By the late nineteenth century, the modernisation of the US state had created an ‘avalanche of printed numbers’. As the story goes, Herman Hollerith’s invention of an electric tabulating machine took industrial production methods and applied them to civic bureaucracy, greatly increasing speeds of data processing. Using Hollerith’s tabulating machine, the US census of 1890 took one year to complete, whereas previously it had been a decade. Canada, Austria and Norway all signed up for the magical counting machine.

Realising the role of categorisation in the modern capitalist state, Hollerith soon began marketing the device to businesses which needed data catalogued and categorised in retrievable forms. Hollerith went on to create the Tabulating Machine Company, later IBM, where data categorisation was equally at home with both railroad companies and the National Socialist German Workers Party.[1] Data-processing and calculating machines would thus seem a key characteristic of the bureaucratic structures of modernity and systems of control in modern states. Today data is meat for the algorithmic grinder where platforms use a mathematical armoury of calculative methods to measure, simulate, rank, rate, model, benchmark and index. Users of platforms are continually classified into specific categories of measurement. Algorithms not only automate the classification process but are further used to predict and target user behaviour.

This in turn has created a whole industry of ‘data brokers’ who buy and sell data to platforms. Data sets can be obtained on everything from tenant screening for landlords, credit history, healthcare information, property ownership, race, gender, mortgage records and employment history. Organisations such as TalkingData in China or Acxiom, Equifax and VenPath in the US all collect, process, analyse and sell data onto platforms like Netflix, Spotify, AirBnB and the likes of Palantir.

And this process applies equally to space. Data sets on urban movement and locations are seen to be the most ‘ubiquitous and valuable of commodities’, with location data brokers now routinely gathering, cleaning and mapping everyday locations and movement patterns from smartphones. A study in The New York Times showed how location data can be collected on commutes, love lives, mental states and drug intake, all from how people navigate in their urban environments.[2] One such company is Factual, a relatively unknown but financially colossal data company, which recently merged with another location-data giant, Foursquare. Factual maps and categorises location information from millions of online sources at a global scale, offering this information to data scientists and platforms like Uber, WeWork and Snapchat, where it in turn provides the basis for their location services.

Location data mapping effectively turns qualitative phenomena typically associated with ‘place’ into numerical and calculable abstractions. This means features of a given area that may feel hyper-localised and specific can be data-fied, made measurable and brought into a giant form of spatial data cartography. At the same time, a process of spatial classification takes place. As sociologist Harrison Smith has explained, these systems ‘automate the reproduction of class hierarchies through ideal types and demonstrate the important – if often unacknowledged – role of commercial sociology in applying social science methods in the service of capital’. This explains the contradictory character of data: it can be objective, but this objectivity stems from reproducing existing social hierarchies in space.

Going back to the electrical tabulating machine, in Franz Kafka: The Ghosts in the MachineBenno Wagner notes the influence of Hollerith’s machines on Kafka’s work via his professor Heinrich Rauchberg, who taught International Law and Statistics. A Hollerith aficionado, Rauchberg was Austria’s most renowned national statistician and in charge of Europe's first census using the electrical tabulating machine in 1890. Kafka is an interesting reference regarding ways to approach the calculating machines underpinning platform urbanism and contemporary obsessions with measurement. In Kafka’s world, machines of techno-rationality take on absurd and irrational qualities. Spaces like the office belong not in the realm of the rational but the fantastical. The stock market and typewriter become supremely mystical things. Could a poetic potential be found in the banal nature of data processing machines today? And what would a platform urbanism taken to absurdist extremes look like?