smart cities and the quality of life - urban systems...

Smart Cities and the Quality of Life A Point of View based on the Urban Systems Collaborative meeting, London, 10‐11 September 2013

Colin Harrison 4/8/2014

Preface

The Urban Systems Collaborative (USC) is a small, unincorporated association established early in 2011

to bring together the many academic and professional communities concerned with the design,

administration, development, and operation of cities. These include administrators, anthropologists,

architects, computer scientists, economists, engineers, environmental scientists, mathematicians, public

health scientists, social scientists, and urbanists, among others. Its goal is to help these communities to

collaborate in understanding how pervasive Information and Communications Technologies (ICT) will

change the way we think about planning, building, operating, and managing public and private

infrastructure and services in cities and about how citizens and businesses will exploit these.

This document describes some of my learning from a workshop held by the Urban Systems Collaborative

at the Imperial College, London on 10‐11 September 2013, hosted by Prof. John Polak. The programme

of the two day workshop is given in the Appendix to this document. Many of the presentation materials

are available on the USC Web site and video recordings of the plenary sessions are available via the

USC’s YouTube channel. The purpose of this document is not to chronicle the debates and talks of the

workshop, but rather to present my own point of view on the topics covered.

The document does not however cover the length and breadth of the many rich discussions during the

meeting. I apologise to the many speakers and participants whose views I have not managed to capture

here.

I wish to thank the many people who helped with the planning and execution of this meeting, the

speakers who generously gave of their time and thinking. I thank especially the workshop session

leaders ‐ Seema Alim, Richard Dawson, Shane Mitchell, Rick Robinson, Marjan Sarshar, and Richard

Tustin, who managed the challenging process of distilling wisdom from wide‐ranging conversations and

whose verbal and written summaries are primary sources for this document. Errors and omissions are

all mine.

Colin Harrison

Brookfield, Connecticut April 2014


Introduction

Urbanisation – the net flow of people from smaller towns and cities into larger, sometimes new,

sometimes existing cities ‐ is producing waves of transformation in regions around the world. The large‐

scale expansion of existing cities and the creation of new cities in Asia are well known. They are best

known in China as that country completes its transition from an agricultural to a mixture of industrial

and post‐industrial economies. Less obvious, but no less challenging are the decline of cities and regions

in Western countries as populations concentrate around a small number of large cities1. Underlining

this transition, research on scaling laws2 in cities emphasises the economic and cultural attractiveness of

larger cities and hence that this process of urbanization may be expected to continue.

Under these forces, cities find themselves in competition for citizens, particularly highly skilled citizens

who will feed the fires of innovation3, hence driving economic growth and in turn driving public and

private investment. From this emerges a desire to measure and track the Quality of Life in and among

cities as an index of attractiveness.

There is also interest in developing a “dashboard” or basic diagnostic of a city’s “health” that could be

used by policymakers to detect underlying causes of citizen dissatisfaction or of economic or

environmental deterioration. Such dashboards would also employ similar factors.

These developments prompt the question: What makes cities attractive to citizens in general and to

highly skilled citizens in particular? It is generally acknowledged that the leading proximate causes of

people moving to cities are for both men and for women the availability of work and for women the

availability of healthcare, schools, and social services for families. But in turn these factors depend on

economic growth driven by a diverse and skilled workforce and on tax bases that can support services.

Cities of all sizes are increasingly interested in a rather undefined concept called the Quality of Life

(QoL). Surveys and assessments attempt to measure this and to rank cities around the world on various

attributes that may contribute to the Quality of Life. Many factors may contribute to these kinds of

metrics including innovative urban design and planning, trees in residential areas, and strong

environmental programmes. For example, WalkScore4 is a metric of the degree to which a city enables

citizens to rely on walking as a primary means of transport. Urban researchers have found evidence that

high “walkability” correlates with increases in property values5.

In recent years, the degree to which is city may be considered to be a “Smart City” has emerged as

component of such indices. Smart Cities may be loosely defined as cities or regions that seek to make

the best use of the knowledge and intelligence of citizens, administrators, and service providers to

improve the design, construction, and operation of the city in various ways. I do not consider the Smart

1 C Harrison, “Endpoint: A World Without Villages?”, http://urbansystemscollaborative.org/endpoint-a-world-without-villages/ 2 G West, ‘Cities, Scaling, and sustainability”, http://www.santafe.edu/research/cities-scaling-and-sustainability/ 3 R Florida, “Who’s Your City?”, Basic Books, 2009, ISBN 0465018092 4 WalkScore, http://www.walkscore.com/ 5 J Cortwright, “Walking the Walk”, https://tinyurl.com/m5bpag3


City to be a worthwhile goal in itself, but rather it is a by‐product of applying intelligent systems

approaches to the existing infrastructure, services, and systems on a city.

It seems likely that the introduction of smartness or intelligence into cities will have some impact on

citizens’ perceptions of Quality of Life. This possible impact was the subject of a workshop organized by

the Urban Systems Collaborative (USC) at the Imperial College, London on 10‐11 September 2103.

Smart Cities and the “City”

In the early days of Smart Cities (ca. 2008), we viewed the challenges mainly as engineering problems:

helping the municipal infrastructure and services to work more efficiently. But even then some of us

realized that we were heading towards a collision between technology and society. It was from this

realization that the USC was born. The USC has subsequently explored a number of aspects of Smart

Cities, but in this meeting on Quality of Life, we jumped right into the middle of that emerging collision

and asked: In what ways can ICT improve how citizens perceive their Quality of Life?

One way to think of a Smart City is as having a digital wrapper around the traditional physical

infrastructure and services. Citizens are then interacting the city as if it were an intelligent system and

we can begin to ask the kinds of questions that we would ask about any intelligent system and about

how this intelligence might contribute to our perception of Quality of Life in the city.

In this meeting the object of study was thus the interactions between citizens and the public and private

services offered by the urban environment. I think of these interactions as the life of the city as it

facilitates the lives of the citizens. I think that the spatial extent of these connected interactions is what

defines the boundary of a city, rather than the political or even economic boundaries. Our interest in

the USC meeting centered on how the introduction of ICT into these interactions improves the citizens’

perceptions of their Quality of Life.

In the rest of this document I use “City” (uppercase) to denote the ensemble of these interactions and

“city” (lowercase) to denote the municipality or the urban infrastructure and public and private services.

Themes

In addition to a number of keynote speakers and panels, the main structure6 of the meeting was as a

workshop with four themes: the Adaptive City, the Personalised City, the Supportive City, and

Perceptions of Quality of Life. These themes were intentionally defined to be far from orthogonal and

heavily overlapping. In fact they were all looking at the nature of “smartness” and its possible impacts

on QoL from different perspectives. The participants in the meeting joined one or more working groups

that initially described one of the themes in terms of scenarios. Subsequently these scenarios were

used to explore the benefits and issues resulting from this aspect of “smartness”. The four workgroups

shared their evolving discussions through plenary panels.

Here I briefly describe the scope of each of these four themes, which are then discussed more

extensively in the following sections.

