1
BOB*: A model for understanding the effect of visual design principles on persuasive
communication and data visualization.
Kassandra Alcaraz, MPH1
Chris Casey, BA1
Heather Corcoran, MFA3
Tina Clarke Dur, PhD2
Giovanina Gardiner, MSW1
Kim Kaphingst, PhD1
Matt Kreuter, PhD1
Doug Luke, PhD1
Lisa Moy, 2
Enrique VonRohr, MFA3
Andrea Spray, BA1
Author affiliations:
1
George Warren Brown School of Social Work and Public Health
Washington University in St. Louis,
One Brookings Drive, Campus Box 1009, St. Louis, MO 63130
2
3
Northern California Cancer Center
2201 Walnut Avenue #300
Fremont, CA 94538
Sam Fox School of Design and Visual Arts
Washington University in St. Louis,
One Brookings Drive, Campus Box XXXX, St. Louis, MO, 63130
2
ABSTRACT
Public health professionals today have access to more data, better analytic and visual design
tools, and more channels through which to share that information with diverse audiences.
Strategic deployment of these data and data-related tools is needed to maximize their social
impact in improving the health of populations. To help guide these efforts, we propose a new
model that integrates principles of information design, information processing and persuasive
communication to understand how health data may influence non-scientific audiences. The
model introduces three major principles of information design (hierarchy, micro and macro,
consistency and variation) and describes how each might affect information processing and
persuasion, specifically attention, understanding and elaboration. Contextual factors and
moderating variables that may influence these relationships are also discussed. We assert that
the model will help communication planners use data more purposefully and effectively, and call
for research to test and refine its propositions.
KEY WORDS:
Information design, visual design, information processing, health communication, cancer
communication, visual displays, transdisciplinary model, data visualization, persuasion, public
health
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INTRODUCTION (Doug Luke) (1-2 pages)
Problem and opportunity statement
The general public is increasingly exposed to sophisticated visual displays of data. The nearly
ubiquitous interactive electoral maps used on cable television news during the 2008 U.S.
presidential election illustrate this trend. Arguably, leading media outlets use more effective data
visualization approaches than many public health scientists and organizations. To keep pace,
public health professionals must develop clear and compelling ways to share new knowledge
with the public and other audiences.
Visual display of quantitative information is the art and science of presenting data in ways
that make them more engaging, understandable and meaningful, and enhance their impact on the
viewer. It spans media and can incorporate words and pictures, graphs, charts, maps, and other
visual devices. Done well, visual displays can transform quantitative information from an
abstract numerical form into a more visceral and sensory experience. They use a direct and
intuitive visual language to show relationships, structure and change, and have distinct
advantages in communicating complex ideas with clarity and efficiency. For example, they can
simultaneously reveal multiple levels of detail (e.g., data for a city block and all blocks in the
city), changes over space (e.g., counties, states, regions) and time (e.g., annual rates), and
multivariate complexity (e.g., combining all of these in a single spatial display, by demographic
characteristics).
Many government and other health agencies have built major infrastructure for collecting and
analyzing population health data (e.g., the National Cancer Institute’s SEER). Less attention has
been paid to maximizing the impact of these data through communication science. Strategic
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presentation of health statistics and other surveillance data to the public, policy makers and news
media may increase awareness and social action for public health.
Cancer registry data are underutilized for both descriptive (e.g., production of cancer
statistics) and analytic (e.g., as a resource for recruiting patients into more detailed research
studies) purposes. Most cancer registries produce a standard roster of statistics – rates and trends,
often with rudimentary graphs or other data visualization – and disseminate these to scientific
and professional audiences. Dissemination to the public, policy makers, news media, and other
non-scientific audiences is less common and less systematic. What is needed is a more strategic,
sophisticated approach to communication with lay audiences.
Such an approach would start with a clear articulation of the main message a given set of data
is intended to convey and the broader goals it will advance (e.g., population awareness, increased
screening, policy advocacy). It would consider not just the types of cancer data to be presented,
but also to whom they would be presented, and what we know about how that audience uses and
understands cancer information.

