The Pennsylvania State University
The Graduate School
College of Earth and Mineral Sciences
THE INFLUENCE OF AFFECTIVELY CONGRUENT
COLOR ASSIGNMENT IN CATEGORICAL MAP INTERPRETATION
A Thesis in
Geography
by
Cary L. Anderson
2018 Cary L. Anderson
Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Science
August 2018
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The thesis of Cary L. Anderson was reviewed and approved* by the following:
Anthony Robinson Assistant Professor of Geography Thesis Advisor
Alan MacEachren Professor of Geography
Cynthia Brewer Professor of Geography Head of the Department of Geography
*Signatures are on file in the Graduate School
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ABSTRACT
Cheerful yellow, calming blue: colors often have emotional connotations. Map data contexts, similarly, are often emotive in nature—either inherently (e.g., climate change, mortality maps), or by design, such as in visual storytelling. Recent work in data visualization has shown that small, perceptually-distinct color palettes—such as those used in categorical mapping—often carry significant affective connotations (Bartram et al., 2017). Though significant research has assessed the role of color in map interpretation with regards to visual perception (e.g., Brewer et al., 1997), little is known about how the affective qualities of color interact with those of data context in map design. We define an affectively congruent color scheme as one that shares similar affective qualities with the topic, theme, or data content of the map to which it is applied. Here, we present the results of a crowdsourced study on the influence of affectively congruent versus incongruent color schemes on categorical map interpretation. We report both objective (pattern detection; area comparison) and subjective (affective quality; appropriateness; preference) measures of map-reader response. The results of this work demonstrate that affectively-congruent colors amplify map- topic affect, affective incongruence confuses map readers, and that affective-congruence is particularly preferred by readers when mapping positive data topics. In closing, we propose future research directions for balancing color congruence with other factors such as visual discriminability and offer preliminary design recommendations for synthesizing color and affective context in thematic map design.
Keywords: Map Design, Emotion, Congruence, Map Context, Cartography, Affective Design
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TABLE OF CONTENTS
LIST OF FIGURES ...... vi
LIST OF TABLES ...... viii
ACKNOWLEDGEMENTS ...... ix
Chapter 1 Introduction ...... 1
Chapter 2 Background and Motivation ...... 4
2.1 Color and Map Design ...... 4 2.2 Color and Context ...... 6 2.3 The Stroop Effect ...... 7 2.4 Affective Priming...... 8 2.5 Color and Emotion in Design...... 9 2.6 Emotional Mapping ...... 10
Chapter 3 Methodology ...... 13
3.1 Stimuli Design ...... 13 3.1.1 Color Scheme Development ...... 14 3.1.2 Map Topic Development ...... 15 3.1.3 Map Stimuli Design ...... 18 3.2 Experiment Design ...... 23 3.2.1 Study Participants ...... 24 3.2.2. Questionnaire Design ...... 27 3.2.3 Pre-Study Questions ...... 27 3.2.4 Part One: Objective Measures ...... 28 3.2.5 Part Two: Subjective Measures ...... 35
Chapter 4 Results ...... 41
4.1 Objective Measures ...... 41 4.2 Subjective Measures ...... 44
Chapter 5 Discussion ...... 47
5.1 Objective Measures ...... 47 5.2 Subjective Measures ...... 53 5.2.1 Ratings of Map Affect ...... 53 5.2.2 Map Preference ...... 55 5.3 Limitations ...... 56
Chapter 6 Conclusions and Future Work ...... 58
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References ...... 60
Appendix A Affective Color Scheme Specifications ...... 66 Appendix B Full map text detail ...... 68 Appendix C Map Specifications for the 544 maps tested in this study ...... 70 Appendix D ...... 90 Pre-Study Materials ...... 90 Study Participant Consent Form ...... 90 Pre-Screening Questions ...... 93 Demographic Questions ...... 94 Full-Color Vision Verification Test...... 95
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LIST OF FIGURES
Figure 1-1. Affective Color Congruence ...... 3
Figure 2-1. Fictional Fruit Sales, adapted from (Lin & Heer, 2014)...... 7
Figure 2-2. Four affective color palettes, adapted from Bartram et al., (2017) ...... 11
Figure 3-1. An Affectively-Congruent (left) and Affectively-Incongruent (right) map ...... 13
Figure 3-2. The Core Affect model, adapted from (Russell, 2003). Relevant affects starred ...... 14
Figure 3-3 Color Schemes and Map Topics on the Core Affect Model ...... 17
Figure 3-4. Cluster Detection Map Distributions ...... 20
Figure 3-5. Area Comparison Map Distributions ...... 21
Figure 3-6. Subjective Map Distributions...... 22
Figure 3-7. Participants’ self-reported age, highest-attained level of education, gender, and cartographic expertise ...... 25
Figure 3-8. Practice Question Maps ...... 28
Figure 3-9. Introduction to Categorical Maps ...... 29
Figure 3-10. Cluster Detection Introduction (shown on mouse hover) ...... 30
Figure 3-11. Area Comparison Introduction ...... 31
Figure 3-12. Cluster task, part one (cluster detection) ...... 32
Figure 3-13. Cluster task, part two (cluster category identification)...... 33
Figure 3-14. Area Comparison task ...... 34
Figure 3-15. The Affective Slider (Betella & Verschure, 2016)...... 36
Figure 3-16. Our adaptation of the Affective Slider ...... 36
Figure 3-17. Suggested implementation of the Affective Slider ...... 37
Figure 3-18. Appropriateness and Preference Ratings ...... 38
Figure 4-1. Cluster selection response times, n = 124 ...... 42
Figure 4-2. Category identification response times, n = 124 ...... 42
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Figure 4-3. Area Comparison response times, n = 148 ...... 43
Figure 4-4. Valence (happiness) ratings for positive and negative data topics ...... 44
Figure 4-5. Arousal (excitement) ratings for positive and negative data topics ...... 45
Figure 4-6. Appropriateness ratings for all data topics ...... 45
Figure 4-7. Preference ratings for all data topics ...... 46
Figure 5-1. Cluster Selection Response times by Affect of Color Scheme ...... 48
Figure 5-2. Area Comparison Response times by Affect of Color Scheme ...... 49
Figure 5-3. Participant response times (in seconds) by question type...... 50
Figure 5-4. An example set of stimuli that may have produced a negative priming effect ...... 51
Figure 5-5. Cluster Detection log response times by distribution ...... 52
Figure 5-6. Relative speed of cluster detection between distributions...... 52
Figure 5-7. Boxplots of happiness ratings for negative data topics ...... 54
Figure 5-8. Calm and negative color schemes: Area comparison response times ...... 56
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LIST OF TABLES
Table 3-1. Color Scheme Design...... 15
Table 3-2. Map Color and Topic Assignments ...... 22
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ACKNOWLEDGEMENTS
There are many people I would like to thank, and without whom this would not have been possible. First, my advisor, Anthony Robinson—for his encouragement, ideas, feedback, and support throughout my time at Penn State. Cindy Brewer, for always inspiring me to be a better student and a better teacher. And Alan MacEachren, for his endless knowledge and helpful insights. To Arif Masrur and Carolyn Fish, for their support—academic and otherwise— thanks for keeping me (somewhat) sane, and for making GeoVISTA feel like home. To Natalie Pawlikowski, Elena Sava, Tom Wert, and the rest of my friends in State College who occasionally forced me to have a life outside of this thesis—I’m thankful for you. To Dr. Meyer and Dr. Hayes at University Health Services (I’m not kidding), you helped me even more than you know. To my students in Geog 260 and 361, for inspiring me (occasionally) and keeping me entertained (always). To Sarah Malsch, Jackie Harkins, and Alyssa Romaine, for keeping me grounded and being there to lift me up. To my parents for their support, and to all the lucky and unlucky happenings that led me here. I’m grateful for all of it.
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This research was funded by the Graduate Enrichment Fund of the Department of Geography of The Pennsylvania State University. Travel to conferences to present research related to this thesis was supported by the Annual Meeting Student Travel Grant of the North American Cartographic Information Society (NACIS), and the International Cartographic Conference Student Travel Grant of the United States National Committee to the International Cartographic Association (ICA).
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Chapter 1
Introduction
The ways in which data are visualized have a great influence on how they are understood by viewers—this basic idea has been a focus in cartography for centuries. Though perception can be altered through the use and misuse of multiple visual variables, our research focuses on color. Color research is well-established in cartography; empirical studies have confirmed the importance of considering visual perception and visual impairment in map color assignment (e.g., Lee et al., 2012; Olson & Brewer, 1997). However, less attention has been given to the importance of data context (i.e., what the map is about) in cartographic color assignment. Given that data often have significant affective connotations, our work aims to evaluate the influence of affectively-congruent color assignments on map interpretation. In particular, we evaluate the influence of such color assignments in categorical thematic mapping. In this way, we build upon previous work in data visualization—including research on contextual color assignment in bar chart interpretation (Lin et al., 2013), and affectively-appropriate color palettes (Bartram et al., 2017). Additionally, we provide a substantial empirical contribution to the burgeoning subfield of emotional cartography (Griffin & McQuoid, 2012). We study affective congruence using categorical maps due to their popularity in a wide range of mapping contexts, and in order to provide a foundation on which further studies of more complex map types can be developed. Even without taking data context into consideration, designing color schemes for maps is a challenging task. Hues must be discriminable, a fact further complicated by the substantial prevalence of colorblindness in the adult population—about 1 in 12 males of Northern European descent are red-green colorblind (National Eye Institute, 2015). Data topics may also lack obvious color connotations, and the understanding of such connotations often varies considerably between audiences and cultures (Brewer, 2005; Cyr et al., 2010). Despite these challenges, empirical assessment of the relationship between
2 color and context affect—and their combinatorial influence on map interpretation—is necessary for several reasons. First, the argument that visual perception constraints should take precedence in map color selection is valid in many cases, but not all. Propaganda maps, for example, are intended to elicit emotion and deliver a persuasive message, rather than simply to visualize data for the sake of imparting knowledge (Muehlenhaus, 2013). In his review of persuasive maps, Muehlenhaus argues that following standard cartographic doctrine, e.g., seeking a “clear portrayal of complexity,” (Tufte, 2001, pg. 191) is only prudent when a clear scientific presentation of the data is the cartographer’s goal—and that this is not always the case. Secondly, data context is deeply intertwined with effective design. The most effective data graphics reflect the congruence principle, in which “the structure and content of the external representation [corresponds with] the desired structure and content of the internal representation” (Tversky et al., 2002, pg. 249). For example, higher data values are traditionally represented with increased color saturation or larger symbols (i.e., “more represents more”) (Robinson et al., 1984). Griffin et al. (2006) cite the congruence principle as a likely explanation for their participants’ greater success in completing tasks with animated maps rather than with small-multiple map sets (i.e., animation more clearly represents the passage of time). As colors have affective connotations, we propose that— dependent on the affective qualities of the data context—color assignments can similarly be affectively-congruent (Figure 1-1) or affectively-incongruent, and that this may influence their utility in categorical mapping contexts.
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Figure 1-1. Affective Color Congruence
We define affective color congruence as a characteristic of a map wherein the emotive connotations of a map’s color scheme match those of the data being visualized (e.g., bright colors feel playful). The term affective congruence is also used in psychology, typically to describe pairs of stimuli in an affective priming study, wherein participants are shown a positive or negative prime stimuli, followed by a positive or negative target stimuli; a pair is considered affectively congruent if their emotions match (e.g., negative-negative), and incongruent if they do not (see Klauer, 1998, for a review). This thesis therefore seeks to assess and evaluate the influence of affective color congruence on categorical thematic map interpretation. To this end, we administer a user study of maps with various color schemes and emotive map topics. Following a review of relevant background and motivating literature, this thesis details our study’s methods and results, as well as a discussion of the implications of our results for cartographic research and practice.
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Chapter 2
Background and Motivation
The following sections provide an overview of six major topics related to this work: Color and Map Design, Color and Context, the Stroop Effect, Affective Priming, Color and Emotion in Design, and Emotional Mapping.
2.1 Color and Map Design
As with any representation of reality, no map is a perfect replica of the real world. And as with all models, without generalization and simplification, maps would not be useful. These processes however, are never without consequence. Any design choice made by the cartographer inherently influences the map-reader’s perception of the place and data that is being mapped. For example, Brewer & Pickle (2002) found that participants answered map- reading questions more accurately when quantile or minimum boundary area classification methods were used, as compared to other choropleth mapping classification methods. In another study, Fabrikant et al. (2010) found that altering the visual hierarchy in their maps altered participants’ accuracy and response-times when completing map-reading tasks. Data visualization studies similarly note the influence of design on task success. Cawthon & Moere (2007) for example, found that their study participants completed data retrieval tasks less effectively—and were more likely to abandon those tasks entirely—when using three-dimensional data visualizations as compared to their two-dimensional counterparts. The human brain acquires more information through the sense of vision than through all other senses together (Ware, 2012). Due to the visual system’s power and efficiency, the dissemination of data through visual graphics is highly useful when done properly. In Semiology of Graphics, Bertin (1983) delineates seven visual variables: position
5 on the x-y plane, size, value, texture, color, orientation, and shape. Cartographers (e.g., MacEachren, 1994) have since expanded upon this list to include other types of visual attributes, though Bertin’s seminal work remains regularly cited in cartography. Visual variables as Bertin describes them are primarily used to display relationships between data elements (Ketil, 2001). For example, recommendations for determining color assignments in choropleth mapping primarily concern the kind of data (sequential; diverging; qualitative) rather than the nature of the data content (Brewer, 1994; Slocum et al., 2009). Similarly, contemporary color selection tools (e.g., ColorBrewer) output color schemes based on user input of kind of data and number of data classes (Brewer, et al., 2003; Harrower & Brewer, 2003). Maps derive their meaning through semiotic relationships—semiotics being the “science of signs” (MacEachren, 1995, pg. 213). In the context of cartography, a sign refers to the relationship between a mark on a map (the sign-vehicle) and the content to which it refers (the referent) (MacEachren, 1995). For example, on most maps in the United States, a square icon containing a capitalized “H” (sign vehicle) represents a hospital (referent). There is a range in the strength of semiotic relationships, which is dependent on how closely a sign vehicle matches its referent (MacEachren, 1995). Robinson & Petchenik (1976, pg. 61) describe this variation as a symbolization continuum from mimetic to arbitrary. This continuum is typically discussed in the context of point symbols, from mimetic (e.g., pictorial symbols) to arbitrary symbols (e.g., geometric shapes) (Robinson et al., 1984, pg. 288). However, point symbols are only one kind of “map mark,” a term which Robinson & Petchenik (1976, pg. 57) use to describe any distinct graphical feature, “such as a red line, a green area, a black star….” Colors used in a categorical map then not only demonstrate each category’s relationship to other categories, but also serve as sign-vehicle for the map-data categories themselves. Similarly, if the enumeration units in a choropleth map constitute map marks, they must exist somewhere on the mimetic to arbitrary continuum. We propose that enumeration units with affectively-congruent color assignments are—regardless of their location on the continuum overall—more mimetic than identical units with affectively- incongruent color.
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2.2 Color and Context
The idea of context-based color assignment is not new in cartography. Maps have traditionally represented physical phenomena in colors consistent with their nature: the ocean is blue, soil is brown, etc. (Robinson, 1952, pg. 94). More abstract color associations can also be traced back to beginning of the 20th century; Rossle’s map of diseases used red for scarlet fever, and blue for tuberculosis—representative of the gray-blue color of a diseased lung (Eckert-Greifendorff, 1939, pg. 41; translation: Robinson, 1952, pg. 94). Though studies are scarce, some cartographers have empirically assessed the role of contextual color assignment in thematic mapping. Cuff (1973), studied students’ performance with temperature maps, and found that traditional monochromatic quantitative color schemes of increasing saturation were more effective than a blue to red (concept-associated) scheme. Bemis & Bates (1989) found the opposite result—they suggest that this might be due to participants’ increased familiarity with the blue-red color scheme by the time of their study. Dent et al. (2009, pg. 258) suggest that cartographers borrow from studies in cognate disciplines such as psychology and art until more detailed research on color association is conducted in cartography. The popularity of color maps has risen dramatically in recent decades. White et al., (2017) conducted a review of 440 quantitative maps published in scientific journals between 2004 and 2013 and found that use of color on maps increased from 18.4 to 69.9 percent over that time frame. This is for good reason—empirical research has shown that not only do people prefer colorful maps over achromatic ones, (properly) colored maps increase map-reading accuracy (Brewer et al., 1997). Recent trends toward web-based map dissemination and color scheme-building tools such ColorBrewer (Harrower & Brewer, 2003) have significantly reduced the expense and expertise needed to produce adequate color maps. Relatedly, digital publication of peer-reviewed articles has become the norm— unlike when journals were print only, publication in color no longer comes with a near prohibitory cost. These factors are encouraging for research on context-relevant color assignment, particularly as the limited studies of the topic in cartography predate these new developments.
