Over the last two decades, data visualisation has diffused into the broader realm of mass communication. Before this shift, tools and displays of data-driven geographic- and information visualisation were mostly used in expert contexts. By now, they are also used in casual contexts, for example on newspaper websites, government data portals and many other public outlets. This diversification of the audience poses new challenges within the visualisation community. In this paper we propose personal relevance as one factor to be taken into account when designing casual data visualisations, which are meant for the communication with non-experts. We develop a conceptual model and present a related set of design techniques for interactive web-based visualisations that are aimed at activating personal relevance. We discuss our proposed techniques by applying them to a use case on the visualisation of air pollution in London (UK).
Traditionally, visualisations have their origin in expert domains. Information visualisation stems from the computer sciences and in particular the domain of computer graphics and human-computer interaction (HCI). Geovisualisation in turn originated at the intersection of these previous fields as well as cartography and geographic information systems (GIS). Although geographic- and information visualisation tend to overlap in the academic community, they are still often perceived as two separate domains due to their academic heritage. Since we are not particularly interested in their distinct visualisation qualities, but rather in their role as communication vehicles, they are subsumed under the unified term data visualisation in this paper.
Apart from their specific significance in expert domains, maps, (rudimentary) geovisualisations, and (simple) information visualisations like bar charts have been part of the public discourse for a long while. However, in recent years, even considerably more complex visualisations have found their way into mass media and other forms of publication that is aimed at a broader public. This rise of casual data visualisations (CDV) in non-expert domains now poses the question if and how laypersons and media recipients actually do profit from these forms of communication of complex information and data. In this context, the term “casual” is used in some visualisation and HCI papers to describe the usage of a technology by a layperson in a non-professional context, such as “casual browsing”. It furthermore often implies an undirected or rather non-intentional and non-goal-oriented interaction. In their investigation of the term and definition of casual information visualisation Pousman et al.  summarised those specific qualities and identified four key differences from traditional visualisation systems: 1) User population (wider spectrum of users), 2) Usage patterns (beyond the work context), 3) Data type (personally important and relevant), 4) Insight (beyond analytical). While many static visualisations such as printed graphics and infographics are also based on data, this paper focuses specifically on interactive data-driven visualisations which are today predominantly distributed through the web.
The diffusion of data visualisation is likewise driven by technological developments and the growing availability of (open) data. Furthermore, the interest in data visualisation as a means of communication that trespasses expert domains is encouraged by a standard rationale that emerged in the academic visualisation community over the years . This rationale encompasses a “deeply pervasive and optimistic rhetoric about the need for, and the value of, information visualisation in an increasingly information-overloaded digital age” [13, p. 17]. While such a positive appraisal regarding the impact and importance of data visualisation puts a clear emphasis on their overall potential, Danziger questions the seamless transfer of techniques and knowledge from academic and expert visualisation contexts into casual contexts. Instead, he emphasises the need to reframe, rethink, and reassess the requirements of data visualisation in casual contexts . With this, he contrasts the generalising standard rationale that suggests that the information overload can, to some extent, be overcome by the means of data visualisation. Although data visualisation in principle might hold the potential to reduce information overload – if not overcome it – this paper follows this line of argument and likewise questions the standard rationale in its all-encompassing optimistic rhetoric. With this paper we focus on the specific challenges that arise when data visualisations are used in casual contexts, especially in journalism or as a general means of communicating complex information to the public, and discuss the influence of personal relevance as a model to approach design solutions.
Let us illustrate our rationale by the example of online news. Before the widespread usage of interactive data visualisations, a traditional news article focusing on, for example, air pollution would have featured a text and maybe a simple static map or chart. Now, in addition to the previous elements, we might find an interactive visualisation that tries to help the user gain more insights and a deeper understanding of the pollution data. In this case, the visualisation aims at making abstract and complex data that was previously primarily accessible by expert audiences equally available to the broader public. What is important to note is the fact that a visualisation in this case also adds more information, instead of reducing the information overload. If, according to the standard rationale, visualisations are a means to reduce the information overload, how can this paradox of adding information by visualisations be solved or evaluated? Why should we add visualisations, for example in online news, if this also leads to an increase in information (and potential overload)? What is the advantage of an interactive visualisation in a casual context? In the following, we discuss the potential of data visualisation when providing information with a focus on personal relevance as one factor that, although it increases the amount of information as a whole, also increases the perceived news value. In Danziger’s discussion of data visualisation for the people , he integrates research that has more experience with mass communication than visualisation research and thus repeatedly takes on the perspective of journalism and infographics. Following his lead, the next section discusses how journalism research dissects the newsworthiness of an information and how we might learn from this perspective when trying to improve the design of CDVs.