The Adaptive City

6 See Appendix A for the workshop programme


From the point of view of the city and its public and commercial capabilities, one outcome for a Smart

City will be the opportunity to adapt these capabilities to the needs of individuals or communities. For

example, consider a community of people living in a certain district, working in another certain district,

and preferring to commute by bicycle. An Adaptive City would notice such people, find safer routes

between the two districts, and make these known.

The systems that are being installed in current Smart City projects can often provide short‐term

flexibility, but adaptation is also needed for longer term changes. After cities have evolved for some

decades it may well be that the original planning assumptions that were made for transportation, for

education, hospitals, emergency response, and recreation are no longer valid. The new digital interfaces

provide information sources for keeping such planning assumptions up to date.

The Personalised City

This is the mirror image of the Adaptive City. How can the individual citizens make better use of the

capabilities of the city? This is again both a short‐term and a long‐term question. It is short‐term in the

sense of the individual making decisions on how to accomplish his or her own plans for the day to the

current status of the city’s capabilities. It is long‐term in the sense of deciding where to seek a new

apartment or a job or a place to start a new business. Whereas the Adaptive City represents a set of

challenges for the municipal or utility administrators, the Personalised City presents several issues for

the individual citizens such as privacy of personal information and how to discover and exploit new

services and affordances.

The Supportive City

In the Adaptive city and the Personalised City we may consider the services and affordances of the City

one by one, the Supportive City considers the interactions among these – a ”system of systems” view –

and seeks to help the City as a whole achieve its collective goals in the face of perturbations to the load

and the capacities of these services. On one hand this involves enabling the citizens to make the best

use of the available services capacities and on the other of enabling the service providers to anticipate

and prepare for changes in service loading.

Quality of Life and the City

Quality of Life in a city is much discussed and poorly defined. Perhaps it reflects a perception of the

individual citizen of the degree to which the city enables him or her to satisfy his or her desires for a

wide range of attributes such as employment, a nice place to live, a variety of restaurants, a range of

stimulating cultural events, easy access to healthcare or education for children, safety from criminal

activity, natural catastrophes, and so forth. Our workshop did not attempt to define QoL, although we

learned about others’ definitions. Instead we focused on some of the underlying factors that seem likely

to contribute to QoL.

In the following sections, I reflect on the reports of the four working groups as well as the comments of

panelists and speakers to point to opportunities and problems in applying “smartness” to improve

citizens’ perceptions of QoL.


The Adaptive City

One of the meanings we might associate with a “Smart City” is that it is aware of us as individuals and

responsive to our needs. This is a basic characteristic we expect of intelligent systems. Traditional

municipal and utility systems on the other hand are “unintelligent” in this sense – exhibiting neither

awareness nor responsiveness. This was the theme explored by the Adaptive City working group.

Short‐term adaptation affects how journeys are made, where food is bought, or how business

operations are managed. It may be as simple as knowing where to cross a street, when the rain will

begin, or where to find the best current price for a commodity. It benefits enormously from greater and

more up to date flows of information about the current status of the city’s capabilities.

Long‐term adaptation reflects the evolution of the spatial distribution of people and infrastructure. It

can be a mutual adaptation in which the city tracks where people are living or working and people track

where new schools or sport facilities are being built. Both kinds of adaptation can benefit from

predictive models that are rooted in the hourly and daily accumulation of real‐world information about

how the City is operating.

The organizational models for the provision of many municipal and utility services such as

transportation, water, energy, education, healthcare, and so forth appear to me to be rooted in the 19th

industrial model. In that model the provider defined a range of standard goods or services that could be

produced reliably in high volumes at low cost. The provider invested capital to create production

facilities – factories, generating stations, transportation infrastructure ‐ equipped with expensive and

complex machines and sought to extract the maximum productivity from these investments. The

resulting goods or services were distributed from the factory via a system or network to delivery points

where individuals could choose whether or not to consume them.

Inherent in this 19th century organizational model were several assumptions:

The management and operational processes for producing high volumes of goods and services

of acceptable quality as efficiently as possible are complex and are understood by relatively few

people. Hence, they must be centralized in a factory with a vertically integrated management

system.

The preferences of the consumers are irrelevant to the production process: “take what you are

given”, “one size fits all”, “any color so long as it is black” were the slogans of the day.

Demand or need for the good or service is sufficiently high and the choices available to the

consumer are sufficiently limited that the consumers will adapt their lives to the provider’s

offerings.

With the emergence during the 20th century of an educated consumer population, of global competition

among providers, and of the ability of ICT to encapsulate complex processes, these assumptions have

become less valid. Slogans such as “mass customization”, “a marketplace of one”, “have it your way”

are now the common experience of consumers in many domains. New organizational and business

models no longer require massive, centralized facilities, but allow smaller scale, highly distributed and

flexible facilities that are better able to adapt to fluctuating demand and are more resilient to

environmental and economic disruptions. Workers lives are less driven by the need to clock‐in and


clock‐out by the factory whistle and in consequence the desire emerges for greater flexibility in how,

when, and where goods and services are consumed. The willingness to “take what you are given” is

diminished. Consumers’ expectations have risen that they will be consulted – directly or indirectly ‐ on

their desires rather than being told what to do.

This transformation from centralized, closed, and rigid means of production to distributed, open, and

adaptable means is hardly reflected in how we experience municipal and utility services today.

Transportation still requires the travelers to organize their lives around the timetable. Waste, water,

and energy utilities still see no need to engage in dialogue with their clients about their needs.

Distributed energy production, which would among other things offer greater resilience to increasingly

unstable weather, remains a still rare exception.

This means that a large opportunity space exists for innovation. Innovation has been an essential

motivation for building cities and for living in cities since their beginning and it is curious that municipal

innovation has stagnated for many years while at the same time civic and commercial innovation have

accelerated.

Let me acknowledge that moving 20 million people a day into and out of a Megacity is not a trivial task.

Likewise the provision of safe water and air or reliable electricity poses difficult challenges. Cities have

historically dealt with the production of services on scales and with reliability that few commercial

organisations could contemplate until the advent of the Age of Information. Today however, it is

common to see private enterprises providing efficient, reliable services to ten or even hundreds of

millions of customers. But I acknowledge that there is much about City infrastructure services that can

only evolve slowly.

What could change here is the adaptation of such services to short‐term and long‐term changes in

demand. Consider short‐term adaptation of transportation, where historically it is the passenger’s

responsibility to show up at the boarding place in accordance with the departures published in the

timetable. The passenger will then board a vehicle that traverses a fixed route and eventually brings

him or her to the destination. In this 19th century model, the service provider has no knowledge on a

given day and time of who wants to travel between points A and B. But in the 21st century model, this

information is knowable and could be used to dispatch point to point transportation services that closely

match capacity to demand and minimize journey times. The Uber7 start‐up is offering “on‐demand” taxi

services in 35 countries. Large‐scale, low‐tech versions have existed for many years in many Asian and

African cities, where private fleets of mini‐buses provide similar customized routes.

