Assignments > Design a Data-Intensive Science Communication Campaign

For your final project, you will design a data communication campaign that tells a story using both quantitative (e.g., charts, maps, statistics) and qualitative (e.g., interviews, stories, documents, media coverage) data.

Your campaign should be designed for a specific audience and should take a clear point of view - not just informing, but persuading, engaging, or surprising. While many students may choose an environmental or resilience-related issue, you are also welcome to explore more cultural, whimsical, or curiosity-driven topics (e.g., the rise of LEGO Star Wars sets, changes in pop lyrics, or the growth of board game culture).

The core goal is the same: to show how data, combined with storytelling, can move people - whether to care about an urgent challenge or to see something familiar in a new light.

Project Overview

1. Topic Selection

Choose a topic that lends itself to storytelling with data. This might be:

  • Environmental & resilience issues (wildfire risk, climate migration, flood preparedness, energy transition, air quality, biodiversity loss).
  • Cultural or lifestyle topics (music trends, LEGO evolution, board games, movies, sports, food culture).

Regardless of the dataset you choose, clearly articulate why this topic matters (to you, your audience, or society) and what perspective or insight you want to highlight.

2. Audience & Goals (Proposal)

  1. Identify a specific audience (e.g., local policymakers, high school students, fans of LEGO, trivia enthusiasts, climate activists).
  2. State the communication goals of your campaign (e.g., raise awareness, inspire curiosity, influence behavior, correct misconceptions, highlight hidden patterns).

3. Data Sources

  1. Quantitative data: Use at least one dataset (from government sources, NGOs, cultural databases, APIs, or open repositories) and visualize it in a clear, compelling way.
  2. Qualitative data: Add depth by drawing on interviews, narratives, media coverage, policy documents, or other sources that highlight human or cultural context.

Document your data sources and choices clearly.

4. Analysis & Visualization

Perform Exploratory Data Analysis (EDA) to understand trends, patterns, and surprises. When you're done, create at least 3 visualizations, including:

  1. One chart (bar, line, scatter, histogram, etc.)
  2. One visualization that goes beyond a “default” chart (e.g., map, network diagram, custom graphic, annotated chart).
  3. One element that integrates qualitative content (e.g., photo with data overlay, timeline annotated with quotes).

Use appropriate labels, scales, and colors - accuracy + clarity come first.

5. Campaign Materials

Create at least one deliverable (and optionally a second) that are appropriate for your audience:

  1. Your first deliverable must be a website with photos, embedded charts, and a clear narrative that helps your audience navigate the visualization.
  2. (Optional) Your second deliverable can be anything you want (earn up to 3 points extra credit applied to your final grade):
    • A social media infographic
    • A public service announcement (poster, video, or podcast script)
    • A fact sheet or one-pager for policymakers
    • A video
    • A zine

Ensure that your deliverable(s) communicate your idea effectively using data.

6. Design & Aesthetics

  • The project should be visually polished (consistent fonts, colors, layout).
  • Use annotations and narrative text to guide the viewer.
  • Strive for professional design quality: this should look like something you’d proudly show in a symposium.

7. Iteration & Feedback

  • Share an early draft with peers.
  • Gather feedback on clarity, design, and storytelling.
  • Submit a revised final version that responds thoughtfully to feedback.

8. Write-Up

Include a short write-up (2-3 pages or equivalent) that:

  • Introduces your question and motivation.
  • Describes your dataset(s).
  • Explains your design choices and iteration process.
  • Reflects on what you learned about your topic and about data communication.

9. Presentation

  • Prepare a 5-7 minute presentation.
  • Summarize your project’s story, audience, and visualizations.
  • Practice: make sure your visuals are legible and your narrative is focused.

Logistics

1. Timeline of Deliverables

  • Week 10 (Oct. 21) - Project proposal due (topic, audience, preliminary data sources)
  • Week 16 (Dec. 2) - Preliminary share-outs and user testing (for peer feedback)
  • Week 16 (Dec. 4) - Final Project Presentations at 3PM
  • Week 17 (Dec. 9) - Final project submission + paper due

2. Evaluation Criteria

Your project will be assessed on:

  1. Minimum Requirements Met - Qualitative and quantitative datasets, required visualizations included, 2 deliverables, etc.
  2. Clarity & Storytelling - Is the message clear and engaging for the chosen audience?
  3. Data Use - Are both quantitative and qualitative data integrated effectively?
  4. Visual Design - Are visualizations accurate, polished, and aesthetically strong?
  5. Iteration & Reflection - Did you test, revise, and reflect thoughtfully?
  6. Presentation - Was your talk clear, confident, and visually effective?

Potential Project Ideas / Datasets

I encourage you to pursue your own questions, but here are some ideas to get you started:

Food, Agriculture & Community

  1. Farmers’ Markets Directory (USDA) - Locations, products sold, seasonality.

    • Storytelling: “How accessible are fresh foods in rural WNC vs. urban areas?”
    • Viz: Bar charts (availability by month), treemaps (product categories).

  2. USDA Crop Production Data - Annual production of apples, Christmas trees, and other WNC staples.

    • Storytelling: “How has local agriculture shifted over time?”
    • Viz: Slope charts, bump charts (crop rankings over years).