2 Personal Information Relevance
The study of “news value”, sometimes also referred to as “news criteria”, is a line of research that has developed a model for analysing the selection of and attention to relevant information. On the one hand, this includes how journalists select which information to turn into a story and how much importance each story should be given to. On the other hand, the focus is put on how news audiences distribute their attention over a set of given stories. Early research in this area focussed primarily on the professional journalistic perspective . Later research was built on a constructivist argument, which considers journalism not as a mere reporting on “reality” but rather as actively constructing reality by selecting and weighting stories . More recent research also investigates news value from the audience perspective and tries to better understand how people select the news they read. Within this audience perspective, news value can be framed by the concept of relevance , . In this context, relevance is seen as an efficient way to reduce complexity. Complexity reduction, hence, is achieved by directing attention to relevant information, whereby relevance encompasses “all aspects of individual meaning for the recipient, no matter whether a stimulus is just interesting for someone or whether something is significant in terms of representing a possible threat to one’s life” [14, p. 6]. In her study, Eilders does not define specific qualities of relevance, she rather stresses that relevance is assigned individually “depending on personal experience and knowledge, personal expectations and goals” [14, p. 6]. Consequently, relevance can be understood as a value that is being applied in an active process of interpretation. A traditional news outlet offers static information (e.g. combination of article, image, and chart) and thus can only represent a certain perspective and only appeal to the individual relevance of a certain preconceived audience. As was already mentioned, we remain critical towards the notion of the standard rationale that visualisations can “solve” the problem of information overload. Instead, we explore another feature within visualisation that focuses more on the question of relevance. To be more precise, this paper proceeds on the hypothesis that interactive data visualisations can increase information relevance by providing multiple individual perspectives that each cater to the personal experience, expectations, and goals of the recipient.
In order to further investigate the potential of CDV, we return to the analysis of news value and specifically the context of relevance regarding the individual recipient. Many of the factors that influence news value are either related to the news cycle (e.g. duration and frequency) or content specific (e.g. dynamic or valence). Adding to that, the factor distance describes the relationship between the event or information and the journalist and her audience. This factor can be discussed in regards to spatial, cultural, political, social, and psychological distance. To exemplify our approach, we specifically focus on and discuss dimensions of spatial relevance. On a very fundamental level, spatial relevance follows Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things” [47, p. 236]. When returning to our example of news, in many instances distance can be perceived as the actual geographic distance between one individual (the recipient) and the event in question (the news). In other instances, distance can relate to a spatial entity that the individual feels connected to or used to be located in. This can encompass, for example, an event in a neighbouring country or an event in the birthplace of the individual. We chose spatiality as an exemplary dimension due to the increasing attention it has gained, for example as part of climate change research and science communication. Here, the potential of highlighting spatial relations and distance when trying to establish individual relevance for global phenomena can be observed. In regards to communicating the risks of global warming, Leiserowitz observed that local threats “are generally perceived as more salient and of greater urgency than global problems” [27, p. 53]. At the same time, an all-encompassing emphasis on the local consequences of global threats can be criticised on the grounds that this disconnection between the local and global scale might lead to the effect that people turn a blind eye on the problem as long as it does not hit them with its full force in their (spatial or emotional) proximity. This might render severe consequences that are already happening on a global scale invisible to local audiences that are not yet affected by them with the same gravity. At the same time, a shift in focus to the global scale might distance the audience from issues of climate change and render it as something which does not concern everyday life and thus obscure the responsibility of each individual on a local scale (cf. ).
In summary, the emphasis of closeness or remoteness in climate change communication are both ambiguously discussed in the field. Nonetheless, on a more general level, public discussion and mediation of discourses that also take into account global scales and non-personalised parameters also do influence people’s view of their own position and also constitute the (political) self (cf. [10, p. 174]). This reading informs our approach towards the design of CDV in that we see a need to integrate both global and local perspectives when striving to encourage individual behavioural changes or acknowledgement of a need for change on a global level that starts with public support on a local level. Thus, the question of “distance” and individual identification with a problem play a significant role in general journalistic and public communication and has already been discussed in relation to personal relevance in climate change communication. In this regard, identification poses as yet another “news factor” which contains the subcategories personalisation and ethnocentricity. Personalisation includes the concept of using an individual at the heart of a narrative regarding a certain information. When reporting on environmental issues, for example, concepts of personalisation can be seen in approaches that centre a story around an individual whose health is negatively influenced by factors in her environment, for example by severe air pollution. Ethnocentricity, on the other hand, has some overlapping with the spatial, cultural, and emotional factor “distance” and, for example, centres a report around the similarity of a certain ethnical group in order to stimulate relevance by engaging an audience that identifies with this group. Through both means, personalisation and ethnocentricity, the audience is encouraged to identify with the information at hand, which in turn is thought to establish and underline the relevance of the information.
