Seeking the noema of big data visualization

30 minutes read

I do not even know what I am searching for, and it makes it hard to explain. But at the same time, that is why it is worth it.

These days, I was stuck in the writing of a long paper. It has been submitted to publication, and the anonymity of the peer-review conflicts with writing a blog post – for now. As an agile developer I feel bad, as I overlooked the mantra: release early, release often. In other terms, if I am not ashamed when I publish, I published too late.

I am not ready yet, so let’s publish something. This post is not my conclusion, but the beginning of my journey.

I started reflecting on the noema of big data visualization (I will characterize those terms), and I found myself lost in a forest of concepts that I do not master. As I just entered these woods, I was barely capable of retracing my steps to the edge. Here I am just consolidating my early steps. I can’t tell whether the path leads somewhere, and once (if) I get somewhere, that path will not be optimal. It’s exploratory.

I want so salvage something

Big data and its visualization are criticized for different reasons, most of which I agree with. I made peace with the disappointing rhetoric of big data in the industry, both as an engineer and as a researcher. I have never believed in the myth. Yet I believe that big data visualization works, that it does something for you. I don’t think it is a scam. A wreckage, maybe, but from which there is something to salvage.

I cannot write that which I want to salvage. I do not have the words, and in fact, I do not know what it its. I endeavored this exploration to find an answer.

My expertise on data visualization gives me the tools to unpack and criticize the big data viz rhetoric. For instance I do not make network visualization invisible as a mediation; I document the multiple layers involved; I argue against misuses; and I criticize the distorted reasons why people rely on it.

Anecdote. For a PhD course, I write a piece titled Big data visualization beyond persuasion, arguing that “big data visualization builds its own abusable regime of persuasion.” As my draft is discussed in the context of the course, I realize that some participants understand “regime of persuasion” as a bad thing per se, as a myth generating fake science. While for me, persuasion can be abused but also useful; for me the success of big data is also due to legit persuasive powers. They ask: who persuades? and of what? Awkwardly, I have no answer. I only realize that I am trying to salvage something despite my criticism. Let me face that.

A distinctive material-semiotic feature

When Ruppert and Scheel observe big data under the angle of the politics of method, they find material-semiotic practices involving visualization. I wrote about their paper in a previous post.

“To be clear, our point is that discursive struggles often work together with digital devices such that the politics of method cannot be reduced to language games.”

Evelyn Ruppert and Stephan Scheel in The Politics of Method: Taming the New, Making Data Official

I suspect that detractors of big data oppose, through it, to the world view of the natural sciences. Such strategy is doomed to fail. Resisting the epistemology of physics modeling is one thing, pretending it does not work is another one. Having a foot in both worlds, I have the feeling that big data visualization is effective despite the well-identified myth. Obviously, big data cannot be at the same time the fuel of surveillance capitalism and the new snake oil: either it performs, or it doesn’t. Denouncing big data visualization as the harmless and replaceable vessel of a convincing myth does not help, as the substance is actually active, even radioactive. Big data visualization is not the symptom, but the agent of a problematic power relation.

This tells me what to search for: a material-semiotic property of big data visualization that grounds both its effectiveness and its specificity.

The question is not to tell whether big data visualization shows real things, or imaginary things. As a mediation, it translates and distorts, so one can argue either its fidelity or its biases. Nobody is fooled by this duality. The question is to determine why one does, or does not, hold the visualization for true.

I seek a piece of the sense-making mechanic. I postulate a single trait that gets interpreted differently depending on your sensibility, but yet characterizes big data visualization for everyone.

Barthes’ noema

I draw inspiration from the French semiotician Roland Barthes. In his book Camera Lucida he inquires the essence of photography, from a phenomenological and personal standpoint, reflecting on images of his recently deceased mother. This book can also be read as a testament, as he died shortly after of a car crash.

