September 12, 2013

Electronic Visualisation in Arts and Culture, eds. Jonathan P. Bowen, Suzanne Keene, and Kia Ng. Springer (2013): 25–37.

This chapter is an updated and extended version of the following paper, published here with kind permission of the Chartered Institute for IT (BCS) and of EVA London Conferences: C.A. Sula, “Quantifying Culture: The value of visualization inside (and outside) libraries, museums, and the academy.” In S. Dunn, J. P. Bowen, and K. Ng (eds.). EVA London 2012 Conference Proceedings. Electronic Workshops in Computing (eWiC), British Computer Society, 2012. http://www.bcs.org/ ewic/eva2012 (accessed 26 May 2013).

The beginning of page numbers from the printed version are indicated in /red/ below.

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Quantifying Culture: Four Types of Value in Visualization

ABSTRACT As cultural heritage work increasingly involves quantitative data, the need for sophisticated tools, methods and representations becomes ever more pressing. The field of information visualisation can make a helpful intervention here. This chapter explores four types of value associated with visualisation (cognitive, emotional, social and ethical/political) and discusses their prospects and limitations, including examples. The chapter concludes with a case study illustrating the value of visualisation.

Cultural Heritage Institutions and Quantitative Data

Cultural heritage institutions have undergone major changes in the past few decades, marked by a noticeable shift toward the digital. Items once preserved carefully in archives – largely sealed from the general public – have now been given new life in digital collections; access, use and sharing have become central values at the most progressive institutions. Within this digital turn, there are two moments of significance. The first is the creation of digital objects (or the capture of “born-digital” ones), which opens up new possibilities for accessing, sharing and using content./26/

The second moment, which is the occasion of this chapter, is the point at which these scans, digital images, digital recordings, etc. become data

Two types of data may be involved with cultural heritage work. One is metadata, which describes these digital objects in a structured format and facilitates information retrieval, organisation and architecture. The second type is the data present in the content of items themselves, especially in the case of digitised records. Birth certificates, census counts and other ambient records are physical instruments for collecting and storing information. They have fields for “given name” or “race” or for more administrative metadata, such as record number or preparer. This information may be transformed into digital data by employing character recognition and also by exploiting the fact that these records are visual materials, whose layouts provide important clues about the types of information being recorded. Names are tagged as “Name,” letters and numbers become “Date of birth,” and so on. These values may even enter into databases where they can be aggregated, compared, merged and reconciled with other datasets.

Born-digital artifacts are even richer in quantitative information. Many photos, tweets and posts now carry embedded geospatial data, and the platforms that host them capture relationships between people and groups, forming large-scale social networks, the scope and documentation of which is unprecedented in human history.

Though data comes in various types (e.g., numerical, geospatial, relational), it may all be discussed under the rubric of “quantitative” information. The defining characteristic of quantitative data is regularisation through fields, value formats and validation. Qualitative data, or “document-centric” information [1], lacks these structures and is open in length and format, often preventing validation. Part of the reason why it is worth considering both types of information as “data” is that both are susceptible to analysis through computational means: statistical processing in the former case and natural language, image, or audio processing in the latter. These analytical methods also bring with them the need for more advanced representations of results. In the case of large collections, one simply cannot process such high-volume longitudinal data in a textual form. Attempting to do so would exhaust limits of human memory and attention long before trends could be noticed.

The field of information visualisation can make a helpful intervention here. Visualisation, broadly defined, sits at the centre of cognitive science, computer visualisation and data analysis. Colin Ware defines the term ‘visualisation’ as “a graphical representation of data or concepts,” specifically designed to harness and augment basic powers of human perception for the task of comprehending large-scale information [2]. Current information visualisations allow viewers to browse through complex datasets, noting top-level patterns and trends and often drilling down into more detailed information. Lin [3] reviews several early studies that identify contexts in which information visualisation is particularly useful: where there is an organisational structure that brings related items together [4], when users are unfamiliar with a collection [5] or its organisation [6], when users have trouble describing their information needs [7], or when information is easier to recognise than describe [8]. All of these instances have wide application to the /27/ materials found in cultural heritage institutions and many are especially relevant to the case of structured, quantitative data.

