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X-RAY THE CITY!

Gideon AschwandenDonald BatesKaren BurnsMark BurryKim DoveyPhilip GoadXiaoran HuangJustyna KarakiewiczGeoff KimmTom KvanNano LangenheimHannah LewiElek Pafka Alan PertStanislav RoudavskiAndrew SanigaPaul WalkerMarcus White

02

Copyright ©Melbourne School of DesignThe University of Melbourne2016

Published byMelbourne School of DesignThe University of MelbourneVictoria 3010 Australiawww.msd.unimelb.edu.au

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publishers.

Printed by Brambra Press6 Rocklea DrivePort Melbourne 3207Australia

Publication Design:Sean Hogan, Trampolinetrampoline.net.au

National Library of Australia Cataloguing-in-Publication entry

X-Ray The City!1. Urban Design2. Architectural History3. Architectural Design4. Urban Analytics

ISBN 978-0-7340-5258-2

DEDICATION

This book is dedicated to the memory of Ernest Fooks (born Ernest Leslie Fuchs, 6 October 1906 – 4 December 1985) and his wife Noemi Fooks.

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Doing Bigness

Stanislav Roudavski

What will architectural design look like in a world of ambient intelligence?

NatureTrader, a project by Gwyllim Jahn, Tom Morgan and Stanislav Roudavski; other credits: Alexander Holland, Julian Rutten.

PocketPedal, a project by Alexander Holland and Stanislav Roudavski; other credits: Julian Rutten.

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Written as a provocation that reflects on some of the relationships between data, habitable environment and design, this article uses the essay called X-Ray the City! that was written by architect and town planner Ernest Fooks in 1946 as its starting point.1 The discussion below compares some of the propositions made by Fooks at that time with two subsequent periods: the situation now, in 2016, and near the symbolic future moment in 2046 when Fooks’ essay will be 100 years old. Specifically, it focuses on one characteristic that is comparable between these periods: a practical attitude towards bigness.

***

Writing in 1995, near the midpoint of the timeline established above, a prominent architect and architectural thinker Rem Koolhaas insisted that “[b]ecause there is no theory of Bigness, we don’t know what to do with it, we don’t know where to put it, we don’t know when to use it, we don’t know how to plan it. Big mistakes are our only connection to Bigness.”2 But was he right?

Big Science

In other domains, bigness pre-existed Koolhaas, for example in the form of “big science” that emerged after the Second World War as a practice that was distinct from the previous forms of science that were “small”. Small science referred to the traditional experimental physics that was done by individuals with local resources, with little collaboration and with rapid returns on personal initiatives. By contrast, big science emerged in the US weapons laboratories

where it was conducted by large teams of physicists and engineers, supported by huge amounts of money and governed by hierarchical bureaucratic processes.3 Fooks’ hopes to underpin design by science are related to the spirit of such undertakings but are modest by comparison. His site of application is comparably large – whole cities, his search for patterns with statistical tools is also similar to the big science approaches but his analysis is a one-man job and his data are obtained from a limited selection of existing sources.

The main device introduced by Fooks is the distance grid or the diagram of population density. This diagram has several core properties: it’s geometry – it is concentric; it’s uniformity – it is made of even cells; its universality – it is meant to be applicable to any city. The contemporary tools are allowing for greater variety and yet, as will be discussed below, the future tools might result in the return of the regular-pattern superposition, the standardisation and the data totalitarianism, even if in a new guise.

Fooks saw defects in the way statistical data was collected and analysed and his proposal was to sample and map the available data differently. And yet, possibilities for such difference were limited: on one hand, by the small number of available – typically, governmental – data sources; and on the other, by the inefficiency of manual processing.

One such defect was to do with “the arbitrary nature of urban boundaries.”4 This question of boundaries, or – more generally – of patterns, remains important in the contemporary, and more fluid, world of data. The data are influenced by their providers, the data collection methods, the

1 Ernest Fooks, X-Ray the City! The Density Diagram: Basis for Urban Planning (Melbourne: Ministry of Post-War Reconstruction, 1946).