As an addition to acknowledging the constructivist logic of representation (e.g. regarding journalism not as a reporting on “reality” but rather as a construction of reality in itself), relevance has to be understood as a relational construct as well. It is not a predefined category or value that each individual actively and consciously uses in order to filter a stream of information – the concept of relevance itself is a product of communication, it is being constructed with each interaction and observance and in relation to previous experiences, beliefs, and emotions. As Eilders sums up, relevance is a matter of perspective and highly subjective – it can “arise from former experience or knowledge or from a possible direct impact on someone’s life” [14, p. 6]. Consequently, the intricacy of the concept of relevance, the subjectivity and interpretational factor that play into it, all have to be acknowledged when trying to activate relevance within a design framework or visualisation model. Thus, just as the general claim of the standard rationale of data visualisation must be scrutinised and questioned, we critically acknowledge that our approach presented in this paper only is an attempt to activate the factor of personal relevance within visualisations – in the knowledge that “personal relevance” only can be approximated, never fully anticipated or addressed. We are interested in understanding if and how visualisations might provide more personal and individual perspectives and thereby possibly increase the relevance for the individual. In the next section we explore academic and applied work which explores the boundaries of personal relevance in CDVs.
3 Related Works
The aspect of relevance, even though often not specifically framed as such, does constitute a general field of interest in the visualisation community. Before we begin the discussion of related works in the area of CDV, we want to exclude a set of projects that deal with task-related relevance in information retrieval. When dealing with search, exploration, or information retrieval tasks in visualisation and HCI, relevance serves as a measure to discuss the decision making of users during specific tasks. Relevance, in this context, means that an information is of importance X to solving task Y (, , ; cf. , , ). The relevance of the actual information is only insofar “personal” as the information retrieval task in itself might be a personal matter of interest. As we described in the introductory section, the focus of this paper lies on casual interactions and not goal-oriented interactions. Even though there are certainly overlaps between personal and task-related relevance, we will exclude works that focus specifically on task-related relevance as part of an information retrieval process. Instead, our review of academic and applied works that revolve around relevance in the context of CDV are centered around two main themes: “personal data” and “visualising the individual”.
3.1 Personal Data
The first examples are of particular interest to our framework since they first of all encompass visualisations that are concerned with personal relevance, while at the same time the underlying data in itself is “personal”. Therefore, they serve as suitable examples for how the personal relevance of an information can be a guiding influence when designing the actual visualisation. Nonetheless, it has to be pointed out that obviously not every visualisation of personal data is focussing on establishing personal relevance. Many examples are simply visualisations of summaries of personal sensor data and activities and are not concerned with the dimensions of relevance as discussed above (e.g. a generic and basic Fitbit User Interface ). The following section will instead focus on a subset of data visualisations that explicitly center around personal experiences – not only in terms of the underlying data, but also in terms of visualisation design, intention, and narrative.
A good example for such research are the works by Alice Thudt, Dominikus Baur, Sheelagh Carpendale et al. on autobiographical visualisations , , . This line of work focuses on visualising spatio-temporal trajectories (see for example figure 1), which are – in the case of “Visual Mementos: Reflecting Memories with Personal Data” – enriched with photos taken by the individual user or “data collector”. Thudt et al. elaborate, amongst other things, on this concept of visual mementos, which the authors describe as an analogy to physical mementos such as photo albums, travel journals, and souvenirs that all “support personal recall of past events and sharing of experiences” [45, p. 371]. One goal within this visualisation approach is to evoke familiarity with the event represented by the underlying data, for example by revealing “patterns that help the data owners recognize their own history in the visual mapping” [45, p. 371]. This line of work strongly resonates with the proposed concept of inducing relevance by visualising personal spatio-temporal events. The authors reflect that their concept can be a “step towards creating meaning with personal data and reintroducing richness and emotional resonance into our experiences with digital data collections” [45, p. 371].
Similar projects on visualising personal spatio-temporal behaviour for reminiscent exploration have also been conducted by for example Move-o-scope . Move-o-scope is a web platform that allows users to upload their movement data and have it visualised as aggregated movement patterns that support, for example, reminiscence after a journey or the identification of patterns in everyday spatio-temporal behaviour. Otten et al. take a similar approach of aggregating movement data but visualise it with a focus on networks or clusters of spatio-temporal behaviour and the relations between places . Their visualisation “Shifted Maps” supports the discovery of recurring patterns in long-term mobility traces and encourages the user to engage with forgotten places and connections .