Though cognitive enhancements are the most frequently discussed benefits of visualisation, they do not exhaust a theoretical account of the value of visualisation. After all, many trends, groupings and hypotheses generated through visualisations require independent, statistical confirmation. Though visualisation may help show the way, or, “answer questions you didn’t know you had” [9], it is not the final or only approach to large data and its value is not limited strictly to its interaction with human cognitive systems. A more complete account would recognise other types of value added by visualisation, including emotional and social value, as well as ethical and political value. The following four sections each develop one type of value associated with visualisation. Each section also highlights examples of visualisation related to cultural information and suggests future areas of research to enhance our understanding of the development, use and evaluation of visualisation.

The Cognitive Benefits of Visualisation

Information visualisation attempts to harness quick perceptual systems for the purpose of processing information. Card, Mackinlay and Shneiderman even define ‘visualisation’ as “the use of computer-supported, interactive visual representations of data to amplify cognition” [10]. In discussing this definition, they list a number of cognitive benefits associated with visualisation:

• Increasing memory and processing resources available,
• Reducing search for information,
• Enhancing the recognition of patterns,
• Enabling perceptual inference operations (which are much faster than logical ones),
• Using perceptual attention mechanisms for monitoring and
• Encoding info in a manipulable medium.

According to Larkin and Simon, many of these benefits are achieved by substituting rapid perceptual inferences for more difficult logical ones [11]. This switch is made possible by preventive processing: low-level tasks in the human visual system that occur less than 200–250 milliseconds from the time an observer sees a visual stimulus. Healey and Enns summarise the range of these tasks as:

• Target detection: users rapidly and accurately detect the presence or absence of a “target” element with a unique visual feature within a field of distractor elements,
• Boundary detection: users rapidly and accurately detect a texture boundary between two groups of elements, where all of the elements in each group have a common visual property,
• Region tracking: users track one or more elements with a unique visual feature as they move in time and space, and
• Counting and estimation: users count or estimate the number of elements with a unique visual feature [12]. /28/

Visualisations that make good use of pre-attentive processing often help viewers to grasp large, complex datasets for the first time. This characterisation is reflected in Franco Moretti’s Graphs, Maps, Trees: Abstract Models for a Literary History [13]. As opposed to the close readings of a single text that typify literary scholarship, Moretti employs a “distance reading” method: “instead of concrete, individual works, a trio of artificial constructs – graphs, maps, trees – [is used] in which the reality of the text undergoes a process of deliberate reduction and abstraction…. fewer elements, hence a sharper sense of overall interconnection. Shapes, relations, structures. Forms. Models” (p. 1). In particular, Moretti’s graph of the rise of the novel in Britain and Japan (1700s), Italy and Spain (1800s) and Nigeria (1900s) provokes new questions about the development of the genre and the underlying forces of industrialisation that account for these trends. “[M]ost radically,” he says of quantitative visualisations, “we see them falsifying existing theoretical explanations, and ask for a theory” (p. 30).

In addition to amplifying cognition, visualisation has also been discussed in the context of aiding decision-making [2], as well as facilitating collaboration, engaging new audiences and fostering higher levels of understanding [14]. Additional social uses of visualisation are discussed in section “Visualisations as Social Objects” of this chapter.

A helpful example of cognitive enhancement applied to cultural materials is “Mapping the Republic of Letters: Exploring Correspondence and Intellectual Community in the Early Modern Period (1500–1800),” based at Stanford University (http://republicofletters.stanford.edu). The primary source material for the project includes over 2,000 correspondents who formed a communication network across Europe, Asia, Africa and the Americas and different project interfaces leverage mapping and network analysis techniques to trace interactions across space and time (Fig. 3.1). A key macroscopic component of this effort is its focus on high-level trends, structures and patterns, rather than the individuals that compose and exist within those larger elements. Such visualisations are no substitute for detailed analysis of primary source documents but rather an alternative method for understanding a set of material. The hundreds of individuals and thousands of connections between them could not be apprehended in textual form, yet visualisation renders these documents quite saliently at a glance.

Visualisation and the Emotions

Cognitive benefits are the most commonly discussed advantage of visualisation, receiving two to three times more attention than emotional and social advantages [15]. Bresciani and Eppler do discuss emotional disadvantages through studies of disturbing content [16, 17], boringness and ugliness [17], personal preferences [18], prior experiences [19] and wrong use of colour [2, 20, 21]. A closer examination of these sources, however, shows only casual reference to the role of emotions; none of the studies are specifically about the role of emotion in visualisation./29/

Even in-depth studies of visualisation aesthetics examine general features such as “beauty” and “ugliness” [22, 23].

