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Technology has fundamentally transformed urban life. But today’s “smart” cities look little like what experts had predicted. Aaron Shapiro shows us the true face of the revolution in urban technology, taking the reader on a tour of today’s smart city. Along the way, he develops a new lens for interpreting urban technologies—logistical governance—to critique an urban future based on extraction and rationalization.
Through ethnographic research, journalistic interviews, and his own hands-on experience, Shapiro helps us peer through cracks in the smart city’s facade. He investigates the true price New Yorkers pay for “free,” ad-funded WiFi, finding that it ultimately serves the ends of commercial media. He also builds on his experience as a bike courier for a food delivery startup to examine how promises of “flexible employment” in the gig economy in fact pave the way for strict managerial control. And he turns his eye toward hot-button debates around police violence and new patrol technologies, asking whether algorithms are really the answer to reforming our cities’ ongoing crises of criminal justice.
Through these gripping accounts of the new technological urbanism, Design, Control, Predict makes vital contributions to conversations around data privacy and algorithmic governance. Shapiro brings much-needed empirical research to a field that has often relied on “10,000-foot views.” Timely, important, and expertly researched, Design, Control, Predict doesn’t just help us comprehend urbanism today—it advances strategies for critiquing and resisting a dystopian future that can seem inevitable.
- rightsPortions of chapter 2 were published in a different version as “Between Autonomy and Control: Strategies of Arbitrage in the ‘On-Demand’ Economy,” New Media and Society 20, no. 8 (2018): 2954–71; copyright 2017 Aaron Shapiro, https://doi.org/10.1177/1461444817738236; and as “Dynamic Exploits: Calculative Asymmetries in the On-Demand Economy,” New Technology, Work, and Employment 35, no. 2 (2020): 162–77. Portions of chapter 3 were published in a different version as “Predictive Policing for Reform? Indeterminacy and Intervention in Big Data Policing,” Surveillance and Society 17, nos. 3–4 (2019): 456–72; copyright 2018 Aaron Shapiro, https://doi.org/10.24908/ss.v17i3/4.10410.
Copyright 2020 by the Regents of the University of Minnesota
- publisherUniversity of Minnesota Press
- publisher placeMinneapolis, MN
- restrictionsAll rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.
- rights holderRegents of the University of Minnesota