Information Design and Voter Education: A Reflection on the 2018 Midterms and How to Design for 2020

The goal of the project was to first identify why Millennials weren’t voting as much as older generations, and ultimately attempt to inspire higher turnout in the local university community.

Courtney Marchese
Associate Professor
Quinnipiac University

In the summer of 2018, a design student-professor collaboration produced a 100-page Midterm Election Guide, that set out to tackle the lopsided statistic that millennial voters (18-35 years old in 2016) had a nearly 20% lower voter turnout in 2016’s presidential election, as compared to Baby Boomers (53-71) despite having a near equal portion of eligible voters (each about 30%).

The goal of the project was to first identify why Millennials weren’t voting as much as older generations, and ultimately attempt to inspire higher turnout in the local university community. Through an initial survey of college-aged students, the vast majority noted that they do not typically vote because they feel like they don’t know enough about the issues at stake and are not educated on the purpose of midterm elections. They further noted which issues are most important to them, which are the issues that are focused on in this guide: the environment, the economy, immigration, foreign policy, the treatment of minority groups, gun policy, healthcare, and women’s rights. While these issues surfaced as top priorities to millennials, it was evident that these topics resonate across generations.

Data from the internationally-recognized Quinnipiac Polling Institute, Pew Research Center and a variety of government websites, was used to create an organized system of timelines, key terms, and data visuals to help explain today’s complex politic issues and seeks to help young voters understand their demographic significance in today’s society. This presentation describes the effect that the guide had in the 2018 midterms, and looks at the evolving strategy for how it will educate voters in 2020.

Visualizing Mental Models

Joshua Korenblat
Assistant Professor
Graphic Design

State University of New York at New Paltz

Visual communicators can work at the center of ideas by understanding mental models. A mental model is an abstract representation of reality that enables thinking, understanding, and knowledge sharing. In his book Visual Complexity, Mapping Patterns of Information, researcher Manuel Lima identifies two broad historical trends in mental models: earlier tree-based models of knowledge, illustrated in the literal form of trees, shift into today’s more abstract, network-based models of knowledge.

As summarized by Raph Koster in his influential book A Theory of Fun for Game Design, thinking is pattern-matching against experience. Patterns are stored in memory as chunks of information. Most of the time, the brain works with these abstract chunks—a type of autopilot—rather than processing incoming information in detail. Poetry breaks us from the autopilot mode through vivid descriptions and figurative verbal language. Like a poem, a visual mental model can break readers from their autopilot mode by allowing them to examine their assumptions in a material way. These diagrams rely upon an elegant visual alphabet. Mental models appear in user experience research as affinity maps and user journeys. Or they can show systems, a set of interdependent parts, below the threshold of events and action. Ultimately, the most vivid mental models allow the reader to see a belief or story.

After presenting historic mental models, I’ll show a simple design case study for how to make a mental model, adapted from systems theorist Derek Cabrera. I’ll then discuss when to represent the model in an abstract way, and when it might benefit the designer to represent the model in a more illustrative way. Designers who wish to create vivid, shareable artifacts of our world can use mental models as a tool to enhance communication, conversation, and action with their constituents.

Data Visualization Research: How It Informs Design and Visual Thinking

Joshua Korenblat
Assistant Professor, Graphic Design
SUNY New Paltz

Design research aligns with the process of researching a data visualization project. Data visualization maps numbers to visual variables; many design projects, meanwhile, have concerns other than numbers and statistics. Yet the research process that contributes to a sound data visualization can offer valuable insights into visual thinking and storytelling. Data visualization is the end result of data analytics, an exploratory process that cultivates a mindset familiar to designers.

Curiosity guides this mindset: observational, descriptive methods allow the creator to understand a topic from multiple angles, ultimately honing clarity in communicating an idea. The process might at times proceed from details to a big picture; other times, from a big picture to details. This data analytics mindset dovetails with emergent processes in design thinking. In both processes, small sprints often yield results more optimal than a grand master plan.