(CAN BE AN EXAMPLE? OR REMOVE?) Cancer registration is mandated in all 50
states. Cancer registry data are the only truly population-based and therefore
representative resources for quantifying cancer morbidity and mortality. However, When
it occurs, it is often reactive (e.g., responding to a cancer cluster concern in a
neighborhood), although several states and the NCI have made interactive online
mapping tools available to the public.
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Our goal with this paper: to introduce a transdisciplinary model for thinking about cancer data
visualization.
OVERVIEW OF THE MODEL (Matt Kreuter) (1/2 – 1 page)
Developing the model
Modern visualization tools and interactive media offer the ability to create novel
representations of geospatial cancer data, but these approaches still need to be tested, evaluated,
and refined. To do this requires a new collaborative team of epidemiologists, experts in design
and development of visual displays of quantitative information, and cancer communication
researchers.

Focus on the box in the diagram

In creating a transdisciplinary model to better understand cancer data visualization, we
drew from three disciplines (scientific data analysis, visual design, and communication).

This project aims to develop a more comprehensive, strategic and evidence-based
approach to visualizing and presenting data. In short, data visualization would be guided
by a systematic framework that builds on the science of cancer communication and the
art of visual display to maximize accessibility, understandability and social impact of
cancer data.
INFORMATION PROCESSING MODELS (Kim Kaphingst) (1/2 – 1 page)
Much of the contemporary research on persuasive communication is based on the early work
of McGuire (1968) and his input/output model of persuasive communication.93 This model, later
termed the Communication/Persuasion Model27 consists of five types of “input” variables
(source, message, channel, receiver and destination) that can influence effectiveness of a
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communication, and 12 possible “outputs,” or outcomes of communication (e.g., attention,
comprehension, attitude change). In the model, these 12 outcomes are successive, from more
affective and cognitive effects to behavioral outcomes. McGuire proposed that for a
communication to achieve higher order effects (e.g., behavior change), outcomes appearing
earlier in this succession have to be achieved.27 For example, the model would suggest that
health information, including data, will be more likely to influence audience members’ attitudes,
decisions and actions if they are exposed to the communication, pay attention to it, understand it,
and learn from it. The progression of communication effects can stop anywhere in this
sequence.94 Thus, persuasive communication that hopes to achieve higher order effects should be
designed to maximize effectiveness at each step.
Petty and Cacioppo’s (1981) elaboration likelihood model (ELM) provides an explanation of
how movement through these steps occurs. The ELM is based on the premise that under many
conditions, people are active information processors -- considering messages carefully, relating
them to other information they’ve encountered, and comparing them to their own past
experiences.21-22 For example, people are more motivated to actively and thoughtfully process
information if they perceive it to be personally relevant or if they care about the topic or issue
being addressed.20 Studies have shown that when information is processed more deeply (i.e.,
“elaborated” upon) it is retained for a longer period of time and is more likely to lead to
permanent change.23-24
Borrowing from these well-established and empirically supported theories of communication,
persuasion and information processing, our model includes a sequence of attention,
understanding and elaboration to describe the general process through which exposure to health
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information can influence outcomes like knowledge, attitudes, beliefs and behaviors (see Figure
X).
Attention

Capturing

Holding

When do we want to capture / hold attention? What problems might this be relevant to?
(Ask this question for next two concepts as well)

Our initial concern with this model was in the ways that visual design may impact the end
user’s desire to attend to the message (capturing attention) and in the maintaining
attention. For the purpose of communication, attention is a necessary predecessor to
understanding, and ultimately elaboration and increased motivation to positively impact
behavior.
Understanding

Understanding

Elaboration

Personal relevance

Motivation to process

Favorable feelings

How much does this relate to me?