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2.3 The Stroop Effect
Despite the scarcity of such research in cartography, evidence from data visualization suggests that contextually-relevant color assignment could help map-readers make more efficient judgments of displayed data (Lin et al., 2013). In a study using bar charts, Lin et al. demonstrate that when color assignments are semantically-resonant (e.g., red for tomatoes), users perform graph-reading tasks with greater speed. These results are presumably relevant to similar types of data visualizations, such as categorical maps.
Figure 2-1. Fictional Fruit Sales, adapted from (Lin & Heer, 2014)
Lin et al. (2013) suggest that their results may be related to the Stroop Effect, where readers are slower and more prone to mistakes when naming the color of words printed in a color which conflicts with their meaning (e.g., red or yellow) (Macleod, 1991). Since its publication, Stroop’s original study on the Color-Word Interference Test (Stroop, 1935) has been widely-referenced, and multiple variants of the effect have been revealed through subsequent studies (Macleod, 1991). For example, Naor-Raz et al. (2003) found that the cognitive interference invoked by atypically-colored objects (e.g., a purple banana) causes people to name them slower than typically-colored objects (e.g., a yellow banana). Results reported by Lin et al. suggest that a similar effect occurs when data visualizations are colored in a way that conflicts with their nature (e.g., blue for apples). Iterations of the Stroop Task related to emotion and emotionally-laden words are also common (e.g., Kahan & Hely, 2008). Reeck & Egner (2011) conducted a study in which they overlaid images of facial expressions with emotion-words (“Happy,” “Fear”), and found that incongruent pairs resulted in less-efficient task performance. Despite the lack of a color
8 variable in this study, it is perhaps most relevant of all Stroop examples to our work, as it explicitly tests the impact of affective congruence—incongruence using text (emotion words) and visual cues (images of emotive facial expressions) (Reeck & Egner).
2.4 Affective Priming
The Stroop Effect is also referenced in psychological literature on affective priming, a paradigm first introduced by Fazio et al. (1986). In an affective priming experiment, a participant is shown both a prime and a target stimulus (usually text) in sequence, then asked to answer some question about the target stimuli (e.g., “is the word good or bad?”) (De Houwer et al., 2002). When the two stimuli are affectively congruent (e.g., death- garbage), participants have been shown to complete the task faster than when the two are incongruent (e.g., rainbow-garbage) (De Houwer et al.). Significant research has attempted to understand the cognitive processes which facilitate this effect, with two main theories suggesting that either (1) faster response with congruent pairs is facilitated by attitude activation that occurs when participants view the prime, or (2) the effect is produced later – incongruent pairs slow down participants as they are selecting a response (i.e., a kind of Stroop Effect) (De Houwer et al.). Wentura (1999) found evidence for a similar effect called negative priming, in which the valence of a target stimulus in any trial n takes longer for a participant to identify if it matches the valence of an incongruent prime in trial n-1. This implies that the results of each trial (and most studies involve multiple trials) cannot always be considered independent. Our work applies the concept of affective congruence to the more abstract affective connotations imbued in color. We also diverge from the affective priming paradigm by testing affective congruence within a single stimulus, rather than within a sequence of a prime and a target. This makes our study more like a classic Stroop experiment, in which both the color of the word and the word itself are presented simultaneously. The core idea, however—that incongruence often leads to response latencies—is the same.
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2.5 Color and Emotion in Design
Data is frequently linked to design through affectively-resonant connotations. Links between color and emotion have been widely studied in psychology, and changes in color saturation and lightness in particular have been found to elicit different emotional responses (Suk & Irtel, 2010; Valdez & Mehrabian, 1994). Studies in marketing and consumer research also point to the importance of affectively-appropriate color usage for products and product categories. A mix of highly-saturated colors, for example, might be associated with playfulness and children’s toys (Labrecque et al., 2010). Such a color palette would likely be deemed inappropriate in a more solemn context, such as in a map of childhood cancer types. Similarly, Hanss et al. (2012) found that the colors deemed most appropriate for particular car types shared similar affective qualities with the car type itself (e.g., red = active; sports car = active; red = sports car). It should be noted that there also exists an artistic tradition of considering emotional color connotations. Historical interpretations suggest that emotions and other phenomena are associated with particular hues (e.g., white = light and triumph; blue = truth and wisdom) (Sargent, 1964). Contemporary color guides (e.g., Eiseman, 2006) also describe semantic and emotive connotations of specific colors, particularly as advice for their use in marketing, communications, and interior design. Though such guidelines are useful in many artistic contexts, they are notably distinct from this research. We draw instead from empirical color research—studies that account for all three dimensions of color (hue; saturation; lightness) and provide sound experimental evidence to support their recommendations (e.g., Bartram et al., 2017; Suk & Irtel, 2010). Though color is perhaps the most prominent example, content-design congruence has been studied through other elements of visual design. Doyle & Bottomley (2002), for example, tested the influence of product-font congruity. When the connotative meanings of a font aligned with those of the product, that product was more likely to be selected by the study participant (Doyle & Bottomley). Substantive additional research on semantic connotations of font style exists, including in cartography. Guidero (2016) analyzed semantic connotations of fonts as used for map labeling. Despite limiting her study to fonts commonly used in professional mapping contexts, she identified significant differences in semantic associations between typefaces. Thus, even design elements that are not explicitly
10 intended to engender semantic associations (e.g., Comic Sans for children’s products) can nonetheless have such an effect.
2.6 Emotional Mapping
Though the volume of work is small when compared to other aspects of map design, cartographers have recently begun to explore the relationship between maps and emotion (e.g., Griffin et al., 2017; Griffin & McQuoid, 2012). Cartographers have long been interested in cognition—and new studies of emotion contribute to that domain—as emotion is deeply intertwined with cognition and human decision-making (Damasio, 1994). In their overview of this nascent field, Griffin & McQuoid delineate three main sub-groups in emotional mapping research: (1) maps of emotions, (2) using maps to collect emotional data, and (3) emotional impacts of maps on users. The first two are often studied in tandem as they focus on peoples’ emotional response to different spaces and environments. Pánek et al. (2017) for example, used maps to collect emotional judgements of the environment from residents of Olomouc, Czech Republic—focusing especially on safety and, conversely—on places that elicit fear. They then used these data to map the spatial distribution of various emotions across the city of Olomouc (Pánek et al.). Similarly, Gartner (2012) used a mobile application called EmoMap to collect affective responses across space in real-time, recognizing that emotions often influence wayfinding decision-making. These data were later used to create a user study, which revealed that routes designed with user comfort ratings in mind—rather than just efficiency—were preferred by users (Huang et al., 2014). Other forms of volunteered geographic information (VGI) have also been used to calculate emotionally-pleasing routes. Quercia et al. (2014) used metadata from Flickr images as a proxy for assessing the level of beauty, calm, and happiness present at different places in Boston and London, and used these data to design walking paths to maximize these respective emotions. The concept of comfort-based routing also exists outside of academic literature. Garmin’s automotive navigation devices, for example, offer routing options for avoiding highways, toll-ways, and unpaved roads, as well as for minimizing challenging driving maneuvers such as left-hand turns (“Garmin International,” n.d.)
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Emotional impacts of maps on users, the third category of emotional mapping research outlined by Griffin & McQuoid (2012), is the category within which our work falls. Though indubitably related to affect across space, research in this category typically focuses on the artistic elements of cartographic design, and how such designs may influence user affect. Fabrikant & Christophe (2012), for example, studied topographic base maps, and found that unconventional color schemes (e.g., blue land and pink water) elicited high emotional arousal in participants, who often described these color schemes as “shocking” or “strange.” Other cartographers have suggested that artistic elements such as music be used to imbue maps with affective qualities (Edsall, 2010). Such creative artistic techniques have been suggested as promising tools for handling the complexities of new “big data” geovisualizations and (geo)visual analytic tools, particularly when those data concern emotional topics (Robinson et al., 2017). Work such as Edsall (2010) and Robinson et al., (2017) primarily focus on the intentional addition of emotive qualities to maps. However, recent research in data visualization reveals that even small, perceptually distinct color palettes (such as those used in thematic mapping) can carry affective connotations (Bartram et al., 2017; Figure 2-2). This suggests that during color scheme selection, cartographers may already be altering the emotive content of their maps—even if they are not aware of it.
Positive Negative Calm Exciting
Figure 2-2. Four affective color palettes, adapted from Bartram et al. (2017)
Our research provides the first in-depth analysis of the interaction between the emotional connotations of map design and those of map-data context. As such, it contributes to the literature on emotional impacts of maps on users (Griffin & McQuoid, 2012), as well as other categories of emotional mapping research, by providing empirically-derived design
12 suggestions applicable both to mapping emotions and collecting emotions using maps and mapping products. The goal of this research was to answer this primary research question: How does the affective-congruence of a color scheme influence objective and subjective measures of categorical map interpretation? Specifically, we evaluate how affective congruence (vs. incongruence) influences: 1. Map-reading task efficiency 2. Reader perceptions of map affect 3. Reader perceptions of map-color appropriateness 4. Overall reader preference
Therefore, our work has two primary aims: (1) To extend upon the work of Lin et al. (2013), by assessing its relevance in an affective data visualization context, and (2) to evaluate how affectively-resonant color palettes such as those developed by Bartram et al. (2017) interact with underlying emotive data context in categorical thematic map interpretation.
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Chapter 3
Methodology
The following sections describe the methods and maps used in a between-subjects study built to assess how affective congruence influences categorical map interpretation. In section 3.1 we discuss the design process and rationale for our stimuli: emotive color schemes and map topics, as well as categorical maps to which these schemes and topics were applied. In section 3.2 we describe our study participants, survey methodology, and research design. Results follow in chapter 4. To achieve our goal of testing congruence vs. incongruence, our study was designed to compare participant responses between pairs of maps—those with affectively congruent and affectively incongruent designs. An example of such a pair is shown in Figure 3-1.
Figure 3-1. An Affectively-Congruent (left) and Affectively-Incongruent (right) map
3.1 Stimuli Design
To create the map pairs such as those as shown in Figure 3-1, first, affectively appropriate color schemes and map data-topics had to be developed. Sections 3.1.1 and 3.1.2 describe these processes.
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3.1.1 Color Scheme Development
Bartram et al. (2017) identified appropriate color palettes for eight affects (calm; exciting; positive; negative; serious; playful; trustworthy; disturbing). Our study focused on two of these affect pairs (Calm-Exciting) and (Positive-Negative), as they approximately represent the four corners of the core affect model (Russell, 2003). In addition to capturing a wide swath of emotion, their opposing nature makes them particularly suited to a study of emotional congruence vs. incongruence. Figure 3-2 illustrates the Core Affect Model (Russell), with our four chosen affects starred.
Figure 3-2. The Core Affect model, adapted from (Russell, 2003). Relevant affects starred
Appropriate color schemes for each of these affects were built using the affective color palettes identified by Bartram et al. (2017). Each palette was transformed into two categorical color schemes (A and B) per affect (Table 3-1). As Bartram et al. designed categorical color palettes, we apply our schemes to categorical maps for direct comparison. The use of two schemes rather than one was done for additional experimental generalizability—to minimize the possibility that results might be limited to a particular color scheme and not generalizable to similar schemes in practice.
15 Colors were derived from work by Bartram et al. (2017) using Adobe Color CC. Care was taken to ensure that colors identified by Bartram et al. as most integral (i.e., the largest or most-connected hues in their network diagrams; Table 3-1) to each affect’s color palette were adequately represented. For this reason, some near-identical hues exist in both A and B color schemes. For example, as shown in Table 3-1, light blue (calm) and bright orange (exciting) appear in both of their respective affect’s color schemes.
Table 3-1. Color Scheme Design. RGB and hex codes available in Appendix A.
Affect (Bartram et. al., A B 2017) Calm Exciting
Positive Negative
3.1.2 Map Topic Development
For each of the affect pairs (Calm-Exciting) and (Positive-Negative), we defined emotionally-similar data contexts which could perceivably be used in a thematic mapping context. These were developed during a workshop on emotion and mapping hosted at Penn State’s GeoVISTA Center. Participants included cartography MS and PhD students, as well as graduate students from other subsets of GIScience and physical geography (nine students in total). Approximately 50 topics for categorical thematic mapping were generated overall. Using the input from workshop participants, the final topics (two per affect) used in this
16 study were as follows:
Calm: Relaxation Techniques; Nature Scenes Exciting: Amusement Rides; Caffeinated Beverages Positive: Kinds of Dessert; Keys to Happiness Negative: Childhood Cancer; Methods of Homicide
These eight maps topics were chosen based on collective agreement by workshop participants that they most closely matched the emotions we intended to test. Though arguably hyperbolic in a traditional cartography context (i.e., maps are more likely to show homicide statistics than homicide methods), they resemble topics described in maps and other data visualizations found alongside so-called “listicle” web articles found on a wide range of sites such as Buzzfeed, Cracked, and WIRED (Edidin, 2014). Listicles present news or other information in a simplified—often media-laden—digital format, intended to draw reader attention in the age of seemingly unlimited digital content (Owens Boltz et al., 2018). Though some argue that such articles do harm by prioritizing style over substance or catering to readers’ tendencies for distraction (Billock & Wudel, 2015), their growing ubiquity has made them a relevant topic for academic study. Our chosen map topics are shown in Figure 3-3 at their relative positions on a simplified diagram of the core affect model (Russell, 2003). Color schemes are also shown in the appropriate position. A map created from matching components (e.g., Caffeine ) is affectively-congruent (AC), while a map created from opposing components (e.g., Caffeine ) is affectively-incongruent (AI). The influence of different—but not opposing—color schemes was not tested (e.g., Happiness ).
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Figure 3-3 Color Schemes and Map Topics on the Core Affect Model
Once appropriate map topics were determined, full titles and data categories were developed for each topic. The eight final map titles were as follows:
1. Most Preferred Relaxation Activity 2. Most Popular Amusement Ride 3. Most Popular Kind of Dessert 4. Most Common Method of Homicide 5. Favorite Scenic View of Nature 6. Most Popular Caffeinated Drink 7. Highest Rated Happiness Factor 8. Most Common Childhood Cancer
To minimize the influence of word-reading speed on map-task completion time, all titles used had 4 or 5 words, and of those that had five, the fifth word was “of.” As suggested by Dillman et al. (2014, pg. 117), we also simplified the map titles by substituting complex
18 words with simpler alternatives whenever prudent—for example, the word “predominant” was originally used in multiple titles: this was later changed to “most common.” All map text was displayed in Segoe UI font, a sans-serif font commonly used in Microsoft user interfaces. Sans-serif fonts are preferred by users for website text, and have been found to score neutrally on personality factors (e.g., creative; happy; dull), which suggests that their use is unlikely to alter the affective connotations of said text (Shaikh et al., 2006). Within each map title, the word that best summarized the map topic was displayed in semi bold text. The intention of this was to encourage participants to read all map titles—as tasks could theoretically be completed by reading only the legend—without straying too far from standard map design. All map data categories (i.e., legend elements) ranged from two to five syllables. Syllable and character counts were calculated for each topic, with various edits made to initial map designs to minimize differences in reading and comprehension speed while maintaining enough realism to demonstrate the appropriate affective qualities. Final categories had a mean length of 9.25 letters (SD = 2.5). Additional map text detail can be found in Appendix B.