Though research into visualisation and the emotions is sorely lacking, emotions have been found to play an important (although infrequently discussed) role in information processing generally [24] and it is reasonable to suspect that emotions enter into perceptions of visualisations, either alone or (more likely) in tandem with cognitive and other factors. Visual elements such as shape, flow, texture, position and colour are likely to elicit emotional responses from viewers, much in the same way that those elements engage preattentive processing to amplify cognition. More extensive studies of emotion and visualisation might explore the ways in which emotions bind to particular visual elements (perhaps differentially); interact with preattentive processing and Gestalt effects; facilitate cognition, meaning and understanding; and influence decision-making and action with respect to visualisation.

Chief among considerations of visualisation and emotions would be inquiries into the special role of colour, widely regarded as having emotional connotations – and one of the most problematic elements of visualisation. MacDonald [25] discusses the three ways that colour perception may vary across instances of observation, all of which involve cultural factors: individual differences, both genetic and developmental; group-level effects, such as gender and expert training; and the context of presentation itself, such as the display medium and colour calibration. Though earlier research attempted to discover universal colour names and associated emotional reactions, the most successful studies found only six to seven cross-cultural colour names [26] and very general emotional valences, such as positive/negative and active/passive [27]. In a controlled experiment, Post and /30/

Greene found that only eight colour categories plus white were consistently named with better than 75 % probability [28] and more recent studies have stressed that the meaning of colour terms varies across cultures, along with the emotions that colours evoke [29, 30]. An ambitious (if questionable) attempt to understand colour in culture has come through David McCandless’s chart of 13 colours and their 85 emotional associations across 10 cultural groupings [31]. (An interactive visualisation is also available [32].) This chart is based on data from “Pantone, ColorMatters, and various web sources,” making it hard to fully evaluate its research methodology for consistency and reliability.

More rigorous research into colour, emotion and visualisation might also reveal best practices for using colour to convey certain messages or, conversely, alert researchers to manipulative uses of colour – all with reference to cultural variations in the emotional significance of colour. In the absence of such research, it is premature to speculate about more systematic relationships between colour, emotion and visualisation.

Visualisations as Social Objects

IBM researcher Martin Wattenberg was among the first to discuss the “social life of visualizations,” [33] in which audience members participate in social data analysis through shared discussions, hypotheses testing and even gameplaying. These and other social uses of visualisation draw attention to the sense in which visualisations, once created, are social objects – artifacts, documents, things – that can be held up, examined, critiqued and shared. Heer similarly notes that such objects can establish shared interpretations (e.g., “do you see what I see?”), create spaces for conversation and break conventional boundaries through expected uses and reinventions of technology [34]. Both researchers point to NameVoyager, an interactive visualisation of baby name data from the 1880s to the present [35], which sparked wide-spread discussion well beyond the intended user community of prospective parents.

More recent research has explored visualisation-based collaboration, both synchronous and asynchronous. This literature generally recognises the complex and specialised nature of information processing in the present day, hoping to meet this task with the joint forces of visualisation, collaboration and social intelligence. Collaborative contexts for visualisation may range from viewing visualisations in group environments (e.g., lectures, presentations), to collaborative interaction/ exploration and sharing/creating of visualisations [14]. Specific tasks accomplished by visualisation include dividing and allocating work; establishing common ground and awareness; providing reference and deixis (context); offering incentives for engagement; promoting identity, trust and reputation; mediating group dynamics; and facilitating consensus and decision making [36]. Specific attention has also been paid to design elements and affordances that facilitate usability and group interaction, including segmented discussion spaces, pointing /31/ and annotation mechanisms, collection creation and linked views [37]; the wide range of skill level different viewers may bring with them to the same visualisation [38]; and “casual” visualisation, including ambient visualisation, artistic visualisation and other examples [39].

In some cases, visualisation also facilitates data collection. A common example is rating and commenting interfaces that also display aggregated feedback through visualisations. Another example is the Transborder Immigrant Tool, a digital art project by Micha Cardenas and Jason Najarro at the University of California San Diego, which uses hacked Nextel cell phones to track immigrant geolocations across the Mexico/U.S. Border. As well as providing undocumented immigrants with access to map information, the application’s creators hope it will “add an intelligent agent algorithm that would parse out the best routes and trails on that day and hour for immigrants to cross this vertiginous landscape as safely as possible” [40].