Furthermore, a thorough literature analysis with additional examples on personal visualisation and personal visual analytics has been conducted by Huang . Without being able to go into detail of the existing examples, they all shed light on the specific narrative of relevance that is at work here: personal data and self-tracking as an “interest in the self”. The concept of continuously and regularly gathering data about oneself, being it by hand, with the help of specialised gadgets, or by using a smartphone app, is most commonly referred to as the “quantified self movement”. It also includes the analysis of the data to produce statistics or visualisations that relate to regular habits, behaviours and feelings . Often, such self-tracking devices and apps are used based on a conscious decision to monitor activities and behaviour in an effort of “self optimisation”. At the same time, the quantified self movement also includes the sharing of data with others, sometimes even publicly. This can be described as a “playful mode of self-surveillance” (ibid.) which introduces disciplinary monitoring on a seemingly voluntary level into formerly non-disciplinary (private) spaces . While the availability of personal data is one of the aspects within a conceptual model of relevance in visualisation, the act – when seen in a critical light – of self-surveillance might also be seen as a bit uncanny. In this context, Foucault’s vocabulary and concept of “self-government” and power (as strategic games) allows us to understand such self-tracking practices not only as a “decipherment of the self by oneself” [18, p. 29] that is built on “self-reflection, self-knowledge, self-examination” (ibid.); Foucault also describes how the individual’s capacity for self-control is linked to power and “forms of political rule and economic exploitation” . However, in Foucault’s understanding, power relations do not necessarily result in a removal of liberty, they can also lead to an ‘empowerment’ or ‘responsibilisation’ of subjects and force them to decision-making in fields of action as part of self-government (ibid.). This reading opens the conceptual space towards an understanding of self tracking as part of a system of power that – besides the threat of exploitation – also carries with it the potential of empowerment and decision-making.
Such critical reflections of self tracking and the gathering of personal data hopefully contribute to a conscious, cautious and informed handling of self tracking devices (or a refusal to use them). Additionally, the interest in collecting personal data exceeds the mere purpose of self improvement: personal data can likewise be used in the examination of spatial behaviour on a collective and broader scale, it can be related to artistic practices and interpretations, and can be intentionally translated into discursive artefacts that relate to such critical discourses .
However, such a focus on personal data and the self might make it harder to communicate events on a global scale or bring up events that do not fall within the actual spatio-temporal experience of a user or reader. Yet, in the context of consciously conceiving CDV as a means to prompt identification and inducing personal relevance, the visualisation of personal (spatial) data could allow users to identify their own “position” in space, support reflection of their experiences in the lived-in world and their everyday life. Thereby, the integration or juxtaposition of personal data in CDVs might help to establish a connection to a broader dataset that aggregates both scientific data, as well as personal data by other individuals. On this basis, it is possible to also emphasise the fact that every aggregated dataset is comprised of information on or provided by a number of individuals, that the data is based on a variety of examinations, represents a multitude of views or positions, and in itself is a result of interpretation. Lastly, by integrating personal data and perspectives we wish to lay the ground for emphasising the need for interpretative and critical engagement with visualisations, both on the production as well as the reception side.
3.2 Visualizing the Individual
As mentioned in the last section, most (non-personalised) data visualisations are built on data that is collected by or that concerns bigger groups of people. Thus, every individual is only represented in an aggregated form, for example in a statistical overview on unemployment (see figure 2) or in calculations of population densities in a certain spatial area. In these examples, a variety of individual information or data is combined into aggregated values that are then visualised, for example, in a colour scheme, pattern, or as symbols. This practice of aggregation can be contrasted with a set of visualisations that take an opposing route: to show the individual in the data.
The first example of such an approach that centres around the “individual in the data” also remains in the sphere of spatial data visualisations. In 2010, Dustin Cable created a dot map for the United States (U.S.), in which every dot represents one person and is color-coded by race and ethnicity (see figure 3) . A similar map, but with an aggregation of 100 persons per dot, was produced by the New York Times (NYT) in 2015 . In contrast to traditional choropleth maps (representing e.g. census data, see figure 4) which show the race and ethnicity with the biggest count in a spatial area, such “dot maps” show each individual and, thereby, represent a more thorough and inclusive perspective on the data. By that, in theory, each U.S. citizen (included in the data) would be able to find him- or herself in the map as a dot.
The next two examples are visualisations on gun deaths in the U.S. Similar to the first two cases, these visualisations use one discrete visual entity for each and every single death instead of an aggregated visual entity that represents many (like for example the BBC graphic see figure 5). The company FiveThirtyEight created a visualisation in which each block of two times two pixels represents one gun death in the U.S (see figure 6). The data visualisation firm Periscopic visualised every death that occurred in one year as one line. The latter visualisation goes a step further and not only visualises the number of deaths, but also interpolates the possible lifespan of each victim. Thereby, the visualisation projects – as the project calls it – the “stolen years” (see figure 7). The general approach of representing each individual in the data does not only support direct identification, it also helps to give the audience a better sense for large numbers.
A related design technique that aims at making complex measures easier to grasp is centred around the idea that the illustration of numbers such as monetary figures can be supported by the inclusion of familiar objects from the real world, for example by utilising magnitudes and units that are visually and kinesthetically experienceable . Jacob Harris reflects on such visualisation techniques in a blogpost and also describes different approaches that aim at “humanising” data, all of which share the same motivation: to evoke empathy in the readers. One particular example is summarised as the Wee People . This design technique is closely related to the news value of identification, in that it does represent each individual with an abstracted and minimised human figure, instead of “only” a dot, line, or other “non-human” visual metaphor. On the downside, as Harris points out, such a visualisation becomes hard to read once it surpasses a certain threshold of data points, or when the goal is to visually compare two amounts over time .