3.1.3 Map Stimuli Design
Map stimuli for our experiment were built using a selection of generalized county- level boundary data from the state of Georgia, USA at a scale of 1: 2,500,000. Participants were presumed to be unable to recognize this location but expected to recognize the areal units as typical of categorical thematic maps. Thus, we strike a balance between maximizing ecological validity (e.g., as with a choropleth map of the United States) and minimizing bias due to place-based knowledge (e.g., as with an abstract hexagonal display). The study had three task-sets: Cluster Detection, Area Comparison, and Subjective Response. For each task-set, 8 different synthetic categorical data distributions were generated. These are shown in Figures 3-4, 3-5, and 3-6. For demonstration purposes, these maps are shown with the ColorBrewer “Qualitative Set2” 5-part color scheme (Brewer, 2012).
19 For each data distribution, categories were assigned in ArcGIS Pro 2.0 using data generated with Mockaroo, an open-source test data generator (“Mockaroo,” 2018). To create the map distributions, we used this tool to generate three data tables (one for each task), each with eight columns (one for each distribution) of random numbers, either between 1-5 (cluster detection) or 0-100 (area comparison; subjective questions). Each data table contained 282 rows—one for each county in the local area, and was joined to a generalized TIGER county boundary file (US Census Bureau, 2017) using ArcGIS Pro. Randomized attribute data were used to avoid revealing the maps’ location—most data freely available (e.g., Census; American Community Survey) showed distinct trends in and around the city of Atlanta, GA, which is located in the upper left corner of our chosen area. For the cluster detection maps, areal units (each with a randomly assigned value ranging from 1 to 5) were symbolized in ArcGIS Pro using the unique values symbolization method. Each map was then manually edited to ensure it had one cluster with eight or more contiguous areas. Though clusters on maps are not always contiguous, we designed them as such to reduce ambiguity about what defines a map cluster when defining them for our novice participants, as explained in section 3.2.4. If two or more clusters were present, the smallest were de-emphasized by changing some of the included counties to values which fell within a differently-colored category. Thus, each map contained only one eight-or-more area cluster, though cluster size and placement varied between the map distributions (Figure 3-4).
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Figure 3-4. Cluster Detection Map Distributions. For reference, distributions are numbered 1-4 (top row; left to right), and 5-8 (bottom row; left to right).
For the area comparison maps, areal units (each with a random value between 0- 100) were classified into five categories using the Natural Breaks (Jenks) method. Unlike if we had used a quantile classification method, this created an unequal distribution of colors based on the distribution of the underlying synthetic data. These breaks were manually adjusted to exaggerate differences in counts of units for the five different colors (categories). In designing these maps, we created a task [“determine which of these categories takes up more area on the map”] that was more challenging than the cluster selection task, but not so challenging as to induce participant breakoffs or item non- response. The use of two tasks (cluster detection; area comparison) with different levels of difficulty borrows from Lin et al. (2013), who similarly used two different types of data- retrieval questions in their study of semantically-resonant color assignments for bar charts. Distributions were considered adequate for the area comparison task once the category containing the greatest number of areas contained approximately 65% more area than the next-largest category. This amount varied slightly between the different map distributions, and the differently-sized units created some additional variation between maps (Figure 3-5). Any clusters (eight or more contiguous areas) were manually broken apart to not distract participants, as cluster detection was not the intention of this task.
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Figure 3-5. Area Comparison Map Distributions. For reference, distributions are numbered 1-4 (top row; left to right), and 5-8 (bottom row; left to right).
To create the subjective rating map distributions (Figure 3-6), areal units (again with random values between 0-100) were classified into five categories using quantiles. This created an approximately equal distribution of the five colors across each map. As the main variable of interest in our study was color, it was important that each map had an adequate representation of each individual color within its assigned color scheme. Like the area comparison maps, any clusters with eight or more contiguous areas were manually broken apart to avoid distracting participants from the task at hand.
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Figure 3-6. Subjective Map Distributions. For reference, distributions are numbered 1-4 (top row; left to right), and 5-8 (bottom row; left to right).
These synthetic data distributions were used to create the maps for this study by adding the appropriate color schemes and data topics. For cluster detection and area comparison maps, the 32 color-content combinations shown in Table 3-2 were applied to each distribution, for a total of (32 x 8) = 256 maps each. This factorial stimuli-design was used as it provided us a way to test maps with various distributions (presumably with slight differences in difficulty) without these differences in difficulty becoming a confounding factor in our analysis.
Table 3-2. Map Color and Topic Assignments
Affectively Congruent (AC) Affectively Incongruent (AI) Topic Affect Map Topic A B A B Calm Relaxation
Nature
Exciting Amusement
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Caffeine
Positive Dessert
Happiness
Negative Cancer
Homicide
Unlike the cluster detection and area comparison map-sets, this factorial design was not used to create the maps for the subjective assessment task. Only 32 maps (one for each context-color combination; Table 3-2) were used for subjective ratings. Multiple distributions (Figure 3-6) were used only so that participants would not be shown obviously duplicated maps. During this task the maps were to be judged holistically, with timing/performance measures not collected, and thus minor differences in their data distributions can be presumed inconsequential. A list describing the design of all 544 maps used in this study can be found in Appendix C.
3.2 Experiment Design
Our human subjects study was reviewed and approved by the Pennsylvania State University Institutional Review Board (STUDY00009022). The following sections describe the survey completed by study participants, as implemented using Qualtrics online survey software. As is needed for all surveys, care was taken in the design of this study to minimize participant confusion, satisficing (e.g., behavior such as straight lining), and break-offs (i.e., when participants quit) (Dillman et al., 2014). The methodological details that follow reflect both the benefits and constraints of survey research in general, and our data collection tool (Qualtrics) in particular. We begin by describing our participants, followed by implementation details for the design and delivery of the experiment using Qualtrics.
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3.2.1 Study Participants
A total of 281 participants were solicited for this study through the crowdsourcing task-completion web service Amazon Mechanical Turk (“Amazon Mechanical Turk”). Our use of crowdsourced participants for this study permitted the recruitment of a large sample, and thus enabled us to test our many stimuli with a between-subjects factorial design. Though lack of expertise is often considered a downside to crowdsourced samples, our study required no advanced knowledge of cartography or data visualization. Figure 3-7 illustrates general demographic characteristics of our sample.
Age
# of Participants # of
Education Level
25
# of Participants # of
Gender
% of Participants
Cartographic Experience
% of Participants
Figure 3-7. Participants’ self-reported age, highest-attained level of education, gender, and cartographic expertise (top to bottom). No participants reported advanced cartographic experience.
Though other crowdsourcing platforms such as Figure Eight (formerly "Crowd Flower; “Figure Eight” 2018) exist, use of Mechanical Turk (MTurk) in related work (e.g., Bartram et al., 2017; Lin et al., 2013) as well as its Qualtrics integration features made it the most appropriate choice for our study. Replications of visual perception studies using MTurk have demonstrated its reliability, and suggest that the greater variance found in crowdsourced data can be offset with larger sample sizes (Heer & Bostock, 2010). Though crowdsourcing experiments on visual perception means that experimenters lose control over some experimental conditions such as display type, lighting, and participants’ viewing distance (Heer & Bostock), crowdsourced conditions more closely mimic real-world data
26 visualization users and scenarios, “effectively swapping experimental control for ecological validity” (Heer & Bostock, 2010, pg. 1). In addition to research in data visualization, MTurk has been widely used in other fields, including Geography. Wallgrün et al. (2018), used MTurk to build a corpus of tagged place names using twitter data. Other recent examples include a study by Sparks et al. (2015) which used MTurk to recruit participants for a land cover classification survey built using Qualtrics, and Limpisathian (2017) who used Mechanical Turk to recruit and compensate participants for a study of visual contrast and hierarchy in cartographic design. MTurk is also regularly used to recruit participants for studies in visual cognition (e.g., Klippel et al., 2015), and psychology (e.g., Betella & Verschure, 2016). Participants recruited for this study were required to have a Mechanical Turk masters qualification, indicating high-performance on previous tasks (“Amazon Mechanical Turk”). To minimize the influence of cultural differences on color perception, we required that all participants be United States residents. Participants were asked to complete the survey on a laptop or desktop computer to provide ample screen-size for map viewing. Browser and screen resolution data were collected through Qualtrics. Though no laws govern wages that crowdsourced participants must be paid, an effort was made to compensate participants reasonably. Based on a completion time estimate (15 min) from pilot studies, the first 200 participants were compensated $2 for their time. Results showed that 86% of these participants submitted the survey within 16.5 minutes, and thus that their compensation was equivalent or greater than the American federal minimum wage of $7.25/hour. In addition to the 200 participants who completed the primary study, an additional 81 participants were recruited to fill in gaps left by randomization procedures and to increase statistical power based on our factorial design. These participants did not complete the subjective response tasks, only the shorter section of quantitative questions. For this reason, they were paid only 60 cents, based on an expected completion time of five minutes (12 cents a minute). Participants completed these shorter surveys in an average time of 4.4 minutes, with 78% of participants completing the survey within our five-minute estimate.
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3.2.2. Questionnaire Design
Given the nature of our study, a significant component of building our survey was the design of its visual aesthetics. Qualtrics is a sophisticated survey development tool with many options for altering a survey’s look and feel. For this survey, we used the Qualtrics “Boxed Questions” theme. The separation of questions into boxes within this theme provided visual comfort to participants, established clear figure-ground in the questionnaire layout, and minimized confusion by separating task information from question text (Dillman et al., 2014, pg. 174; Mahon-Haft & Dillman, 2010). A light grey background and white question boxes were chosen, and Qualtrics’s custom CSS function used to eliminate the default blue color from on-screen buttons—ensuring that all surveys were entirely achromatic. Research has shown that even questionnaire background color can alter survey responses (Weller & Livingston, 1988), and we wanted additionally to avoid using any colors that might either compliment or clash with the colored stimuli in our study.
3.2.3 Pre-Study Questions
After providing consent, participants began the user study by answering several demographic and screening questions. Those who self-identified as under 18, cognitively- impaired, or visually impaired (including color deficiencies) were excluded from the study. Screening questions for cognitive and visual impairment, as well as map-reading expertise, were adapted from the study by Limpisathian (2017) on visual contrast and map aesthetics for cartographic design. The participant consent form, as well as all screening and demographic questions, are available in Appendix D. Participants who met initial screening criteria completed a modified five-plate version of the Ishihara (1972) test for colorblindness (Appendix D). While complete success on this test is fairly conclusive evidence of adequate color vision, failure cannot be considered definite proof of vision impairment (Ishihara). Thus, while data from the nine participants who failed the test were ultimately excluded from analysis, these participants were permitted to continue with the study and compensated for their time.
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3.2.4 Part One: Objective Measures
After completing the colorblindness test, participants were randomly assigned to complete either cluster detection or area comparison tasks (between-subjects variable). These tasks were chosen as they correlate with questions often asked and answered using categorical maps, such as “are there any spatial clusters of [data category x]?” and “which is more prevalent, [data category x] or [data category y]?”
3.2.4.1 Practice Questions
To prepare them for the timed questions, all participants completed a set of untimed practice questions from whichever task condition they were assigned. These tasks were nearly identical to those in the main section, but the data used was devoid of context. As shown in figure 3-8, maps were simply titled “Example Map”, and categories were listed with filler words (e.g., Category A; Category B). The color scheme used was the ColorBrewer Qualitative “5-class Set2” color scheme (#66c2a5, #fc8d62, #8da0cb, #e78ac3, #a6d854) (Brewer, 2012). This color scheme has been empirically validated for use in qualitative mapping (though it is not colorblind friendly) and does not match any of the affective color palettes evaluated in the main portion of our experiment.
Figure 3-8. Practice Question Maps, cluster detection (left); area comparison (right)
We did not assume that participants had significant mapping or map reading experience, so practice sets began with a brief introduction to categorical mapping, shown in Figure 3-9.
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Figure 3-9. Introduction to Categorical Maps
Practice questions were intended to get participants into the flow of answering questions with maps, but also to delineate the specifics of how to answer our questions. The cluster detection task, in particular, needed to be clearly defined. Cluster detection tasks asked participants to “select the largest cluster”—though there is not an authoritative definition of what exactly defines a cluster in cartography. To remedy this, we defined a cluster as a grouping of eight or more areas, and gave this definition, as well as an interactive visual example, to participants up front (Figure 3-10).
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Figure 3-10. Cluster Detection Introduction (shown on mouse hover)
Similarly, participants assigned to the area comparison condition were given additional details about their task (Figure 3-11). Though “which category is assigned to the most areas” and “which category covers the most area,” are both valid questions that could be answered with a categorical map, the latter is a more directly cartographic query, and we clarify in this introduction that this is the question that participants would be expected to answer.
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Figure 3-11. Area Comparison Introduction
After participants completed the practice questions, they were sent to the main task. These tasks are described in more detail below.
3.2.4.2 Cluster Detection Cluster detection questions contained two parts: (1) cluster detection, and (2) cluster category identification. Participants first identified the largest cluster on the map on- screen by clicking anywhere within it with their cursor (Figure 3-12).
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Figure 3-12. Cluster task, part one (cluster detection)
Clicks were recorded and identified as either within or outside of the appropriate cluster using the Qualtrics “heat-map” question format. Next, participants saw the same map again, and were asked to identify the category that the cluster they had just selected was associated with (Figure 3-13). Though the cluster detection task was useful as it is a common map-oriented inquiry, it could theoretically be completed without ever reading the map title or categories. Thus, we added the second element (“to which category does the cluster belong?”) to ensure that participants were reading the words—and therefore, aware of the data context.
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Figure 3-13. Cluster task, part two (cluster category identification)
With each question, participants saw a count-up timer at the top of their screen. This reminded them that they were being timed and was intended to add a sense of urgency to the task. A count-up rather than count-down timer was used so as to not prime participants with response-time expectations, and to differentiate slow from incomplete responses. Each participant was randomly assigned to test one of 32 sets of eight maps each. These maps were presented to participants in a randomized order. All saw a mix of affectively-congruent and incongruent maps—though none saw the same color scheme, data topic, or data distribution twice. Although previous work suggests that mixing congruent and incongruent stimuli together may decrease Stroop-type effects (Naor-Raz et al., 2003; Macleod, 1991), we chose this study design as it better reflects exposure to maps in the real world.
3.2.4.3 Area Comparison The area comparison task was simpler, though also—as intended—more challenging. Participants were simply asked to judge which of two categories covered more
34 area on the map.
Figure 3-14. Area Comparison task
3.2.4.4 Hypotheses
The following hypothesis was tested in part one (objective measures) of our study:
H1: Affectively congruent colors increase map reading efficiency.
Maps with emotionally-congruent color assignments are expected to elicit more efficient (fast and accurate) responses from respondents, compared to those with emotionally-incongruent color assignments. In our study, congruence in color selection applies to the whole color scheme, unlike in work by Lin et al. (2013), where it refers to individual categories. Thus, affectively- resonant colors cannot be expected to decrease user reliance on the legend, a suggested explanation for the increased efficiency seen in visualizations with semantically-resonant colors by Lin et al. However, strong incongruence between the data and colors’ emotive properties may cause some level of Stroop-type interference, and this may be enough to
35 slow down participants or cause a significant increase in errors. This hypothesis is supported by the affective priming paradigm, wherein affectively congruent prime-target pairs are responded to faster and more accurately by study participants than incongruent pairs (Klauer, 1998).
3.2.5 Part Two: Subjective Measures
In part two, participants rated maps along several scales to measure perceived map affect, appropriateness, and overall preference.
3.2.5.1 Rating Map Affect
After completing the timed map-reading tasks, participants rated eight maps using a modified version of the Affective Slider (Betella & Verschure, 2016). The maps participants saw in this section matched the color-context combinations they had seen in the objective question section, but with the subjective assessment data distributions applied. The intention of this was to not confuse participants by showing them the same data context twice with opposing congruence (e.g., a congruent map of homicide followed by an incongruent map of homicide in the following section). Maps again were displayed in a randomized order. The Affective Slider (Figure 3-15) is a digital scale which was developed as a contemporary tool for measuring arousal (top) and valence (bottom), which define the two dimensions of the aforementioned core affect model (Betella & Verschure, 2016; Russell, 2003).