2 Rem Koolhaas et al., S, M, L, XL (New York, NY: Monacelli Press, 1995), 509, 510.

3 Andrew Pickering, The Mangle of Practice: Time, Agency, and Science (Chicago; London: University of Chicago Press, 1995), 43. Derek J. de Solla Price, Little Science, Big Science (New York: Columbia University Press, 1963).

4 Fooks, X-Ray the City!, 43.

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NatureTrader, or where it might lead. An experience of the world where all experience is data-dependent. All environment is mapped. All mapping units are standardised, named, indexed. All units are sentient and can act. All units trade. Everything is commodified, everything is one market.

61Stanislav Roudavski

suitability of particular phenomena for quantification as well as by the character of specific systems, data streams, data owners, and so. As the city becomes increasingly cyber, the nature of boundaries becomes more general. Boundaries take form of pattern discontinuities that can appear as indices, identities, standards, database formats, communication protocols, resolution choices, metadata specifications and so on. It is impossible to understand or process large volumes of data manually and many of these boundaries come to the fore because they are intrinsic to automation. On the other hand, contemporary – and future – data-collection techniques can overcome many traditional boundaries such as those that are to do with physical space or site ownership. The types of data defects change with technology but some defects always remain. Data continues to be highly political, decidedly contingent, dependent on craftsmanship, reliant on human imagination.

Big Data

Today is characterised by the potential of big-data tools to make decisions on small-grain, local and dynamic information, making arbitrary boundaries still further obsolete. As reported by the big-data narratives, historically, data have been time-consuming and expensive to generate, analyse and interpret.5 It provided static and, often, coarse representations of phenomena. Consequently, good-quality data were valuable, proprietary and expensively traded. With the advent of networked computing, data have retained their value, but their production has become significantly easier and the result is an increasingly overwhelming flow of relational data that is finely differentiated, timely and of high resolution. Such data come from heterogenous sources, at multiple scales and can be fertile for exploratory data analysis. Often, these data are of low cost and, increasingly, – openly available

and easily accessible. Availability of such data led to the emergence of new data-analytic toolsets that are designed to cope with the data abundance rather than with data scarcity.

The resulting analytics can be descriptive – reporting on the past; predictive – modelling the future from the past trends; or prescriptive – using models to specify optimal actions with resulting approaches going by the names such as data mining, predictive analytics, data science and business intelligence. Such tools provide new support for the design approaches compatible with the Fooks’ insistence that “[a] town must be regarded as a flexible shell, able to meet the constantly changing needs of the population.”6 The relationship with data enabled by such methods is much more active than before, however, they still primarily focus on the understanding and interpretation of the already-existing environments.

And yet, the reverse influence, that of data on the environment, is becoming increasingly more apparent, for example through such visions as the Internet of Things. This network of objects is predicted to link many billions of devices, some say more than 50 billion by 2020. When every person will have several connected devices, the whole world will turn into a network of connected objects. Many of the common things are already connected. Pets. Livestock. Fridges. Tennis rackets. Most objects that have a name already exist in versions that can make, use and transmit data. Such connected devices do not need independent interfaces. Smart phones and tablets provide universal windows into the relationships of interconnected entities and support dashboards with which these objects can be controlled.

That these new hybrid ecologies are more tightly integrated with the surrounding environments is only right, given the newly common appreciation for the environmental concerns. Such concerns were

6 Fooks, X-Ray the City!, 32.5 E.g., see Rob Kitchin, The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (London; Thousand Oaks, CA: Sage, 2014).

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STOP/GO BRAIN GO, GO — YEAH!

RISK BRAIN TOO RISKY!

SPEED BRAIN FASTER, FASTER, FASTER!

TURN BRAIN SHARP RIGHT!

THE PHONE The virtual cycling world is accessed through a device familiar all.

HAZARD BRAIN CAR!

PocketPedal, or one way to resist. An approach to design that places stakeholders in the midst of data. All data is felt, performed, played. Designing occurs in the magic circle. All design is negotiated. All action is rehearsed. Every decision is supplied with an alternative.