Dependent on cues from the message but also might be dependent on individual
characteristics
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
Consistency and variation seem to have the biggest impact
Knowledge, attitudes, beliefs

Integration – the outcome
PRINCIPLES OF VISUAL DESIGN (Heather Corcoran, Enrique VonRohr) (3-6 pages)
Hierarchy

Contrast: color, “one versus another” but also may be scale contrast

Scale: Relative size to other objects, thus making object large or small.

Color: Or Hue, Includes use of or change value, intensity, shade, tint and saturation.
Colors can be primary, secondary, tertiary, complimentary or analogous.

Weight: Tufte’s maximizing data; typographic (density of page)

Grid: A grid is a network of lines use as a tool for generating form, arranging images, and
organizing, information. It is not seen but rather is used as guide.

Impacts both attention and understanding: examples (color can capture attention,
intensity of colors can encourage end users to look more closely, which paves the way for
increasing understanding. The use of intense hues can enhance understanding; for
example, darker hues may indicate greater values)
Macro & Micro

Figure/ground: A figure (form) in relation to what surrounds it (ground, or background).

Framing: What content you elect to highlight.

Layers: Multiple number or levels of information in one visual.

Transparency: Visual technique, name suggests meaning although an opaque set of
images can imply transparency
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
Impacts understanding
Consistency & variation

Small multiples: parsing information into “easy sequences”

Compare/contrast: something small versus something big

Impacts understanding and elaboration
FIGURE 1. (Design team creates) USING ACTUAL VISUALS
Hierarchy
• expected
outcomes
Micro & Macro
• Expected
outcomes
Consistency &
Variation
• Expected
outcomes
APPLYING THE MODEL (1-2 pages)
Context
Data formulation (TINA CLARKE DUR)

Prioritize and organize data

Determine degrees of localization

How do you select a good data story?
Communication objectives (CHRIS CASEY)

Message you want to communicate
Audience analysis (MATT KREUTER)

Who you are communicating to (constructing personas)
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
Audience background (SES, prior knowledge, learning style)
Moderators (ANDREA SPRAY)
The capacity of the visual principles listed above to enhance attention, understanding and
elaboration will only be realized if the visualization is appropriate for the user (i.e., the person
consuming the information) and the environment (i.e., where she is exposed to the visualization).
Knowing who will use the visualization, and in what environment, drives decisions about factors
that, while invisible to the user, can nonetheless enhance or diminish the visualization’s effect on
attention, understanding, and elaboration. These moderating factors form the armature over
which the visualization is draped.
Channels
For the purpose of this article, channel is defined as the media format by which the user may
be exposed to the visualization and includes television, the Internet, print, radio, and telephone.
It is selected based on a number of factors including knowledge about the target audience. The
channel must be appropriate for the target user in order for him to even be exposed to the
visualization. However visualizations must also be appropriate for the channel; a visualization
that is effective in one channel may not be effective in another (6).
Interactivity
Interactivity is a quality of visualization on a spectrum from low to high. Interactivity most
strongly correlates to attention, as a target user must at least cognitively interact with even a print
visualization in order to facilitate cognitive processing. As the visualization increases in
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interactivity it must incorporate appropriate affordances, “those fundamental properties that
determine just how the thing could possibly be used”, that provide clues to its operation (1).
Explanation
Explanation, for the purposes of this article, is defined as supplementary narrative that
describes the data contained in the visualization and contributes to understanding. The
explanation can either lie outside of the visualization or be encompassed by it and it can take
various forms including audio or text.
Usability
Underlying all of these factors is the usability of the visualization, the degree to which
“people who use the product can do so quickly and easily to accomplish their own tasks” (5).
Usability impacts each stage of the information-processing model and has a cumulative effect as
the user progresses from one stage to the next. The data contained in the visualization cannot
well be understood if the interface itself is standing in the way. Usability is commonly
associated with five key principles: accuracy, ease of use, efficiency, learnability, memorability
and user satisfaction (4). Moreover, in the field of health, credibility of the data source also
plays a significant role and will be included for the purposes of this article. The particular
usability heuristics change depending on the channel.
CONCLUSION (Matt Kreuter and Tina Clarke Dur)
Call for research
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
Currently, best practice guidelines for presenting geo-coded cancer registry data are
available from the North American Association of Central Cancer Registries.[4] This
handbook describes cartography basics, types of maps and media, and how to match data
with presentation mode. These guidelines are very helpful, but base decisions about
visual display on characteristics of the data, rather than the communication goals of the
display.

