MS&E 237 Assignment 4 – Gamification and Geo-Location (Part A)
Due Date: May 19th, 2011 (By Noon)
Learning Goals:
· Learn about social engineering, incentive design systems, and gamification
· Apply / Design Incentives / Create Hypothesis / Make Predictions

Submission Details:
Please answer the questions on the following Google Form:

Assignment Details:
Earlier in the quarter, you all received a Social Data Revolution t-shirt with a unique QR code. Then, in DF4, you investigated Rio Akasaka’s JustNear.Me platform, which provides you with a platform to track the spread of your QR code when it’s scanned. The site also allows you to associate your QR code with a content page that is displayed each time someone scans your code. Using your QR code on the JustNear.Me platform, you will take advantage of this content page to "create a movement" (i.e. think of an interesting way to use your QR code to promote the spread and proliferation of your content page).
In Part A of the assignment, you will design an incentive structure to promote your QR code with the goal of increasing both virality and engagement. Later, in Part B of the assignment, you will get a chance to download the entire class’ scan data and analyze the geo-location data associated with these scans, with the hope of detecting social data patterns and forming meaningful insights which will be shared with the rest of the class!
As a further incentive to do well, at the end of the assignment, the top ranking QR code(s) will win prizes based on:

  1. Originality of your content page idea
  2. Number of visitors (virality)
  3. Overall user engagement

Step 1
Design an incentive system to promote either virality, engagement, or both. Part of this will mean first thinking of something compelling to put on your content page – perhaps you want to support a cause (i.e. Tsunami Relief), or perhaps you want to promote a new tech startup or a new web service that you recently stumbled across, or perhaps even you just want to gain virality through promoting a new YouTube video or news story that you particularly enjoyed. What you can do with your content page is totally up to you – but make sure that the content fits nicely with your end incentive system.

Step 2
Once you have figured out what your incentive system is going to be and what your content page is going to look like, you’ll need to answer the following questions (in the Google Form):
1. What was your incentive design for having your QR code scanned in the real world?
2. What was your incentive design for spreading your QR content page in the online world?

MS&E 237 Assignment 4 – Gamification and Geo-Location (Part B)
Due Date: May 31st, 2011 (By Noon) -- extended to Fri June 3rd, 2011 noon [aweigend]
PDF Version:

Learning Goals:
  • Learn about data analytics for tracking both real world and online visitors
  • Learn about geo-location based data and how to extract social behaviors from geo-location data
  • Learn about visualizations of geo-location data



Submission Details:
Submit a DOC/PDF to with the subject line “HW4 – [Your SUNet ID]”. Your DOC/PDF should contain the following:
  1. Your visualization from Step 1 as well as comments on whether or not your visualization reflects your incentive design decisions for the physical spread that you submitted in HW4A
  2. Your visualization from Step 2 as well as a your summary of what you see
  3. Your plot from Step 3 as well as your brief analysis of the plot
  4. A summary of the assignment – what viral techniques/design incentives worked well, which did not and possible explanations for why they did not do well, etc
  5. An Appendix with all code that you used (i.e. whether in Python, MATLAB, R, etc)

Assignment Details:
We hope you’re having fun promoting your QR code! In this second part of the assignment, we will focus much less on the promoting/spreading of your QR code, and much more on analyzing what happened with both your QR code along with the rest of your peers’ QR codes. Specifically, we will focus on analyzing geo-location data, as well as virtual spread data.
Step 1Let’s start by analyzing the geo-location data. The first thing we want you to look at is how YOUR QR code spread physically. More specifically, what you have is a data set that contains the class’ aggregate scan/check-in data, but first we want you to isolate those scans/check-ins that were for your QR code and we want you to visualize this by overlaying the locations of those scans on a map.
More specifically, we’d like you to use map overlay markers and drop them with labels corresponding to the day at which the scan happened (i.e. first day = 1, second day = 2, etc, see PDF for example plots), that way we can easily see how your QR code spread as a time series.
Finally, comment on whether or not the spread of your QR code in the physical world was consistent with what you intended (i.e. did what you design in HW4 Part A actually come to fruition).
Step 2
Then, we are going to switch focus to the aggregate geo-location data from the class. Now, instead of visualization for how your QR code spread as a time series on a map, we just want to look at where the most scan activity occurred. Namely, we want you to overlay all the geo-location data from entire class, and describe to us where you saw the most activity (don’t worry about labels). This visualization in essence will be like a heat map and should show you which areas around campus had the highest/lowest density of scans.

Step 3Now, we’re going to veer away from the geo-location data and look instead at the online world. Again, focusing on the class’ aggregate data, we want you to create a visualization that shows how many visits there were to each student’s content page in total over the week (this is essentially a histogram where the x-axis is buckets of visits).
You have some freedom in this plot as we’ll let you choose the bucket size of visits (i.e. bucket students by buckets of size 5, 10, 20, 50, etc), but in the end your plot will have visits as the x-axis, and number of students in that corresponding bucket in the y-axis. We hope (though can’t guarantee) that with the class’ data you’ll encounter a real life instance of an event that obeys the power law (, which if you spend enough time in this field you’ll quickly learn that many other phenomena in fact follow power laws. Finally, just provide a brief analysis of the plot you produced, why you chose the bucket size you chose, etc.
Step 4And lastly, we just want you to think about what viral techniques/design incentives you did worked well and which ones failed. For those that failed, why did they fail? What would you do differently next time? Please provide a summary of your findings at the end.
We hope you had fun with this last assignment, and we hope you had a great time working through these difficult real-life industry problems in this class! Thanks for taking MS&E237!

For those people who (for whatever reason) were unable to implement their incentive design structure from Part A and were therefore unable to obtain sufficient data to create these visualizations, please use the data associated with QR ID sdrlom as a substitute and please mention this on the top of your assignment.