63

unfamiliar to Fooks and the discourse of his time. His essay is concerned with humans only. “It is the human scale which has to be the guiding principle. Human beings, their collective needs, their grouping, their distribution and redistribution, become the primary concern of urban planning.”7 Today, the need to pay attention to or consult with nonhuman stakeholders is becoming increasingly evident. It is by now uncontroversial in relationship to living ecosystems and is becoming more accepted in regard to artificial agents.

Big Cognition

The uniformity of standardising tools such as Fooks’ distance grid can miss local variations; their simplification is necessarily lossy. Future techniques promise substantially greater data resolution but they also make it impossible for humans to peruse this data; requiring some form of automation. In addition, and more significantly, stakeholder relationships themselves are changing under the impact of data. For example, the role of “spatial nearness” as a condition for “community life” – a relationship emphasised by Fooks – is diminishing as new social aggregations become possible through electronic networks and the proximity to data and data sources emerges as more important than the nearness to physical locations.8 The exact nature and influence of these new relationships is far from obvious. To illustrate: if spatial nearness is no longer significant, why are cities still growing so rapidly? The fact that utility services such as water supply, garbage disposal, drainage, sewerage, gas, electricity and cultural institutions such as schools or kindergartens are harder to distribute might be one of the reasons. Fooks argues that technical achievements of 1946 were “not able to diminish the importance of SPATIAL NEARNESS for creating community life.”9 In this, his argument is compatible with more

historically nuanced understandings of technological development than those that are commonly promoted by the pervasive-computing and big-data enthusiasts.10

At the same time, the logic of Fooks’ method as an all-revealing x-ray breaks down in these new conditions. He claimed that his “method can be compared to an X-Ray of the human body, the single maps forming parts of an “anatomic atlas” of the urban entity.”11 This metaphor stops to work when tools, such as x-rays, become grown into the bodies under study.

The situation where the amount of available data is overwhelming, and where there is no clear distinction between data producers and data consumers, the environment and its users or the data and the city motivates the introduction of new toolsets, attitudes and behaviours. This new paradigm, first conceptualised in the early 1990s, is termed here Big Cognition and can also be encountered under the names of automated analytics, ambient intelligence, cognitive computing and deep learning.

As the number of connected entities grows, the task of managing them becomes harder and more expensive. The ambition of the industry is, therefore, to define communication standards and procedures that can support autonomous operation without human interference. Already now, organisations are building proof-of-concept machines that automate aspects of decision-making. Their ambition is to construct cognitive technologies that can support multiple applications. The result can take form of modular services or so-called “cognitive platforms”. Such services can comprise analytics, analysis of behaviour, visual recognition, natural-language parsing and so. Such ambitions are seen by some as a pervasive threat of automation while others see this trend as a radical opportunity to construct

11 Fooks, X-Ray the City!, 95

7 Ibid., 96.

8 For “nearness” and “community life”, see Ibid., 26.

9 Ibid., 28.

10 For the criticism of solutionism motivated by over- enthusiastic embrace of networked technologies see Evgeny Morozov, To Save Everything, Click Here: Technology, Solutionism and the Urge to Fix Problems that Don’t Exist (New York: Public Affairs, 2013).

Stanislav Roudavski

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systems that can self-improve through the running of continuous experiments and by doing this, shift from historical enumeration to real-time, predictive, actionable intelligence. Many different types of processes from shopping, to plant growth, to traffic, to energy fluctuations can be seen, analysed and affected as they occur, in real time; redirecting data toolsets from accumulation to action and from storage to value-making.

In 1995, Koolhaas claimed that “[n]ot all architecture, not all program, not all events will be swallowed by Bigness. There are many “needs” too unfocused, too weak, too unrespectable, too defiant, too secret, too subversive, too weak, too “nothing” to be part of the constellations of Bigness.”12 Today, it seems that all architecture, all program, all events or – to put it differently – all matter, all processes and all life are delectable for the bigness of Big Cognition.