An example of long‐term adaptation would be for the service provider to monitor the evolution of

common commuting journeys. As cities evolve, so does where people live and work, but bus and train

routes often do not evolve at the same rate and disjunctions emerge between the journeys people need

to make and the service routes provided. This re‐planning benefits travelers, but also the service

operator, since it can eliminate unneeded capacity. In Dubuque, a city of 60,000 people, IBM showed

7 Uber, https://www.uber.com/cities


how to reduce operating costs by 30% through simple re‐mapping of the transit routes and IBM went on

to apply this approach in Istanbul, a city of some 15 million people.

A further example, raised by Prof. Richard Dawson, concerns the adaptation of cities to climate change.

In the context of the eastern coast line of England, this concerns rising high tide levels, mainly driven by

stronger onshore winds during storms in the North Sea. The ability to predict storm surges by region

and under various storm scenarios provides early warning of the need either to erect defensive sea

walls, as is common in Japan, or to inform property and infrastructure owners of the risks of erosion of

the coast line and flooding along estuaries. With these predictive models citizens, local and central

governments, and insurers can develop mitigating policies.

This indifference of municipal and utility services to individual needs must contribute to the broken links

between the individual and the municipality. In an Adaptive City each citizen’s needs and behaviours

can play a role in shaping the evolution of the services provided, thus realizing Jane Jacob’s view that

every citizen could take part in the design of the city. This adaptation represents a softening of the hard

boundary that normally exists between municipal administrators and citizens and a significant advance

in civic governance.

These changes lead towards participative governance as advocated by Eleanor Olstrom8. They pave the

way for services based on the “third model of economic production” in which the knowledge commons9,

for example information about travelers’ needs, is used to manage transportation planning and

operation collectively among municipal, civic, commercial, and individual stakeholders. Breaking down

the barriers between municipal governments and utilities and their citizens or clients would be a

constructive move in the direction of restoring engagement among these parties and in the restoration

or development of a sense of place, a sense of ownership. The Open Data movement to date is a partial

implementation of participative government.

We might ask whether municipal and central governments still need to provide some of these

traditional services. When such services were first established some centuries ago, only governments

had the skills to develop and operate such complex services. They also often felt the need to control

these services. Today knowledge and skills for the management and operation of complex, large‐scale

services is widely accessible and supported by ICT tools. It is no longer reserved for large organisations

whether public, civic, or private, thus opening the door to the transformation, where appropriate, from

closed, centralized systems to open, distributed systems. Not every system can or should be open and

distributed; some systems, for example public safety, depend on bringing together information that is

spatially distributed to generate, for example, overall understanding and decision‐support in an

emergency situation then propagate this integrated information to decentralized systems.

Decentralised systems, for example ham radio, can also bring resilience to centralized system. Perhaps

8 E Olstrom, “A theoretical framework for exploring the capability of participative and collective governance in sustainable outcomes”, http://www.earthsystemgovernance.org/lund2012/LC2012-paper96.pdf 9 C Hess & Elinor Olstrom, “Understanding Knowledge as a Commons: From Theory to Practice”,ISBN-10: 0262516039


it is time to evolve towards the “third model” of participative economic production suggested by Hess

and Olstom7 that is neither public nor private but a collaboration among these for the common good.

This theme of adaptation is one of the main motivations found among municipal and civic leaders for

the adoption of smart city methods in cities that aspire to improve their attractiveness to (highly skilled)

citizens and to industry.

The Personalised City

In addition to being aware and responsive, we also expect intelligent systems to be configurable, so that

the user or citizen is able to configure how the system looks, what features are prioritised for access,

and how the system performs. This also emphasizes the ability of the citizen to easily discover the

offerings or affordances of the system. We might call it Ease of Use. How can the citizen make better

use of the capabilities of the city? How can a wide range of users differing in age, gender, language,

physical abilities, education, economic status, life‐styles, and cultures using a variety of access devices –

kiosks, basic mobile telephones, smart telephones, near‐field devices, cameras, and so forth ‐ configure

the city in ways that meet their expectations, preferences, and needs? This was the topic studied by the

Personalised City working group.

Personalisation has also both a real‐time and a long‐term aspect. It is real‐time in the sense of the

individual deciding how to reach short‐term goals based on the current status of the City’s capabilities.

It is long‐term in the sense of deciding where to seek a new apartment or a job or a place to start a new

business.

Three of our city themes – the Adaptive, Personalised, and Supportive Cities – imply the exchange of

information among the services and citizens comprising the City. These are processes of mutual learning

that lead to mutual adaptation in both the short term and the long term. These exchanges are also the

basis for the knowledge commons mentioned above. These exchanges of information raise difficult

issues including:

The modalities of interaction

The ownership and governance of the information exchanged

The role of mutual learning in fostering engagement

Consider the challenges of interaction in a Smart City. While the participants in the USC meeting and, no

doubt, most of the readers of this document belong to the techno‐elite – highly‐educated, relatively

wealthy, globally mobile, and so forth – a City must embrace a wider range of demographics. The tails

of the demographic distribution have long posed problems for interactions. In particular in the lower

tail there are issues such as literacy and comprehension, the authorities’ expectations of identity tokens

or credentials and fixed addresses, limited mobility, physical and mental handicaps, and the ability to

navigate complex bureaucratic processes. In recent years to these issues we have added the Internet

divide, the rapid evolution in complex access methods such as kiosks and mobile devices, online

credentials, and the costs of smart mobile telephones and tablets and the associated

telecommunications services.


A clear example of the problems these issues of interaction can create was the 2010‐2011 rollout of

Smart Meters10 by the Pacific Gas & Electric (PG&E) utility in northern California. Although this area

includes Silicon Valley, San Francisco, and other regions inhabited by wealthy, well‐educated, and

techno‐savvy citizens, the process produced uproar from the utility’s consumers, who clearly did not

understand the technology or the purpose of installing it in their homes. There was deep suspicion

about who would benefit from of this technology.

The original 2008 vision for Masdar11 also stirred similar fears. While the utopian, technological vision

was inspiring, I questioned the project leaders as to whether such an environment could easily support a

balanced demographic. More broadly, I feared that the use of such techno‐centric access methods and

systems in existing cities would disenfranchise some of the citizens.

There are also complex issues of ownership, use, and governance of the shared information. It is

effectively impossible to take part in a Smart City without divulging personal information that may be

sensitive for some citizens. Much of that information is only relevant in aggregate, related to questions

such as: How many people are crossing the bridge at the moment? In such cases merely counting heads

can preserve anonymity. But other information, particularly information needed for the most valuable,

personalised services is specific to an individual such as : Where is the nearest hospital that can help

me? While allowing the utility to read my electricity consumption hour by hour instead of month by

month may not seem a particularly sensitive change, over a period of weeks that information would

reveal when my home is likely to be unoccupied. Likewise smart transportation services would need

accurate, real‐time information about my current location.