Why do we hold for true what we know being an illusion? Anyone can relate to the feelings of Barthes looking at the image of his deceased mother. Barthes articulates how the sincerity of his affect can derive from a treacherous image. Despite his mother posing for the photographer; despite the artificial setting; despite the photographer’s touch; the photograph is the trace of a moment that actually happened. Despite the fabrication, it has been. For Barthes, this is the essence of a photographic image. He calls it the photographic noema: that-has-been, “ça a été.”

The noema is a phenomenological concept introduced by Husserl, but Barthes uses it in a pretty liberal way. As there is a long-lasting controversy on its exact meaning, and as it does not matter much here, I just stick to Barthes’ loose usage.

The photographic noema is a powerful pivot to deconstruct a photo. It does not work in isolation, but altogether with other concepts such as the studium, the spectrum, and the punctum. I think it is better to skip this theory here, but it is a very rewarding (and famous) piece of semiotics. It suffices to understand how the photographic noema allows unpacking why we hold the illusion for true. Here, McLuhan is a legit shortcut: if the medium is the message, then the message of photography is that-has-been. Any photograph implies “that has been”, and its meaning derives from there.

I must warn against a possible misunderstanding. For me, the photographic noema does not require that-has-been to be true, or that one agrees with it. Barthes just tells us that it the noema is an inevitable part of the meaning; thus I assume that the truth of that meaning can of course be assessed independently. It is precisely assessed, in fact, against it. A drawing of you hugging the pope is not deceptive, but a photo is, because the drawing does not tell that-has-been. It might not be what Barthes meant, but since photographic manipulation has been commoditized, the situation has changed.

The noema does for Barthes two interesting things. Firstly, it gives a single point of origin for multiple interpretations. A same image may mean different things to different people, their meanings can still be unpacked starting from the same underlying assumption – some may believe you hugged the pope and others not. Secondly, the noema is specific to photography. It defines its particular semiotic character.

Ordeals

I can imagine different propositions for the noema of big data visualization. I need to evaluate them. My next step is to formalize ordeals to help me in that task.

(1) The noema must be specific to big data visualization.
I characterize big data as Kitchin’s “3 Vs”: Volume, Velocity and Variety, to which I add relational data (networks). It must in particular mark the difference with two other forms of visualization: the map, and the chart (e.g. a bar chart). Maps or bars are not forbidden, but the “big data” qualifier requires at least one of those traits: outstanding volume, dynamic, heterogeneity, and/or links.

In this post I borrow a practical case from Ruppert and Scheel (see the figure below). It represents the population density of Ljubljana, as derived from mobile data. It qualifies by its volume and dynamics.

(2) The noema allows formulating both the critique and the defense of big data visualization.
It must be compatible with the idea of a myth of big data, but also account for the reasons why it can be held for true, and of course acknowledge big data visualization as a mediation: conveying information but with a distortion, constructed but not independent of what it represents, etc.

(3) The noema is rooted in material-semiotic properties
It is important that the noema is not just the product of the discourses surrounding big data. Sure, these discourses matter as they impact our interpretation; big data visualization is culturally situated. But the noema is nevertheless the key to unpacking how the interpretation of big data visualization derives from its material-semiotic features.

(bonus) A minimal statement
It is clear now that I am not philosophizing freely for the purpose of it. I am crafting a concept. There is a design to it. The noema empowers you this way: you convoke its statement as a fulcrum, a pivotal landmark for generating a discussion on how an image makes sense. It is a key to unpacking the meaning of the image. As a key, it must be transportable, lightweight. It works better if you can remember it easily, and state it shortly. I also want my noema to be minimal.

Possible noema and their problems

a. that-has-been

When I look at the picture above, the high number of data points makes an important difference with the usual bar chart: the data have been reduced less. The visualization is closer to the raw data, it does not make any effort to convey a curated message to a broad audience. Of course there is still a reduction, an aggregation (e.g. the grid on which the bars are based), but the trade-off between realism and readability leans towards the former (in the figures below, it leans on the left). Then, why not just reuse the photographic “that-has-been”?