The inclusion of human figures in data visualisations or graphics is not a new approach, whatsoever. Most famously, Marie Neurath, Otto Neurath, Gerd Arntz, and their team established the “Vienna Method of Pictorial Statistics” in the 1920ies and 1930ies, also known as “Isotype” (International System Of Typographic Picture Education). They not only theorised how to visually communicate abstract phenomena to the public, but also applied their Isotype system in public exhibition projects. The language of pictograms which were developed by Neurath and their colleagues were used to explain data on societal structures and developments to the public. In their visualisations, each pictogram represents a certain count of units (see figure 8). It is therefore an intermediate path between completely aggregated visualisations and the previously named examples where the focus lied on revealing the individual. Neurath and their team used visual metaphors that resemble the underlying entity to support a more intuitive understanding of the represented units. This pictorial visualisation technique is still popular today as it allows to create relatable illustrations in contrast to more abstract information visualisation techniques like bar charts. The New York Times used the technique for example to illustrate casualties in the iraq conflict (See figure 9 left) , as did Neil Halloran in his video on the fallen of world war II (see figure 9 right) .
Boy et al. have recently explored this spectrum of visualizing the individual in the data in casual contexts . In their study they developed a model that differentiates between abstract representations of individuals in the data (e.g. dots or lines), abstracted icons, and realistic shapes of human beings. While we used relevance as our conceptual model to discuss the design and perception of visualisations in casual contexts, Boy et al. explore how the design spectrum (abstract shapes, abstracted icons, realistic shapes) affects the level of empathy in the visualisation’s audience. The concept of empathy has recently attracted the interest of the visualisation community. It was discussed, amongst others, by the “Responsible Data Forum on Visualizations” in early 2016  and the conversation was then continued by Jon Schwabish in his PolicyVis Podcast, where he debated the topic with Kim Rees and Mushon Zer-Aviv .
While relevance focuses mostly on the guidance of personal attention and interest, the concept of empathy extends this and can be regarded as a precursor to action. High relevance could, thus, be a first step and an influential factor in later creating the feeling of empathy and it seems useful to integrate empathy into our conceptual model of personal relevance. Following this thought, it is plausible to suggest that it might be easier to associate with an individual that is more similar to us (e.g. on a level of spatial, social, or political distance) both when addressing personal relevance as well as in the creation of empathic encounters.
4 Establishing Personal Relevance as a Design Space for Visualisations
We have started this paper by proposing relevance as a perspective in conceptualising and designing data visualisations for casual contexts. We used research from fields such as visualisation, journalism, and climate change communication to suggest a framework around the concept of relevance. In the related works discussion, we illustrated how relevance, even though often not explicitly emphasised as such, is a concept that can be applied to many existing visualisation concepts and projects. In their discussion of CDV, Pousman et al. contrast “traditional” information visualisation with examples that can be described as artistic or ambient visualisations, which they use as illustration of their argument for less analytical use cases . They also put a strong focus on personal data. From our perspective, their study neglects the potential of casual data visualisation in regards to also being able to support analytical insights in less goal-oriented settings. Therefore, our design space is not limited to personal data and instead also tries to establish relevance for data sets which might not present a personal connection at first sight.
The following paragraphs translate the conceptual model of personal relevance, as well as some aspects highlighted in the related works section, into a model that establishes a design space for CDVs. Most of the previously discussed examples made use of the factor of “distance” when trying to enforce personal relevance. Even though this encompasses different concepts such as spatial, emotional, cultural, or political distance – which hardly can be translated directly into an euclidean distance – we will use this idea of distance as a metaphor in our design model. This will help us to address personal relevance based on distance or closeness in the design process of interactive CDVs. In order to do so, we will perceive the dataset at hand as a conceptual n-dimensional space. The statistical approaches and clarifying figures described in the following section only serve as metaphors for a data-driven perspective and are not meant to illustrate actual casual visualisation examples.
4.1 Taking Perspective Through Multidimensional Scaling
In a n-dimensional data space, each dimension can represent a certain attribute. When thinking about CDV and the question of relevance, our first aspect within the design model is to allow the individual reader to relate to some of these data attributes. This might include a relation by religion, political affiliation, ethnicity, residence, occupation, language, or more trivial aspects such as taste in music or choice of hobbies. By allowing an individual to relate themselves to at least some of the n-dimensions of attributes, they might be encouraged to mentally position themselves within a data space. This act of “mental self-positioning” is similar to multidimensional scaling in the sense that the individual searches for a location in the n-dimensional space that is characterised by data attributes with the greatest similarity to themselves (see figure 10). As an example for such a space metaphor, we would like to go back to Dustin Cable’s dot map . The dot map’s data space reduces the complexity of citizenship and identity to two dimensions: ethnicity and space (e.g. residence). On the spatial dimension, individuals can mentally position themselves, for example, in an area that is similar to their residence and thus evokes “closeness”. Or they can mentally position themselves in an area that they feel closely connected to on an emotional level. The same is true on the second dimension, the ethnicity. By combining the two dimensions and relating them to their own identity attributes, individuals can establish a relation to the displayed data. In the case of the dot map, this might be the identification of the area where one lives and a comparison of one’s own ethnicity with the other represented ethnicities in the same area. Or one could relate to other areas where there is an agglomeration of people that identify with the same ethnicity as oneself and thus establish a connection and feeling of “closeness”. Through both dimensions, a connection to each individual can be established. When applying this approach to other dimensions of data, the recipient needs to be able to take on an individual perspective on the overall data and mentally position themselves within this data space.