Many public and private services claim to protect personal information through “anonymisation”

techniques, for example removing names, addresses or other identifiers. There is much scientific and

legal debate12 on whether personal information can in fact be protected in this way. If various sets of

anonymised information are correlated, it is often possible to extract identities. The idea of a

knowledge commons is admirable in many ways, but it assumes that my personal information can be

protected.

These issues are typical of the social problems that arise from the introduction of new technologies.

When automobiles were first introduced in Britain, they were required to be preceded by a man

carrying a flag to warn pedestrians and horse riders. This and other limitations greatly reduced their

utility on roads that had been created for pedestrians and horses. Eventually the vehicles and roads

were adapted to one another and society came to accept the associated deaths, pollution, noise, and so

forth as the costs of greatly increased mobility. Civic organisations such as the AAA (USA)13 and AA

(UK)14 played a leading role in mediating this technological transition. Organisations such as the Open

10 “New Electricity Meters Stir Fears”, http://www.nytimes.com/2011/01/31/science/earth/31meters.html?pagewanted=all&_r=0 11 “Masdar Plan”, http://www.economist.com/node/12673433 12 “Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization", http://www.uclalawreview.org/?p=1353 13 AAA –History, http://newsroom.aaa.com/about-aaa/history/ 14 AA – The Early Years, http://www.theaa.com/aboutaa/history.html


Knowledge Foundation15 and the Open Data Institute16, supported by Open source tools such as CKAN,

may eventually take on this role successfully. I note also the important role in this space of the

Electronic Frontier Foundation17.

Equally controversial is the issue of who controls and can make use of such information. Clearly in

disclosing my current location to the transit management system I want that system to use it, but do I

want this information to be passed on to an advertising broker so that I can be informed of local

offerings? Possibly yes. Do I want this information to be aggregated with that of others and offered for

sale to anyone? Probably no.

Setting these challenges aside, it appears that the Personalised City would have much to offer In terms

of QoL. By allowing the individual citizens to tailor their experience of the City to their needs, they could

have the impression that the City was designed around them, was aware of their goals and interests,

and was responsive to them. Personalisation could increase the accessibility of the capabilities and

affordances of the City and thereby enable these to be exploited in differing ways by citizens with

varying characteristics. This could then have the outcome of giving them a sense of a richer City with an

improved QoL.

Personalisation could have many dimensions:

Means of interaction (kiosk, mobile telephone, smart telephone, PC, camera, RFID)

Modality of interaction (text, speech, visual, gesture)

Language

Accessibility (visual, speech, physical or mental handicaps)

Cultural or community context

Role (young child, student, worker, visitor, senior)

Goals (Maslow’s Hierarchy, mobility, entertainment, minimum cost, minimum delay)

…and so forth.

This working group identified a key impediment to this approach in the extent to which citizens are

willing to take time to configure systems. The Nest18 intelligent thermostat was given as an example of a

device that does not require the use to enter a detailed programme for the heating or cooling of a

home, but rather learns from the inferred comfort levels of the occupants. Samir Menon also suggested

that many people would simply want a device such as a thermostat with three buttons:

“I feel super‐green today – turn it way down”

“Somewhere half‐way”

“I’m a caveman – turn it way up”

15 Open Knowledge Foundation, http://okfn.org/ 16 The Open Data Institute, http://theodi.org/ 17 Electronic Forntier Foundation, https://www.eff.org/ 18 Nest Learning Thermostat, www.nest.com


This is an important aspect of decision‐making in the context of city services and elsewhere: many users

do not want a large range of choices and in fact the offered choices in the City will often be limited by

practical constraints such as existing road ways.

So much may depend on services that can observe and infer the citizen’s configuration preferences.

Sensitivities over privacy might be addressed by rendering configuration at the level of identifiable

communities or demographic groups, leaving those that are highly motivated to add their own

refinements.

A side effect of this ability to personalize the city could be a dilution of the character of the city. The

indifference of municipal services to the individual and the requirement that the individual conform to

their inflexible capabilities are important contributors to this character. If we adopt personalisation, as

argued here, then we reduce the common experience of the city. On the other hand, different sub‐

cultures cohabiting in the same city have long exploited its services and affordances In different ways to

support differing lifestyles.

This reduction of a common experience is also manifest in the experience of international cities and the

changing relationship between our physical location and other, connected locations. In my grand‐

parents’ generation in the north of England, it was still common for someone to spend his entire life

within 20 km of his birthplace. As a mobile knowledge worker, I can say that given light, power, and

network connectivity, I can work anywhere in the world and maintain close contact with a large network

of family, friends and colleagues.

Even time can be configurable with some pain. I have known co‐workers who had US‐based jobs, but

chose to live in Europe or Japan and to accept the need to adapt their daily cycles. Senior executives

with global roles have the freedom to work any 24 hours of the day that they choose.

So to a large degree, my physical location, whether that is my home town or a city in China, becomes an

instance of a collection of services and affordances that support my way of life. My perception of a City

as a mobile worker therefore depends on how easily I can get it to resemble and behave like my

abstraction. I realize that this is a rather crass devaluation of the identity of cities around the world and

note that this view applies primarily to meeting my working needs. I do in fact very much appreciate the

varied character and sense of place of the many cities I am privileged to visit. But if every city I visit has

Starbucks, Novotels, Australian wine, and Indian restaurants and can be configured to meet my specific

needs, then how much of its unique character do I encounter?

We defined four archetypes for the emergence of the Personalised City together with current example

to illustrate how these are being enacted in our cities today:

1. Passive – citizens consume information, receive services from official agencies, or data sources

a. Personal Travel Assistant (Amsterdam, Seoul19)

b. London, Transport for London Apps20

c. Smart Meters21

19 Personal Travel Assistant, Seoul, http://www.smart2020.org/case-studies/personal-travel-assistant-pta/ 20 London Transport Live, https://play.google.com/store/apps/details?id=com.toson.londontransportfree


d. PlacR22 – new re‐combinations of open data sources

2. Active – crowdsourced, user generated, providing a symmetrical, virtuous cycle of inputs and

outputs, which is enriched through heterogeneous interactions

a. Waze23 – community based traffic and navigation App

b. HistoryPin24 – user generated place history

c. Photosynth25 – fractal world through 3D imagery of place

d. Songkick26 –personalised music recommendations based on preferences with a

feedback loop from Pandora

3. Pragmatic – a personal trade off on personal privacy, data flows captured while using a service,

with an imputed net benefit to the individual.

a. Google Glass27 – real time, personal data, video, search, reacting to personal interests

and preferences

b. Foursquare28 – personal location awareness

c. Nest29 – Learning thermostat, with personal data being collected

d. Gamification ‐ Angry Birds30 collects personal location data using the game as a front

end

4. Constructive – an open source, hacktivist approach to developing information which is

otherwise not openly provided to citizens

a. OpenStreetMap31 – a peer mapping platform based on a knowledge commons

b. Xively32 ‐ a platform for the Internet of Things, such as sewer sensors in New York,

radiation level sensors in Fukushima

One area that we did not manage to explore in this meeting is the Smart City as a Design problem. That

is, with the advent of a rich set of digital interfaces between citizens and the public and private

capabilities of the city, how should these interfaces be designed to maximize conventional design

characteristics such as ease of use, discovery of capabilities, teaching or training about capabilities,

accessibility of capabilities, integration of capabilities, and so forth? To date these digital interfaces

have been created as extensions or replacements of legacy physical interfaces with little thought given

to how they relate to one another and to the goal‐driven, problem solving needs of the citizens. If

instead we were to think of the city’s capabilities as an integrated, intelligent system, how would we

want to interact with the city through these interfaces? What would we want these systems to learn

about our goals and needs?