Understanding Comics, Scott Mc Cloud, 1993
The “Pédofil” of Boa Vista, Bruno Latour, 1995

Big data visualization shares something with photography, in the sense that it is a relatively raw record. However, one does not recognize the same way what they represent. When I look at a photo of my mother, I see her; I recognize her effortlessly. When I look at the visualization of population densities in Ljubljana, I recognize nothing, not even Ljubljana. I must make an effort to understand what I am looking at (read the caption etc.). I do not know what that is right away.

The that of that-has-been is much less problematic in the case of photography. The mechanical process of fixing the scene on a substrate is quite close to how our eyes function; close enough that it makes sense to understand it as if I was there. The that of that-has-been is both the that of the image and the that-as-if-I-was-there. But it does not work with big data visualization, because the representation is nothing like the represented phenomenon.

b. that-has-been-manipulated

This is what Bachimont proposes as the noema of the digital (in French). I do not reproduce his point here. Big data visualization is digital, so it is part of it, but not specific enough.

c. something-is-brewing

My next try is to emphasize the rawness of the record without relying on a that. It does not seem so problematic, as in my experience it is quite difficult to put words on what a big data visualization represents – and I mean it in a good sense, not as a failure, since ambiguity is a feature of the world. To me, it seems fair to assume that one does not really see what is going on. Sure, in the case of Ljubljana, there is something to see – as stated in the caption. However (1) there is potentially more to see and (2) if you curate the data differently, even in the absence of a main message, it remains a big data visualization. It does not require that we understand what is going on – but something is going on. Is that the noema?

I like the idea that big data visualization is visualized rawness. For me there is a rawness to the data, but the visualization itself, of course, is all but raw. Actual rawness would be something like a series of 0 and 1 in a computer. Like photography, there is a mechanical dimension to the reproduction of the represented phenomenon. But the rawness we see is fabricated, because our cognition is not compatible with the order of that materiality. Even represented as a spreadsheet, which is already quite a significant processing, it remains too raw. The rawness we see is staged rawness. Once again, I do not mean it in a negative way; the point of visualization is precisely to provide a good staging. However there is a paradox to this well-cooked rawness. I want to capture that contrast.

I like the something-is-brewing expression because it suggests “under the surface”. In French I would pick “il se trame quelque chose”, an idiomatic expression where “trame” also means “pattern”. I am considering here the implicit message that big data visualization makes the invisible visible, albeit in an indefinite way.

However, this candidate has two issues.

Firstly, something-is-brewing applies a fortiori to other data visualizations, where we often know what is brewing. A bar chart may well tell the same. It is not specific to big data visualization.

Secondly, I am not convinced that the “making the invisible visible” angle is rooted in material-semiotic features. To me, it sounds more like a promise that the promoters of big data associate to it. What is visible or invisible depends too much on the audience. Something might be visible to some, and not to others. For that reason, it is less specific to the material-semiotic features of big data visualization.

d. that-is-how-it-is

I now explore now more directly the idea of rawness. This time, instead of dealing with the problematic that by getting rid of it, I split it in two: we have a that, and a it. One is the representation, and the other one is the represented phenomenon.

This proposition addresses most issues, but not with equal success.

Is it specific to big data visualization? It must not work for other things like photos, maps and charts.

For a photo, that-is-how-it-is sounds technically true, but not to the point. Indeed the photography assumes the fidelity of the mechanical reproduction, but from there, it poses the question of the realism of the image itself. Obviously staged photographs still have been, even though the situations they depict are not realistic (e.g. family portraits). Conversely, that-is-how-it-is asserts a fidelity to the phenomenon, realism. In the case of the photograph, depending on what that is, the stage or everyday life, the statement is either obvious (as in “Mom’s pose in the studio was how it is depicted”) or false (“In everyday life, Mom was how it is depicted”).

That-is-how-it-is does not apply well either to traditional charts. These visualizations are visibly processed, the reduction is apparent. However, once again it is technically true, in some sense, that most visualizations aim at being faithful to what it represents.

