Uberising the Urban. Labour, Infrastructure and Big Data in the Actually Existing Smart City of Toronto

Namberger, Fabian. 2022. Uberising the Urban. Labour, Infrastructure and Big Data in the Actually Existing Smart City of Toronto. Doctoral thesis, Goldsmiths, University of London [Thesis]

Text (Uberising the Urban. Labour, Infrastructure and Big Data in the Actually Existing Smart City of Toronto)
SOC_thesis_NambergerF_2022.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (10MB) | Preview

Abstract or Description

This thesis explores how Uber reformats the urban and vice versa. Rather than taking for granted Uber’s success in remoulding the emerging ‘smart city’ in its own image, Uberising the Urban pays close attention to the contradictory, variegated and far from frictionless encounters between Uberisation and urbanisation. The thesis is particularly interested in those neuralgic points of contact where the abstract logics of Uber’s business model – its vectors of data extraction, labour exploitation and platform expansion – hit the urban ground of existing social and physical geographies. The Uberisation of the urban – such is this thesis’s main argument – does not take place in a material and social void; it unfolds in, with and against the dense social and material thickness of existing urban space.

This argument is deepened in three case studies. Zooming in from different angles, these case studies show how the vectors of Uberisation have come up against the multiscalar and variously uneven urban grounds of the actually existing smart city of Toronto. While the first case study provides a detailed discussion of the conflictive processes leading up to the legalisation of Uber in Toronto and the parallel ‘regulated deregulation’ of the city’s taxi-cum-ridehail market, the second case study tackles the next subsequent ‘stage’ of Uberisation in Toronto: the proliferation of various public-private ridehail partnerships (PPRPs) between Uber and Lyft on the one hand and local and regional transit agencies in the GTA on the other. The third case study is concerned with Uber’s self-driving car programme and, in particular, the invasive practices of data extraction that Uber has implemented in Toronto – turning the city into a real-life urban data reservoir for the development of its self-driving software. A conclusion, shedding light on a potential reconfiguration of Uber towards more socially emancipatory ends, rounds out the dissertation.

Item Type:

Thesis (Doctoral)

Identification Number (DOI):



Uber, Smart City, Smart Urbanism, Toronto, Platformisation, Taxi, Big Data, Regulation, Labour, Automation, Artificial Intelligence, Self-Driving Vehicle

Departments, Centres and Research Units:



30 June 2022

Item ID:


Date Deposited:

04 Jul 2022 14:32

Last Modified:

07 Sep 2022 17:20



View statistics for this item...

Edit Record Edit Record (login required)