
  • October 16, 2013: Meeting with MicroStrategy
  • October 17, 2013: Group meeting for data and mock ups
  • October 23, 2013: Mock up completes.
  • October 28, 2013: Consolidated mock-up.
  • November 2: Group meeting on development.
  • November 4: Group meeting: Tool evaluation and individual tasks.
  • November 11: Group meeting: Discuss current demo and combine code. C-Flow
  • November 24: First demo built + Pilot study by classmate.
  • November 26 & 27: First usability test.
  • November 29: Adjust project based off of usability test.
  • December 6, 2013: Draft Paper, Video and Demo
  • December 13, 2013: Final Version of Paper, Video and Demo
  • December 16, 2013: Slides and Presentation

JS Packages

Packages

References

  1. Tauber, Edward M. "Why do People Shop?." The Journal of Marketing (1972): 46-49.
    • This paper illustrates the relationship between walking distance and customer concerns, which provide background introduction for our project.
  2. Phan, Doantam, Ling Xiao, Ron Yeh, and Pat Hanrahan. "Flow map layout." In Information Visualization, 2005. INFOVIS 2005. IEEE Symposium on, pp. 219-224. IEEE, 2005.
  3. Maneesh Agrawala. "Visualizing Route Maps." Ph.D. Dissertation, Stanford University, January 2002.
    • A Ph.D. dissertation on route visualization
  4. Maneesh Agrawala Chris Stolte. "Rendering Effective Route Maps: Improving Usability Through Generalization." SIGGRAPH 2001
    • The prior work of rendering route maps, which inspires us for foot traffic flow rendering.
  5. Sean, Gallagher. "We’re Watching: Malls Track Shopper’s Cell Phone Signals to Gather Marketing Data." ArsTechnica (2011) [Online] http://arstechnica.com/business/2011/11/were-watching-malls-track-shoppers-cell-phone-signals-to-gather-marketing-data/
    • This article provides motivation of our project. Actually, we are writing a visualization tool for this.
  6. James. Visualizing the IKEA shopping experience. http://maptd.com/visualizing-the-ikea-shopping-experience/. 2011
    • A floor plan visualization mock-up for IKEA.
  7. David P. Dobkin, Emden R. Gansner, and Eleftherios Koutsofios, and Stephen C North,. Implementing a General-Purpose Edge Router. Springer-Verlag, 262-271. 1997.
    • Previous algorithms on path generation on a map.
  8. Raghubir, Priya, and Aradhna Krishna. "As the Crow Flies: Bias in Consumers' Map-based Distance Judgments." Journal of Consumer Research (1996): 26-39.
    • This paper has a prior study on walking distance and number of consumers.
  9. Card, Stuart K., and Jock Mackinlay. "The structure of the information visualization design space." Information Visualization, 1997. Proceedings., IEEE Symposium on. IEEE, 1997.
    • This classical paper proposed an Internet traffic visualization idea in Figure 7.
  10. As the Crow Flies: Bias in Consumers' Map-Based Distance Judgments Priya Raghubir and Aradhna Krishna Journal of Consumer Research , Vol. 23, No. 1 (Jun., 1996), pp. 26-39
    • Rhaghubir and Krishna examine consumer footpaths (like our users will be doing) to better understand the psychology of distance estimates. This work could help us understand additional factors that a user might consider.
  11. Wu, Fuqu, and Melanie Tory. "PhotoScope: visualizing spatiotemporal coverage of photos for construction management." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2009.
    • This paper provides some insights for secondary visualization (cubes) within a floor plan visualization.
  12. Tysons Corner Center. [Online] http://www.shoptysons.com/Map/?tid=75282; http://www.tysonsgalleria.com/Content/Img/Maps/TysonsGalleria.jpg
    • A simple visualization of a floor-plan without foot traffic data.
  13. Koutamanis, Alexander. "Digital architectural visualization." Automation in Construction 9.4 (2000): 347-360.
    • This paper presents an early illustration for floor plans without information on it.
  14. Jung, Thomas, Mark D. Gross, and Ellen Yi-Luen Do. "Sketching annotations in a 3D web environment." CHI'02 Extended Abstracts on Human Factors in Computing Systems. ACM, 2002.
    • This paper might inspire us with annotation visualization for floor plan.
  15. Mike Bostock, Shan Carter. "Counties Blue and Red, Moving Right and Left" The New York Times (2012) [Online] http://www.nytimes.com/interactive/2012/11/11/sunday-review/counties-moving.html?_r=0
    • This visualization provides an inspiration for foot traffic visualization.
  16. T. Asahi, D. Turo, and B. Shneiderman, “Using TreeMaps to Visualize the Analytic Hierarchy Process,” Inf. Syst. Res., vol. 6, no. 4, pp. 357–375, 1995.
    • We might use TreeMaps for profits, sales, foot traffic visualization.
  17. Alex Goldmark. Visualization: London Bike Share Usage on Day of Tube Strike. 2011.
    • An inspiring visualization on temporal bike usage data.
  18. Damien Demaj. Geovisualizing spatio-temporal patterns in tennis: An alternative approach to post-match analysis. Esri. http://gamesetmap.com/