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Figure 3-15. The Affective Slider (Betella & Verschure, 2016)
The use of opposing affect-pairs (e.g., positive-negative) in our study design makes such scales an ideal format for assessment. As noted by Betella & Verschure (2016), the Affective Slider was intentionally developed in an achromatic color scheme, so as not to bias responses due to the emotional connotations of color. This is particularly relevant to our study, as it concerns the affective qualities of color and map context. Our implementation of this tool (Figure 3-16) has two notable differences from that by Betella & Verschure (2016). First, we replaced the digital slider with radio buttons. Research in survey design has demonstrated that web slider scales (as compared to radio buttons) result in longer response times and higher participant break-off rates, particularly among those with less formal education (Funke et al., 2011). Radio buttons also have the advantage of requiring no starting location—making item non-response easily differentiable from neutral responses, and preventing responses from anchoring near the slider’s starting position (Funke, 2016).
Figure 3-16. Our adaptation of the Affective Slider (Betella & Verschure, 2016)
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Second, we labeled the endpoints of our Affective Scale. Though the emoticons in the original scale by Betella & Verschure (2016) make it reasonably self-explanatory, proper implementation of their tool requires including the words “Pleasure” and “Arousal” to describe the scales to survey respondents, as shown in Figure 3-17.
Figure 3-17. Suggested implementation of the Affective Slider (Betella & Verschure, 2016)
Rather than labeling our scale as shown above, we used endpoint names that represent empirically-validated endpoints of semantic scales of pleasure and arousal (Mehrabian & Russell, 1974, pg. 26). This is consistent with best practices in survey design, where including both the positive and negative descriptor (e.g., “unhappy” and “happy”) is recommended to avoid biasing responses in one direction (Dillman et al., 2014, pg. 134). Removal of the words “pleasure” and “arousal” was an additional benefit, as these words have other connotations that may have influenced respondent results in undesirable ways.
3.2.5.2 Rating Appropriateness and Preference
After submitting affective ratings for all assigned maps, participants were shown the same eight maps again, and asked to rate them on two final dimensions—appropriateness, and overall valuation (preference). These questions are shown in Figure 3-18. We used bold
38 text to draw participants’ attention to essential words in the question stem (Dillman et al., 2014, pg. 174).
Figure 3-18. Appropriateness and Preference Ratings
The first question, on map-color appropriateness, was adopted from a study on font congruity by Doyle & Bottomley (2002). They introduced their participants to the idea of font appropriateness as follows: "Companies spend a lot of time, effort and money in presenting what they hope is the right image to consumers," …. the purpose of this study was to "... explore what people feel about different typefaces for different types of products." (Doyle & Bottomley, 2002, pg. 117)
After this introduction, participants rated each stimuli on an 11 point-scale from 0 (absolutely inappropriate) to 10 (absolutely appropriate) (Doyle & Bottomley, 2002). Implementation in our study was similar, though the introductory explanation was shortened to: “Map designers like to use colors that are appropriate for the topic of their maps.” The randomization technique used in our study design meant that this explanation appeared multiple times, so brevity was more important in our study than in that by Doyle & Bottomley. “Absolutely Appropriate” and “Absolutely Inappropriate” remained as the endpoint labels in our study, but we reduced the scale length from 11 to 7 points. The other rating scales in our study design used 7-point scales, and we felt that consistency was important— particularly at the end of the survey, when participants may be somewhat fatigued, and thus more prone to skimming instructions and/or making mistakes. Additionally, survey
39 research has demonstrated that there is no significant benefit in increasing the length of Likert-type scales beyond seven points (Lozano et al., 2008). The opinion scale was added at the end to capture participants’ overall feelings about the maps. No further instructions were given, as this was intended to capture the raw subjective feeling of preference (or otherwise) from participants, rather than a more calculated value. Consistent with all other scales in our survey, opinion ratings were made on a 7-point bipolar Likert-style scale that ranged from low (“I really don’t like it”) to high (“I really like it”).
3.2.5.3 Hypotheses
The following hypotheses were tested in part two (subjective measures) of this study.
H2: Affectively-congruent colors amplify judgments of map affect.
When map context and color are emotionally congruent, we expect this to increase the magnitude of the reported affect. For example, a map with a positive, cheerful context would be expected to elicit higher ratings of emotional valence when designed with bright, playful colors rather than dreary ones.
H3: Affectively-congruent colors are more appropriate.
Appropriateness ratings will be higher for maps with affectively-congruent colors. This is fairly self-evident. Of primary interest is whether participants are cognizant of affective congruence and incongruence. Testing this hypothesis also serves to evaluate the quality of our stimuli (i.e., were the map topics and color schemes successful in conveying the selected emotions as intended?)
H4: Affectively-congruent colors are preferable.
40 Preference (opinion) will be higher for maps with affectively-congruent colors. We expect these schemes to facilitate increased processing fluency (Reber et al., 2004). If this occurs, we expect this to translate into higher preference ratings, as they will match user expectations and thus require less effort to interpret. If the results found by Lin et al. (2013) were primarily a consequence of less reliance on the legend, rather than a Stroop-type interference, we are likely to find no difference between AC and AI maps in either accuracy or response speed. However, similar to how participants in the study by Fabrikant & Christophe (2012) found bright-pink oceans to be “shocking” and “strange,” participants were expected to find affectively-incongruent color schemes to be confusing, sloppy, or distasteful, and to rate them lower—even if objectively they are no less useful. It should be noted that we do not expect color assignments to be more important than data context in preference ratings. For example, participants should not prefer affectively-congruent maps of childhood cancer to emotionally-incongruent maps of dessert. However, within all maps of a specific topic (e.g., childhood cancer) we expect that those with the most appropriate color schemes will be most liked.
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Chapter 4
Results
Survey data were downloaded from Qualtrics in comma separated values (csv) format; cleaned and formatted for analysis in Microsoft Excel. Data from 9 participants were excluded due to failure on the colorblindness test. Data from the remaining 272 participants were analyzed using the R statistical software package. The following sections report results for both objective (4.1) and subjective (4.2.) tasks.
4.1 Objective Measures
H1: Affectively congruent colors increase map reading efficiency.
Support was not found for this hypothesis. Among the cluster detection tasks, no significant difference was found between affectively-congruent (AC) and affectively- incongruent (AI) stimuli in either response times or error rates. This study contained two different cluster-related tasks (“select the largest cluster”; “to what category does the cluster belong?”) and one area comparison task (“which category takes up more area on the map?”). Each question appeared on its own page in the survey. For all tasks, response time was measured at the time of participant page submission. As is typical for response times, results followed an approximately log-normal distribution. Thus, response times were log-transformed for visualization (Figure 4-1) and analysis. A one-way ANOVA showed no significant influence of affective congruence on response time for any objective task.
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log transformed transformed log
Response time (sec.) time Response
Figure 4-1. Cluster selection response times, n = 124
Log response times for the second task, category identification (“to what category does the cluster belong?”) are shown in Figure 4-2. Results from the area comparison task are shown in Figure 4-3.
log transformed transformed log
Response time (sec.) time Response
Figure 4-2. Category identification response times, n = 124
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log transformed transformed log
Response time (sec.) time Response
Figure 4-3. Area Comparison response times, n = 148
To assess whether results were confounded by individual differences in participant skill levels, an average AC and average AI response time was calculated for each participant (for each task), and paired t-tests performed. Again, no significant differences were found between AC and AI response times. It was also discovered that several participants used their keyboard to select responses; this increased the speed with which they were able to complete some tasks. To test whether this influenced the pattern of overall response times, statistical tests were re- run with these participants excluded. Still, one-way ANOVA showed no significant effect of resonance on response time. In summary, based on theoretical support for H1, complimentary repeated analyses were performed to seek evidence of a Type 2 error. No evidence of such an error was found. Though response time was the main variable of interest, map-reading accuracy was analyzed as well. As expected, participants made more errors with the area comparison task (94% correct) than with the cluster detection (96% correct) or cluster category identification (98% correct) tasks. However, even for area comparison tasks the error rate was low, and the difference in error rates between task types was not statistically significant. Additionally, no significant differences in error rates were found between affectively-congruent and incongruent stimuli for any task.
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4.2 Subjective Measures
H2: Affectively-congruent colors amplify judgments of map affect.
Results show that affective congruence has a significant influence on map affect ratings. Affect ratings for (congruent-incongruent) pairs were analyzed with the non- parametric Mann-Whitney U test. Significant debate (e.g., Carifio & Perla, 2008; Jamieson, 2004) surrounds the issue of whether Likert-type data such as that generated by this study are interval data, and thus should be considered appropriate for parametric analyses such as ANOVA. For this analysis, we take the conservative approach and conduct non- parametric (and thus, rank-order) analyses—working under the assumption that intervals between response categories that measure subjective feelings cannot be assumed to be equal (Jamieson, 2004).
* **
*** *** *** *** Figure 4-4. Valence (happiness) ratings for positive and negative data topics
As shown in Figure 4-4, for maps with positive data contexts (dessert; happiness), participants rated maps with affectively-congruent (AC) colors as significantly higher (Mann-Whitney U test; p < .001***) in valence than those with incongruent (AI) colors. The opposite was true for negative data topics (cancer; homicide). These maps were rated significantly lower in valence when shown in affectively-congruent colors.
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*** *** *** ***
Figure 4-5. Arousal (excitement) ratings for positive and negative data topics
Similar results occurred with (calm-exciting) map contexts. Maps with calm data contexts (nature; relaxation) were rated significantly lower in arousal, and thus calmer (Mann-Whitney U test, p < .001) when shown in AC rather than AI colors. For exciting map topics (amusement; caffeine), maps with AC colors were rated significantly higher in arousal than their AI counterparts (Mann-Whitney U test, p < .001). Therefore, we find that affective color congruence intensifies map topic affect.
H3: Affectively-congruent colors are more appropriate.
As expected, for all affects, maps with affectively-congruent colors were judged by participants as more appropriate than incongruent maps (Mann-Whitney U test, p <.001). As shown in Figure 4-6, this effect was particularly pronounced for negative and positive map topics.
*** *** *** ***
Figure 4-6. Appropriateness ratings for all data topics
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H4: Affectively-congruent colors are preferable.
For all affects (calm; exciting; negative; positive), maps with affectively-congruent colors were judged by participants as preferable to those that were affectively-incongruent (Mann-Whitney U test, p <.001). As shown in Figure 4-7, this effect was particularly pronounced for positive map topics; the effect was smallest for negative map topics.
*** *** *** *** Figure 4-7. Preference ratings for all data topics
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Chapter 5
Discussion
The following sections extend the results described above by relating them back to the theoretical basis and objectives of this work.
5.1 Objective Measures
It is notable that no significant differences were found between congruent and incongruent maps for either of the objective task types. This is a departure from the finding by Lin et al. (2013) that contextually-congruent colors increase data-retrieval efficiency. Here we posit several possible explanations for this different result. First, in the study by Lin et al. (2013), congruent colors were applied to individual marks (i.e., one bar of a bar chart) rather than to the visualization overall. Lin et al. suggest that the reason for participants’ increased efficiency with congruent colors might be due to decreased reliance on the chart legend, rather than a Stroop-type interference in the incongruent condition. Were this the case, we would not expect to see a difference between the congruent and incongruent stimuli in our study. Secondly, work by Lin et al. (2013) applied colors based on semantically, rather than affectively resonant colors. Despite substantive evidence that colors have generalizable emotional connotations (Suk & Irtel, 2010; Bartram et al., 2017), “light blue is calming” and “red represents tomato,” are not equally universal, with the former perhaps being not enough so to generate a statistically significant effect. Third, the differences in readability of the various affective color palettes may have been a confounding factor. Color scheme development was focused first and foremost on closely emulating the affective color palettes identified by Bartram et al. (2017). Colors used
48 were sufficiently discriminable, but each color palette was not necessarily equivalent with regards to visual perception. To test whether the color scheme itself was a significant factor, a one-way ANOVA was performed on log transformed response times for all tasks. For the cluster selection and area comparison map tasks, ANOVA showed a significant effect of color scheme affect (irrespective of data context) on task completion time (p < .05). Given the significance of these omnibus tests, pairwise post-hoc independent t-tests were performed. To adjust for the increased probability of finding significance when conducting multiple comparisons, Holm-adjusted p-values are reported. The Holm adjustment method is similar to the oft- used Bonferroni correction method, but maintains a higher amount of statistical power (Aickin & Gensler, 1996).
log transformed transformed log
Response time (sec.) time Response
* *
Figure 5-1. Cluster Selection Response times by Affect of Color Scheme
For the cluster detection tasks, a significant effect on response time was found between exciting and negative color schemes (p < .05). This is demonstrated in Figure 5-1. As shown, maps with negative color schemes took participants longer to read than those with exciting schemes, regardless of their data content (Figure 5-1). A trend (Holm-adjusted p = .07) was also seen between negative and positive color schemes. These affects’ color schemes differ significantly when analyzed alone but fall just outside the bounds of the widely accepted p < .05 level of significance after the Holm multiple comparisons correction. We suggest that such a result—theoretically supported by the positive and
49 exciting color schemes’ similar use of high saturation and significant between-category color discriminability—should be considered practically meaningful for map design. Similar to cluster detection tasks, ANOVA showed a significant effect of color scheme affect on response time for area comparison tasks (Figure 5-2), with exciting color schemes facilitating faster response times than negative schemes (Holm-adjusted p < .05). Positive schemes also show a suggestion of a trend toward faster response times than negative schemes, though less so than the cluster detection tasks (Holm-adjusted p = .10)
log transformed transformed log
Response time (sec.) time Response
* *
Figure 5-2. Area Comparison Response times by Affect of Color Scheme
This diminished influence of color scheme affect on response time in the area comparison vs. cluster detection condition echoes a similar finding by Lin et al. (2013). In their study, Lin et al. found that more challenging tasks resulted in a decreased influence of semantic-resonance, presumably as the time-lag induced by color incongruence in more challenging tasks represented a smaller percentage of the total task time. As shown in Figure 5-3, participants took significantly longer to complete area comparison tasks as compared to those in the cluster detection condition.
50
Cluster Category Identification
Cluster Detection Question Type Question
Area Comparison
Figure 5-3. Participant response times (in seconds) by question type.
In Figure 5-3, unlike similar boxplots in this thesis, response times are shown prior to log-transformation to demonstrate the real time it took participants to complete these tasks. One-way ANOVA did find a significant difference between all question types for log- transformed response times (Holm-adjusted p < .0001), but this in itself is not an interesting result. Cluster category identification questions were answered even faster than cluster detection questions, though no effect of color scheme on response time for this question type was found. This is perhaps not surprising, however. As cluster category identification questions always referred to the same map as the cluster detection question that preceded it, any response latencies due to color scheme-reading difficulties with that map are more likely to be seen in the first question (cluster detection) than the second (cluster category identification). It appears that once a participant identified the largest cluster on the map, matching it to its appropriate category was easy. Finally, we cannot rule out the possibility that a kind of negative priming may have influenced these results. Consider, for example, the map sequence shown in Figure 5-4.
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Figure 5-4. An example set of stimuli that may have produced a negative priming effect
If we consider the map’s color scheme to be analogous to the prime in the affective priming paradigm (it has an affective influence on the reader before they have read the map), a map sequence such as the one in Figure 5-4 (left to right) may have slowed participant responses. The presence of an incongruent prime (the positive color scheme) in trial n-1 may have caused participants to repress this affect, making them slower to identify it as the now-congruent affect in trial n (Wentura, 1999). Our randomization procedures did not record how often or in which instances this or similar map sequences occurred. It should also be noted that, as expected, one-way ANOVA identified some differences in response time between the various data distributions. Figure 5-5 shows log- transformed response times for the cluster detection task for all eight distributions (as illustrated previously in Figure 3-4). As discussed in Chapter 3, our study was designed so that results would be valid despite such differences.
52
ime (sec.) ime
log transformed transformed log
Response t Response
Distribution
Figure 5-5. Cluster Detection log response times by distribution
Distribution three was found to be the most challenging, significantly more so than distributions 1**, 4**, 5***, 6***, and 8*** (Holm adjusted p < .01**; p < .001***). It is unsurprising that larger and more compact clusters were easier for participants to detect. It is notable, however, that cluster positioning in the upper-left corner, a location often found to be vital in web-based visual search (e.g., Goldberg et al., 2002) appeared not to make the cluster in distribution three more visible. Instead, centralized clusters were more easily detected, replicating findings such as those by Enoch (1958) that visual searches of spatial graphics are concentrated in the center of the field of view.
Slower Faster Figure 5-6. Relative speed of cluster detection between distributions: three (left), one (center), and five (right).