To extend the statistical metaphor, we could assume that in some cases certain attributes might be unknown or uncertain from the user’s perspective. Through a k-nearest-neighbour (KNN) approach, a visualisation or rather algorithm could then suggest certain attributes (see figure 11 left) that is based on similar items. As an example, a KNN approach could calculate a likely political orientation by having a user position themselves within a selection of opinions on certain topics. If an overwhelming number of items is present in a data set, using k-means clustering could group items. Thereby, a subset of items is created that shares some similarity with the individual perspective and can therefore serve as a selection which the user can identify herself with (see figure 12). Such a personal perspective can then serve as an entry point to the overall data space.
4.2 Overcoming Limitations by Overview and Zoom / Details
The concept described above presents us with an approach that aims at highlighting the personal relevance of a dataset to an individual, or, to be more precise, allow the individual to establish a personal relation to the data at hand. At the same time, these approaches distort the perspective on the overall data space. By encouraging the individual who interacts with such a visualisation to take on a distinct and personal perspective onto the data space, their focus might be limited to reflecting their own relation to the data which could then lead to a strongly biased perspective and render the global perspective or non-personalised significance invisible. When thinking of ways to overcome this, we can keep in mind the climate change communication literature discussed above. Climate change communication tries to increase relevance by introducing local perspectives, which are then reconnected to the global perspective in order to incorporate the local into the big picture. Turning Ben Shneiderman’s “visualisation mantra” (overview first, zoom and filter, details-on-demand)  on it’s head, a detailed and personal perspective could serve as an entry point that triggers personal association and supports personal relevance. In a second step, zoom and filter interactions could help to understand the “depth” of the data space and the associated topics by also introducing perspectives that go beyond the individual’s mental position. This intermediate stage might, for example, include a multitude of individual positions within the data space and thus lead over to more aggregated views. The third step can then introduce an overview on the global perspective and highlight the non-personalised significance of the topic at hand. Preferably, when applied in a visualisation, these three stages would support continuous shifts of perspective.
4.3 Visualising the Individual and Using Less Abstract Visualisations to Increase Identification
While the previous design techniques are primarily focussed on the data, the last technique focuses on the visual representation of the data itself. The related work has presented us with two connected approaches to visualise data in a more relatable way: visualising individual data points with abstract shapes or using visual metaphors that resemble the actual entity (e.g. humans or familiar objects from the physical world). The main idea suggests that, instead of showing aggregated visualisations, the individual data point within a display of data has a “higher resolution” and provides more level of detail and singularity, which then allows the recipient to relate to the individual cases behind the data points. Building upon this general idea, the design spectrum laid out by Boy et al. (see figure 13) suggests that less abstract visualisations might in turn lead to increased identification and relevance . Such an approach that aims at humanising abstract data was also illustrated by the NYT’s icon visualisations that introduces a personal perspective on the casualties in the Iraq war .
4.4 Challenges of Abstract Data
With the illustration of those three approaches within a design space we wish to transfer the theoretical concept of personal relevance in CDVs into an applicable concept. The implications in the design space are based on the conceptual model that we introduced in the first part of the paper and influenced by our reading of the related work. Most of the related work that we used to discuss some general ideas and concepts are either based on personal data (collected by individuals) or they are centred around human-related data. In both cases, the user’s connection to the underlying data is very obvious, as the data either portrays their own data or another individual’s data. The next section now illustrates how our concept of personal relevance as a design space for visualisations could also be applied to an abstract dataset that has no direct connection to either personal data or human-related data. Or, to be more precise, how we can establish a personal connection to an abstract dataset by, for example, introducing factors or data of personal relevance.
4.5 The Individual in the Data: Relevance and Personal Perspectives
With the following use case we illustrate how the design techniques described in the last section can be combined and applied to an actual dataset. We will particularly exemplify how this design space also allows us to address personal relevance with a dataset that is not directly connected to human identities, attributes, or traits. The basis for this visual exploration is air pollution data from the city of London . Traditionally, visualisations of air pollution either represent aggregated overviews or static maps of air pollution. We chose this example since it is quite abstract and, similar to the example of climate change, only affects a subset of the population directly, at least to such a degree that it is perceivable. A majority of the population is only indirectly affected, even though everyone obviously contributes to the problem at hand.
