Visualizing the Weird and the Eerie
An essay written for the alt.VIS workshop at IEEE VIS 2023
(…what follows is an abridged version of an essay I wrote for the alt.VIS workshop at IEEE VIS 2023, a “venue for work that is otherwise difficult to place in the main conference for reasons of form, format, or topic.” – the full version of my essay is available as a PDF here…)
In his 2016 essay collection The Weird and the Eerie, the late writer Mark Fisher set out to define these two terms. He references a selection of critically notable novels, films, television series, and pieces of music that evoke the weird and the eerie. And his writing led to my realization that data visualization may also be capable of instilling these unique affects.
Fisher argues that the weird and the eerie are distinct from one another, even though they are both often associated with the genres of horror, science fiction, and post-apocalyptic fiction. However, the weird and the eerie are not necessarily horrific or devastating.
According to Fisher, the weird is “that which does not belong,” that is, the presence of something that seems wrong or at least strange; the placement of unfamiliar things within a familiar context. And it’s not necessarily a negative affect; there can be enjoyment in seeking out the weird.
In contrast to the weird, Fisher defines the eerie as a reaction to situations “when there is something present when there should be nothing, or when there is nothing present when there should be something.” In both cases, there is a question of agency, a question of who or what is responsible for the lack of absence or the lack of presence, and what motivated — or continues to motivate — this unseen agent.
In my essay, I contextualize the weird and the eerie by discussing a few related affects in visualization and HCI research: fear, thrill, uncertainty, ambiguity, doubt, a profound feeling of loss, and even humor.
Ultimately, the affective goal of communicative visualization is persuading viewers to take action. To compel to action, however, first requires strengthening or changing viewers’ beliefs. Encountering the weird is an opportunity to change what one believes; Fisher remarked that the weird forces us to acknowledge that “the weird thing is not wrong [—] it is our conceptions that must be inadequate.”
Another motivation for this essay stems from my interest in visualization techniques that integrate semantic cues into encodings of data. While it may be easy to imagine semantic cues that indicate how particular visual patterns exhibited by the data are good or bad, it is less clear how to effectively communicate that some patterns are simply weird or strange. Similarly, it is not universally obvious how visualization designers can communicate that a particular pattern is present or absent, as well as how to express a lack of an explanation for these presences or absences.
Visualizing the weird
Visualization practitioner Andy Kirk remarked that people “are naturally drawn to gaps and exceptions and things that don’t really fit in with the rest.” And yet, designing representations of data that emphasize these aspects can be “a very fiddly challenge.”
Real datasets “frequently contain outliers, missing data, and “just plain weird” distributions.” [Leland Wilkinson and colleagues, 2006] A professional data analyst is trained to spot these weird distributions, distributions that seem strange or wrong given the context of the data. But how can the analyst effectively communicate that these distributions are weird to a lay audience?
Despite the precision and generalizability that scatterplots and their variations offer, using other forms of representation can, according to Enrico Bertini and colleagues, promote “serendipitous discovery, educational impact, hedonic response, or changes in behavior”. Like the hedonic enjoyment of the weird in popular culture, it is similarly possible to enjoy weird visualization design choices.
In other cases, weird choices of representation can accentuate weird patterns in data. Consider the connected scatterplot: it can resemble a line chart, but it can also exhibit visually prominent line reversals and loops, indicative of weird events in the relationship between two variables. Absent weird events, the connected scatterplot is often a poor choice for showing bivariate temporal relationships
The connected scatterplot is not alone in its capacity to draw attention to the weird. Visualization designer Maarten Lambrechts has amassed a catalog of techniques that he calls xenographics, or “weird but (sometimes) useful charts.” The concept of xenographics suggests that given a weird observation in data, there may (or should) exist a suitably weird way to represent it
Visualizing the eerie
Fisher’s definition of the eerie as a failure of presence brought to mind Nicole Hengesbach and colleagues’ review of the four types of missingness in data. If an entity exists in reality and is either completely or partially captured as data, the representation is complete (or at least partially complete). However, if the entity is present in reality but unaccounted for in the data, this is an absence, while the converse is an emptiness. Lastly, there is nothingness: entities are neither present in reality or represented in the data. Given these categories or missingness, an absence or an emptiness could be seen as eerie if there is ambiguity with respect to the processes of data collection and redaction: who was responsible, what their motivations were, and if any external forces acted to bring about absences or emptiness. Nothingness can also be eerie, even when the processes of data collection are relatively transparent, and particularly in cases where something or anything was expected.
