Project 3: Interactive Visualization
For this assignment, you will explore the issues involved in implementing interactive visualizations. You will build a visualization that enables interactive exploration of a dataset and deploy it on the web.
One goal of this assignment is to gain familiarity with implementing interaction techniques for visualizations. For example, the zipdecode and NameGrapher applications apply the interactive technique of dynamic queries – as explored in the earlier HomeFinder application – to the problem of uncovering patterns in zip codes and baby names.
Similarly, treemaps were originally developed to display hierarchical file structures and were later adapted to the problem of interactively depicting a map of the stock market. We have seen many other examples of interactive visualization techniques in class. The goal of this assignment is not only for you to gain hands-on implementation experience, but also for you to think about the effectiveness of the specific techniques for your chosen data domain.
One challenge is to scope the assignment so that you can finish it within two weeks. Focus on designing a limited yet compelling visualization that enables interactive exploration along a few critical dimensions. The NameGrapher application is a nice example that uses a simple but elegant interaction design to enable engaging explorations. A tightly focused, well-implemented interactive graphic is much preferred to a sprawling design that attempts too much!
Team Registration
For this assignment, you should work in teams of 3-4 students. You might want to find project partners that you will continue to work with for the final project.
As soon as you have formed a team and chosen a dataset, submit the team registration form. Your form must include the names, UCSD email addresses, and PIDs of each team member, along with a concise title for your interactive visualization. The visualization title need not be exact, just a rough title should be a good start. If you are looking for project partners, please post to Piazza to find classmates with similar interests! You should submit your team registration form by Wed 10/29, 11:59 pm. Please ensure that the team has been finalized before filling the form, and that the details are correct.
You will submit your interactive prototype as a webpage hosted on GitHub Pages.
Deliverables
Design an interactive graphic aimed at enabling understanding of a compelling question. You’re welcome to use any dataset of your choice. To determine what subset of the data and which interactive options are most promising, you may want to perform additional exploratory analysis. What aspects of the data reveal the most interesting discoveries or stories? Do not feel obligated to try to convey everything about the data: focus on a compelling subset.
Your graphic must include interactions that enable exploration. Possible techniques include panning, zooming, brushing, details-on-demand (e.g., tooltips), dynamic query filters, and selecting different measures to display. You are free to also consider highlights, annotations, or other narrative features intended to draw attention to particular items of interest and provide additional context.
Implement your interactive graphic and deploy it to the web. You may use only D3.js for this project. Vega-lite/Plotly/Altair/any other high-level plotting library or tool are strictly not allowed for this project. You can use other non-plotting related JavaScript libraries, e.g. Lodash, Svelte, Moment, etc. Your graphic should not require customized server-side support; you should simply load data from a static data file or public web API.
You will use GitHub pages to host your visualization from your project repository. Make sure that you make your repository “Public”. We recommend keeping everything (development files and website) in your master branch: either serve your website from the root folder or the “/docs” folder. Your repo must also contain the (unobfuscated) source code for your visualization.
Your deployed web page should also include a write-up with the following components:
A rationale for your design decisions. How did you choose your particular visual encodings and interaction techniques? What alternatives did you consider and how did you arrive at your ultimate choices?
An overview of your development process. Describe how the work was split among the team members. Include a commentary on the development process, including answers to the following questions: Roughly how much time did you spend developing your application (in people-hours)? What aspects took the most time?
You can either include your write-up on the same page as your visualization or link to a separate file.
Grading Criteria
Projects that squarely meet the requirements for the assignment will receive a score of 12/14.
Going beyond the call of duty can net additional points, for example:
- Advanced interaction techniques
- Novel visualization elements
- Effective multi-view coordination
- Thoughtful and elegant graphic design
- Insightful & engaging exploration experience
Point deductions will be made when projects suffer from:
- Errors or broken features
- Ineffective visual encodings
- Confusing interface design or unhelpful interactions
- Lack of exploratory interaction techniques
- Incomplete or insufficient write-up
Rubric
The assignment is out of 14 points possible. Submissions that squarely meet the project requirements (Satisfactory column) will get 12/14 points.