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No differences were found between data distributions for the second cluster-based task (cluster category identification). We posit a similar explanation for this as for the lack of influence of color scheme on response time for this task—participants had already become accustomed to the map in question. The task was easy, and visual design differences at that point had become irrelevant. We also found no significant differences between any of the eight synthetic data distributions used in the area comparison task. The reason for this is less certain, though likely related to the fact that this was a more time- consuming and challenging task, with between-participant variability based on skill level obscuring any differences between the objective difficulties of the various map distributions. For each task, map topic (irrespective of color assignment) was found not to have influenced participant response time (one-way ANOVA). This suggests that differences in reading times between map text-sets were negligible (as intended).
5.2 Subjective Measures
5.2.1 Ratings of Map Affect
As expected, affective congruence amplified ratings of map-topic affect. For example, as shown in Figure 5-7, negative topics were rated as sadder when mapped with affectively congruent colors (i.e., ; ). Interestingly, this also means that negative topics were rated as happier when mapped with affectively incongruent colors.
54
Figure 5-7. Boxplots of happiness ratings for negative data topics
Maps with negative topics are highly prevalent in the media, particularly in data journalism (e.g., gun violence, climate change, mortality maps, political conflict, poverty statistics...). The results of this study suggest that the choice of color in such a map can either amplify or mitigate these negative emotions. If the intention is to tell a compelling and emotionally-engaging story, a negative color scheme would benefit the map designer. However, if the intention is to motivate behavior (e.g., pro-environmental behavior in response to climate change), this design decision may be counterproductive. Though visualizations that arouse fear or similar negative emotions capture attention, these emotions are often unproductive—even counterproductive—at motivating positive behavioral change (O’Neill & Nicholson-Cole, 2009). Thus, a more positive-leaning color scheme might benefit a map of a distressing topic around which the designer wishes to inspire action. Though affectively-incongruent color schemes did diminish ratings of map-topic affect, their ratings included significantly more variance. This suggests that incongruent schemes are confusing to map readers. Affectively incongruent schemes were also valued (based on overall opinion ratings) lower overall than their affectively congruent counterparts, even for maps of negative topics. This suggests that despite an incongruent scheme’s ability to alter a map’s emotional connotations—even to make it seem happier— map-readers are put off by this incongruence.
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A possible solution to this for a cartographer who aims to make a map of a negative topic that is deemed appropriate by readers, but not overly sad, would be to use an affectively-neutral color scheme. Theoretically, this would avoid the color scheme either increasing the topic’s negative affect or creating an incongruence that is considered inappropriate by map-readers. Whether an affectively-neutral color scheme exists, however, is unclear. Even achromatic color schemes likely have affective connotations.
5.2.2 Map Preference
Maps with affectively-congruent colors were consistently judged as more appropriately designed and better liked by study participants. When assessing preference ratings, it is notable that affective color congruence had the greatest preference effect on positive map topics, and the least effect on negative map topics. This suggests that emotions brought about by negative data topics may be less malleable via visual design. As negative color schemes were found to be harder to read than positive and exciting color schemes, this is another possible explanation for their low ratings. Participants can be presumed to have rated maps lower if they found them objectively less useful. Additionally, decreased processing fluency—such as occurs when a stimuli is harder to read—is associated with diminished feelings of positive affect (Reber et al., 2004). The calm map topics and color schemes tested in this study, however, provide a useful control for which to evaluate the likelihood of objective utility vs. emotional discomfort as a cause for negative map topics’ lower preference scores. Like negative color schemes, calm color schemes were somewhat muted, with less inter-category visual discriminability than exciting or positive color schemes. And as illustrated by Figure 5-8, no significant difference was present in task-efficiency between calm and negative color schemes. Meanwhile, affective congruence had a greater effect on calm than negative color schemes with regards to preference ratings.
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log transformed transformed log
Preference Rating Preference Response time (sec.) time Response
Figure 5-8. Calm and negative color schemes: Area comparison response times (left, insignificant); preference ratings (right, significant)
The idea that you can make a map about homicide look appropriate—but you can’t make people like it—is not particularly surprising. It does however, point to a sophistication in map-readers’ appreciation and comprehension of nuance in emotionally congruent (or incongruent) design. Additionally, we found no significant differences in map opinion or appropriateness ratings based on age or education level. That the influence of affective congruence is so robust even among novice, crowdsourced participants suggests that it ought to be a design consideration for cartographers across diverse types of media.
5.3 Limitations
Although these results provide evidence of the importance of affective-congruence in categorical map design, they may not be transferrable to other thematic mapping techniques – in particular, to sequential choropleth maps. Qualitative maps, like those studied here, typically rely on differences in hue to denote category assignment (Harrower & Brewer, 2003). Sequential maps, conversely, generally use lightness steps within single- hue schemes (Harrower & Brewer). Though sequential maps may also benefit from context- relevant color assignment, the mechanism by which this would alter map interpretation is likely distinct.
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Additionally, emotional color connotations vary between cultures, and map-color selection without considering the map’s intended audience can have considerable negative consequences. For example, America’s most popular corporate color—blue—is often viewed as ominous and cold in East Asia (Schmitt, 1995). Limiting our study to United States residents provided us with a useful increase in experimental control, but accordingly limits our results’ generalizability to international mapping contexts. Color preferences have also been found to vary not only across cultures, but by gender (Park & Guerin, 2002), and even in accordance with seemingly unrelated factors such as marital status (Whitfield, 1984). Individual differences in color preference, similarly, are likely to influence affective responses and map preference. In addition to differences in preferences, the participants in our study likely had varying levels of spatial abilities, as well as differing knowledge of thematic maps and how they work. As mentioned previously, participants were asked rate their experience with cartography at the beginning of the study, and none considered themselves to have advanced knowledge. However, experts are most likely to underestimate their abilities— and novices most likely to overestimate them—as experts understand the gaps in their knowledge (Kruger & Dunning, 1999). Thus, asking participants to self-assess their level of expertise has known limitations.
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Chapter 6
Conclusions and Future Work
This study examined the influence of affective congruence (vs. incongruence) on thematic map interpretation. Our results demonstrate that affective congruence magnifies user perceptions of map-topic affect, incongruence confuses map readers, and affective congruence is particularly preferred by readers when applied in positive-leaning map contexts. Though no difference was found in map-reading efficiency between affectively congruent and incongruent maps, efficiency is not the only consideration in map design. As affectively-congruent colors are preferred, their use in maps is likely to hold readers’ attention. This is important not only in commercial contexts, such as in the development of new web-based mobile applications, but also in academic ones—where readers who enjoy a map’s design are more likely to read it, to believe it, and to understand the information that it contains. Contextual considerations such as map topic emotions have typically been a side note in cartography. However, current research in cognate fields (e.g., Lin et al., 2013; Stone, 2017), as well as an emerging interest in emotion within cartography itself (Griffin & McQuoid, 2012), has brought them into the forefront. Maps have become ubiquitous (such as in mobile phone applications), and geovisual analytics tools are now used to explore big spatial datasets. User affect and preference—as measured in this study—are of great importance in these and many other mapping contexts. The use of positive colors in positive mapping contexts, for example, significantly increases ratings of positive affect, which has been shown to correlate with increased processing fluency (Reber et al., 2004). Such increased processing fluency would likely befit users of geovisual analytic tools, particularly when working with big, messy data and/or when completing cognitively-challenging tasks. Map-based affect also likely has everyday implications—though the relationship is complicated, affect is a crucial element in decision-making, particularly under conditions of
59 risk (Loewenstein et al., 2001). Emotions elicited by map design are therefore likely to influence user responses in real-world mapping situations under conditions of uncertainty, such as when driving with a personal navigation device, or during emergency response. Despite some differences in legibility between the color schemes in this study, it is worth noting again that all color palettes used fit within the realm of academically acceptable cartographic design. Thus, we do not—and cannot, based on these results alone—recommend that map designers abandon well-established cartographic color conventions in favor of facilitating affective congruence. However, the results of our study do affirm a significant influence of affective congruence on map-reader responses, and thus we suggest that affective congruence is one of multiple important design constraints to consider in cartography. Out of all map-topic affects tested in the study, results from maps with negative data topics show particular promise for future study. The tension between a reader’s understanding of what is appropriate design—and their feeling of what makes a pleasing design—is clearly demonstrated in our results. Given the complex nature of negative emotions as compared to positive emotions (Morgan & Heise, 1988) and the abundance of maps of negative data contexts in data journalism, further study of affective colors’ influence on negative data interpretation is a clear direction for future research. Participants in our emotional mapping workshop found negative map topics by far the easiest to generate, reiterating the profusion of such maps in the media. Our study tested maps of topics which were highly negative (childhood cancer; homicide)—and when these maps were incongruently-colored, participants found them inappropriate and unlikable. Future studies could test additional negative topics across a range of magnitudes (e.g., rising rent prices (low); disease outbreaks (medium); child sex trafficking (high)). There is likely a tipping point at which affective incongruence becomes disturbing rather than mediating; knowledge of this could provide further color-selection guidance to cartographers and data journalists alike. In closing, this research established empirical support for the importance of considering affective congruence in cartographic design. Despite the lack of support for an objective benefit (i.e., more efficient task-completion) with affectively-congruent colors, such colors serve both to intensify map topic emotions, and to create preferable maps. We
60 suggest that cartographers consider these results when selecting color for their maps, particularly when their maps depict positive topics.
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Appendix A
Affective Color Scheme Specifications
(Bartram, Patra, and A Hex RGB B Hex RGB Stone 2017) #BD82BE (189, 130, 190) #4F9E9C (79, 158, 156)
#A6D4E4 (166, 212, 228) #A8D3E2 (168, 211, 226)
#3DC8C4 (61, 200, 196) #DDD8BA (221, 216, 186)
Calm #ADD9A1 (173, 217, 161) #9796BB (151, 150, 187)
#DFB0C7 (223, 176, 199) #ADD9A1 (173, 217, 161)
#F3D027 (243, 208, 39) #6C6DD1 (108, 109, 209)
#00C9C4 (0, 201, 196) #FF901F (255, 144, 31)
#F9468C (249, 70, 140) #32BFF2 (50, 191, 242)
Exciting #FF8000 (255, 128, 0) #23BF0C (35, 191, 12)
#23BF0C (35, 191, 12) #F81E27 (248, 30, 39)
#FF8000 (255, 128, 0) #F67FAE (246, 127, 174)
#3DB87F (61, 184, 127) #80D947 (128, 217, 71)
#79C0ED (121, 192, 237) #F69C42 (246, 156, 66)
Positive #F3D027 (243, 208, 39) #EFCF38 (239, 207, 56)
#A6DE55 (166, 222, 85) #1FD5D0 (31, 213, 208)
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#4A4A4A (74, 74, 74) #3C3C3C (60, 60, 60)
#51692B (81, 105, 43) #9F9F9F (159, 159, 159)
#77493E (119, 73, 62) #7C4C41 (124, 76, 65)
Negative #30355F (48, 53, 95) #64795F (100, 121, 95)
#8E8DAB (142, 141, 171) #A73930 (167, 57, 48)
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Appendix B
Full map text detail
Below are the titles and categories for each map topic (1-8). Map topics are bolded.
1. Most Preferred Relaxation Activity a. Yoga b. Meditation c. Deep Breathing d. Massage e. Acupressure
2. Most Popular Amusement Ride a. Roller Coaster b. Tilt-a-Whirl c. Log Flume d. Drop Tower e. Bumper Cars
3. Most Popular Kind of Dessert a. Ice Cream Cake b. Cookies c. Cupcakes d. Brownies e. Frozen Yogurt
4. Most Common Homicide Method a. Gunshot b. Drowning c. Poisoning d. Strangulation e. Stabbing
69
5. Favorite Scenic View of Nature a. Waterfall b. Ocean Waves c. Sunrise d. Sunset e. Night Sky
6. Most Popular Caffeinated Drink a. Iced Coffee b. Hot Coffee c. Soda Pop d. Energy Drink e. Frappuccino
7. Highest Rated Key to Happiness a. Relationships b. Sense of Purpose c. Physical Health d. Friendships e. Money
8. Most Common Childhood Cancer a. Lymphoma b. Adrenal c. Leukemia d. Retinal e. Wilms Tumor
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Appendix C
Specifications for the 544 maps tested in this study
Map Code Key (separated by underscores): Task Type, Topic Affect, Congruence (AC or AI), Distribution (1-8), Color scheme (a or b). Subjective assessment map codes do not have distribution codes, as it is not relevant (each map topic was only applied to one distribution).