The Ethics and Power of Visualisation

The problem of bias has long been discussed with reference to acts of collection and curating, especially where cultural materials are concerned. Decisions over which items to collect, preserve and digitise, as well as how to categorise and disseminate them, all position cultural heritage institutions as contested sites of power. How visualisation might change, mediate, or interact with such power is a pressing ethical question.

The data foundation of visualisations often bestows a false air of objectivity and neutrality upon them. As Huff pointed out long ago, it is always possible to lie with statistics [41] and so too is it possible to lie with the datasets that form the basis of visualisations, if not the visual representations themselves. No matter how neutral or objective a dataset or collection purports to be, there may be residual biases in measurement design, modelling techniques or background assumptions. Cathy Davidson puts the point nicely: “Data transform theory; theory, stated or assumed, transforms data into interpretation. As any student of Foucault would insist, data collection is really data selection. Which archives should we preserve? Choices based on a complex ideational architecture of canonical, institutional, and personal preferences are constantly being made” [42]. In this respect, a more robust “ethics of visualisation” is needed to guide practitioners toward transparent and critical approaches to their data.

On occasion, visualisation can be of help in bringing questionable data to the fore of discussion. If large portions of continuous trend data are missing or a significant number of outliers present, such omissions or deviations will be plainly visible in faithful representations. These visual cues invite questions about whether trends are indeed as they appear, whether outliers are genuine outliers or something else (e.g., perhaps nothing more than an innocent keystroke error during data entry). Visualisations can even be used to represent imperfect data, as shown in a recent study that examined uncertainty across 18 different subject domains [43]. Though /32/ the arts and humanities were largely absent from this study, the five cross-domain categories for understanding uncertainty (measurement, completeness, inference, credibility and disagreement) are easily transferable to many disciplines. Visual techniques may not be able to address all types or degrees of uncertainty, but they can represent many of them more fully than statistical measures – especially measures of central tendency – and help to reduce the impression that findings are determinate or at least more certain than they are. Such techniques must be incorporated in the design process more frequently to be successful and Boyd Davis et. al. (Chap. 17) note how unusual it is for historical visualisations to bother representing imprecision or uncertainty.

Still, we must be on guard about the power of visualisations to misrepresent and mislead – as all representations can. Though some resources exist for visualisers, especially journalists [44–46], their guidance is mainly confined to case-based design studies and question of accuracy. Similar examples are found outside the world of journalism [47, 48], but they go little beyond questions of accuracy and design. Subtler effects of omission, framing, emotional manipulation and other ticks are rarely discussed – nor anything about the way in which visualisation might be used to good purpose by raising awareness, providing insight, or correcting false beliefs.

At present, it is worth noting that the shift toward quantitative data provides a level of empirical verifiability that is not found in many non-quantitative forms of visualisation. This shift provides wronged parties with a framework within which to question claims, seek redress and present counter-narratives, in much the same way that human rights advocates have historically advanced empirical realities in the service of greater equality. This process is far from perfect; evidence can be ignored and powerful bodies often have more resources to produce data than those with less privilege. Nevertheless, an empirical framework is, in principle, more disinterested on the whole. The victors can still write history, but only insofar as they can measure it – and cannot avoid all measurements of it, even those that challenge established narratives.

Visualisation, however, can do more than just reduce harm through minimising bias, error and false completeness; it can also help individuals and groups, especially those that are unrepresented or underrepresented in the past or present. A prominent example here is Invisible Australians (http://invisibleaustralians.org), which documents Indigenous Australians and thousands of non-Europeans – including Chinese, Japanese, Indians, Afghans, Syrians and Malays – who faced discriminatory laws and policies. The site draws together government records of these individuals, including archival photos (Fig. 3.2) and attempts to “link together their lives.” While the site currently focuses on more qualitative aspects of their lives, the quantitative possibilities abound, from frequency charts and line graphs of their history to geo-spatial mapping and network graphs of their activities and connections.

