Assignment 3: Ethical and Deceptive Visualization

With visualizations increasingly playing a role in how the general public consumes news and information, it is important to remember that the particular visualization design has the power to influence the types of insights that a reader may uncover and later recall about the data. In this assignment, you will identify a dataset of interest and design two static visualizations to communicate the data. One design will be an ethical representation of the data, whereas the other will be a deceptive visualization that aims to mislead the viewer.

Assignment

Your task is to design two static (i.e., single image) visualizations of your chosen dataset. One visualization should aim to effectively and ethically communicate insights in the data, whereas the other visualization design should aim to mislead the viewer into drawing the wrong conclusions. You will also provide a short write-up (no more than 4 paragraphs) describing your design rationale for both the ethical and deceptive visualizations.

For the purposes of this assignment, we will consider an ethical visualization to be one where:

• The visualization is clear and easy to interpret for the general population
• The visual encodings are appropriate and effective for the intended task
• Data transformations are clearly and transparently communicated
• The underlying data sources (and any potential bias) is clearly communicated

On the other hand, a deceptive visualization may exhibit the following properties:

• The visual representation is intentionally inappropriate or misleading
• Titles are skewed to intentionally influence the viewer's perception
• The data has been transformed, filtered, or processed in an intentionally misleading way
• The existence or source of bias in the underlying data is unclear

For the ethical visualization, your goal is to be as clear and transparent as possible to help viewers answer your intended question. For the deceptive visualization, your goal is to trick the viewer (and graders) into believing that the visualization is legitimate and ethical. It should not be immediately obvious which visualization is trying to be deceptive. Subtle ineffective choices in the design should require close and careful reading to be identified.

For both visualization designs, start by choosing a question you'd like to answer. Design your visualization to answer that question either correctly (for the ethical visualization) or incorrectly (for the deceptive visualization). You may choose to address a different question for each visualization. Be sure to document the question as part of the visualization design (e.g., title, subtitle, or caption) or in your assignment write-up.

Your write-up should contain the following information:

• Your name and UW netid
• A description of which file is designed to be ethical and which is deceptive
• The specific question each visualization aims to answer
• A description of your design rationale and important considerations for each visualization

Recommended Data Sources

To get up and running quickly with this assignment, we recommend using one of the following provided datasets or sources, but you are free to use any dataset of your choice. You must use the same dataset for both visualizations, but you may transform the data differently or choose to address a different question for each design. These datasets are intentionally chosen to cover politically charged topics for the simple reason that these are typically the type of data where ethical visualization is important.

The World Bank Data, 1960-2017

The World Bank has tracked global human development by indicators such as climate change, economy, education, environment, gender equality, health, and science and technology since 1960. We have 20 indicators from the World Bank for you to explore. Alternatively, you can browse the original data by indicators or by countries. Click on an indicator category or country to download the CSV file.

Data: https://github.com/ZeningQu/World-Bank-Data-by-Indicators

Greenhouse Gas Emissions, 1990-2017

The Organization for Economic Co-operation and Development (OECD) has compiled data for the emissions of all participating countries broken out by the pollutant (e.g., carbon monoxide, methane, etc.) and by different sources (e.g., energy, agriculture, etc.). While the linked interface can be somewhat hard to navigate, you are free to browse alternate themes from the panel on the left (such as education or health). You can download the data by selecting "Export" at the top of the chosen table.

Data: Greenhouse Gas Emissions

DEA Pain Pills Database

The Washington Post has published a significant portion of a database maintained by the Drug Enforcement Administration (DEA) that tracks every opioid from their manufacturer, through to distributors, and into pharmacies in towns and cities across the United States. Note that this is a very large dataset with many different facets, so you may want to focus on a particular area or set of attributes of interest.

Important Note: In order to download and use this data you may need to enter your email in order to access the Washington Post Data Access page.

Data: Washington Post Data Access Page

Here are some other possible sources to consider. You are also free to use data from a source different from those included here. If you have any questions on whether a dataset is appropriate, please ask the course staff ASAP!

• data.seattle.gov - City of Seattle Open Data
• data.wa.gov - State of Washington Open Data
• nwdata.org - Open Data & Civic Tech Resources for the Pacific Northwest
• data.gov - U.S. Government Open Datasets
• U.S. Census Bureau - Census Datasets
• IPUMS.org - Integrated Census & Survey Data from around the World
• Federal Elections Commission - Campaign Finance & Expenditures
• Federal Aviation Administration - FAA Data & Research
• NOAA Daily Weather - NOAA Daily Global Historical Climatology Network Data
• yelp.com/dataset - Yelp Open Dataset
• fivethirtyeight.com - Data and Code behind the Stories and Interactives
• Buzzfeed News - Open-source data from BuzzFeed's newsroom
• Kaggle Datasets - Datasets for Kaggle contests
• List of datasets useful for course projects - curated by Mike Freeman

Grading

The assignment score is out of a maximum of 20 points. We will determine scores by judging the soundness of your visualization designs, the duplicity of your deceptive visualization, and the quality of the write-up. Here are examples of aspects that may lead to point deductions:

• Obvious identification of the ethical and deceptive visualizations.
• Ineffective visual encodings for your stated goal.
• Missing indication of the main analysis question.
• Missing or incomplete design rationale in write-up.

We will reward entries that go above and beyond the assignment requirements to produce effective (and deceptive) graphics. Examples may include outstanding visual design, effective annotations and other narrative devices, exceptional creativity, or deceptive designs that require the write-up to properly identify the misleading design components.

Submission Details

This is an individual assignment. You may not work in groups.

Your completed visualizations designs and write-up are due Monday 5/03, 11:59pm PT on Canvas. Submissions will be reviewed as part of a subsequent peer evaluation assignment (due: Monday 5/10), so be sure to avoid a late submission; assignments submitted late will not be included as part of the peer evaluation and will not receive any peer reviews.

You must submit your assignment using Canvas. Please upload a single zip file named using the pattern "uwnetid_a3.zip" (replacing "uwnetid" with your UW network login - this is the same as your @uw email address, not a numeric id number). The zip archive should contain three files: a plain text file named "readme.txt" and two .png or .jpg image files, one named "ethical" and one named "deceptive" accordingly.

Please use the correct file names for your submission ("ethical" and "deceptive"); typos that require manual correction by the course staff may result in point deductions. Remember, the visualization itself should not give away which design is ethical and which is deceptive; the file names will be randomized by the course staff prior to the peer evaluation assignment. Please also use the correct file extensions for your images (either .png or .jpg) and be sure your image is sized for a reasonable viewing experience. Viewers should not have to zoom or scroll in order to view your submission!

The readme.txt file should contain your write-up, as described above. Please be sure to include both your name and UW netid in your readme.

Acknowledgments

The design of this assignment is largely based on the experience and recommendations of Niklas Elmqvist at the University of Maryland, College Park, and a similar assignment from Arvind Satyanarayan from the Massachusetts Institute of Technology.