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Exploring Qualitative Displays and Interfaces

Windsock on Burgh Island. Devon

by Dan Lockton, Delanie Ricketts, Shruti Aditya Chowdhury (Imaginaries Lab, Carnegie Mellon School of Design) and Chang Hee Lee (Royal College of Art)

Much of how we construct meaning in the real world is qualitative rather than quantitative. We think and act in response to, and in dialogue with, qualities of phenomena, and relationships between them. Yet, quantification has become a default mode for information display, and for interfaces supporting decision-making and behaviour change.

There are more opportunities within design and human-computer interaction for qualitative displays and interfaces, for information presentation, and an aid to help people explore their own thinking and relationships with ideas. Here we attempt one dimension of a tentative classification to support projects exploring opportunities for qualitative displays within design.

This blog post is a slightly edited version of a late-breaking work submission presented at CHI’17, May 06–11, 2017, Denver, CO, USA, and published in the CHI Extended Abstracts at http://dx.doi.org/10.1145/3027063.3053165

Download this article as a PDF.

Water trapped in train carriage door is a form of qualitative display of the train’s acceleration, deceleration and inertia.

Introduction

Outside of the digital, we largely live and think and act and feel in response to, and in dialogue with, the perceived qualities of people, things and phenomena, and the relationships between them, rather than their number.

Much of our experience of — and meaning-making in — the real world is qualitative rather than quantitative. How friendly was she? How tired do I feel right now? Who’s the tallest in the group? How windy is it out there? Which route shall we take to work? How was your meal? Which apple looks tastier? Which piece of music best suits the mood? Do I need to use the bathroom? Particularly rarely do we deal with quantities in relation to abstract concepts — two coffees, half a biscuit, three children, but rarely 0.5 loves or 6.8 sadnesses.

And yet, quantification has become the default mode of interaction with technology, of display of information, and of interfaces which aim to support decision-making and behaviour change in everyday life [27]. We need not elaborate here the phenomena of the quantified self [36, 42] and personal informatics more widely [24, 12], except to note the prevalence of numerical approaches (Figure 1) and the relative unusualness of non-numerical, pattern-based forms (Figure 2).

Figure 1: A typical form of quantitative interface: a Fitbit’s display of number of steps taken.
 

Figure 2: The Emulsion activity tracker, by Norwegian design studio Skrekkøgle, contains two immiscible liquids. Movement splits the colored liquid into smaller drops, making patterns.
 

But what might we be missing through this focus on quantification? It seems as though there might be opportunities for human-computer interaction (HCI) to explore forms of qualitative display and interface, as an approach to information presentation and interaction, as an aid to help people explore their own and each other’s thinking, and specifically to help people understand their relationships and agency with systems.

In this article, we discuss qualitative displays and interfaces, and attempt one dimension of a tentative classification supporting design projects exploring this space.

Leaves as a qualitative interface for the wind

What could qualitative displays and interfaces be?

Here we define a qualitative display as being a way in which information is presented primarily through representing qualities of phenomena; a qualitative interface enables people to interact with a system through responding to or creating these qualities. ‘Displays’ are not necessarily solely visual — obvious to say, perhaps, but not always made explicit.

Before exploring some examples, we will look at some theoretical issues. The terms ‘qualitative interface’ or ‘qualitative display’ are not commonly used outside of some introductory human factors textbooks, but forms of interface along these lines are found in lots of projects at CHI, TEI, DIS, Ubicomp (all academic human-computer interaction conferences) and other venues, without authors explicitly drawing our attention to the concept — it is perhaps just too obvious and too broad to merit specific comment in HCI and interaction design research. But, assuming the idea does have value, what are some characteristics?

A human face is a qualitative interface, perhaps the earliest we encounter [e.g. 40] along with the voice. We learn to read and interpret emotions in others’ expressions, to recognize commonalities and differences across people, to make inferences about internal and external factors affecting the person, and monitor the effects we or others are having on that person. We understand that the face and voice and our ability to read them are abstractions, interpretations, not perfect knowledge, but a model which enables us to make decisions in conjunction with our reading of our own emotions.

In a sense, the whole world, as we perceive it, is a very complex qualitative interface. The most accurate model of a phenomenon is the phenomenon itself, but it is only useful to us to the extent we can understand what we are observing, detect the patterns we need to, and recognize that we are constructing the ‘reality’ we perceive. We are always creating a model [14] and that model is necessarily not reality itself; all displays of information are representations of a simplified model of phenomena in the world. Levels of indexicality [32], drawing on Charles Peirce’s semiology, are relevant here, addressing the “causal distance” between the phenomenon and how it is displayed.

