Assignment: Expository Visualization

An expository article requires the author to investigate an idea, evaluate evidence, expound on the idea, and set forth an argument concerning that idea in a clear and concise manner.

In this assignment, you will design an expository visualization to clearly communicate an idea based on the city climate dataset. In addition, you must provide a rigorous rationale for your design choices. You should in theory be ready to explain the contribution of every pixel in the display towards your expository goals.

We expect most students will use Vega-Lite, but you are free to use a different web-based visualization library (for example, Observable Plot) if you prefer.

Assignment Description

Your task is to design a static (i.e., single image) visualization that you believe effectively communicates an idea about the data, and provide a short write-up (no more than 4 paragraphs) describing your design. Start by choosing a question you’d like your visualization to answer. Design your visualization to answer that question. The title of your graphic should either be your question or your answer to the question that the chart conveys.

While you must use the data set given, you are free to transform the data as you see fit. Such transforms may include (but are not limited to) log transformation, computing percentages or averages, grouping elements into new categories, or removing unnecessary variables or records. You are also free to incorporate additional external data. Your chart image should be interpretable without recourse to your write-up. Do not forget to include title, axis labels, or legends as needed!

As different visualizations can emphasize different aspects of a data set, your write-up should document what aspects of the data you are attempting to most effectively communicate. In short, what story are you trying to tell? Just as important, also note which aspects of the data might be obscured due to your visualization design.

Your write-up should provide a rigorous rationale for your design decisions. Document the visual encodings you used and why they are appropriate for the data and your specific question. These decisions include the choice of visualization type, size, color, scale, and other visual elements, as well as the use of sorting or other data transformations. How do these decisions facilitate effective communication?

Grading

We will determine scores by judging both the soundness of your design and the quality of the write-up. We will also look for consideration of audience, message, and intended task. Here are examples of aspects that may lead to point deductions:

We will reward entries that go above and beyond the assignment requirements to produce effective graphics. Examples may include outstanding visual design, meaningful incorporation of external data to reveal important trends, demonstrating exceptional creativity, or effective annotations and other narrative devices.

Submission Details

This is an individual assignment. You may not work in groups. Submit your assignment by completing this page and publishing it to your GitLab repository. The rendered page should be viewable on your GitLab pages site.

Second, you must submit your write-up and a screenshot image of your visualization to Gradescope. The image must be in either PNG or JPG format, and properly sized to be comfortably viewable on a screen without scrolling or zooming.

Visualization

Write Up

The heatmap titled “Visualizing the Annual Cycle of Rainy Days Across U.S. Cities: A Wetmap” provides a comparative overview of the frequency of precipitation days throughout the year for selected cities: Seattle, San Francisco, Chicago, Houston, New York, and Miami. By displaying months along the x-axis and cities along the y-axis, with color intensity, in blues, representing the number of precipitation days, the visualization offers an immediate understanding of monthly precipitation patterns across diverse climates.

In designing this heatmap, specific visual encodings were chosen to enhance clarity and interpretability. The x-axis presents months in chronological order from January to December, facilitating intuitive comprehension of temporal trends. The y-axis lists the cities, allowing for straightforward comparison. Since the axis titles are self-evident due to directly labeled axis fields, these titles were purposefully omitted to provide a cleaner look and feel; a less-is-more approach which reduces the viewer’s cognitive load. A sequential color scheme, ranging from light to dark shades of blue, represents the increasing number of precipitation days; lighter hues indicate fewer days, while darker hues denote more frequent precipitation. This color scheme is discretized into quadrants to give the viewer a more accurate sense of discrepency between months and cities. This color gradient also effectively highlights variations and patterns within the data since we associate water with the color blue. Tooltips were added to provide the audience access to explicit values. Additionally, grid lines were enabled within the chart to further facilitate ease of interpretation.

The visualization emphasizes the temporal distribution of precipitation days across different cities, making it easy to identify seasonal trends and regional differences. For example, one can observe that Seattle experiences a higher number of precipitation days during the winter months, while Miami shows more precipitation days during the summer. However, the heatmap does not convey the intensity or amount of precipitation, meaning that a day with light drizzle is treated the same as a day with heavy rainfall. Additionally, the visualization does not account for the duration of precipitation events within each day.

The design choices, including the use of a heatmap format, sequential color gradient, and clear axis labeling, were made to facilitate quick visual comparisons and to highlight patterns across multiple dimensions—time (months) and location (cities). The heatmap’s structure allows viewers to discern trends and anomalies at a glance, supporting effective communication of the data’s story. By focusing on the number of precipitation days, the visualization provides insights into the frequency of precipitation, which is valuable for understanding regional climate behaviors and for applications in sectors such as agriculture, urban planning, and travel.