The proposition might not make such a good job at pointing to rawness, as opposed to, more generally, trying to represent something.

That-is-how-it-is also works for maps. There is an important overlap between big data visualization and maps, and I am wondering how relevant it is to mark the difference. But I would still mark it better if I can.

All in all, this proposition is moderately specific to big data visualization (ordeal 1), but it could be improved by finding something closer to the idea of rawness.

Ordeal 2 is where the proposition shines. Indeed, it allows formulating both the critique and the defense of big data visualization. With this noema, the image implies that that is how it is. For the ones, it is a virtue: big data visualization demonstrates the fidelity of the data. For the others, it is a vice: the image evokes a self-evidence that does not exist; it hides the mediation. Both views can be expressed using the noema.

Big data visualization is about manifesting visually the volume, velocity, and/or variety of the data, and/or the presence of links. Each of those four traits prevents a strong reduction of the visualization. Reducing them before visualizing them would produce a classic chart. By definition, what I characterize as a big data visualization is irreducible.

This non-reduction of the data grounds the that-is-how-it-is effect. From the perspective of the reader of the image, the proliferation of signs is overwhelming. It must then have another meaning than conveying a message, as the big data visualization cannot be understood otherwise. The proliferated signs are not arbitrary, they are always ordered by a principle, a key to reading the image – regardless of how hard the decoding might actually be. I think that this ordered proliferation is at the source of the meaning of the image. It may showcase an internal property of the data: volume, velocity, variety, or links. It may be an invitation to explore the data visually. It may just showcase the ability to obtain and organize the data. In any case, the organized proliferation of signs refers to an internal structuration of the data.

I like that this possible noema is rooted on material-semiotic properties of big data visualization. However, it is does not convey specifically the staged rawness I try to capture; it is not specific enough.

e. there-is-an-order-to-that-chaos

I think I overlooked the fact that there is always a pattern, a shape to read. The signs in a big data visualization tell three things:

  1. The proliferation of signs tells “you cannot comprehend that”
  2. The internal order of signs tells “that is computable”
  3. The pattern tells “that has a meaning” (but which one?)

This triplet captures a tension, but also leaves aside the question of fidelity. It makes sense that big data visualization does not, by itself, tell “I am real”. I see two justifications: (1) we do not recognize a “real thing”, and (2) the “3V” criteria do not require the data to refer to reality. For instance the picture below qualifies as a big data visualization, even though it plots a simulation. In a different context, one could believe it represents empirical measurements.

https://www.youtube.com/watch?v=ncRj2uyAeBY

Big data visualizations do not necessarily tell “that has been”, after all. But the visual patterns emerging from the mass of signs still tell that “something is brewing”. The patterns create a kind of reality effect, in the sense that they manifest something – but what?

“There is an order to that chaos” evokes the contrast between the proliferation of signs and the patterns. It also deals with a lot of our constraints: it is specific to big data visualizations, as it does not apply to photography, to classic charts, or to (most) maps; it allows formulating both the critique and the defense of big data, and it is rooted in material-semiotic properties; I only regret that it does not directly refer to the self-evidence effect.

This candidate makes the best noema so far. Let me wrap it up.

A semiotic model for big data visualization

I made a number of points, and they came up when it made sense for the exploration. I now state the general argument, and make the same points but in a more logical order.

I also take the opportunity to get rid of the clumsy notion of “big data visualization”. Now I can characterize those more directly.

Note that this is a tentative argument. The assertive style here purely serves clarity. After posting this piece, I will engage more with the literature on the topic, and it might well change my argument.


I refer to a specific type of data visualization, commonly associated with big data, and sharing the following characteristics:

  • A proliferation of signs. The visualization features a high number of signs, usually associated with data points. This includes dynamic visualizations where those are distributed over time.
  • An internal order. As the image or video pictures data, there are presupposed rules or constraints to these elements. Those may be stated in a key or caption, or left implicit but visible in the image.
  • Emergent patterns. Together, the signs display shapes not prescribed by the internal order. This emergent order may or may not be recognizable.