| Component | Excellent | Satisfactory | Not Satisfactory |
|---|---|---|---|
| Visual Encodings | The visualization does not violate the expressiveness criteria and the design choices (marks and encoding channels) are clear, evocative, and effective. (+4 points) | The visualization does not violate the expressiveness criteria, but another set of design choices (marks and encoding channels) could have been more effective (e.g. using the area visual channel instead of position when possible). (+3 points) | The visualization violates the expressiveness criteria (e.g. heavy overplotting, encodings that imply incorrect readings, etc.), and these violations are not addressed using the available interactions (e.g. overplotting that cannot be filtered or zoomed into). (+2 points) |
| Data Transformations | Â | The visualization uses appropriate data transformations (e.g. filtering, aggregating) and clearly describes the transformations to the reader (e.g. in the subtitle of the plot). (+2 points) | The visualization used an inappropriate set of data transformations (e.g. filtering out important outliers), or did not clearly describe these transformations to the reader. (+1 point) |
| Interaction | The interactive elements of the plot are polished, are mostly bug-free, and guide the reader to quickly and easily discover interesting patterns in the data. (+5 points) | The interactive elements of the plot are functional and enable the reader to discover interesting patterns in the data, but a static plot (perhaps with a different encoding) could have conveyed the same information just as effectively. (+4 points) | The plot would have been substantially more effective as a static plot without the interaction; or, the interaction has major bugs that preclude use. (+2 point) |
| Writeup |  | The writeup clearly describes the motivation for the visualization and the rationale for its design decisions (e.g. the visual encodings, data transformations, color palette, etc.). It also contains an overview of the team’s development process. (+3 points) | One or more required elements of the writeup has major issues (e.g. rationale misses important parts of the visualization design), is missing, or is clearly generated by an AI tool. (+2 points) |
| Creativity and Originality | The submission exceeds the assignment requirements, with original insights or particularly engaging visualizations. (+1 bonus point) | The submission meets the assignment requirements. (+0 points) | Â |
Submission Details
Team Registration
Submit your team registration form by Wed 10/29, 11:59 PM.
Checkpoint Submission
The submission must be made on Canvas by Fri 10/31, 11:59 PM. For this submission, submit an mp4 video no longer than 1 minute with the following details:
- Name of your team and team members
- Dataset chosen. Explain the aspects of the datasets you plan to use.
- 5-6 exploratory graphs you have come up with (static/dynamic) that lead you to choose the (below) question.
- Title of your graph / the question you are aiming to answer.
- Current progress on the final dynamic graph.
Please ensure that the video has an associated audio/voiceover rather than just text. The video recording does not need to include all members of the team.
Only submit one video per team. It is important to add all members of your team to the submission.
Final Submission
You must have a working prototype online by Fri 11/07, 11:59 pm. Your prototype should be accessible on GitHub pages using a publicly viewable URL.
In addition, you must submit the URL on Canvas. One submission per team is sufficient.
Submissions will be reviewed as part of a subsequent peer review assignment, so try to avoid a late submission; assignments submitted late may not be included as part of the peer review and thus not receive peer feedback.
Data Sources
You’re welcome to use any dataset of your choice. If you don’t have one in mind, we’ve provided four suggested datasets below:
Human Development Indicators, 1960–2020. The World Bank has tracked global human development by indicators such as economy, education, environment, gender equality, health, and science and technology since 1960. The linked repository contains indicators that have been cleaned and formatted to simplify visual analysis and visualization design. However, you’re also welcome to browse and use the original data by indicator or by country. Click on a category to download the CSV file on the right-hand sidebar.
Climate Change Indicators. Similar to the previous dataset, the International Monetary Fund (IMF) tracks a number of metrics related to climate change including greenhouse gas emissions, strategies for mitigating or adapting to the impacts of climate change, transitioning to a low-carbon economy, climate finance, and the impact on weather. The data is structured as “crosstabs” (also known as “wide” data) where every time period (e.g., quarter or year) is given its own column. To facilitate visualization and visual analysis, you might wish to pivot the data into a “long” format.
Civilian Complaints Against New York City Police Officers. This is a dataset compiled by ProPublica, an independent, nonprofit investigative journalism newsroom. It contains more than 12,000 civilian complaints filed against the NYPD, with demographic information about the complainant and officer, the category of the alleged misconduct, and the result of the complaint.
Abortion Data by U.S. State from the Guttmacher Institute The Guttmacher Institute is a research and policy organization committed to advancing sexual and reproductive health and rights. They maintain a variety of global data related to global reproductive rights. This data set contains data about abortion rates, abortion providers, and abortion seekers, aggregated to the state level.