Cluster Detection Maps Map Topic Topic Distribution Color Scheme Map Code Number Affect
1 Calm Relaxation Cluster 1 Calm A CD_C_Re_AC_1_a
2 Calm Relaxation Cluster 2 Calm A CD_C_Re_AC_2_a
3 Calm Relaxation Cluster 3 Calm A CD_C_Re_AC_3_a
4 Calm Relaxation Cluster 4 Calm A CD_C_Re_AC_4_a
5 Calm Relaxation Cluster 5 Calm A CD_C_Re_AC_5_a
6 Calm Relaxation Cluster 6 Calm A CD_C_Re_AC_6_a
7 Calm Relaxation Cluster 7 Calm A CD_C_Re_AC_7_a
8 Calm Relaxation Cluster 8 Calm A CD_C_Re_AC_8_a
9 Calm Relaxation Cluster 1 Calm B CD_C_Re_AC_1_b
10 Calm Relaxation Cluster 2 Calm B CD_C_Re_AC_2_b
11 Calm Relaxation Cluster 3 Calm B CD_C_Re_AC_3_b
12 Calm Relaxation Cluster 4 Calm B CD_C_Re_AC_4_b
13 Calm Relaxation Cluster 5 Calm B CD_C_Re_AC_5_b
14 Calm Relaxation Cluster 6 Calm B CD_C_Re_AC_6_b
15 Calm Relaxation Cluster 7 Calm B CD_C_Re_AC_7_b
16 Calm Relaxation Cluster 8 Calm B CD_C_Re_AC_8_b
17 Calm Relaxation Cluster 1 Exciting A CD_C_Re_AI_1_a
18 Calm Relaxation Cluster 2 Exciting A CD_C_Re_AI_2_a
71
19 Calm Relaxation Cluster 3 Exciting A CD_C_Re_AI_3_a
20 Calm Relaxation Cluster 4 Exciting A CD_C_Re_AI_4_a
21 Calm Relaxation Cluster 5 Exciting A CD_C_Re_AI_5_a
22 Calm Relaxation Cluster 6 Exciting A CD_C_Re_AI_6_a
23 Calm Relaxation Cluster 7 Exciting A CD_C_Re_AI_7_a
24 Calm Relaxation Cluster 8 Exciting A CD_C_Re_AI_8_a
25 Calm Relaxation Cluster 1 Exciting B CD_C_Re_AI_1_b
26 Calm Relaxation Cluster 2 Exciting B CD_C_Re_AI_2_b
27 Calm Relaxation Cluster 3 Exciting B CD_C_Re_AI_3_b
28 Calm Relaxation Cluster 4 Exciting B CD_C_Re_AI_4_b
29 Calm Relaxation Cluster 5 Exciting B CD_C_Re_AI_5_b
30 Calm Relaxation Cluster 6 Exciting B CD_C_Re_AI_6_b
31 Calm Relaxation Cluster 7 Exciting B CD_C_Re_AI_7_b
32 Calm Relaxation Cluster 8 Exciting B CD_C_Re_AI_8_b
33 Exciting Amusement Cluster 1 Exciting A CD_E_Am_AC_1_a
34 Exciting Amusement Cluster 2 Exciting A CD_E_Am_AC_2_a
35 Exciting Amusement Cluster 3 Exciting A CD_E_Am_AC_3_a
36 Exciting Amusement Cluster 4 Exciting A CD_E_Am_AC_4_a
37 Exciting Amusement Cluster 5 Exciting A CD_E_Am_AC_5_a
38 Exciting Amusement Cluster 6 Exciting A CD_E_Am_AC_6_a
39 Exciting Amusement Cluster 7 Exciting A CD_E_Am_AC_7_a
40 Exciting Amusement Cluster 8 Exciting A CD_E_Am_AC_8_a
41 Exciting Amusement Cluster 1 Exciting B CD_E_Am_AC_1_b
42 Exciting Amusement Cluster 2 Exciting B CD_E_Am_AC_2_b
43 Exciting Amusement Cluster 3 Exciting B CD_E_Am_AC_3_b
44 Exciting Amusement Cluster 4 Exciting B CD_E_Am_AC_4_b
45 Exciting Amusement Cluster 5 Exciting B CD_E_Am_AC_5_b
46 Exciting Amusement Cluster 6 Exciting B CD_E_Am_AC_6_b
72
47 Exciting Amusement Cluster 7 Exciting B CD_E_Am_AC_7_b
48 Exciting Amusement Cluster 8 Exciting B CD_E_Am_AC_8_b
49 Exciting Amusement Cluster 1 Calm A CD_E_Am_AI_1_a
50 Exciting Amusement Cluster 2 Calm A CD_E_Am_AI_2_a
51 Exciting Amusement Cluster 3 Calm A CD_E_Am_AI_3_a
52 Exciting Amusement Cluster 4 Calm A CD_E_Am_AI_4_a
53 Exciting Amusement Cluster 5 Calm A CD_E_Am_AI_5_a
54 Exciting Amusement Cluster 6 Calm A CD_E_Am_AI_6_a
55 Exciting Amusement Cluster 7 Calm A CD_E_Am_AI_7_a
56 Exciting Amusement Cluster 8 Calm A CD_E_Am_AI_8_a
57 Exciting Amusement Cluster 1 Calm B CD_E_Am_AI_1_b
58 Exciting Amusement Cluster 2 Calm B CD_E_Am_AI_2_b
59 Exciting Amusement Cluster 3 Calm B CD_E_Am_AI_3_b
60 Exciting Amusement Cluster 4 Calm B CD_E_Am_AI_4_b
61 Exciting Amusement Cluster 5 Calm B CD_E_Am_AI_5_b
62 Exciting Amusement Cluster 6 Calm B CD_E_Am_AI_6_b
63 Exciting Amusement Cluster 7 Calm B CD_E_Am_AI_7_b
64 Exciting Amusement Cluster 8 Calm B CD_E_Am_AI_8_b
65 Positive Dessert Cluster 1 Positive A CD_P_De_AC_1_a
66 Positive Dessert Cluster 2 Positive A CD_P_De_AC_2_a
67 Positive Dessert Cluster 3 Positive A CD_P_De_AC_3_a
68 Positive Dessert Cluster 4 Positive A CD_P_De_AC_4_a
69 Positive Dessert Cluster 5 Positive A CD_P_De_AC_5_a
70 Positive Dessert Cluster 6 Positive A CD_P_De_AC_6_a
71 Positive Dessert Cluster 7 Positive A CD_P_De_AC_7_a
72 Positive Dessert Cluster 8 Positive A CD_P_De_AC_8_a
73 Positive Dessert Cluster 1 Positive B CD_P_De_AC_1_b
74 Positive Dessert Cluster 2 Positive B CD_P_De_AC_2_b
73
75 Positive Dessert Cluster 3 Positive B CD_P_De_AC_3_b
76 Positive Dessert Cluster 4 Positive B CD_P_De_AC_4_b
77 Positive Dessert Cluster 5 Positive B CD_P_De_AC_5_b
78 Positive Dessert Cluster 6 Positive B CD_P_De_AC_6_b
79 Positive Dessert Cluster 7 Positive B CD_P_De_AC_7_b
80 Positive Dessert Cluster 8 Positive B CD_P_De_AC_8_b
81 Positive Dessert Cluster 1 Negative A CD_P_De_AI_1_a
82 Positive Dessert Cluster 2 Negative A CD_P_De_AI_2_a
83 Positive Dessert Cluster 3 Negative A CD_P_De_AI_3_a
84 Positive Dessert Cluster 4 Negative A CD_P_De_AI_4_a
85 Positive Dessert Cluster 5 Negative A CD_P_De_AI_5_a
86 Positive Dessert Cluster 6 Negative A CD_P_De_AI_6_a
87 Positive Dessert Cluster 7 Negative A CD_P_De_AI_7_a
88 Positive Dessert Cluster 8 Negative A CD_P_De_AI_8_a
89 Positive Dessert Cluster 1 Negative B CD_P_De_AI_1_b
90 Positive Dessert Cluster 2 Negative B CD_P_De_AI_2_b
91 Positive Dessert Cluster 3 Negative B CD_P_De_AI_3_b
92 Positive Dessert Cluster 4 Negative B CD_P_De_AI_4_b
93 Positive Dessert Cluster 5 Negative B CD_P_De_AI_5_b
94 Positive Dessert Cluster 6 Negative B CD_P_De_AI_6_b
95 Positive Dessert Cluster 7 Negative B CD_P_De_AI_7_b
96 Positive Dessert Cluster 8 Negative B CD_P_De_AI_8_b
97 Negative Homicide Cluster 1 Negative A CD_N_Ho_AC_1_a
98 Negative Homicide Cluster 2 Negative A CD_N_Ho_AC_2_a
99 Negative Homicide Cluster 3 Negative A CD_N_Ho_AC_3_a
100 Negative Homicide Cluster 4 Negative A CD_N_Ho_AC_4_a
101 Negative Homicide Cluster 5 Negative A CD_N_Ho_AC_5_a
102 Negative Homicide Cluster 6 Negative A CD_N_Ho_AC_6_a
74
103 Negative Homicide Cluster 7 Negative A CD_N_Ho_AC_7_a
104 Negative Homicide Cluster 8 Negative A CD_N_Ho_AC_8_a
105 Negative Homicide Cluster 1 Negative B CD_N_Ho_AC_1_b
106 Negative Homicide Cluster 2 Negative B CD_N_Ho_AC_2_b
107 Negative Homicide Cluster 3 Negative B CD_N_Ho_AC_3_b
108 Negative Homicide Cluster 4 Negative B CD_N_Ho_AC_4_b
109 Negative Homicide Cluster 5 Negative B CD_N_Ho_AC_5_b
110 Negative Homicide Cluster 6 Negative B CD_N_Ho_AC_6_b
111 Negative Homicide Cluster 7 Negative B CD_N_Ho_AC_7_b
112 Negative Homicide Cluster 8 Negative B CD_N_Ho_AC_8_b
113 Negative Homicide Cluster 1 Positive A CD_N_Ho_AI_1_a
114 Negative Homicide Cluster 2 Positive A CD_N_Ho_AI_2_a
115 Negative Homicide Cluster 3 Positive A CD_N_Ho_AI_3_a
116 Negative Homicide Cluster 4 Positive A CD_N_Ho_AI_4_a
117 Negative Homicide Cluster 5 Positive A CD_N_Ho_AI_5_a
118 Negative Homicide Cluster 6 Positive A CD_N_Ho_AI_6_a
119 Negative Homicide Cluster 7 Positive A CD_N_Ho_AI_7_a
120 Negative Homicide Cluster 8 Positive A CD_N_Ho_AI_8_a
121 Negative Homicide Cluster 1 Positive B CD_N_Ho_AI_1_b
122 Negative Homicide Cluster 2 Positive B CD_N_Ho_AI_2_b
123 Negative Homicide Cluster 3 Positive B CD_N_Ho_AI_3_b
124 Negative Homicide Cluster 4 Positive B CD_N_Ho_AI_4_b
125 Negative Homicide Cluster 5 Positive B CD_N_Ho_AI_5_b
126 Negative Homicide Cluster 6 Positive B CD_N_Ho_AI_6_b
127 Negative Homicide Cluster 7 Positive B CD_N_Ho_AI_7_b
128 Negative Homicide Cluster 8 Positive B CD_N_Ho_AI_8_b
129 Calm Nature Cluster 1 Calm A CD_C_Na_AC_1_a
130 Calm Nature Cluster 2 Calm A CD_C_Na_AC_2_a
75
131 Calm Nature Cluster 3 Calm A CD_C_Na_AC_3_a
132 Calm Nature Cluster 4 Calm A CD_C_Na_AC_4_a
133 Calm Nature Cluster 5 Calm A CD_C_Na_AC_5_a
134 Calm Nature Cluster 6 Calm A CD_C_Na_AC_6_a
135 Calm Nature Cluster 7 Calm A CD_C_Na_AC_7_a
136 Calm Nature Cluster 8 Calm A CD_C_Na_AC_8_a
137 Calm Nature Cluster 1 Calm B CD_C_Na_AC_1_b
138 Calm Nature Cluster 2 Calm B CD_C_Na_AC_2_b
139 Calm Nature Cluster 3 Calm B CD_C_Na_AC_3_b
140 Calm Nature Cluster 4 Calm B CD_C_Na_AC_4_b
141 Calm Nature Cluster 5 Calm B CD_C_Na_AC_5_b
142 Calm Nature Cluster 6 Calm B CD_C_Na_AC_6_b
143 Calm Nature Cluster 7 Calm B CD_C_Na_AC_7_b
144 Calm Nature Cluster 8 Calm B CD_C_Na_AC_8_b
145 Calm Nature Cluster 1 Exciting A CD_C_Na_AI_1_a
146 Calm Nature Cluster 2 Exciting A CD_C_Na_AI_2_a
147 Calm Nature Cluster 3 Exciting A CD_C_Na_AI_3_a
148 Calm Nature Cluster 4 Exciting A CD_C_Na_AI_4_a
149 Calm Nature Cluster 5 Exciting A CD_C_Na_AI_5_a
150 Calm Nature Cluster 6 Exciting A CD_C_Na_AI_6_a
151 Calm Nature Cluster 7 Exciting A CD_C_Na_AI_7_a
152 Calm Nature Cluster 8 Exciting A CD_C_Na_AI_8_a
153 Calm Nature Cluster 1 Exciting B CD_C_Na_AI_1_b
154 Calm Nature Cluster 2 Exciting B CD_C_Na_AI_2_b
155 Calm Nature Cluster 3 Exciting B CD_C_Na_AI_3_b
156 Calm Nature Cluster 4 Exciting B CD_C_Na_AI_4_b
157 Calm Nature Cluster 5 Exciting B CD_C_Na_AI_5_b
158 Calm Nature Cluster 6 Exciting B CD_C_Na_AI_6_b
76
159 Calm Nature Cluster 7 Exciting B CD_C_Na_AI_7_b
160 Calm Nature Cluster 8 Exciting B CD_C_Na_AI_8_b
161 Exciting Caffeinated Cluster 1 Exciting A CD_E_Cf_AC_1_a
162 Exciting Caffeinated Cluster 2 Exciting A CD_E_Cf_AC_2_a
163 Exciting Caffeinated Cluster 3 Exciting A CD_E_Cf_AC_3_a
164 Exciting Caffeinated Cluster 4 Exciting A CD_E_Cf_AC_4_a
165 Exciting Caffeinated Cluster 5 Exciting A CD_E_Cf_AC_5_a
166 Exciting Caffeinated Cluster 6 Exciting A CD_E_Cf_AC_6_a
167 Exciting Caffeinated Cluster 7 Exciting A CD_E_Cf_AC_7_a
168 Exciting Caffeinated Cluster 8 Exciting A CD_E_Cf_AC_8_a
169 Exciting Caffeinated Cluster 1 Exciting B CD_E_Cf_AC_1_b
170 Exciting Caffeinated Cluster 2 Exciting B CD_E_Cf_AC_2_b
171 Exciting Caffeinated Cluster 3 Exciting B CD_E_Cf_AC_3_b
172 Exciting Caffeinated Cluster 4 Exciting B CD_E_Cf_AC_4_b
173 Exciting Caffeinated Cluster 5 Exciting B CD_E_Cf_AC_5_b
174 Exciting Caffeinated Cluster 6 Exciting B CD_E_Cf_AC_6_b
175 Exciting Caffeinated Cluster 7 Exciting B CD_E_Cf_AC_7_b
176 Exciting Caffeinated Cluster 8 Exciting B CD_E_Cf_AC_8_b
177 Exciting Caffeinated Cluster 1 Calm A CD_E_Cf_AI_1_a
178 Exciting Caffeinated Cluster 2 Calm A CD_E_Cf_AI_2_a
179 Exciting Caffeinated Cluster 3 Calm A CD_E_Cf_AI_3_a
180 Exciting Caffeinated Cluster 4 Calm A CD_E_Cf_AI_4_a
181 Exciting Caffeinated Cluster 5 Calm A CD_E_Cf_AI_5_a
182 Exciting Caffeinated Cluster 6 Calm A CD_E_Cf_AI_6_a
183 Exciting Caffeinated Cluster 7 Calm A CD_E_Cf_AI_7_a
184 Exciting Caffeinated Cluster 8 Calm A CD_E_Cf_AI_8_a
185 Exciting Caffeinated Cluster 1 Calm B CD_E_Cf_AI_1_b
186 Exciting Caffeinated Cluster 2 Calm B CD_E_Cf_AI_2_b
77
187 Exciting Caffeinated Cluster 3 Calm B CD_E_Cf_AI_3_b
188 Exciting Caffeinated Cluster 4 Calm B CD_E_Cf_AI_4_b
189 Exciting Caffeinated Cluster 5 Calm B CD_E_Cf_AI_5_b
190 Exciting Caffeinated Cluster 6 Calm B CD_E_Cf_AI_6_b
191 Exciting Caffeinated Cluster 7 Calm B CD_E_Cf_AI_7_b
192 Exciting Caffeinated Cluster 8 Calm B CD_E_Cf_AI_8_b
193 Positive Happiness Cluster 1 Positive A CD_P_Ha_AC_1_a
194 Positive Happiness Cluster 2 Positive A CD_P_Ha_AC_2_a
195 Positive Happiness Cluster 3 Positive A CD_P_Ha_AC_3_a
196 Positive Happiness Cluster 4 Positive A CD_P_Ha_AC_4_a
197 Positive Happiness Cluster 5 Positive A CD_P_Ha_AC_5_a
198 Positive Happiness Cluster 6 Positive A CD_P_Ha_AC_6_a
199 Positive Happiness Cluster 7 Positive A CD_P_Ha_AC_7_a
200 Positive Happiness Cluster 8 Positive A CD_P_Ha_AC_8_a
201 Positive Happiness Cluster 1 Positive B CD_P_Ha_AC_1_b
202 Positive Happiness Cluster 2 Positive B CD_P_Ha_AC_2_b
203 Positive Happiness Cluster 3 Positive B CD_P_Ha_AC_3_b
204 Positive Happiness Cluster 4 Positive B CD_P_Ha_AC_4_b
205 Positive Happiness Cluster 5 Positive B CD_P_Ha_AC_5_b
206 Positive Happiness Cluster 6 Positive B CD_P_Ha_AC_6_b
207 Positive Happiness Cluster 7 Positive B CD_P_Ha_AC_7_b
208 Positive Happiness Cluster 8 Positive B CD_P_Ha_AC_8_b
209 Positive Happiness Cluster 1 Negative A CD_P_Ha_AI_1_a
210 Positive Happiness Cluster 2 Negative A CD_P_Ha_AI_2_a
211 Positive Happiness Cluster 3 Negative A CD_P_Ha_AI_3_a
212 Positive Happiness Cluster 4 Negative A CD_P_Ha_AI_4_a
213 Positive Happiness Cluster 5 Negative A CD_P_Ha_AI_5_a
214 Positive Happiness Cluster 6 Negative A CD_P_Ha_AI_6_a
78
215 Positive Happiness Cluster 7 Negative A CD_P_Ha_AI_7_a
216 Positive Happiness Cluster 8 Negative A CD_P_Ha_AI_8_a
217 Positive Happiness Cluster 1 Negative B CD_P_Ha_AI_1_b
218 Positive Happiness Cluster 2 Negative B CD_P_Ha_AI_2_b
219 Positive Happiness Cluster 3 Negative B CD_P_Ha_AI_3_b
220 Positive Happiness Cluster 4 Negative B CD_P_Ha_AI_4_b
221 Positive Happiness Cluster 5 Negative B CD_P_Ha_AI_5_b
222 Positive Happiness Cluster 6 Negative B CD_P_Ha_AI_6_b
223 Positive Happiness Cluster 7 Negative B CD_P_Ha_AI_7_b
224 Positive Happiness Cluster 8 Negative B CD_P_Ha_AI_8_b
225 Negative Cancer Cluster 1 Negative A CD_N_Cr_AC_1_a
226 Negative Cancer Cluster 2 Negative A CD_N_Cr_AC_2_a
227 Negative Cancer Cluster 3 Negative A CD_N_Cr_AC_3_a
228 Negative Cancer Cluster 4 Negative A CD_N_Cr_AC_4_a
229 Negative Cancer Cluster 5 Negative A CD_N_Cr_AC_5_a
230 Negative Cancer Cluster 6 Negative A CD_N_Cr_AC_6_a