When one looks at such image, one cannot deny that it is not completely disorganized, that its apparent chaos is partially organized. As this specific constraint only exists for this kind of visualization, one should consider it its essence, its “noema”: there is an order to that chaos.

That which I see in such image is all at once disorganized and partially structured. A visual signal stands out of the noise. I may not know what it means, but I recognize it as a sign, as an observation deserving an explanation, as a clue in a potential investigation.

The noema there-is-an-order-to-that-chaos may be met with indifference, as a self-evident property. In that case the order is received as a direct emanation of the data. It leads to matter of concerns such as: what is that order? can I delineate it? describe it? reproduce it? understand it?

But one can also be aware of the noema. In such image the presence of a pattern is never metaphorical, but it does not mean that it is true. Patterns can be illusions: artifacts of the data, of the visualization. To certain readers, these data visualizations evoke how problematic visual patterns can be; these readers might not easily agree to hold the patterns for true.

The noema derives from the interplay between two material-semiotic properties: the proliferation of signs, and the presence of patterns.

The proliferation of signs has multiple effects. Firstly, it overwhelms the reader. The eye does not know where to land, the image requires an effort. Secondly, it shows that these signs have not been placed manually to convey a specific message. Assuming that the image explicitly visualizes data, it tells that those have not been reduced (or not much). Thirdly, assuming that the image is intentional (i.e. not a random screenshot illustrating a process), it tells that it is open to interpretation. Indeed, if there was a clear message to convey, the data would have been reduced further to remove unnecessary visual noise. The proliferation of signs tells “chaos”, “raw data” and “potential for interpretation”.

The presence of patterns, often highlighted in the title or caption, tells “order”. Of course, we must exclude the structures trivially explained by the construction of the image. Importantly, the meaning and the shape of the patterns may well be undefined, provided that one can agree on their presence. For instance we may agree on the presence of visual clusters, but disagree on where they are, or how many. It does not matter that the “order” is unspecified, as long as its presence is assured.

These material-semiotic features of “patterns in an proliferation of signs” specifically imply that there is an order to that chaos.

Examples

Here are a few examples of such visualizations. The proliferation of signs is always obvious, but the patterns may not. I will make at least some patterns explicit for each case, but I will not explain them in detail.

Trajectories. It plots the trajectories of players during a sports game, as seen from above.

Patterns: Symmetry; it shows that both teams play evenly on both sides. Accumulation of trajectories along certain lines and curves; it indicates that players circulate more in certain places – can you guess the sport?

Dos Juegos, Laura Castro

A 3D interactive dynamic map bar chart of the Manhattan population. Try the live version.

Patterns: Certain bars do spikes at certain places, certain dates; it shows that some areas are more crowded. At certain hours, all the bars are higher; it means that many people leave Manhattan at night.

Manhattan Population Explorer, Justin Fung

A network map. The dots and labels represent websites, the lines represent hyperlinks. By design, connected websites tend to be closer.

Patterns: The dots spread more horizontally; it means we have two connected clusters with more internal links, two communities. There are two colors; it means that each cluster has a different position on climate change (acknowledging or denying its human origin).

Climate Change on the Web, Sciences Po médialab

A map with many dots. One dot for each resident, colored by race, in the city of Minneapolis.

Patterns: More dots in certain areas; it indicates denser neighborhoods. White areas; nobody leaves there (rivers). Colors cluster in certain areas; it shows racial communities.

Racial dot map – University of Virginia

Bonus: misleading patterns. I you plot usage data on a map, it will often follow the distribution of population. You see patterns, but those are not related to what one may think. It has become a classic joke in the community of data visualization, see below.

Heatmap
https://xkcd.com/1138/, Randall Munroe

Did I salvage something?