21 Southern California Edison – Smart Meters, https://tinyurl.com/mkcxwx6 22 PlacR, http://www.placr.com/ See also http://theodi.org/case-studies/placr-case-study 23 Waze, https://www.waze.com/ 24 HistoryPin, http://www.historypin.com/ 25 Photosynth, http://photosynth.net/ 26 Songkick, http://www.songkick.com/ 27 Google Glass, http://www.google.com/glass/start/ 28 Foursquare, https://foursquare.com/ 29 Nest, https://store.nest.com/ 30 Angry Birds, http://www.angrybirds.com/ 31 Open Street Map, http://www.openstreetmap.org/#map=5/38.007/-95.844 32 Xively, https://xively.com/whats_xively/


The Supportive City

We are social animals33. We need relationships with others for our own well‐being. Cities exist to allow

large numbers of us to live in close proximity. One of the earliest written documents is a collection of

stories from the Sumerian civilization known as The Epic of Gilgamesh34. Among these stories is the

construction of a wall that defined the city of Uruk. The purpose of the wall was to separate the people

from nature, where their lives were ruled by biological Darwinism, and instead to create a space in

which a new set of rules – social Darwinism – could be developed. Hence one view of life in a city is the

creation of social niches and the competition for ever better niches. QoL is a metric of our satisfaction

with our current niche.

A 2013 discussion at the Santa Fe Institute lead me to think of cities in terms of Darwinian evolution.

Through our ability to modify the environment in which we as individuals and communities find

ourselves, we have greatly expanded the ecological niches in which we can survive and this has

permitted a great expansion of homo sapiens. There are of course negative aspects to these

modifications which have in the past lead to the collapse of large societies35 and may indeed threaten

the current global society in the coming centuries. But at least in the relative short‐term our cities may

be viewed as providing a range of niches combining ecological, societal, and economic support for broad

demographics.

The Darwinist view of “fitness” in natural selection refers to the ability of the individual organism to

propagate specific characteristics (“allele”) into future generations. We might take “urban fitness”

metaphorically as the ability of a citizen to find or to create a niche in which he or she can sustainability

meet his or her needs and desires and which may have the potential for further elaboration or

specialisation. In the context of the USC’s London meeting, we might consider this as an important

aspect of “quality of life”.

This Darwinist model is useful in another way. It points to the city as a collection of ecosystems – social,

economic, environmental, commercial, cultural – that result from the interactions of citizens and

suppliers of goods and services. These ecosystems are normally invisible unless we are directly involved

in them. The advent of the Internet of Things gives us a ghost‐like image of their activities through the

many interactions that are captured by public and private, real and virtual sensors.

While the Adaptive City and the Personalised City take the individual or specific sub‐communities with

the city as the objects of concern, the Supportive City is concerned with the overall outcome for the

citizens as a whole. It also goes further in considering the interactions among the urban services and the

citizens using them to detect problems in a service and to anticipate the impacts that this may have on

other services. The Supportive City attempts to understand what the City is trying to do, minute by

minute, and then make adjustments to the services to facilitate these collective goals.

What is a collective goal? Citizens are human beings gifted with free will. Who knows what they want

to do collectively? The free will argument is true, but in practice almost all individuals have habits or

33 D Brooks, “The Social Animal”, Random House, 2011, ASIN: B00BQ1Y9YA 34 The Epic of Gilgamesh, http://en.wikipedia.org/wiki/Gilgamesh_epic 35 J Diamond, ”Collapse: How Societies Choose to Fail or Succeed”, Penguin Books, 2011, ISBN 0143117009


patterns of daily or weekly behavior that form the rhythm of life. On any given day, an individual may

deviate from his or her habits for a thousand different reasons. En masse, these deviations disappear to

a large degree – a given individual may or may not go to a concert on a given evening, but we know that

a certain number of people will go.

The Supportive City relies on observing these large scale patterns of behavior over weeks and months

and years and using these to develop expectations of the demand for urban services minutes by minute

on a given day. At our current level of understanding, this is rather like a statistical approach to weather

forecasting in which the sampling density in time and space has strongly improved in recent years36.

Such models exist or can be readily developed for many individual urban services. Thus electrical

utilities have observed patterns of energy consumption at the level of the distribution networks that

enable accurate hour by hour predictions of demand on a given day under various influences, such as

weather. These are essential to timely forward purchases in energy markets and to the overall

operation of regional energy production. The introduction of Smart Meters facilitates the extension of

the detection of patterns to the level of individual consumers and of the collection of data in real‐time.

For example, the utility may detect directly or indirectly the beginning of a clothes washing or drying

cycle and hence anticipate the load that will be sustained for the next hour or so.

Likewise transportation managers have long had the ability to detect the level of activity of a given

station or bus stop and the overall patterns of traveler volumes. The detailed, real‐time information

now available from contactless payment methods such as London’s Oyster card or Japan’s Suica card

may eventually provide the ability to observe minute by minute usage of the transportation system, to

detect fluctuations or anomalies, and to make useful predictions about fluctuations in transportation

volumes one to two hours ahead37. Similarly traffic sensor data can be used to detect patterns of

vehicle flow that may then be leading indicators for congestion in specific districts.

So the identification of patterns of normal behavior, the detection of deviations or fluctuations in this

behavior and the prediction of short‐term future service demand are one strand of the Supportive City.

A second strand is help understanding of the impact of fluctuations in the activity or performance of one

service, for example transit, on other services and other parts of the City. It is a truism that the City is a

“system of systems”. The services underlying the City interact with one another through both causality

and correlation. Thus a failure of water main causes a disruption of road traffic. Such traffic disruptions

affect the times when people arrive at work in the morning or at home in the evening and hence

correlate with changes in demand for water, energy, and communications services. The Supportive

City’s accumulated knowledge allows it to understand these causal and correlative impacts, to make

real—time adaptations to services to cope where possible with these impacts, and to make predictions

about impacts in the near future.

36 Note: Actual weather forecasting is (mainly) based however on models of the atmosphere based on physical science and it is my hope that over time we will develop an urban science that will replace these statistical methods. 37 Prof. M Barry noted during the meeting that in many cases it is not possible to extract origin and destination information. For example, in London passengers are detected when boarding a bus, but they are not detected when disembarking.


Overall every city’s systems work. Citizens can get to work and get home. They get food and shelter.