These and other such uses of visualisation belong to a broader study of “liberation technology” [49–51]. As Diamond [52] points out, though internet and communications technology can be used for censorship, it can also allow individuals to “report news, expose wrongdoing, express opinions, mobilise protest, monitor elections, scrutinise government, deepen participation, and expand the horizons of /33/ freedom” (p. 70). More generally, it can help create a pluralistic sphere of public discussion before democratic rights are even present. Though much of this literature is centred around contemporary notions of democracy, it should also be noted that the vast amount of cultural heritage materials available for visualisation speak to a range of political, economic and social arrangements of power.

A Case Study of the Occupy Wall Street Project List

On September 17, 2011, an encampment protest began in New York City’s Zuccotti Park, blocks away from Wall Street and the New York Stock Exchange. Occupy Wall Street, as it came to be known, drew in thousands of residents and tourists for conversations, criticism and direct actions, and generated solidarity groups in all 50 states before its forcible eviction two months later. Reflecting on this movement in December 2012, Time magazine noted a shift toward what it dubbed “Occupy 2.0,” a transition from physical occupation to partnerships with local communities and community organisations: “less than a year after the last protester was removed from New York City’s Zuccotti Park, the movement has re-emerged as a series of laser-focused advocacy groups that, loosely organised under the Occupy umbrella, are trying to effect change in a variety of sectors, financial and otherwise” [53]. /34/

Such a claim has obvious importance, both for those interested on the legacy of the Occupy movement and for the American political landscape in general. Though many have offered speculations about the ultimate impact of Occupy, compelling empirical data has yet to fully emerge. Part of this effort has been taken up by branches of the Occupy movement, including OccupyData NYC, which hosts regular hackathons to analyze and visualise data produced by and about the Occupy movement and related issues.

This case study, led by the author over several hackathons, investigates three moments in the Occupy movement drawn from three issues of the Occupy Wall Street Project List published between February and July 2012. Each issue lists several dozen projects and participating organisations, including Occupy-related groups, community organisations, political organisations, religious/spiritual organisations and unions. From these lists, relational data was extracted about partner organisations, providing a window into the shifting structure of the Occupy movement within the larger American landscape. Organisations listed in the directory of the New York City General Assembly (oriented around the physical occupation of Zuccotti Park) were categorised separately from larger Occupy-related groups to study the special role of space in the movement. Notably, the data was all generated by the Occupy community itself, which provides a degree of ethological validity lacking in external interpretations of the movement. Non-Occupy organisations were classified through web research.

In a series of network visualisation (Fig. 3.3), each project is represented as set of lines between partner organisations. The resulting force-directed network graphs provide powerful, macroscopic views of (sub)cultures arising from these projects and hint at larger patterns of growth, development, division and perhaps even replication within the movement.

Two trends are particularly noticeable across these visualisations. The first is a shift in structural relationship between NYCGA and Occupy-related groups /35/ and community organisations (shown in black). In February 2012, NYCGA and Occupy-related groups are found in dense clusters, often separated from community organisations on the fringe of the movement. By the end of the period, these organisations are more fully integrated into topical clusters around financial, political, educational, health care, labour, arts and culture and other areas of advocacy (all viewable in the detailed online version). The second trend is a shift in the overall structure of partnership from a highly centralised network to a looser, chain-and-link model, with major NYCGA and Occupy-related groups connecting the various issue-based clusters. Such observations seem to support the description of Occupy 2.0 presented in Time and raise further questions about the causes and significance of these shifts.

These images again underscore the social nature of visualisations: the sense in which they and their contents may be discussed and disseminated among broader audiences. Colour versions of these images were exhibited at the James Gallery at the Graduate Center of the City University of New York in March 2013 along with other materials produced by OccupyData NYC. Each image was printed and placed in a small petri dish, evoking themes of surveillance, monitoring and control as well as the use of visualisation for self-reflection, understanding and intentional practice. Informal observations of visitors noted a range of reactions to these images, with some seeing fragmentation and discord and others noting a broader base of support and work with community organisations.

Conclusion

Though cultural heritage institutions are faced with a deluge of digital information, the process of presenting such materials is greatly facilitated by visualisation, which holds vast potential for providing context, insight and perspective with large-scale datasets. The empirical foundations of such datasets also support visualisations that reduce bias and represent individuals, groups and events more fully. While significant work remains in developing and preserving visualisations, the field provides exciting ground for the task of quantifying – and visualising – culture.

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