At least I found what I was trying to salvage. That is a beginning. It matters to me that the pattern is real. Sure, sometimes it is an illusion, but illusions are real – in the phenomenological sense.

Acknowledging the reality of the pattern helps me separate interesting practical questions from pointless ones (for me).

We should always discuss:

  • whether the pattern is an artifact, of the data; of the visualization;
  • where the pattern is, and if there is one (when our readings differ);
  • what the pattern means;
  • which different purposes the visualization serves.

These questions seem pointless to me:

  • Is the pattern the product of a discourse on big data?
    (if you see it, it is a material inscription somewhere)
  • If people disagree on a pattern, is it real?
    (Aka the “post-truth” argument. Same answer.)
  • Why do some people believe these visualizations to be scientific?
    (The pattern is real, so its interpretation is as legit as any falsifiable hypothesis in a Popperian scientific method)

For the record, I do not deny that some people use these kinds of visualizations for other purposes, leveraging the same material-semiotic properties. For instance the proliferation of signs may suggest technical mastery, fidelity to data, accurate measurements. And because it is cognitively overwhelming, it suggests the necessity to rely on sophisticated algorithmic approaches to extract hidden values hinted at by the patterns. I think these statements are, in fact, poorly supported by such data visualizations.

Improving the critique of big data

Let me circle back to my starting point. With these temporary conceptual tools, I try to fix something that bothers me in Ruppert and Scheel’s analysis of big data in The Politics of Method: Taming the New, Making Data Official.

I cannot say a thing on their field work, beyond the fact that it does not conflict with my own observations. But I think I am well aligned with their points overall. I agree with their analysis of the rhetoric of big data, and the politics of methods per se; but when it comes to data visualization specifically, I feel frustrated by their analysis. I would like to improve it.

The first demonstration of their paper is titled “Enacting Mobile Populations as Self‐evident Realities.” They feature dynamic visualization of the Estonian population over time, obtained from mobile data. I reproduce their screenshot below.

The visualization has many moving dots, that I assume correspond to data points representing mobile users. The patterns that I can identify are clusters around cities and along circulation axes. To me, the data seems coherent with what one would expect – which is never a given. But I do not see anything surprising. Full disclosure: I know nothing of Estonia.

I will now cite a section of their paper, and comment within the text. I do not skip passages – the quotes are contiguous.

The setting observed by the authors is the company MOBDATA, owning and promoting the data, showcasing their product to Estonian officials.

Despite its simplicity, the map succeeds in generating astonishment, as a MOBDATA staff member responsible for sales in Estonia stresses: “Oh yes, people are impressed… it’s catchy, and it’s nice… people like to see things like this.”

I don’t know exactly why the authors feature that quote here. I want to mention that I see no reason to interpret it as if MOBDATA acknowledged that they would rather do something catchy than scientific. Those traits are not mutually exclusive. On a factual level, their visualization is, indeed, appealing.

This astonishment is caused by what the moving dots enact and make intelligible: commuting patterns in Estonia.

I agree on the pattern and its interpretation.

In contrast to other statistical accounts of mobility, such as static charts and tables, MPD [(mobile positioning data)] seems to speak for itself precisely because it moves.

I do not fully understand this notion of data speaking for itself – I will rather rely on the rest of the text. Surely the signs do tell things, yes; but I do not see how the movement, specifically, implies a special way to mean.

I do think, however, that the dots have a spatial and temporal resolution that tells something. This is what I labelled earlier “proliferation of signs”, although the number of data points is more obvious over space (we see many dots) than over time (the same dots just move). As these points cannot have been placed manually, they must come from somewhere else – tracking mobile devices. The proliferation also suggests that there might be more to discover, but that remains to be proved.

There is no reason to doubt that the (unsurprising) patterns we see reflect a structure within the data. The data have captured the commuting patterns in Estonia. The visualization does not mean it “for itself precisely because it moves”, it does not mean it in a special, suspicious way. It means it the normal way, the way we are supposed to read a visualization. I see no reason to be suspicious about the conclusion that the data reflect population movements.