One advantage of interfaces seeking to provide a qualitative display is that they have the potential to enable the preservation of at least some of the complexity of real phenomena — representing complexity without attenuating variety [2] — even if we do not pay attention to it until we actually need to, in much the same way as certain phenomena in the real world become salient only when we need to deal with them. Looking out of the window or opening the door to see and feel and hear what the weather is like outside presents us with complex phenomena, but we are able to interpret what actions we need to take, in a more experientially salient way than looking at some numbers on a weather app.

Figure 4: It’s easy to imagine the feel of the wind on ourselves when we watch this scarf tied around a lamp post flapping in the breeze. Figure 5: A windsock gives us more sense of the wind’s qualities than a numerical display.
 

The feel of the wind on our skin, or watching the wind affect the environment, gives us a better sense of whether we need a scarf or coat than knowing the quantitative value of the wind speed and direction (Figures 3, 4 and 5). We can see, hear and feel not just wind speed and direction, but other qualities of it — is it continuous? in short gusts? damp, dry?

Qualitative displays could enable us to learn to recognize patterns in the world (and in data sets), and the characteristics of state changes, similarly to benefits identified in sonification research [35]. We should consider that ‘qualitative’ does not simply imply the absence of numbers. The examples we use in this paper might involve elements that could easily be quantified (rain drops, ink in a pen) but are given meaning through their display in a way that emphasises a quality or characteristic of the phenomenon. We recognise that this is potentially an ambiguous area, and are open to evolving the concept.

A possible spectrum of one dimension of qualitative displays: directness of connection

Here’s a tentative spectrum of one dimension of qualitative displays, relating phenomena to the display in terms of how directly they are connected.

(Levels 0–1 involve direct use of a real-world phenomenon in the display; from about Level 2 up to Level 5, they involve increasing degrees of translation or transduction of the phenomena. This parallels ideas in indexical visualisation [32] and embedded data representation [41] in terms of ‘situatedness’ or causal distance to phenomena.)

  • Level 0: The phenomenon itself ‘creates’ the display directly
  • Level 1: The display is an ‘accidental’ side-effect of the phenomenon
  • Level 2: The side-effect is ‘incorporated’ into a display that gives it meaning
  • Level 3: The display is a designed side-effect of the phenomenon
  • Level 4: Some minor processing of the phenomenon creates the display
  • Level 5: Major processing of the phenomenon creates the display

Figure 6: Some examples of displays from Levels 0, 1 and 2. Level 0: The pattern of raindrops hitting a translucent umbrella — frequency, coverage, and sound — directly creates a ‘rain display’ for the user, providing insight into the current state and enabling decisions about whether the umbrella is still needed; City lights create a display showing the shape of the city’s districts and indicator of population density; Water trapped in a train carriage window moves as the train ac-/de-celerates, creating a dynamic display of the train’s motion; A transparent pen is a physical progress bar for the amount of ink remaining — it could be quantified, but it is perhaps the quality of being not-yet-run-out which matters to the user. Level 1: A worn patch on a map accidentally provides a display of ‘you are here’; Use marks [5] from previous users demonstrate how to use a swipe-card for entry to a building; A spoon worn through decades of use is an accidental display of the way in which it has been used [31]; Footprints in the snow ‘accidentally’ provide a display of previous walkers’ paths. Level 2: ‘This Color For Best Taste’ label gives ‘meaning’ to the colour of a mango’s skin for the consumer (Photo used with permission of Reddit user /u/cwm2355); Writing ‘Clean Me’ or other messages in dust on a car gives meaning to the dusty property; Admiral Robert Fitzroy’s Storm Glass, as used on the voyage of the Beagle (1831–6), incorporates crystals whose changing appearance was believed to enable weather forecasting (Photo: ReneBNRW, Wikimedia Commons, public domain dedication); George Merryweather’s Tempest Prognosticator (1851[30]) incorporates “a jury of philosophical councillors”, 12 leeches whose movement on detecting an approaching storm causes a bell to ring (Photo: Badobadop, Wikimedia Commons, CC-BY-SA).
Figure 7: Some examples of displays from Levels 3, 4 and 5. Level 3: IceAlert is designed so that freezing temperatures cause the blue reflectors to rotate to become visible; A ‘participatory bar chart’ by Dan Lockton along the lines of [22, 33, 16], designed so that ‘voting’ increases the visible height of the bar, though the votes are not numbered; A non-numerical weighing scale by Chang Hee Lee designed so liquid trapped under glass changes shape; Toilet stall door lock designed so display rotates from ‘Vacant’ to ‘Engaged’ — the position of the lock itself gives us a display of actionable information. Level 4: Chronocyclegraphs (1917) by Frank and Lillian Gilbreth, tracing manual workers’ movements [10] (Photo from [15], Archive.org, out of copyright]; Live Wire (Dangling String) by Natalie Jeremijenko (1995)[39] moved a wire in proportion to local network traffic; Melbourne Mussel Choir, also by Natalie Jeremijenko with Carbon Arts [6] uses mussels with Hall effect sensors to translate the opening and closing of their shells into music; Availabot (2006), by Schulze & Webb, later BERG [3], is a USB puppet which “stands to attention when your chat buddy comes online”. Level 5: Powerchord by Dan Lockton [29] provides real-time sonification of electricity use, translating it into birdsong or other ambient sound; Immaterials: Ghost in the Field by Timo Arnall [1] visualizes “the three-dimensional physical space in which an RFID tag and a reader can interact with each other”; Ritual Machine 2 by the Family Rituals 2.0 project [23] uses patterns on a flip-dot display to visualize the countdown to a shared event for two people; Tempescope by Ken Kawamoto [21] visualizes weather conditions elsewhere in the world through re-creating them in a tabletop display (Photo used from Tempescope Press Kit).
 