The first visualisation bears some resemblance to the traditional pollution maps (see figure 14). By adding multiple interaction elements, we then enable the user to locate themselves in the data or, rather, on the map. A locator plugin allows the user to identify and zoom onto their current position. In a similar way, a georeferencing plugin allows the user to search for an address (e.g. home, a friend’s house, work, a neighbourhood considered for relocation). Furthermore, prominent and well known locations are added as navigational cues. As previously discussed, this example highlights a typical problem in communicating pollution and climate change: on the local level, some people might not seem directly affected at first sight. A person living in a minor road near to a public park might be under the impression that air pollution is not a problem for them, thereby ignoring the overall impact on the whole city.
In order to overcome this spatial bias, we explore the temporal dimension in two additional visualisations which both focus on the bigger picture. The goal of both visualisations was to allow users to relate their own activities on a temporal dimension to the air pollution in the whole city of London. Instead of only displaying spatio-temporal difference between weekdays and weekends, the first visualisation (see figure 15) goes a step further and emphasises the individual’s connection to air pollution. It therefore connects a time use survey with the pollution data. Time use surveys are conducted via diaries in which the participants record their activities in certain time intervals. The UK time survey uses a 10-minutes interval, resulting in 144 activity descriptions per day . Weekday and weekends show very distinct patterns. By separating these two categories and then correlating the named activities with the pollution data, we are able to highlight the impact of traveling (e.g. commuting) on air pollution. We therefore selected an exemplary roadside sensor from the city of London and combined it with the time use survey (see figure 15). Thereby, we wish to encourage people to reflect their own activities throughout a day to either “find themselves” in the data, or assume a different perspective “through the eyes of others”. We acknowledge that this comparison is difficult on many levels. First, the time use survey data is not restricted to London but represents data from the whole of the UK. Second, air pollution caused by motorised travel is a strong contributor to urban air pollution, but not the only one. After analysing the overall temporal patterns of all sensors in London we can say that the general pattern of two peaks on weekdays during rush hour in the morning and afternoon, as well as a slowly rising and descending curve on the weekend applies to most locations, beside the inner city. The inner city usually shows high traffic throughout the day, which is only higher on weekdays.
In an attempt to increase the factor of identification even more, we developed a novel visualisation technique that combines video with a geospatial and a timeline visualisation (see figure 16). We recorded videos of bike commutes from several parts of London into central London. The GPS trajectories of those commutes were combined with the (almost) real-time pollution maps of London. Thereby we were able to create a timeline of pollution exposure for the cyclist. In this case we decided to only use a generic visual indicator for the individual moving through the city: a dot on the map that shows the current position. In this case, the humanising factor is integrated into the visualisation by synchronising the dot that indicates the position with the video images which, for each position on the route, allows the user to view the surrounding from the cyclist’s point of view. As a result, this visualisation gives a very vivid example of the exposure of air pollution on a person moving through the city. It furthermore illustrates the traffic impact on air pollution. A good example is the intersection at Elephant and Castle, which usually exhibits high congestions during rush hour. The videos clearly show the dense traffic, while the map and timeline correspondingly show the high pollution levels (see figure 17).
Through the examples above we illustrated how some aspects laid out in the design space for personal relevance could be applied to a very abstract and non-personal dataset. We did so by exploring ways that might allow the users to find themselves in the data or identify with aspects of it, especially through the factors of space, time, and daily routines. In the process of experimenting with ideas, we realised that such techniques can only be applied up to a certain level of complexity (see for example figure 18, which presents the recipient with a complex cluster of overlapping levels of information).
Through the discussion of these examples we identified additional perspectives and potential approaches that are based on the design space. One of these approaches could, for example, include using k-means to cluster the time use survey data based on activity patterns which would allow users to pick a group they can identify with. Another solution would be to use less abstract values. As an example, instead of only using abstract pollution scales, information on the actual impact on individuals could be included in the visualisation. This could include health related information, such as the impact of air pollution on persons with heart diseases or asthma, or information related to air quality and its influence on sporting performance or general well-being.
5 Future Works
With this paper we intend to open a new perspective on the design and development of CDVs. We are aware that we might have raised more questions than we have answered. We see the relevance of building CDVs as part of a thirst for information and a right to access public data. At the same time, we acknowledge that the design and development of CDV requires new perspectives that take other qualities into account than traditional visualisation and HCI research does. In a next step we need to substantiate the proposed design framework and evaluate the actual impact of personal relevance on the acquisition of knowledge and the diffusion of information. We are also interested in gaining more insights on the validity of the standard rationale, which is one driving force behind the continuing diffusion of CDV into mass communication channels.