Data analytics involves spatial visual thinking skills that designers—all of whom work with points, lines, planes, and color—have the ability to understand. One of the leading visualization packages for the open source statistic package R is called the “grammar of graphics,” akin to verbal and visual language. I will use an accessible information graphic that compares Presidential biographies at the time of first election. This case study will detail how the analytic process conducts along a circular track: gathering data, structuring it, finding an insight, and visualizing that insight in a memorable, authentic, and persuasive way for a specific audience. Designers, and designers interested in storytelling, can identify familiar experiences at each step of the process. For designers who have yet to work in data analytics and visualization, accessible methods of sketching with data can exercise observational skills and visual thinking processes that propel many design and teaching practices—even those unconcerned with data visualization as the end result.

Hearing What Isn’t Said: Visualizing Non-Verbal Responses In Data Analysis

Sanda Katila
Associate Professor
School of Visual Communication Design
Kent State University

Evidence suggests that people ages 60 and over take at least five different medications daily for distinct health issues, and that subsequent drug interactions create significant health problems. Secondary research shows that few patients can define the phrase, “drug interaction”. Despite this, little is written about what patients understand about drug interactions, and what is written lacks valuable data contributed by patients through nonverbal cues.

This paper examines the process of seeing patients’ nonverbal communication by visually mapping data, and suggests that mapping allows designers to look at data in fresh ways, which will ultimately open doors to further research in the area of drug interactions in an aging population. Visual theorist Johanna Drucker states that, “A basic distinction can be made between visualizations that are representations of information already known and those that are knowledge generators, capable of creating new information through their use.” (Drucker 2014, 65) Such design-generated knowledge has the potential to move the needle toward the reduction of harmful drug interactions.

The mapping process in this research specifically highlights connections between researchers’ questions and patients’ nonverbal responses. 13 non-verbal responses such as confidence, laughs, questioning, pause or deep breath, flat tone, etc. were coded to 31 questions to see if there are correlations between nonverbal responses and answers. Designers then, through visualizing these connections, may provide valuable new pathways in examining the qualitative research. More broadly, this paper will demonstrate the kind of value that designers can bring to qualitative research across a spectrum of data-rich fields.

Visualizing Pesticide Use in Controlling Zika

Courtney Marchese
Assistant Professor of Interactive Media + Design
Quinnipiac University

Information graphics help condense large amounts of data into comprehensive visuals. One of the most critical topics for the general public to understand is issues of public health. Zika virus has come to the forefront as one of the most threatening mosquito-transmitted diseases in the Americas, with proven complications that include microcephaly and Guillain-Barré syndrome. Although there is currently no cure for Zika, there are a number of pesticides used in the affected areas in hopes of controlling the spread of the virus. In collaboration with scientists and other experts in the field, I will harvest and deliver the most important data to the general public. Through data visualization, we can track which pesticides are being used where, and how efficiently they are controlling the spread of the virus-carrying mosquitos.

My methodology in creating the information graphics is to research both data visualization techniques as well as pesticide use in the Americas as it related to controlling Zika transmission. I will also interview and collaborate with experts as I collect and analyze the necessary layers of data. From there, many iterations of potential visualizations will be created and critiqued until the best possible solutions have been created. My hope is that these graphics will help provide a comprehensive overview of the relationship between various pesticide use and the spread of Zika virus.

Colloquium 1.3: Call for Submissions

Deadline: November 26, 2014

The  2014 winter colloquium will be held at Parsons, The New School. We invite all Communication Design researchers to submit abstracts for consideration by our panel of peers.

For more details, see the Submission Process description.
Event Date: Tuesday, December 2, 2014

The New School University Center
65 5th Ave, Academic entrance (corner of 13th St)
New York, New York
Room 617

3PM – 5PM

Please RSVP if you plan on attending.