4602-Mozilla

Due November 4, 2019 @ 11:59pm through GitHub Classroom

Projects may be submitted up to 3 days late, with a 10% penalty per day

Overview:

Mozilla (the same company that created the Firefox web browser) recently conducted a survey on people's perceptions of privacy in our modern, highly connected world. The survey was aimed at understanding how comfortable people from all over the world are with various technology and how that comfort varies with things like device ownership or tech savvy. You can learn more about their data here: https://blog.mozilla.org/blog/2017/11/01/10-fascinating-things-we-learned-when-we-asked-the-world-how-connected-are-you/?utm_source=newsletter-mofo&utm_medium=email&utm_campaign=IOTsurveyresults&utm_content=callout&utm_term=4434975

The challenge is that, while they have a rich set of data, they don't have strong ways of exploring that data beyond basic spreadsheets and descriptive statistics. Your goal is to create a set of visualizations that allows them to engage with their data. The raw data is available at: https://drive.google.com/file/d/0B5UMbl9u1_wQc2l0ZTU0dTdoYnM/view

To do this, create visualizations that illustrate at least two insights into their data. The above blog post has some insights you can use to start thinking about this dataset, but I encourage you to think outside of these ideas as well.

Minimum Requirements:

Your project must:

• Include a README.md file that outlines:
  • Information about your visualizations and what they show. Include information about interactions, preprocesses, and design as appropriate. Note what tasks the visualization allows you to accomplish to derive this insight and how your design is tailored to support these tasks.
  • Your design process (e.g., how did you go about designing, building, and refining your system? Why did you choose these representations?)
  • Your team roles for each individual
  • How to run your project
• Include at least two unique visualizations:
  • One visualization must include some quantitative data
  • One visualization must include categorical data
  • Each visualization must be interactive
  • Your visualizations should support at least one meaningful comparison between related data attributes
  • Your visualizations should visualize at least five data attributes total
• Be able to work with any dataset of this format (e.g., the numbers are interchangable but the columns and document titles are fixed).

Above and Beyond:

The above requirements are the minimum for a passing grade on this project. Some ideas to improve your project include:

• Unusual Representations: Draw on some of the examples from class to represent data in ways beyond a typical scatterplot or bar chart.
• Style: Keep the style consistent across all your views, with an eye towards intelligently applying visual design.
• Geography: Incorporate maps or other geospatial data components into your visualization.
• Interesting Tasks: Derive insight into the data beyond that provided in Mozilla's current post. Highlight these insights in your readme and describe how the visualization enables them.
• Perceptually-Informed Design: Integrate perceptual concepts into your visualization design and discuss how you've integrated those concepts in your readme.
• Coordinated Views: Have two or more visualizations that interact with one another as you move through the data.

Platforms:

You can use any development platform you'd like so long as your final project runs in the browser without having to install anything new. Your project readme should include step-by-step instructions on how to run your projects and it should run without me having to modify the code. You are welcome to use different platforms for each visualization.

Some platforms to look at include:

• D3
• R with ggplot
• WebGL or Three.js
• ProcessingJS
• Google Maps API
• Open Street Map API
• Bokeh

If you would like to use a platform that will push you in creative ways but may not support all of the requirements of the project, please come talk to me.

Submissions:

All submissions must be made through GitHub with a timestamp by 11:59pm on 11.4. Your submission files should include:

• Your README
• Your code and/or project

Note that each group only needs to submit one file.

Project Teams

Group 1: Savannah Bornstein, Joshua Paup, Elise Bergmann, Conner Sinjem

Group 2: Lanea Blackburn, Priya Panati, Edgar Mendoza, Jacob Boeckenstedt

Group 3: Hannah Weber, Trevor Buck, Clark Mousaw

Group 4: Madeline Cupchak, Hunter Rief, Kathleen Anderson, Yizhen Wu

Group 5: Jiaheng Zhao, Jiahao Wang, Juliet McFarlane, Michael Rogers

Group 6: Mary Yoder, Julia Merten, Paige Stockebrand, Lu Liu

Group 7: Jihoon Jang, Caden Bradbury, Talia Colalancia

Group 8: Joshua Barker, Angus MacDonald, Malik Tefridj, Keaton Whitehead

Group 9: Anthony Camacci, Dilon Clark, Steven Yatko