Their children get medical care and education. A given City may not work as well as its citizens might

wish, for example Mexico City, but it does work, however painful it may be. If the City did not work,

then, according to economic theory at least, people would leave or, in the worst case, die. In many

cities during the present period of urbanization services are heavily over‐stressed. If a given service

acquires new capacity, well‐informed citizens will seek it out and re‐distribute the overall load as they

seek to improve their QoL. The Supportive City should assist them by enabling them to discover positive

and negative fluctuations in service performance that are relevant to their real‐time goals.

In the very short‐term ‐ minutes to an hour ‐ this assistance will be in the form of information about

fluctuations in service capacity that enables the individual citizen to understand the personal impact of

the fluctuations and what alternatives or adaptations are possible. Simple examples of this are the

Variable Message Boards, for example on the Boulevard Periphérique in Paris, that give information

about waiting times at junctions. While the delay information has face value, experienced drivers can

assess the magnitude of the delays and decide whether to take alternate routes.

In the medium term – a few hours – the impact of a given fluctuation must be propagated to all services

that will be affected by the “knock‐on” impacts of the fluctuation. Thus, the time of peak energy

demand may be shifted or demand may become more or less peaked. Or a major accident may require

the closing of roads and re‐routing of traffic in order to enable rapid access by emergency workers in the

coming hours, thus putting unexpected load onto alternate routes.

This set of interactions is what I mean by the “system of systems”. While urban planners work to

ensure, for example, balance among the static capacities of multi‐modal transit systems, the “system of

systems” is a dynamic phenomenon that emerges from the ways that citizens use the City. Such

complex, real‐world systems are never in equilibrium, but are constantly adjusting to changes in

capacity and demand. This re‐balancing is an example of resilience in which a complex system, such as a

set of urban services, adjusts its operating points to correct for changes in capacity or demand, in what

we might call a “stable disequilibrium”. The Supportive City plays an active role in enabling this re‐

balancing to occur effectively and fairly. By effectively I mean keeping the set of systems within their

linear operating regimes so as to avoid tipping one or more services into an operating state from which

it will not quickly recover. By fairly I mean that re‐balancing loads will in general degrade QoL for some

segment of the population in order to avoid an even greater degradation for others; this is a matter of

municipal policy.

In addition to these tactical goals, the Supportive City also assists municipal governments with their

strategic goals. For example in the early 21st century, many cities set goals to reducing overall

greenhouse gas emissions by, say, 20% by 2015. To a small degree the municipality can contribute to

this goal through how it manages, maintains, and operates its own facilities. However, the larger part

must come from encouraging or requiring citizens to contribute through how they manage, maintain,

and operate their own facilities and how they use the city’s services. Analogous policies for public

health, education, public safety, and so forth go back some centuries. Municipal governments have long

had a guiding hand on how their citizens live their lives, sometime by encouragement and sometimes by


enforceable laws. The Supportive City offers new methods for implementing these policies in ways that

represent the desire of citizens to make their own decisions.

While the Supportive City lies some years in our future the growth of information about the City and of

our ability to predict the future with useful accuracy suggest the need for public discussion on the

balance between well‐meaning policy and the individual’s free will. Prof. Richard Dawson38 gave us an

excellent preview of this in reviewing his department’s work on assessing risk in coastal areas of the UK

and central and local government policy for mitigating and adapting to these risks.

What the Supportive City may eventually bring is the presentation of choices to the individual citizens

that, if accepted, will sustain or improve the overall QoL of the city. In some situations this will be at the

expense of the QoL of the individual. That dilemma lies at the heart of our decision some millennia ago

to live together in cities.

Perceptions of QoL

It appears inevitable that cities will make increased use of ICT. How will this affect the citizens’

perceptions of Quality of Life? In this fourth theme, we considered how these benefits might influence

an index, loosely called here “Quality of Life” as a measure of citizens’ satisfaction with their lives in a

given city or region. The individual’s perception of QoL is inherently subjective and difficult, if not

impossible, to quantify. No doubt social scientists will rightly take great exception to technologists

contemplating this challenge largely in ignorance of how societies work. And yet this intersection

between technology and social science is at the heart of our desire for Smart Cities and the increasing

availability of information describing how and how well cities work for their citizens gives us new ways

to pursuing this goal.

Mike Weinstock gave a thoughtful review of traditional sources of QoL. He began with the observation

that the physical layout of cities embeds much of the culture its citizens. We may contrast the central

districts of traditional villages with their common land, their places of worship, and their burial grounds

with the gridded structure of American cities, which emphasises compactness, ease of transportation,

and extensibility. He went on to wonder how these values are being changed by the Age of Information,

noting that changes are not necessarily bad or disruptive. Since the Enlightenment, we have seen a

drive to individuation at the expense of the historical acceptance of belonging to the community first

with personal rights coming second. Attendant upon this change came the sense of privacy.

For the older generations, down to the Baby Boomers, the personal sense of a neighbourhood remains,

but this feeling is not true of younger generations. The commercial streets are viewed only as places to

make money, and when there is no money to be made, shops are first rented more cheaply to poorer

businesses, and finally to become boarded up. In the high streets of London only 10% of the shops are

boarded up, but in the North of England this rises to some 30%. To a considerable degree the high

street as a place face competition from malls and big box stores on the outskirts of cities and towns.

While the pricing advantage of the “big box” stores has a strong role in this, the traditional high street

38 Prof. R Dawson, https://www.youtube.com/watch?v=VbC4rdWjqgs


simply does not appear to be as attractive as a modern shopping mall in many cities. Economists see

this as a temporary problem that market forces will correct, but the phenomenon has been evolving for

over twenty years.

It appears that traditional urban planning is not by itself capable of making streets that have a sense of

place and attract citizens. For Gen Y and Millennial citizens today the sense of community is driven in

large part by social media, though these are less permanent than bricks and mortar of traditional town

centres. They do on the other hand offer a wider range of community than traditional town centres, not

requiring a special place for “meeting” and having much greater fluidity in evolution. The idea of

community remains, but has been separated from the built environment and may as easily be global as

local.

As municipal, civic, and business leaders of both declining cities and of shiny new cities struggle to make

their Cities more attractive, the desire for a way of measuring something that we might call QoL

emerges. This comes from several directions:

Elected officials in local and central government would like a diagnostic of the “health” of their

city. What makes their citizens happy and unhappy? What are the trade‐offs between different

contributing factors?

Economic development leaders would like a simple way to rank their city’s attractiveness (for

specific demographics) in order to motivate commercial investment in business and in property.

Economists would like to have behavioural models of various demographics based on the

hypothesis that each citizen seeks rationally to maximize some utility. Understanding each

demographics’ view of QoL would then enable the development of micro‐economic models of

cities and regions and the exploration of policy options. It could also shed light on how

(rational) people make decisions, for example about how to use multi‐modal transit services.

Municipal and private service providers would like to understand factors influencing satisfaction

or dissatisfaction with their offerings.