The boundaries between levels here are dependent on observers’ interpretations of what is signified (whether an effect is accidental or deliberate is a common question in design (teleonomy [25])). Nevertheless, this spectrum permits a classification of some examples and is being applied by the authors in undergraduate design studio projects. We note the absence of screen-based examples: this is not intentional, and we welcome adding relevant examples. There are many intersecting research areas we aim to explore; in current HCI research, the most relevant are data physicalisation, embedded data representation, tangible interaction, sonification, and glanceable displays.

The work of Yvonne Jansen, Pierre Dragicevic and others [20] in data physicalisation, including compilation of examples, and embedded data representation [41], provides us with many instances of qualitative display, mostly at what we are calling Levels 2–5; likewise, development of ubiquitous computing, tangible interaction and tangible user interfaces [39, 18, 17] and Hiroshi Ishii’s subsequent vision of tangible bits [19] offers a huge set of projects, many of which provide qualitative interfaces for data or system interaction (usually at Levels 4–5).

Sonification [35] and glanceable displays [e.g. 9, 34] also offer us diverse sets of examples often using non-numerical representation, also largely at levels 4–5. As noted earlier, qualitative does not just mean non-quantitative, and the boundaries may be blurred: if a sonification directly maps numerical values to tones, is it much different to an unlabelled line chart? Or are sparklines [37], for example, a way of turning quantitative data into a form of qualitative presentation?

Even with a quantitative display, how a person interprets it may have a qualitative dimension: Figure 8 shows an electricity monitor used by a study participant [28] who accidentally set it to display kg CO2/day equivalent; this “meant nothing” to her but she interpreted the display such that “>1” meant “expensive”. ‘Annotations’ of values as users construct their own meaning [11] may fit here; the aim must, however, be to avoid the kind of reductive ‘qualitative’ nature of a limited set of labels [13].

Figure 8: A quantitative electricity display that was used ‘qualitatively’ by a householder (see text). Figure 9: An example of MONIAC, the Phillips Machine, at the Reserve Bank of New Zealand (Photo by Kaihsu Tai, Wikimedia Commons, public domain dedication).
 

Analogy and metaphor are important here, and the almost-forgotten field of Analogue Computing offers us an intriguing perspective. By “build[ing] models that created a mapping between two physical phenomena” [7], some analogue computers effectively operated as ‘direct’ displays of an analogue of the ‘original’ phenomenon — a kind of meta-level 2 type qualitative display, with devices such as the 1949 Phillips Machine [4] (Figure 9), which performed operations on flows of coloured water to model the economy of a country, enabling an interactive visualization of a system in operation as it operates (there are parallels with Bret Victor and Nicky Case’s work on explorable explanations [38, 8], and the development of visual programming languages).

Other areas of pertinent research and inspiration, are synaesthesia and mental imagery: sensory overlaps, fusions and mappings offer a fertile field for exploring qualitative displays of phenomena.

Conclusion: What use is all of this?

We’re interested in using qualitative displays and interfaces for supporting decision-making, behaviour change and new practices through enabling new forms of understanding — as an aid to help people explore their own and each other’s thinking, and specifically to help people understand their relationships and agency with the systems around them [26]. Projects using qualitative displays are unlikely simply to be de-quantified ‘conversion’ of existing numerical displays; instead, the aim will be to make use of the approach to represent and translate phenomena appropriately, in ways which enable users to construct meaning and afford new ways of understanding, enabling nuance and avoiding reductiveness.