In these conditions, design actions have diverging potentials for activism. This may be illustrated by the contrast between two radical approaches. One of these seeks to formulate new labour demands presuming the inevitability of automation at all levels: in manufacturing, data production, communication and analysis.13 The next step within this logic is to not just accept but to demand full automation and with it such seemingly counter-intuitive arrangements as the right to be lazy and the guaranteed basic minimum income. The second and contrasting approach seeks to encourage general scepticism for all solutionism. The solutionism believes that network technologies can find the answer to most of the world’s problems and optimise most of the existing life-patterns. This scepticism towards such beliefs rejects the fascination with the Internet along with the presumption that the network is an eternal entity with intrinsic and immutable properties, deserving of the unquestioning respect.14 The logic of this second approach is to see that imperfection,

12 Koolhaas et al., S, M, L, XL, 515, 516.

ambiguity, amorphousness, self-contradiction, and other such phenomena are not necessarily the problems that need fixing. Instead of being “bugs”, they can function as valuable “features”. These features can be valuable because they are historically unique expressions of complexly interrelated behaviours. Elimination of such features can lead to severe restrictions on the operation of known systems including, not unimportantly, the restriction on freedoms such as the freedom to mention, the freedom to act or the freedom to know. In this light, characteristics that common sense interprets negatively and Big Cognition promises to eliminate – including hypocrisy, inconsistency, ambiguity and mendacity – can be as essential to the operation of the inclusive political processes as the similarly unfancied inefficiency, redundancy and opportunism are necessary for the robust operation of living systems.

References

Fooks, Ernest. X-Ray the City! The Density Diagram: Basis for Urban Planning. Melbourne: Ministry of Post-War Reconstruction, 1946.

Kitchin, Rob. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London; Thousand Oaks, CA: Sage, 2014.

Koolhaas, Rem, Bruce Mau, Jennifer Sigler, Hans Werlemann, and Office for Metropolitan Architecture. S, M, L, XL. New York, NY: Monacelli Press, 1995.

Morozov, Evgeny. To Save Everything, Click Here: Technology, Solutionism and the Urge to Fix Problems that Don’t Exist. New York: Public Affairs, 2013.

Pickering, Andrew. The Mangle of Practice: Time, Agency, and Science. Chicago; London: University of Chicago Press, 1995.

Price, Derek J. de Solla. Little Science, Big Science. New York: Columbia University Press, 1963.

Srnicek, Nick, and Alex Williams. Inventing the Future: Postcapitalism and a World without Work. London: Verso, 2015.

14 Morozov, To Save Everything, Click Here.

13 Nick Srnicek and Alex Williams, Inventing the Future: Postcapitalism and a World without Work (London: Verso, 2015).

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BIKE LANE Try to stay within the bike lane! Here, your bike health will slowly recharge. Sticking to the bike lane means you will gain more points, and have enough health to survive a crash or two.Being in the bike lane has its own dangers: watch out for those opening cars doors!

HAZARDS Colliding with traffic decreases your road health per the severity of the collision. On low bike health, impacting an obstacle will cause your cyclist to crash, ending the game.

SCORE You are awarded points for every ten metres successfully cycled towards the city. You are much more likely to end your ride in a high score by cycling safely than simply riding at breakneck speeds.

THE PLAYER This cyclist is you. You’re a hipster girl; a MAMIL; a reckless guy in his twenties. Tap to pedal, tap the sides of the phone to turn.

HEALTH How safe is you riding? The health indicator reflects how safely you ride.Compliance with road rules, remaining within the bike lane, and navigating obstacles increases health.Riding outside bike lanes and colliding with traffic decreases it.

YOUR GOAL You are a cyclist riding along St Kilda Road, Melbourne. Can you get to the city?

THE ROAD St Kilda Rd lacks proper cycling infrastructure. On your way to the city you’ll have to negotiate a route full of traffic. Some vehicles pay attention to you; others not so much.

PocketPedal, the game interface and mechanics.

Stanislav Roudavski