The apparent impossibility of this task stems from the heterogeneity of citizens and cities. Thus a city

might be designed with the intent of being attractive to Ph.D. engineers; the original MASDAR vision

being a case in point. But this would probably make it highly unattractive, to the point of being

unlivable, for children, seniors, non‐technologists of all kinds, and others not members of that elite.

Even within a given segment of the population, personal circumstances – health, employment, failed

relationships, and so forth – may produce strongly negative feelings for some members on any given

day; or vice versa.

Citizens have multiple roles, for example as workers, as drivers, as pedestrians, and so forth and may

have different priorities in their various roles. Citizens also differ in their appetite for complex decision‐

making. Some may seek a wide range of choices and will take the time to evaluate different

combinations to achieve high satisfaction, while others prefer a few, simple choices that require less

effort while yielding a less satisfying result.

Perceptions of QoL are also believed to be influenced by trends as well as absolute values. Thus the

citizens of New York have been heartened in recent years by a strong decline in crime rates, even


though the absolute value of the crime rate is still well above that of, say, London. Improvements may

produce momentary increases in satisfaction, but these become absorbed into a new norm and thus

discounted.

The definition or composition of a QoL index will also vary among cities. Indeed that variation is an

insight into the character of a city. As Glaeser39 points out, the people who choose to live in Houston

have different tastes, interests, and ambitions from those who choose to live in New York City. Each City

selects for certain types of immigrants and this process acts to reinforce the character of the City.

Widely differing cities are also effectively impossible to compare. As Samir Menon pointed out in his

talk at the USC meeting, it is not meaningful to compare QoL between, say, New York City or even

Mumbai and a small town in one of the under‐developed – hence more natural – states of India.

Maslow’s hierarchy of needs40 proposed twenty factors representing common needs. These are widely,

but not globally, accepted as a framework for assessing the degrees to which an individual’s needs are

being satisfied. The framework does not appear to be used directly in assessing QoL, but does inform

the several indices described below.

Notwithstanding these and other difficulties, many such indices – some specific others very broad –

have been developed. As Samir Menon pointed out, when we visit a family doctor, he or she does not

immediately attempt a deep assessment of our state of health, but rather takes a few measurements –

weight, temperature, blood pressure, pulse rate, and so forth. None of these simple measurements will

directly reveal, for example, cancer. But, when tracked over time and across many patients, they

become indicators of whether our basic health is better or worse. So we might imagine that it could be

possible to define and calibrate a QoL index that is both general across demographics and cities.

Samir Menon41 and Stan Curtis gave us several examples of more general QoL indices that have been

developed. These are listed briefly here and more extensive descriptions, in particular of the factors

evaluated are given in Appendix B.

Genuine Progress Indicator (GPI) The GPI indicator is one of a number of metrics developed in

the late 20th century as alternatives to Gross Domestic Product (GDP). These often incorporate

factors indicating that GDP growth is associated with some negative impacts. GPI42 is based on

the concept of sustainable income, presented by economist John Hicks (1948). The sustainable

income is the amount a person or an economy can consume during one period without

decreasing his or her consumption during the next period. In the same manner, GPI depicts the

state of welfare in the society by taking into account the ability to maintain welfare on at least

the same level in the future.

Social Progress Index (SPI) The SPI43 is an example of a genuine progress indicator that has been

developed and assessed over some 50 countries by Michael Porter. “Social progress is defined

as the capacity of a society to meet the basic human needs of its citizens, establish the building

39 E Glaeser, “Triumph of the City”, http://www.triumphofthecity.com/ 40 Maslow’s hierarchy of needs, http://en.wikipedia.org/wiki/Maslow%27s_hierarchy_of_needs 41 Samir Menon, https://www.youtube.com/watch?v=ngiuOgt0-FY 42 GPI, http://en.wikipedia.org/wiki/Genuine_progress_indicator 43 Social Progress Imperative, http://www.socialprogressimperative.org/


blocks that allow citizens and communities to enhance and sustain the quality of their lives, and

create the conditions for all individuals to reach their full potential.”

Human Development Index (HDI) The HDI44 was created by the Pakistani economist

MahbubulHaq and the Indian economist Amartya Sen in 1990 and was published by the United

Nations Development Programme. It is supported by the World Bank.

Gross National Happiness (GNH) GND45was conceived in 1972 by the king of Bhutan and

developed by the Centre for Bhutan Studies. The four pillars of GNH are the promotion of

sustainable development, preservation and promotion of cultural values, conservation of the

natural environment, and establishment of good governance. A second generation version of

the GNH index was developed in 2006 as an index function of the total average per capita of

seven factors.

Beyond GDP This originated with a conference of the European Commission, the Club of Rome,

the OECD, and the World Wildlife Fund and was taken up by Joseph Stiglitz46.

Better Life Index (BLI) The BLI47 was developed by OECD and published in 2011, building in part

on Stiglitz’ work on “Beyond GDP”.

These indices are not directly equivalent to the concept of QoL that was the subject of the USC meeting.

Some, in particular the GNH, are not easily quantified and many of their factors have no recognized

standard metrics. The HDI and the GNP are valuable at a national level, but less so at the level of a city

or region. However the OECD Better Life Index comes close in several areas, where the kinds of effects

ascribed above to Adaptive, Personalised, and Supportive cities might be measureable.

While defining indices is useful, they become far more valuable when they are used and data is

accumulated across many cities. The Global City Indicators Facility (GCIF)48 has defined a set of about

one hundred metrics grouped into City Services and Quality of Life. These metrics are being reported

by over one hundred cities worldwide. The definitions are being incorporated in ISO draft standard

3712049. This draft standard is entitled “Sustainable development and resilience of communities” and

concerns indicators for city services and quality of life. It will be published early in 2014.The GCIF

indicators, the draft ISO 37120 standard and related ISO standards50 appear to be the most substantive

work focused on QoL in Cities.

It seems conceivable to develop metrics based on Smart City data for some parts of these indices,

although, because of the diversity among cities, it will be necessary to perform local calibrations for

each metric. From these it could be possible to design a research agenda at the junction of technology

and social science to assess the degree of influence that Smart Cities methods can bring to improving

the Quality of Life in cities.

44 Human Development Index, http://en.wikipedia.org/wiki/Human_Development_Index 45 Gross National Happiness, http://en.wikipedia.org/wiki/Gross_national_happiness 46 Joseph Stiglitz, “The Stiglitz Report”, http://ec.europa.eu/environment/beyond_gdp/index_en.html 47 Better Life Index, http://en.wikipedia.org/wiki/OECD_Better_Life_Index 48 Global City Facilitators, http://cityindicators.org/ 49 ISO 37120 draft standard, http://www.iso.org/iso/catalogue_detail.htm?csnumber=62436 50 ISO 13.020.20 Environmental Economics, https://tinyurl.com/mu2vgnw


Conclusion

Metaphors of cities drawn from biology and medicine have frequently come to my mind in recent

times. Luis Bettencourt, José Lobo, Geoff West, and others have shown how the scaling laws51 of

(American) cities resemble those of biological systems and hence that the structure and functioning of

cities resemble the morphology and physiology of biological systems. My own hope is that we can

achieve the kinds of revolutions that took place throughout the twentieth century in unifying the

perspectives of the many academic and clinical disciplines that study and treat the human body and that

we can thereby deal with cities in terms of their underlying pathology rather than, as today, their

symptoms.