The spectrum of the ‘directness’ dimension introduced here provides a possible starting point for this work, by giving a framework for analysing examples and suggesting ways of handling phenomena to be displayed, and is currently being used by the authors to brief an undergraduate design studio project on materialising environmental phenomena to reveal hidden relationships. We welcome the opportunity to learn from others who have thought about these kinds of ideas to inform our future explorations of this area.

Acknowledgements

Thanks to Dr Delfina Fantini van Ditmar, Dr Laura Ferrarello, Flora Bowden, Gyorgyi Galik, Stacie Rohrbach, Ross Atkin, Shruti Grover, Veronica Ranner and Dixon Lo for discussions in which some of these ideas were formulated and explored, and to the CHI reviewers. Unless otherwise noted, photos are by the authors.

References

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Design Students Explore Landscape Metaphors for Project Modeling

Delanie Ricketts and Dan Lockton

This article originally appeared on the Carnegie Mellon School of Design website

We often use landscapes as metaphors in everyday speech, particularly to talk about complex systems—understanding a complex information system as an “information landscape”, for example, helps convey the idea that such a system, like a landscape, is vast and encompasses many interacting variables. However, while landscape metaphors are common in speech—terms like “stakeholder landscape”, “lie of the land”, “ocean of possibilities”, “food desert”, even the word “field”—landscape metaphors have been used more rarely in visual applications.

On March 30th, 45 Juniors from Carnegie Mellon University’s School of Design’s “Persuasion” class, taught by Michael Arnold Mages, Dan Lockton, and Stephen Neely, took part in a workshop to explore practically how physical and visual landscape metaphors could help elicit new insights about complex experiences—in this case, modeling and reflecting on group design projects. Facilitated by MA Design student and Research Assistant Delanie Ricketts and Assistant Professor Dan Lockton, as part of the School of Design’s new Imaginaries Lab, the workshop involved students collaboratively creating ‘landscape’ models representing projects they have worked on, using simple paper cut-outs of features such as hills, trees, weather, and people. Each group used the elements in different ways to represent different aspects of their projects, through creating ‘timeline’ landscapes in both two and three-dimensional formats.

Some projects started with rocky beginnings, represented by different cones or hills, in order to show how difficult that part of the project was. Other projects started with trees, rivers, and stars, representing periods of calm ideation, research, or general feelings of optimism. When projects encountered new difficulties later on, many groups represented these periods with lightning, rain, hills, and cones. Several groups used (and came up with names for) metaphors within the general landscape metaphor to represent specific parts of their project experiences, such as a “plateau of exhaustion” before the project came to an end.

Delanie’s previous prototypes of the landscape metaphor visuals, as part of her research assistantship project, have focused on how they could facilitate individual reflection on one’s own career path. However, while people found the metaphor and elements to be a useful and creative reflection tool, several expressed that it was difficult to show how their perspective changed over time within a two-dimensional format. In this second iteration of elements, we aimed to provide greater variation as well as enable three-dimensional expression. In addition, we wanted to explore how the metaphor could be used to think through a different topic, project planning or reflecting rather than career, and in a group rather than individual context.

Students’ responses to trying out this second iteration of landscape elements, applied to group projects rather than individual career paths, suggested that they found the process fun and creative, while also abstract. Many participants commented that the tool helped them understand their project and teammates’ perspectives better, especially in terms of stress, productivity, and overall emotional satisfaction at different points throughout a project’s lifetime. The format is more useful for surfacing — and reconciling — overarching understandings than probing deeper insights about the specifics of complex experiences, but, in triggering discussion, it has value in enabling members of a team to understand and interrogate each other’s perspectives and mental models of a situation (echoing ideas from organizational systems thinking experts such as Peter Senge).

We aim to develop the landscapes kit further, through iterations with application in individual reflection, project planning, and research settings.

Many thanks to Chris Stygar, Josiah Stadelmeier, and the whole School of Design 3D Lab for their help in developing the materials for the project, the Design graduate students and juniors for taking part in the different stages of the project, and Manya Krishnaswamy for helping facilitate. Thanks to Joe Lyons for putting the article on the School website.

Mental Landscapes
Mental Landscapes
Mental Landscapes
Mental Landscapes
Mental Landscapes
Mental Landscapes
Mental Landscapes
Mental Landscapes
Mental Landscapes
Mental Landscapes