It should be noted that the evaluation of the effects of visualisations developed for non-expert audiences, for example the perceived relevance or level of empathy, is not an easy task. Traditional evaluations of visualisations are inspired by the academic heritage of information visualisation: computer sciences and HCI. On the one hand they often focus on the performance and applicability of visualisation algorithms and systems and on the other hand they are interested in measures like efficiency and usability. Those studies offer compelling quantitative empiric insights, but might be less suitable for our needs. An evaluation setup that allows us to truly measure the impact of personal relevance on the perception and selection of information would have to take the respective “personal experience and knowledge, personal expectations and goals” [14, p. 6] into account when analysing the responses from our user group. Furthermore, the weighing of the responses might also be hard to define. In contrast to traditional visual analytics (where a certain task needs to be fulfilled), analysing a personal emotional response is a lot more difficult. Therefore, the next step is to further investigate the interrelationship between visualisation and audience and develop quantitative and qualitative ways to assess their impact. A first promising step will be the analysis of the influence of perceived spatial distance on the relevance of and identification with news.
In the context of assessing the purpose of visualisations and their potential impact on empathic encounters, the question has been raised if such a “personal” parameter as “empathy” is applicable to the concept of data visualisation at all. Our focus on “personal relevance” could be met with the same questioning. In a discussion with other visualisation researchers on twitter, Alberto Cairo questions the factor of empathy within visualisations; his understanding of visualisations – keeping the title of his latest book “The Truthful Art” in mind – seems to imply that they should convey data-based truth , . Cairo, in this context, names mathematical equations as the most effective tools for representing logical proofs. When concerned with visualisations in a scientific setting that are intended to present an “objective” view on the data, we understand that any of the suggested design techniques that target empathy or personal relevance might blur the originally intended functionality of a visualisation (of “conveying data-based truth”). Nonetheless, this presupposition of possible “objectiveness” ignores that in many contexts visualisations are not used as seemingly objective visual analysis. Instead, they function as argumentative parts in a rhetoric of mass communication. In a world solely based on logic, a scientific summary of the signs and data of climate change should be enough argument to make all of us adapt our lifestyle accordingly and radically reduce greenhouse gas emissions. But, being humans, we are emotional and sometimes logic is not the best way to support our understanding of complex connections. Instead, helping us to relate a global phenomenon to our own life and lived-in world might be the more successful approach.
While we as academics might regard this emotional nuisance as false play, or fear it might make us accomplices in “not conveying data-based truth”, the question of how to convey complex information in such a way that non-experts actually act upon this information needs to be investigated and addressed. On another level, we would like to put emphasis on the notion that no visualisation can actually ever be objective and that thus an argument of asserted truth – versus emotional rhetoric – obscures the subjective character of any visualisation (for more on this subject cf. , , ).
Similar to any communication process, the act of developing and designing a visualisation is intentional and to a certain degree subjective in the selection of data, the selection of visualisation techniques, the selection of how to publish it. In the same way as we understand that a journalist takes part in the construction of reality by writing a news story (instead of seeing it as solely reporting on “reality”), we need to understand that visualisations are part of the construction of a “data-based truth” (instead of seeing it as solely translating “truthful” data into visual forms). With that, every visualisation designer has to acknowledge their active role in this construction of “reality” and “data-based truth” and thus consciously decide what “truth” they want to contribute to. This is especially important when using visualisations to argue, which inherit a certain “communicative objectivity”  towards their audience. Using rhetoric, being it visual or verbal, has always been a part of communication, both in science related contexts as well as the media, and thus should be reflected as such also in relation to data visualisation.
The paper started by questioning the standard rationale of data visualisation in casual contexts and proposed the need for a reframing and reassessment of requirements and opportunities of data visualisations in casual contexts. Instead of solely relying on the claims of the standard rationale that visualisations are a suitable means for complexity reduction, the concept of personal relevance was introduced. This paper illustrated the conceptual model of personal relevance in the context of related work and proposed a design space built upon the conceptual model. As discussed in the future works section, we see substantial obstacles for empirically substantiating our model. We still believe that through discussing the topic at hand and translating it into actual use cases, such as the one presented in this paper, we will be able to already get a better perspective on the requirements of data visualisations in casual contexts. This, in turn, might inspire novel techniques and applications and result in further studies. We hope that this will then also lead to the refinement and consolidation of the assumptions of the standard rationale and, finally, help to establish data visualisation as a viable and critically reflected means of mass communication to the public.
About the authors
Sebastian Meier is a data scientist at the Technologiestiftung Berlin. He graduated in Communication and Interface Design and is currently finishing his PhD in Geoinformatics at Potsdam University. His research focus lies on spatial data analytics and visualisation as well as human-centred perspectives on software interfaces.
Katrin Glinka is a research associate and lecturer at the University of Applied Sciences Potsdam. She has a background in cultural sciences and integrates aspects of sociology, critical theory, and cultural studies into interdisciplinary projects that apply digital technologies to cultural collection data, spatial knowledge, or social practices. She is a PhD candidate at the Department of Cultural History and Theory at the Humboldt-University Berlin.
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