  3. Food Security Data (Feeding America, County Health Rankings) - households that are food insecure, access to stores.

    • Storytelling: “Who has access to nutritious food, and who doesn’t?”
    • Viz: Small multiples comparing counties, dot plots of disparities.

Health & Social Vulnerability

  1. CDC Social Vulnerability Index - Composite scores (poverty, disability, minority status, housing).

    • Storytelling: “Which communities are more vulnerable before disaster even strikes?”
    • Viz: Radar charts, parallel coordinate plots, beeswarm plots.

  2. CDC PLACES Health Data - County/city-level rates of asthma, diabetes, physical inactivity, etc.

    • Storytelling: “How environmental risks intersect with health burdens.”
    • Viz: Heatmaps, scatterplots linking health + environment.

  3. EPA EJScreen (Environmental Justice) - Exposure indicators + demographics.

    • Storytelling: “Where are environmental risks highest, and for whom?”
    • Viz: Bubble plots (risk vs. population), stacked bars (pollution burdens).

Energy, Economy & Infrastructure

  1. EIA (Energy Information Administration) - State/county energy use, sources (coal, solar, hydro).

    • Storytelling: “How is NC’s energy mix changing?”
    • Viz: Sankey diagrams (energy flows), stream graphs (shifts over time).

  2. Open Transit / Mobility Data - Transit routes, ridership data (Asheville Rides Transit publishes GTFS feeds).

    • Storytelling: “Who has mobility access in a car-dependent region?”
    • Viz: Timelines of ridership, chord diagrams (origin/destination).

  3. Electric Vehicle Registrations (NC DOT) - Growth in EV adoption.

    • Storytelling: “Are EVs catching on in WNC, or only in big cities?”
    • Viz: Growth curves, slope graphs comparing counties.

Outdoor Recreation / Nature

  1. Recreation & Parks Data (NC Parks & Recreation, USGS recreation stats) - Visitor numbers, trail usage, outdoor recreation trends.

    • Storytelling: “Are our natural spaces under more pressure post-COVID?”
    • Viz: Time-series, ridgeline plots for seasonal use.

  2. Citizen Science Projects (iNaturalist, eBird) - Observations of species in WNC.

    • Storytelling: “What biodiversity trends are citizens noticing?”
    • Viz: Animated scatterplots, seasonal species calendars.

Pop Culture & Media

  1. Billboard / Spotify Charts (Kaggle or APIs)

    • Storytelling: “How has pop music changed over decades (tempo, danceability, mood)?”
    • Viz: Streamgraphs (genre trends), scatterplots (energy vs. popularity).

  2. Lyrics Datasets (e.g., Genius scrapes)

    • Storytelling: “What words define different decades of music?”
    • Viz: Word clouds, bump charts of word frequency.

  3. Movie Datasets (IMDb, TMDb, Rotten Tomatoes)

    • Storytelling: “Are movies getting longer? Do sequels get worse ratings?”
    • Viz: Histograms of runtimes, scatterplots of budget vs. rating.

  4. Streaming Data (Netflix, Spotify playlists, TV Tropes)

    • Storytelling: “What shows dominate binge-watching culture?”
    • Viz: Heatmaps of release timing, chord diagrams of actor collaborations.

  5. Books Dataset (Goodreads / Project Gutenberg)

    • Storytelling: “What genres dominate bestsellers, and how have they shifted?”
    • Viz: Slope charts, stacked area charts by genre.

Random / Fun

  1. BoardGameGeek Dataset (Kaggle)

    • Storytelling: “Which mechanics (deck-building, worker placement) exploded in popularity after 2010?”
    • Viz: Beeswarm plots of game ratings, ridgeline plots of release years.

  2. LEGO Set Database (Rebrickable / Kaggle)

    • Storytelling: “How has LEGO evolved — more pieces, more Star Wars, fewer castles?”
    • Viz: Sankey diagrams (theme flows), scatterplots (set size vs. year).

  3. Sports Stats (Basketball, Soccer, Baseball — plenty on Kaggle)

    • Storytelling: “What’s changed more: NBA scoring or defense?”
    • Viz: Time-series, bar charts of scoring averages.

  4. Video Game Sales (VGChartz / Kaggle)

    • Storytelling: “Which genres are thriving — RPGs, shooters, or indie hits?”
    • Viz: Treemaps, bump charts of best-sellers by year.

  5. Jeopardy! Questions (Kaggle)

    • Storytelling: “What categories stump contestants the most?”
    • Viz: Bar charts of accuracy by category, word clouds of common answers.

  6. Pokemon Stats (Kaggle)

    • Storytelling: “Which Pokemon type is most powerful? How have new generations shifted balance?”
    • Viz: Radar charts, scatterplots of attack vs. defense.

  7. Star Wars / Marvel Data (character networks, movie box office)

    • Storytelling: “Who’s the most central Avenger?”
    • Viz: Network graphs, bar charts of screen time.

  8. World Happiness Report

    • Storytelling: “Do money and happiness move together? Which countries punch above their weight?”
    • Viz: Scatterplots, small multiples of trends.