Environmental Data

Jussi Parikka

Environmental data can be understood as the set of quantified techniques and modes of representation that are central to the current governance of climate, biodiversity, energy, food, clean water and other issues, as identified, for example, in the UN Sustainable Development Goals. Data functions as a particular layer of abstraction through which modelling of the Earth takes place and becomes actionable, at least in principle. Environmental data is thus both the ontology and the instrument of current environmental policy. Beyond policy, it is of course the central ground of conflict too: how and where is data produced (from the planetary orbit to the ground), what labour is included or excluded, what sort of measures are read in and through data? (Goldstein and Nost 2022).

Numbering has created an invisible layer across planet Earth that becomes manifested in different forms of visual data culture: tables, graphs, models, GIS maps, dashboards, and other visualisations. In other words, enumeration has its material existence too; its material repercussions become concrete in such forms and formats that we occasionally call media.

While this layer of quantified and abstract planetarity seems a comprehensive data realm in its own right, it speaks to a particular geography and place(ment) of data too (Loukissas 2022). One can track the historical modes of abstraction of resources and produce as data (including economic transactions and financial data) in this register of space and architecture (Pickren 2022).  As part of the broader discussions about the materiality of information, software, and data (Dourish 2017), the spatial set that emerges is particularly helpful in both tracking the lifespan of data as a finite entity and in highlighting the spatial impact of data.

Similarly, researchers have started to look at the historical sets of containment that define (environmental) data, such as the Linnean “universal” nomenclature, on a practical level of botanical collections and herbaria (Çelik Alexander 2020). In short, these are built environments for data as it concerns samples and classifications embodied in architectural design. In other words, it is through the partial that claims and powers of universality are produced and routed.

Such approaches on space and architecture can be coined as architectures of environmental data that also in the contemporary form must include the built environments for investigation, storage, and analysis of data such as research centres (a good example would be the large-scale National Oceanographic Institute in Southampton, UK) but also (open) field stations, spatial infrastructures of data gathering and storage, and for example the variety of socio-political ways that sites and terrains are mapped, catalogued, cleaned, and organized in data labour (Lin 2020). Architectures of environmental data refer in this sense to a broader research program that I propose as one way of tying contemporary architectures of multiscalar environmental data with the historical spatial metaphors and literal cultural techniques that also define measures for management of data. It ties together two seemingly opposed poles: architectural concreteness with multiscalar abstractions (on these points, see, e.g., Gil-Fournier and Parikka 2024, Parikka 2023).

In other words, we can also include sites of data gathering into the extended architectures of data, so far as remote sensing, for example, defines such sites in specific ways as technical lands. This develops the notion of environmental data through the various cultural techniques as envelopes and wrappings (see McCormack 2018). These envelops and wraps can take different shapes from numbering sensor data to architecture as the expanded notion helps us to focus on the fabrication of environments while also not diminishing the impact of environments as elemental forces in their own right, a point also McCormack (2018: 29). Such a view of environmental data is also closely related to discussions in environing media: an analysis of the historically situated ontogenetic role media plays in producing environments (Wickberg and Gärdebo 2023).

These kinds of methodologically premised geographical, architectural, and historical angles start to underline that environmental datafication concerns a fragmented space of practices reliant on infrastructural operations. Such operations define how data is sensed, captured, and put together in the first place, on top of which it is managed and maintained in a variety of labour practices. Such topics are at the core of the broader field of critical data studies and environmental datafication (see e.g. Gray, forthcoming). Hence, the key repertoire of politics of data-ownership, agency, extractivism, and bias is valid in many of the cases that also concern more-than-human data subjects and objects.

It does, however, also raise the question as to how critical data studies transform as environments are part of the methodological, empirical, and theoretical questions. It means not just applying critical data studies as it is to environments, but asking how environmental data changes some of the points put forward in critical data studies.

Furthermore, additional questions are raised: Is all environmental sensing surveillance? (Cf. Bratton 2021). Probably not. What are the forms of agency of more-than-human data subjects? This hints at accounting for multispecies sensing (see the entry on “Multispecies Sensing” in this glossary) as well as non-conscious intelligence as part of the question of environments that are read as data. In short, how does the field take into account some of the discussions, for example, in critical posthumanities about non-anthropocentric points of cultural theory (see e.g. Braidotti 2016)? While such points are important form the ethico-aesthetic set of questions (Ivakhiv 2024) about the ecology of datafication in question, they also route back to how the earlier mentioned envelopes of architectures of environmental data can map a historical angle to many of the current debates that drive also much of e.g. climate, forest, agriculture, and biodiversity related areas of inquiry and operations. In short, spatial angles to datafication can help both pinpoint issues of socio-political significance while mapping the different sorts of wraps in which data becomes contained, but also logistically mobilised.

Bibliography

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Bratton, Benjamin. 2021. The Revenge of the Real: Politics for a Post-Pandemic World. London: Verso.

Çelik Alexander, Zeynep. 2020. “Managing Iteration: The Modularity of the Kew Herbarium.” In Iteration: Episodes in the Mediation of Art and Architecture, edited by Robin Schuldenfrei, 1-24. Abingdon: Routledge.

Dourish, Paul. 2017. The Stuff of Bits: An Essay on the Materialities of Information. Cambridge, MA: The MIT Press.

Gil-Fournier, Abelardo and Jussi Parikka. 2024. Living Surfaces. Images, Plants, and Environments of Media. Cambridge, MA: The MIT Press.

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Gray, Jonathan. forthcoming. Public Data Cultures. Cambridge: Polity.

Lin, Cindy. 2020. “How to Make a Forest.” e-flux, April 2020. https://www.e-flux.com/architecture/at-the-border/325757/how-to-make-a-forest/.

Loukissas, Yanni. 2022. All Data Are Local. Thinking Critically in a Data-Driven Society. Cambridge, MA: The MIT Press.

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Parikka, Jussi. 2023. “‘A Natural History of Logistics’ and Other Studio Briefs: Problem Spaces for Planetary Design.” Cultural Politics 19 (2): 177-199. https://doi.org/10.1215/17432197-10909672.

Pickren, Graham. 2022. “Data’s Metropolis: The Physical Footprints of Data Circulation and Modern Finance.” In: The Nature of Data Infrastructures, Environments, Politics, edited by Jenny Goldstein and Eric Nost, 21-36. Lincoln: University of Nebraska Press.

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