231 Negative Cancer Cluster 7 Negative A CD_N_Cr_AC_7_a
232 Negative Cancer Cluster 8 Negative A CD_N_Cr_AC_8_a
233 Negative Cancer Cluster 1 Negative B CD_N_Cr_AC_1_b
234 Negative Cancer Cluster 2 Negative B CD_N_Cr_AC_2_b
235 Negative Cancer Cluster 3 Negative B CD_N_Cr_AC_3_b
236 Negative Cancer Cluster 4 Negative B CD_N_Cr_AC_4_b
237 Negative Cancer Cluster 5 Negative B CD_N_Cr_AC_5_b
238 Negative Cancer Cluster 6 Negative B CD_N_Cr_AC_6_b
239 Negative Cancer Cluster 7 Negative B CD_N_Cr_AC_7_b
240 Negative Cancer Cluster 8 Negative B CD_N_Cr_AC_8_b
241 Negative Cancer Cluster 1 Positive A CD_N_Cr_AI_1_a
242 Negative Cancer Cluster 2 Positive A CD_N_Cr_AI_2_a
79
243 Negative Cancer Cluster 3 Positive A CD_N_Cr_AI_3_a
244 Negative Cancer Cluster 4 Positive A CD_N_Cr_AI_4_a
245 Negative Cancer Cluster 5 Positive A CD_N_Cr_AI_5_a
246 Negative Cancer Cluster 6 Positive A CD_N_Cr_AI_6_a
247 Negative Cancer Cluster 7 Positive A CD_N_Cr_AI_7_a
248 Negative Cancer Cluster 8 Positive A CD_N_Cr_AI_8_a
249 Negative Cancer Cluster 1 Positive B CD_N_Cr_AI_1_b
250 Negative Cancer Cluster 2 Positive B CD_N_Cr_AI_2_b
251 Negative Cancer Cluster 3 Positive B CD_N_Cr_AI_3_b
252 Negative Cancer Cluster 4 Positive B CD_N_Cr_AI_4_b
253 Negative Cancer Cluster 5 Positive B CD_N_Cr_AI_5_b
254 Negative Cancer Cluster 6 Positive B CD_N_Cr_AI_6_b
255 Negative Cancer Cluster 7 Positive B CD_N_Cr_AI_7_b
256 Negative Cancer Cluster 8 Positive B CD_N_Cr_AI_8_b
Area Comparison Maps
Map Topic Affect Topic Pattern Color Scheme Map Code Number
257 Calm Relaxation Distribution 1 Calm A AC_C_Re_AC_1_a
258 Calm Relaxation Distribution 2 Calm A AC_C_Re_AC_2_a
259 Calm Relaxation Distribution 3 Calm A AC_C_Re_AC_3_a
260 Calm Relaxation Distribution 4 Calm A AC_C_Re_AC_4_a
261 Calm Relaxation Distribution 5 Calm A AC_C_Re_AC_5_a
262 Calm Relaxation Distribution 6 Calm A AC_C_Re_AC_6_a
263 Calm Relaxation Distribution 7 Calm A AC_C_Re_AC_7_a
264 Calm Relaxation Distribution 8 Calm A AC_C_Re_AC_8_a
265 Calm Relaxation Distribution 1 Calm B AC_C_Re_AC_1_b
266 Calm Relaxation Distribution 2 Calm B AC_C_Re_AC_2_b
80
267 Calm Relaxation Distribution 3 Calm B AC_C_Re_AC_3_b
268 Calm Relaxation Distribution 4 Calm B AC_C_Re_AC_4_b
269 Calm Relaxation Distribution 5 Calm B AC_C_Re_AC_5_b
270 Calm Relaxation Distribution 6 Calm B AC_C_Re_AC_6_b
271 Calm Relaxation Distribution 7 Calm B AC_C_Re_AC_7_b
272 Calm Relaxation Distribution 8 Calm B AC_C_Re_AC_8_b
273 Calm Relaxation Distribution 1 Exciting A AC_C_Re_AI_1_a
274 Calm Relaxation Distribution 2 Exciting A AC_C_Re_AI_2_a
275 Calm Relaxation Distribution 3 Exciting A AC_C_Re_AI_3_a
276 Calm Relaxation Distribution 4 Exciting A AC_C_Re_AI_4_a
277 Calm Relaxation Distribution 5 Exciting A AC_C_Re_AI_5_a
278 Calm Relaxation Distribution 6 Exciting A AC_C_Re_AI_6_a
279 Calm Relaxation Distribution 7 Exciting A AC_C_Re_AI_7_a
280 Calm Relaxation Distribution 8 Exciting A AC_C_Re_AI_8_a
281 Calm Relaxation Distribution 1 Exciting B AC_C_Re_AI_1_b
282 Calm Relaxation Distribution 2 Exciting B AC_C_Re_AI_2_b
283 Calm Relaxation Distribution 3 Exciting B AC_C_Re_AI_3_b
284 Calm Relaxation Distribution 4 Exciting B AC_C_Re_AI_4_b
285 Calm Relaxation Distribution 5 Exciting B AC_C_Re_AI_5_b
286 Calm Relaxation Distribution 6 Exciting B AC_C_Re_AI_6_b
287 Calm Relaxation Distribution 7 Exciting B AC_C_Re_AI_7_b
288 Calm Relaxation Distribution 8 Exciting B AC_C_Re_AI_8_b
289 Calm Nature Distribution 1 Calm A AC_C_Na_AC_1_a
290 Calm Nature Distribution 2 Calm A AC_C_Na_AC_2_a
291 Calm Nature Distribution 3 Calm A AC_C_Na_AC_3_a
292 Calm Nature Distribution 4 Calm A AC_C_Na_AC_4_a
293 Calm Nature Distribution 5 Calm A AC_C_Na_AC_5_a
294 Calm Nature Distribution 6 Calm A AC_C_Na_AC_6_a
81
295 Calm Nature Distribution 7 Calm A AC_C_Na_AC_7_a
296 Calm Nature Distribution 8 Calm A AC_C_Na_AC_8_a
297 Calm Nature Distribution 1 Calm B AC_C_Na_AC_1_b
298 Calm Nature Distribution 2 Calm B AC_C_Na_AC_2_b
299 Calm Nature Distribution 3 Calm B AC_C_Na_AC_3_b
300 Calm Nature Distribution 4 Calm B AC_C_Na_AC_4_b
301 Calm Nature Distribution 5 Calm B AC_C_Na_AC_5_b
302 Calm Nature Distribution 6 Calm B AC_C_Na_AC_6_b
303 Calm Nature Distribution 7 Calm B AC_C_Na_AC_7_b
304 Calm Nature Distribution 8 Calm B AC_C_Na_AC_8_b
305 Calm Nature Distribution 1 Exciting A AC_C_Na_AI_1_a
306 Calm Nature Distribution 2 Exciting A AC_C_Na_AI_2_a
307 Calm Nature Distribution 3 Exciting A AC_C_Na_AI_3_a
308 Calm Nature Distribution 4 Exciting A AC_C_Na_AI_4_a
309 Calm Nature Distribution 5 Exciting A AC_C_Na_AI_5_a
310 Calm Nature Distribution 6 Exciting A AC_C_Na_AI_6_a
311 Calm Nature Distribution 7 Exciting A AC_C_Na_AI_7_a
312 Calm Nature Distribution 8 Exciting A AC_C_Na_AI_8_a
313 Calm Nature Distribution 1 Exciting B AC_C_Na_AI_1_b
314 Calm Nature Distribution 2 Exciting B AC_C_Na_AI_2_b
315 Calm Nature Distribution 3 Exciting B AC_C_Na_AI_3_b
316 Calm Nature Distribution 4 Exciting B AC_C_Na_AI_4_b
317 Calm Nature Distribution 5 Exciting B AC_C_Na_AI_5_b
318 Calm Nature Distribution 6 Exciting B AC_C_Na_AI_6_b
319 Calm Nature Distribution 7 Exciting B AC_C_Na_AI_7_b
320 Calm Nature Distribution 8 Exciting B AC_C_Na_AI_8_b
321 Negative Homicide Distribution 1 Negative A AC_N_Ho_AC_1_a
322 Negative Homicide Distribution 2 Negative A AC_N_Ho_AC_2_a
82
323 Negative Homicide Distribution 3 Negative A AC_N_Ho_AC_3_a
324 Negative Homicide Distribution 4 Negative A AC_N_Ho_AC_4_a
325 Negative Homicide Distribution 5 Negative A AC_N_Ho_AC_5_a
326 Negative Homicide Distribution 6 Negative A AC_N_Ho_AC_6_a
327 Negative Homicide Distribution 7 Negative A AC_N_Ho_AC_7_a
328 Negative Homicide Distribution 8 Negative A AC_N_Ho_AC_8_a
329 Negative Homicide Distribution 1 Negative B AC_N_Ho_AC_1_b
330 Negative Homicide Distribution 2 Negative B AC_N_Ho_AC_2_b
331 Negative Homicide Distribution 3 Negative B AC_N_Ho_AC_3_b
332 Negative Homicide Distribution 4 Negative B AC_N_Ho_AC_4_b
333 Negative Homicide Distribution 5 Negative B AC_N_Ho_AC_5_b
334 Negative Homicide Distribution 6 Negative B AC_N_Ho_AC_6_b
335 Negative Homicide Distribution 7 Negative B AC_N_Ho_AC_7_b
336 Negative Homicide Distribution 8 Negative B AC_N_Ho_AC_8_b
337 Negative Homicide Distribution 1 Positive A AC_N_Ho_AI_1_a
338 Negative Homicide Distribution 2 Positive A AC_N_Ho_AI_2_a
339 Negative Homicide Distribution 3 Positive A AC_N_Ho_AI_3_a
340 Negative Homicide Distribution 4 Positive A AC_N_Ho_AI_4_a
341 Negative Homicide Distribution 5 Positive A AC_N_Ho_AI_5_a
342 Negative Homicide Distribution 6 Positive A AC_N_Ho_AI_6_a
343 Negative Homicide Distribution 7 Positive A AC_N_Ho_AI_7_a
344 Negative Homicide Distribution 8 Positive A AC_N_Ho_AI_8_a
345 Negative Homicide Distribution 1 Positive B AC_N_Ho_AI_1_b
346 Negative Homicide Distribution 2 Positive B AC_N_Ho_AI_2_b
347 Negative Homicide Distribution 3 Positive B AC_N_Ho_AI_3_b
348 Negative Homicide Distribution 4 Positive B AC_N_Ho_AI_4_b
349 Negative Homicide Distribution 5 Positive B AC_N_Ho_AI_5_b
350 Negative Homicide Distribution 6 Positive B AC_N_Ho_AI_6_b
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351 Negative Homicide Distribution 7 Positive B AC_N_Ho_AI_7_b
352 Negative Homicide Distribution 8 Positive B AC_N_Ho_AI_8_b
353 Positive Happiness Distribution 1 Negative A AC_P_Ha_AI_1_a
354 Positive Happiness Distribution 2 Negative A AC_P_Ha_AI_2_a
355 Positive Happiness Distribution 3 Negative A AC_P_Ha_AI_3_a
356 Positive Happiness Distribution 4 Negative A AC_P_Ha_AI_4_a
357 Positive Happiness Distribution 5 Negative A AC_P_Ha_AI_5_a
358 Positive Happiness Distribution 6 Negative A AC_P_Ha_AI_6_a
359 Positive Happiness Distribution 7 Negative A AC_P_Ha_AI_7_a
360 Positive Happiness Distribution 8 Negative A AC_P_Ha_AI_8_a
361 Positive Happiness Distribution 1 Negative B AC_P_Ha_AI_1_b
362 Positive Happiness Distribution 2 Negative B AC_P_Ha_AI_2_b
363 Positive Happiness Distribution 3 Negative B AC_P_Ha_AI_3_b
364 Positive Happiness Distribution 4 Negative B AC_P_Ha_AI_4_b
365 Positive Happiness Distribution 5 Negative B AC_P_Ha_AI_5_b
366 Positive Happiness Distribution 6 Negative B AC_P_Ha_AI_6_b
367 Positive Happiness Distribution 7 Negative B AC_P_Ha_AI_7_b
368 Positive Happiness Distribution 8 Negative B AC_P_Ha_AI_8_b
369 Positive Happiness Distribution 1 Positive A AC_P_Ha_AC_1_a
370 Positive Happiness Distribution 2 Positive A AC_P_Ha_AC_2_a
371 Positive Happiness Distribution 3 Positive A AC_P_Ha_AC_3_a
372 Positive Happiness Distribution 4 Positive A AC_P_Ha_AC_4_a
373 Positive Happiness Distribution 5 Positive A AC_P_Ha_AC_5_a
374 Positive Happiness Distribution 6 Positive A AC_P_Ha_AC_6_a
375 Positive Happiness Distribution 7 Positive A AC_P_Ha_AC_7_a
376 Positive Happiness Distribution 8 Positive A AC_P_Ha_AC_8_a
377 Positive Happiness Distribution 1 Positive B AC_P_Ha_AC_1_b
378 Positive Happiness Distribution 2 Positive B AC_P_Ha_AC_2_b
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379 Positive Happiness Distribution 3 Positive B AC_P_Ha_AC_3_b
380 Positive Happiness Distribution 4 Positive B AC_P_Ha_AC_4_b
381 Positive Happiness Distribution 5 Positive B AC_P_Ha_AC_5_b
382 Positive Happiness Distribution 6 Positive B AC_P_Ha_AC_6_b
383 Positive Happiness Distribution 7 Positive B AC_P_Ha_AC_7_b
384 Positive Happiness Distribution 8 Positive B AC_P_Ha_AC_8_b
385 Positive Dessert Distribution 1 Negative A AC_P_De_AI_1_a
386 Positive Dessert Distribution 2 Negative A AC_P_De_AI_2_a
387 Positive Dessert Distribution 3 Negative A AC_P_De_AI_3_a
388 Positive Dessert Distribution 4 Negative A AC_P_De_AI_4_a
389 Positive Dessert Distribution 5 Negative A AC_P_De_AI_5_a
390 Positive Dessert Distribution 6 Negative A AC_P_De_AI_6_a
391 Positive Dessert Distribution 7 Negative A AC_P_De_AI_7_a
392 Positive Dessert Distribution 8 Negative A AC_P_De_AI_8_a
393 Positive Dessert Distribution 1 Negative B AC_P_De_AI_1_b
394 Positive Dessert Distribution 2 Negative B AC_P_De_AI_2_b
395 Positive Dessert Distribution 3 Negative B AC_P_De_AI_3_b
396 Positive Dessert Distribution 4 Negative B AC_P_De_AI_4_b
397 Positive Dessert Distribution 5 Negative B AC_P_De_AI_5_b
398 Positive Dessert Distribution 6 Negative B AC_P_De_AI_6_b
399 Positive Dessert Distribution 7 Negative B AC_P_De_AI_7_b
400 Positive Dessert Distribution 8 Negative B AC_P_De_AI_8_b
401 Positive Dessert Distribution 1 Positive A AC_P_De_AC_1_a
402 Positive Dessert Distribution 2 Positive A AC_P_De_AC_2_a
403 Positive Dessert Distribution 3 Positive A AC_P_De_AC_3_a
404 Positive Dessert Distribution 4 Positive A AC_P_De_AC_4_a
405 Positive Dessert Distribution 5 Positive A AC_P_De_AC_5_a
406 Positive Dessert Distribution 6 Positive A AC_P_De_AC_6_a
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407 Positive Dessert Distribution 7 Positive A AC_P_De_AC_7_a
408 Positive Dessert Distribution 8 Positive A AC_P_De_AC_8_a
409 Positive Dessert Distribution 1 Positive B AC_P_De_AC_1_b
410 Positive Dessert Distribution 2 Positive B AC_P_De_AC_2_b
411 Positive Dessert Distribution 3 Positive B AC_P_De_AC_3_b
412 Positive Dessert Distribution 4 Positive B AC_P_De_AC_4_b
413 Positive Dessert Distribution 5 Positive B AC_P_De_AC_5_b
414 Positive Dessert Distribution 6 Positive B AC_P_De_AC_6_b
415 Positive Dessert Distribution 7 Positive B AC_P_De_AC_7_b
416 Positive Dessert Distribution 8 Positive B AC_P_De_AC_8_b
417 Negative Cancer Distribution 1 Negative A AC_N_Cr_AC_1_a
418 Negative Cancer Distribution 2 Negative A AC_N_Cr_AC_2_a
419 Negative Cancer Distribution 3 Negative A AC_N_Cr_AC_3_a
420 Negative Cancer Distribution 4 Negative A AC_N_Cr_AC_4_a
421 Negative Cancer Distribution 5 Negative A AC_N_Cr_AC_5_a
422 Negative Cancer Distribution 6 Negative A AC_N_Cr_AC_6_a
423 Negative Cancer Distribution 7 Negative A AC_N_Cr_AC_7_a
424 Negative Cancer Distribution 8 Negative A AC_N_Cr_AC_8_a
425 Negative Cancer Distribution 1 Negative B AC_N_Cr_AC_1_b
426 Negative Cancer Distribution 2 Negative B AC_N_Cr_AC_2_b
427 Negative Cancer Distribution 3 Negative B AC_N_Cr_AC_3_b
428 Negative Cancer Distribution 4 Negative B AC_N_Cr_AC_4_b
429 Negative Cancer Distribution 5 Negative B AC_N_Cr_AC_5_b
430 Negative Cancer Distribution 6 Negative B AC_N_Cr_AC_6_b
431 Negative Cancer Distribution 7 Negative B AC_N_Cr_AC_7_b
432 Negative Cancer Distribution 8 Negative B AC_N_Cr_AC_8_b
433 Negative Cancer Distribution 1 Positive A AC_N_Cr_AI_1_a
434 Negative Cancer Distribution 2 Positive A AC_N_Cr_AI_2_a
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435 Negative Cancer Distribution 3 Positive A AC_N_Cr_AI_3_a
436 Negative Cancer Distribution 4 Positive A AC_N_Cr_AI_4_a
437 Negative Cancer Distribution 5 Positive A AC_N_Cr_AI_5_a
438 Negative Cancer Distribution 6 Positive A AC_N_Cr_AI_6_a
439 Negative Cancer Distribution 7 Positive A AC_N_Cr_AI_7_a
440 Negative Cancer Distribution 8 Positive A AC_N_Cr_AI_8_a