In communicative visualization design, evoking an eerie affect around absences in data requires establishing what expected patterns look like or how they have manifested in the past, contrasting presence with absence, and withholding or deferring possible explanations for this absence. An example of this formula is Isao Hashimoto’s 1945 – 1998, a multimodal combination of visualization and sonification, animating every atomic detonation during this time span on a world map, with different colors and tones indicating the nation responsible for each detonation. Following several cacophonous decades with hundreds of detonations and a barrage of color and sound, the last decade is markedly quieter: a relatively eerie calm has set in. By withholding any verbal or written explanation in the video itself accentuates an eerie affect.
An eerie affect induced by a failure of absence could arguably be easily triggered given our propensity to apophenia, a bias in which we recognize patterns and attribute their appearance to an entity (or entities) that have agency. In most cases, however, there is no agent, and the pattern is fleeting, illusory, or insignificant. Visualization designers must exercise care to ensure that their viewers do not succumb to apophenia unless this is the intent, such as in Michael Brenner’s humorous Viz in the Wild project, in which spurious visual patterns appearing in photographs are captioned as charts.
However, in select cases, there is an explanation for salient patterns where none were expected or designed. Dietmar Offenhuber’s notion of autographic visualization is useful here, as the interpretation of material traces left by environmental phenomena can often yield meaningful measurements as well as revelations regarding the process of data generation. Meaningful measurements depend on several design operations, including the juxtaposition of frames, annotations, and decoding scales alongside material traces. By undertaking these operations and measurements, practitioners can arrive at explanations for the patterns, which may include an attribution of agency, such as air pollution caused by human industry and transportation.
A sense of the eerie is instantiated when an autographic visualization interpretation either fails to attribute this agency or fails to explain the motivations of an identifiable agent. It is similarly eerie when an expected material trace vanishes, and we are bereft of any explanation for this disappearance. I’ll offer a personal example: for years I regularly visited a Florida beach that was pockmarked with tiny holes made by sand fleas, a visual pattern distributed evenly along the shoreline. One year, this pattern had vanished altogether along one stretch of the beach, and in that moment I could not explain this lifeless failure of presence. (I would later learn that the adjacent resort hotel had began importing sand from elsewhere, destroying the natural habitat of the fleas.)
Visualization for weird and eerie time
We are living in weird times: from weird weather fluctuations to invasive species, algal blooms, and disruptions to currencies, real estate markets, and supply chains. How we represent these and other weird happenings, whenever we capture them as data, should be commensurately weird, particularly if we are to communicate just how weird they are.
For example, new visual idioms like Ed Hawkins’ Warming Stripes are useful in communicating how weird extreme weather events are, though even these elicit questions of how we should visualize future extreme values: while we could renormalize the color palettes to make deep blues and deep reds more apparent, renormalization may fail to capture the severity of the next weird event.
Our world is also increasingly eerie. Jenny Odell recently wrote about the feeling of living in apocalyptic times, which is fitting given how post-apocalyptic films and novels often evoke a sense of the eerie. Biodiversity loss has resulted in eerie landscapes and oceans, while human migration has resulted in eerily empty urban centers, empty rural settlements, and conversely crowded interstitial spaces: Why are there absences in places where entities are expected, but present in places where they are not?
Odell’s writing also mentions that the etymology of apocalypse is Greek, meaning to reveal, and that prior to its modern English usage signifying ‘an ending’, apocalypse was closer in meaning to insight. Given the old adage “the purpose of visualization is insight, not pictures”, could there be some utility in the notion of “apocalyptic data visualization”? In experiencing the eerie, each viewer can arrive at some degree of insight, that is, to identify the forces acting on world and the data collected.
In conclusion, for much of what could be constituted as weird or eerie in our world, we have the potential, via data visualization to document this reality, and ultimately, the potential to change what viewers believe. I leave you with several design implications, including some (*) that I didn’t discuss here, so please read my full essay if you want to go further down the rabbit hole of the weird and the eerie:
Acknowledge situations where it is appropriate for data visualization to make viewers uncomfortable.
Avoid manipulating viewers’ impressions by pairing an expected or familiar data observation with a weird choice of representation, reserving the grotesque art of xenographics * for communicating truly weird patterns in data.
Employ egocentric perspectives and doorway motifs for distinguishing the familiar from the weird.*
Provide subtle indications of agency via animation.*
Withhold or delay revelatory explanations or annotations for failures of absence and failures of presence.