The moving red dots become not only a vehicle for the data, but first and foremost for its claimed self‐evidence.

I disagree with this statement. The moving red dots are not “first and foremost” suggesting self-evidence, because the way they suggest it is precisely grounded in how they represent the data.

The dots themselves are nothing without the patterns. Obviously, randomly moving dots do not produce the same effect. The patterns are establishing the link with the data, the confidence that population movements have been captured.

The confidence must not be confused with self-evidence. The visual patterns convincingly refer to the commuting patterns. I refuse to downplay the semiotic strength of this visualization on the motive that it also serves a marketing discourse.

MagrittePipe.jpg

Magritte famously painted a pipe with the caption: “this is not a pipe”. He called the painting The Treachery of Images. It is funny precisely because in everyday life one can point at the image and say: “this is a pipe”. Conflating the image and what it represents is not a scandal. But indeed, it matters that we do not completely forget the mediation, that we remain capable of retrieving it if necessary.

The red moving dots of the Estonian mobility data are in the same situation. The pattern reasonably represents commuting – the authors acknowledge it themselves. For me, it is then rational to conflate the visual pattern with the phenomenal pattern. This suspension of the mediation does not have to be definitive, as we may agree but remain critical. But then, indeed, the conflation looks like self-evidence. And surely, that presents the danger of forgetting the mediation.

Now, one does not have to be convinced: the picture may not resemble a pipe. In that case, self-evidence is not possible. But it does not mean that the picture is “first and foremost” a vessel for “claimed self-evidence”. Here self-evidence is not the fruit of malice, but the consequence of people trusting the image for legit although debatable reasons.

The red dots moving along Estonia’s main transport routes suggest that they correspond to the commuters they are meant to represent.

The authors acknowledge the pattern of correspondence.

Through this “realist trick” (Law 2012) mobility is enacted as a reality that exists independently of the methods that are used to describe it. There appears to be a seamless correspondence between the visualization (the moving dots) and the reality (commuting patterns in Estonia) it represents and renders “the phenomenal world (as if it) were self‐evident and the apprehension of it a mere mechanical task” (Drucker 2011, 2).

I contest that the correspondence is “seamless”, and that it only “appears” to be real. For me the correspondence is debatable, but legit. I see no reason to consider it as an illusion. The pattern is real.

It is precisely because the visual pattern corresponds to the phenomenal world that the conflation is possible, that it can become self-evident. The correspondence is not a trick.

The trick is to forget the mediation. The trick directly gets its strength from the correspondence. The more grounded the correspondence, the more convincing the trick.

In this way MPD is constituted as the perfect method for tracing the movements and locations of increasingly mobile populations, a method that offers an unrestricted vision from above, a vision that allows, in tradition of the “god trick” described by Donna Haraway (1988, 581), to see “everything from nowhere.”

The “god trick” is to make you forget that god’s plunging viewpoint “from nowhere” is actually situated somewhere. The trick is powerful precisely because the view from above is efficient.

In this case the notion of “from above” is mostly metaphorical, and can be better understood as “from nowhere”. It does not matter much that the map is seen as from a satellite. It matters more that the data seems omniscient, tracking each person with an intrusive but invisible accuracy. The high resolution of the data points, typical of big data, is where the viewpoint is godlike.

The trick is to make you believe that this kind of knowledge is disembodied. But at the same time, these data sets are living evidence of where the big data’s bodies lie.

It bothers me that Ruppert and Scheel’s excellent piece tries to establish that big data visualizations falsely claim self-evidence, as if unveiling it could a strike a blow against it. I believe on the contrary that self-evidence is a natural by-product of the very real and legit convincing powers of big data. We should not deny them for two reasons. Firstly, because big data can do something for us, and we should seize the opportunity – but we must impose our conditions! Secondly, because any critique that confuses big data’s force for a weakness is doomed to miss its blow.


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