Just as health communication has examined textual elements of communication, we can
and should be analyzing visual elements

All propositions laid out within this paper still need to be empirically tested
Implications for practice (Tina)

If you’re in the business of communicating data, you should be using these guidelines
References
National Cancer Institute: Geographic Information Systems – Overview of GIS at NCI. 2009.
National Cancer Institute: The NCI strategic plan for leading the nation to eliminate the suffering
and death due to cancer Washington, DC: U.S. Department of Health and Senior Service
National Institutes of Health, 2006.
National Cancer Institute: The NCI strategic plan for leading the nation to eliminate the suffering
and death due to cancer. Washington DC: U.S. Department of Health and Human Services, 2006.
4. Baron RC, Rimer BK, Coates RJ, et al.: Client-Directed Interventions to Increase Community
Access to Breast, Cervical, and Colorectal Cancer Screening: A Systematic Review. American
Journal of Preventive Medicine 2008; 35(1): S56-S66.
13
5. Tufte ER: The visual display of quantitative information, 2nd ed. Cheshire, CT: Graphics
Press, 2001.
6. Cartwright W, Crampton J, Gartner G, et al.: Geospatial information visualization user
interface issues. Cartography and Geographic Information Science 2008; 28(1): 1-19.
7. Glaser SL, Clarke CA, Gomez SL, O'Malley CD, Purdie DM, West DW: Cancer surveillance
research: a vital subdiscipline of cancer epidemiology. Cancer Causes and Control 2005; 16(9):
1009-1019.
8. Benz CC, Clarke CA, Moore DHn: Geographic excess of estrogen receptor-positive breast
cancer. Cancer Epidemiology Biomarkers Prevention 2003; 12(12): 1523-12527.
9. Clarke C, Glaser S, West D, et al.: Breast cancer incidence and mortality trends in an affluent
population. Breast Cancer Research 2002; 4(6): R13.
10. Clarke CA, Glaser SL, Uratsu CS, Selby JV, Kushi LH, Herrinton LJ: Recent declines in
hormone therapy utilization and breast cancer incidence: clinical and population-based evidence.
Journal of Clinical Oncology 2006; 24(33): 49-50.
11. Clarke CA, Glaser SL: Declines in breast cancer after the WHI: apparent impact of hormone
therapy. Cancer Causes and Control 2007; 2007(18): 8.
12. Cockburn M, Swetter SM, Peng D, Keegan TH, Deapen D, Clarke CA: Melanoma
underreporting: why does it happen, how big is the problem, and how do we fix it? Journal of the
American Academy of Dermatologists 2008; 59(6): 1081-1085.
13. Linos E, Swetter S, Cockburn M, Colditz G, Clarke C: Increasing Burden of Melanoma in
the United States. Journal of Investigative Dermatology 2009; E-pub ahead of print
14
14. Schootman M, Jeffe D, Lian M, Gillanders W, Aft R: Geographic clustering of adequate
diagnostic followup after abnormal screening results for breast cancer among low-income
women in Missouri. Annals of Epidemiology 2009; 169: 554-561.
15. Lian M, Jeffe DB, Schootman M: Racial and geographic differences in mammography
screening in St. Louis city: A multilevel study. Journal of Urban Health 2008; 85: 677-692.
16. Schootman M, Jeffe D, Gillanders W, Yan Y, Jenkins B, Aft R: Geographic clustering of
adequate diagnostic follow-up after abnormal screening results for breast cancer among lowincome women in Missouri. 2007; 17: 704-712.
17. Schootman M, Sterling DA, Struthers J, et al.: Positional accuracy and geographic bias of
four methods of geocoding in epidemiologic research. Annals of Epidemiology 2007; 17: 464470.
18. Schootman M, Jeffe D, Kinman E, Higgs G, Jackson-Thompson J: Evaluating the utility and
accuracy of a reverse telephone directory to identify the location of survey respondents. Annals