441 Negative Cancer Distribution 1 Positive B AC_N_Cr_AI_1_b
442 Negative Cancer Distribution 2 Positive B AC_N_Cr_AI_2_b
443 Negative Cancer Distribution 3 Positive B AC_N_Cr_AI_3_b
444 Negative Cancer Distribution 4 Positive B AC_N_Cr_AI_4_b
445 Negative Cancer Distribution 5 Positive B AC_N_Cr_AI_5_b
446 Negative Cancer Distribution 6 Positive B AC_N_Cr_AI_6_b
447 Negative Cancer Distribution 7 Positive B AC_N_Cr_AI_7_b
448 Negative Cancer Distribution 8 Positive B AC_N_Cr_AI_8_b
449 Exciting Caffeinated Distribution 1 Calm A AC_E_Cf_AI_1_a
450 Exciting Caffeinated Distribution 2 Calm A AC_E_Cf_AI_2_a
451 Exciting Caffeinated Distribution 3 Calm A AC_E_Cf_AI_3_a
452 Exciting Caffeinated Distribution 4 Calm A AC_E_Cf_AI_4_a
453 Exciting Caffeinated Distribution 5 Calm A AC_E_Cf_AI_5_a
454 Exciting Caffeinated Distribution 6 Calm A AC_E_Cf_AI_6_a
455 Exciting Caffeinated Distribution 7 Calm A AC_E_Cf_AI_7_a
456 Exciting Caffeinated Distribution 8 Calm A AC_E_Cf_AI_8_a
457 Exciting Caffeinated Distribution 1 Calm B AC_E_Cf_AI_1_b
458 Exciting Caffeinated Distribution 2 Calm B AC_E_Cf_AI_2_b
459 Exciting Caffeinated Distribution 3 Calm B AC_E_Cf_AI_3_b
460 Exciting Caffeinated Distribution 4 Calm B AC_E_Cf_AI_4_b
461 Exciting Caffeinated Distribution 5 Calm B AC_E_Cf_AI_5_b
462 Exciting Caffeinated Distribution 6 Calm B AC_E_Cf_AI_6_b
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463 Exciting Caffeinated Distribution 7 Calm B AC_E_Cf_AI_7_b
464 Exciting Caffeinated Distribution 8 Calm B AC_E_Cf_AI_8_b
465 Exciting Caffeinated Distribution 1 Exciting A AC_E_Cf_AC_1_a
466 Exciting Caffeinated Distribution 2 Exciting A AC_E_Cf_AC_2_a
467 Exciting Caffeinated Distribution 3 Exciting A AC_E_Cf_AC_3_a
468 Exciting Caffeinated Distribution 4 Exciting A AC_E_Cf_AC_4_a
469 Exciting Caffeinated Distribution 5 Exciting A AC_E_Cf_AC_5_a
470 Exciting Caffeinated Distribution 6 Exciting A AC_E_Cf_AC_6_a
471 Exciting Caffeinated Distribution 7 Exciting A AC_E_Cf_AC_7_a
472 Exciting Caffeinated Distribution 8 Exciting A AC_E_Cf_AC_8_a
473 Exciting Caffeinated Distribution 1 Exciting B AC_E_Cf_AC_1_b
474 Exciting Caffeinated Distribution 2 Exciting B AC_E_Cf_AC_2_b
475 Exciting Caffeinated Distribution 3 Exciting B AC_E_Cf_AC_3_b
476 Exciting Caffeinated Distribution 4 Exciting B AC_E_Cf_AC_4_b
477 Exciting Caffeinated Distribution 5 Exciting B AC_E_Cf_AC_5_b
478 Exciting Caffeinated Distribution 6 Exciting B AC_E_Cf_AC_6_b
479 Exciting Caffeinated Distribution 7 Exciting B AC_E_Cf_AC_7_b
480 Exciting Caffeinated Distribution 8 Exciting B AC_E_Cf_AC_8_b
481 Exciting Amusement Distribution 1 Calm A AC_E_Am_AI_1a
482 Exciting Amusement Distribution 2 Calm A AC_E_Am_AI_2_a
483 Exciting Amusement Distribution 3 Calm A AC_E_Am_AI_3_a
484 Exciting Amusement Distribution 4 Calm A AC_E_Am_AI_4_a
485 Exciting Amusement Distribution 5 Calm A AC_E_Am_AI_5_a
486 Exciting Amusement Distribution 6 Calm A AC_E_Am_AI_6_a
487 Exciting Amusement Distribution 7 Calm A AC_E_Am_AI_7_a
488 Exciting Amusement Distribution 8 Calm A AC_E_Am_AI_8_a
489 Exciting Amusement Distribution 1 Calm B AC_E_Am_AI_1_b
490 Exciting Amusement Distribution 2 Calm B AC_E_Am_AI_2_b
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491 Exciting Amusement Distribution 3 Calm B AC_E_Am_AI_3_b
492 Exciting Amusement Distribution 4 Calm B AC_E_Am_AI_4_b
493 Exciting Amusement Distribution 5 Calm B AC_E_Am_AI_5_b
494 Exciting Amusement Distribution 6 Calm B AC_E_Am_AI_6_b
495 Exciting Amusement Distribution 7 Calm B AC_E_Am_AI_7_b
496 Exciting Amusement Distribution 8 Calm B AC_E_Am_AI_8_b
497 Exciting Amusement Distribution 1 Exciting A AC_E_Am_AC_1_a
498 Exciting Amusement Distribution 2 Exciting A AC_E_Am_AC_2_a
499 Exciting Amusement Distribution 3 Exciting A AC_E_Am_AC_3_a
500 Exciting Amusement Distribution 4 Exciting A AC_E_Am_AC_4_a
501 Exciting Amusement Distribution 5 Exciting A AC_E_Am_AC_5_a
502 Exciting Amusement Distribution 6 Exciting A AC_E_Am_AC_6_a
503 Exciting Amusement Distribution 7 Exciting A AC_E_Am_AC_7_a
504 Exciting Amusement Distribution 8 Exciting A AC_E_Am_AC_8_a
505 Exciting Amusement Distribution 1 Exciting B AC_E_Am_AC_1_b
506 Exciting Amusement Distribution 2 Exciting B AC_E_Am_AC_2_b
507 Exciting Amusement Distribution 3 Exciting B AC_E_Am_AC_3_b
508 Exciting Amusement Distribution 4 Exciting B AC_E_Am_AC_4_b
509 Exciting Amusement Distribution 5 Exciting B AC_E_Am_AC_5_b
510 Exciting Amusement Distribution 6 Exciting B AC_E_Am_AC_6_b
511 Exciting Amusement Distribution 7 Exciting B AC_E_Am_AC_7_b
512 Exciting Amusement Distribution 8 Exciting B AC_E_Am_AC_8_b
Subjective Assessment Maps
Map Topic Topic Distribution Color Scheme Map Code Number Affect
513 Calm Relaxation Distribution 1 Calm A SQ_C_Re_AC_a
514 Calm Relaxation Distribution 1 Calm B SQ_C_Re_AC_b
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515 Calm Relaxation Distribution 1 Exciting A SQ_C_Re_AI_a
516 Calm Relaxation Distribution 1 Exciting B SQ_C_Re_AI_b
517 Calm Nature Distribution 5 Calm A SQ_C_Na_AC_a
518 Calm Nature Distribution 5 Calm B SQ_C_Na_AC_b
519 Calm Nature Distribution 5 Exciting A SQ_C_Na_AI_a
520 Calm Nature Distribution 5 Exciting B SQ_C_Na_AI_b
521 Negative Homicide Distribution 4 Positive A SQ_N_Ho_AI_a
522 Negative Homicide Distribution 4 Positive B SQ_N_Ho_AI_b
523 Negative Homicide Distribution 4 Negative A SQ_N_Ho_AC_a
524 Negative Homicide Distribution 4 Negative B SQ_N_Ho_AC_b
525 Positive Happiness Distribution 7 Positive A SQ_P_Ha_AC_a
526 Positive Happiness Distribution 7 Positive B SQ_P_Ha_AC_b
527 Positive Happiness Distribution 7 Negative A SQ_P_Ha_AI_a
528 Positive Happiness Distribution 7 Negative B SQ_P_Ha_AI_b
529 Positive Dessert Distribution 3 Positive A SQ_P_De_AC_a
530 Positive Dessert Distribution 3 Positive B SQ_P_De_AC_b
531 Positive Dessert Distribution 3 Negative A SQ_P_De_AI_a
532 Positive Dessert Distribution 3 Negative B SQ_P_De_AI_b
533 Negative Cancer Distribution 8 Positive A SQ_N_Cr_AI_a
534 Negative Cancer Distribution 8 Positive B SQ_N_Cr_AI_b
535 Negative Cancer Distribution 8 Negative A SQ_N_Cr_AC_a
536 Negative Cancer Distribution 8 Negative B SQ_N_Cr_AC_b
537 Exciting Caffeinated Distribution 6 Calm A SQ_E_Cf_AI_a
538 Exciting Caffeinated Distribution 6 Calm B SQ_E_Cf_AI_b
539 Exciting Caffeinated Distribution 6 Exciting A SQ_E_Cf_AC_a
540 Exciting Caffeinated Distribution 6 Exciting B SQ_E_Cf_AC_b
541 Exciting Amusement Distribution 2 Calm A SQ_E_Am_AI_a
542 Exciting Amusement Distribution 2 Calm B SQ_E_Am_AI_b
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543 Exciting Amusement Distribution 2 Exciting A SQ_E_Am_AC_a
544 Exciting Amusement Distribution 2 Exciting B SQ_E_Am_AC_b
Appendix D
Pre-Study Materials
Study Participant Consent Form
Consent for Research The Pennsylvania State University
Principal Investigator: Cary Anderson Email: [email protected] Advisor: Dr. Anthony Robinson, Department of Geography Advisor Email: [email protected]
We are asking you to be in a research study. This form gives you information about the research. Whether or not you take part is up to you. You can choose not to take part. You can agree to take part and later change your mind. Your decision will not be held against you. Please take your time to make your choice.
1. Why is this research study being done? This research is being done to assess the effectiveness of different maps.
2. What will happen in this research study? If you agree to participate, you will complete a survey which involves answering questions about maps. Most questions will be closed-ended (multiple choice), while a few will ask you to write short answers. You are free to discontinue the survey at any time if you choose.
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3. What are the risks and possible discomforts from being in this research study? There is a risk of loss of confidentiality if your information or your identity is obtained by someone other than the investigators, but precautions will be taken to prevent this from happening. The confidentiality of your electronic data created by you or by the researchers will be maintained to the degree permitted by the technology used. Absolute confidentiality cannot be guaranteed.
4. How long will you take part in this research study? If you agree to participate, it will take you about 15 or 20 minutes to complete this research study.
5. How will your privacy and confidentiality be protected if you decide to take part in this research study? Efforts will be made to limit the use and sharing of your personal research information to people who have a need to review this information. In the event of any publication or presentation resulting from the research, no personally identifiable information will be shared. We will do our best to keep your participation in this research study confidential to the extent permitted by law. However, it is possible that other people may find out about your participation in this research study. For example, the following people/groups may check and copy records about this research: The Office for Human Research Protections in the U. S. Department of Health and Human Services, The Institutional Review Board (a committee that reviews and approves research studies) and The Office for Research Protections. Some of these records could contain information that personally identifies you. Reasonable efforts will be made to keep the personal information in your research record private. However, absolute confidentiality cannot be guaranteed.
6. What are the costs of taking part in this research study? There are no costs associated with taking part in this study, aside from your time.
7. Will you be paid or receive credit to take part in this research study? You will be paid $2 through your Amazon Mechanical Turk account in exchange for your
92 time. Compensation is managed and distributed through Mechanical Turk.
8. What are your rights if you take part in this research study? Taking part in this research study is voluntary. You do not have to be in this research. If you choose to be in this research, you have the right to stop at any time. If you decide not to be in this research or if you decide to stop at a later date, there will be no penalty or loss of benefits to which you are entitled.
9. If you have questions or concerns about this research study, whom should you contact? Please contact the head of the research study (principal investigator), Cary Anderson at [email protected] if you have questions, complaints or concerns about the research, or believe you may have been harmed by being in the research study. You may also contact the Office for Research Protections at (814) 865-1775, OR [email protected] if you have questions regarding your rights as a person in a research study or have concerns or general questions about the research. You may also call this number if you cannot reach the research team or wish to offer input or to talk to someone else about any concerns related to the research.
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Pre-Screening Questions
What is your age? a. Less than 18 years old b. 18-24 c. 25-34 d. 35-44 e. 45-54 f. 55-64 g. 65-74 h. 75 years or older
Do you suffer from any cognitive, perceptual, or visual impairments such as color-blindness that may impact your ability to read and understand maps? a. Yes b. No
Do you fall under any of the following exclusion criteria: (Adults who do not speak English; Adults who are not sighted; Adults who are unable to consent; Prisoners)? a. I do not fall under any exclusion criteria b. I fall under one or more of these exclusion criteria
Participants excluded by one or more of these questions (less than 18 years old, cognitively or visually impaired, or who met exclusionary criteria) saw the following message: Sorry, you do not meet the selection criteria for this study. Thank you for your interest.
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Demographic Questions
Please answer the following demographic questions to the best of your ability. Your answers will not affect your eligibility for this study.
What is your gender? a. Please specify: ______b. Prefer not to say
Please indicate your highest level of education: a. Some high school b. High school graduate or equivalent c. Some college d. Associate's degree e. Bachelor's degree f. Graduate or professional degree
Please indicate your level of experience with cartography and map design: a. No experience b. Some experience c. Intermediate experience d. Advanced experience
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Full-Color Vision Verification Test The following few questions are to test your color vision. There is a number in each circle: please enter it in the box using your keyboard. If you do not see a number, leave the box blank.
1.
2.
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3.
4.
5.