of Epidemiology 2005; 15(2): 160-166.
19. Kreuter M, Skinner C, Holt C, et al.: Cultural tailoring for mammography and fruit and
vegetable intake among low-income African American women in urban public health centers.
Preventive Medicine 2005; 41(1): 53-62.
20. Kreuter M, Buskirk T, Holmes K, et al.: What makes cancer survivor stories work? An
empirical study among African American women. Journal of Cancer Survivorship 2008; Epub
ahead of print.
21. Kreuter M, Green M, Cappella J, et al.: Narrative communication in cancer prevention and
control: A framework to guide research and application Annals of Behavioral Medicine 2007;
33(3): 221-235.
15
22. Hinyard L, Kreuter M: Using narrative communication as a tool for health education
behavior change: A conceptual, theoretical, and empirical overview. Health Education &
Behavior 2007; 34(5): 777-792.
23. Wallack L, Woodruff K, Dorfman L, Diaz I: News for a change: An advocate's guide to
working with the media. Thousand Oaks, CA: Corwin Press, 1999.
24. Thompson V, Cavazos P, Jupka K, Caito N, Gratzke J, Tate K: Evidential preferences:
Cultural appropriateness strategies in health communication. Health Education Research 2007;
(Epub ahead of print).
25. Caburnay C, Luke D, Cameron G, et al.: The Ozioma News Service: Using customized
cancer news releases to increase quantity and quality of cancer coverage in Black newspapers.
American Journal of Public Health Under review.
26. Nicholson R, Kreuter M, Lapka C, et al.: Unintended effects of emphasizing disparities in
cancer communication to African Americans Cancer Epidemiology Biomarkers & Prevention
2008; 17: 2946-2953.
27. Luke D: Getting the big picture in community science: Methods that capture context. In:
Menard S, ed. Handbook of Longitudinal Research: Design, Measurement, and Analysis across
the Social Sciences: Academic Press, 2005.
28. Harris J, Luke D, Burke R, Mueller N: Seeing the forest and the trees: Using network
analysis to develop an organizational blueprint of state tobacco control systems. Social Science
& Medicine 2008; 67(1669-1678).
29. Luke D, Harris J: Network analysis in public health: History, methods, and applications
Annual Review of Public Health 2007; 28: 69-93.
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30. Luke D, Esmundo E, Bloom Y: Smoke signs: Patterns of tobacco billboard advertising in a
metropolitan region. Tobacco Control 2000; 9: 16-23.
31. Shelton S, Herbers S, Mueller N, Luke DA, Harris J: The Lay of the Land: Mapping
Missouri’s Tobacco Control Funding: 2006-2008. Report produced for the Missouri Foundation
for Health, 2008.
32. McGuire W: Theoretical foundations of campaigns. In: Rice R, Atkin C, eds. Public
Communication Campaigns, 2nd ed. Newbury Park: Sage, 2001; 43-65.
33. Petty R, Cacioppo J: The elaboration likelihood model of persuasion. Advances in
Experimental Social Psychology 1986; 19: 123-205.
34. Witte K, Allen M: A meta-analysis of fear appeals: Implications for effective public health
campaigns. Health Education and Behavior 2000; 27: 591-615.
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Journals
Journal of Clinical Oncology
Information Design
Journal of Health and Mass Communication
American Journal of Health Behavior
American Journal of Health Promotion
Cancer
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Communication Research
Journal of Epidemiology and Community Health
Journal of the National Cancer Institute
Preventing Chronic Disease
American Journal of Preventive Medicine
Health Literacy
Social Science and Medicine
Journal of Biomedical Informatics