Human needs and desires can be highly diverse and may evolve quite rapidly under the influence of

changing personal circumstances or of a changing environment. We might consider “life in the City”

then as a continuous interaction between citizens – both individually and as groups with similar needs

and desires – and the environment of the City. As the City evolves or more exactly as the citizens’

perception of the City evolves, these individuals and groups adapt their behaviours to extract the

maximum benefit.

As a personal example, I have moved several times between countries in Europe. My own observation is

that European countries – for example Germany, Great Britain, Switzerland ‐ provide a narrower range

of niches than the United States. The latter provides almost a continuum of niches, but these

individually contain few resources and the citizen has much work to do in constructing a sustainable

life. In Europe the choices are more limited, but each niche is relatively rich in resources. On occasions

when I have moved from the USA to Europe, I have found myself initially struggling against the limit of

the niches I could occupy. But after a while I have (re‐)discovered how to exploit the resources available

to me and how to live very comfortably within one such niche. And when I return to the USA, I struggle

with the need to re‐construct my life from multiple niches.

Following this line of thought, we might ask whether the relative “fitness” of various individuals and

various groups may be in competition. Changes in the built environment almost inevitably impinge on

existing niches. We are all familiar with the “NIMBY” conflict in which an individual or group perceives –

often correctly – that its interests will be jeopardized by some proposed change in the environment that

is often intended to benefit some other individual or group or the community at large. Such stresses

may result in polarization of the citizenry and a weakening of the fitness of the city as an organism. Can

the rapidly increasing flows of information among the citizens and between the citizens the city mitigate

such stresses and thereby maintain or enhance the overall perception of “quality of life”?

The infusion of ICT into cities is but one of many changes currently underway in our cities and societies.

As my urban planning colleagues frequently remind me, we should not over‐estimate the positive

impact that such intelligence can have on life or QoL. Equally, I think we should not exaggerate the

negative impacts. Cities are inevitably the laboratories in which we must conduct experiences of this

kind. It was clear to me from my first experiences with Masdar that the development of Smart Cities is

51 L Bettencourt, Jose Lobo, D Helbing, C Künert, and G West, “Growth, innovation, scaling, and the pace of life in cities”, http://www.pnas.org/content/104/17/7301.full.pdf


too important and complicated to be left to engineers and yet there is no other way to test these

approaches than learn from the other professions that study cities and then to try them out – carefully –

in cities.

Volker Buscher remarked during his talk52 that it seems inevitable that the use of technology in cities will

increase. This is the trajectory of our civilization: Society invents technology and then technology re‐

invents society. Taking heed of the impact of the automobile on 20th century cities, we need to think

more carefully about how ICT will impact life in the City.

I think that our host, Prof. John Polak, in closing the meeting53 captured very well the challenges that we

face in three points (very slightly paraphrased):

“First, our engineering capabilities for sensing, control, and actuation, currently exceed our capacity

to know what to do with them. We don’t quite know what to do with all this stuff. We are lacking

the intellectual capability to understand the totality in any meaningful way. We do not yet have the

tools to understand everything that is going on in a city. The gap between capability and capacity is

largely a result of that. This poses challenges for educators, for researchers, and for ICT businesses

to close these gaps.

Second, we have under‐estimated some of the issues associated with trust and legitimacy. The

realization of the benefits of Smart Cities requires on the part of the operating organizations and of

the citizens a degree of trust that we need to work very hard to achieve. This applies to the new

systems and technologies, but also we are talking about delivery mechanisms for those technologies

that are already untrusted. Public bodies and large corporations are not trusted. We need to think

carefully about how we engender trust. To the degree that these entities are mistrusted, they will be

perceived as lacking legitimacy.

Third, we need to think about how the deployment of these ideas impacts large policy questions,

such as “who collects the tolls”? It is usually easily to see who is gaining financial benefit from new

tolls, taxes, and other sources of revenue. But it is much harder to see exactly who benefits from the

deployment of smart systems.”

I hope that this workshop and my ruminations on what I learned from it may begin to address these

three simple but difficult challenges that Prof. Polak raised. I hope that at least they begin to give us a

vocabulary and a set of frameworks for exploring these on which others can build.

52 V Buscher, https://www.youtube.com/watch?v=_Pmfh6BfVYA 53 J Polak, https://www.youtube.com/watch?v=yD2eGxPQ_HY


Appendix A – USC Workshop Programme

Tuesday 10 September 2013

Time Theme Presenter / Facilitator

09:00 Welcome Prof. John Polak, Imperial College

09:15 Panel – The City as a Design

Problem

Prof. Michael Batty, University College London;

Shane Mitchell, Cisco Urban Innovation;

Michael Weinstock, Architectural Association.

Facilitator Colin Harrison (USC)

10:00 Break

10:30 Summary thus far Colin Harrison

10:45 Breakout Sessions and Facilitators

The Adaptive City Richard Dawson, University of Newcastle

The Personalised City Shane Mitchell, Cisco

The Supportive City Martin Rieser, Institute of Creative

Technologies, De Montfort University,

Leicester

Perceptions of Quality of Life Rick Robinson, IBM UK

13:30 Talks on Illustrative Projects Prof. Michael Batty, Centre for Advanced

Spatial Analytics, UCL

Prof. Richard Dawson, Earth Systems

Engineering, University of Newcastle

Prof. Martin Rieser, Institute of Creative

Technologies, De Montfort University

15:00 Break


15:30 Summary Thus Far Colin Harrison, USC

15:45 4 break out group discussions

to further develop scenarios.

Themes and leaders as before

17:00 Report by group rapporteurs Jurij Paraszczak, IBM Research

17:30 Keynote Presentation Peter Madden, CEO of the Future Cities

Catapult

18:00‐ 20:00 Student Posters and Reception

Skempton Building

A l l

Wednesday 11 September 2013

Time Theme Presenter / Facilitator

9:00 Summarise Day 1 Colin Harrison

9:15 Discuss scenarios from Day 1

theme workshops

Jurij Paraszczak and the rapporteurs.

10:30 Break

11:00 4 break out group discussions

to refine scenarios, cross‐

cutting.

Themes and leaders as before

13:00 Lunch

14:00 Talk on “Quality of Life” Samir Menon, Eco Sustainability Services, Tata

Consulting Services

15:00 Break

15;30 Industry Views on People and

Smart Cities

Volker Buscher, Arup; Shane Mitchell, Cisco,

and

JurijParaszczak, IBM. Facilitator Colin Harrison,

USC


16:30 Closing panel by workshop

rapporteurs

JurijParaszczak, IBM

17:15 Wrap up John Polak, Imperial College and Jurij

Paraszczak, IBM


Appendix B – Quality of Life Indices

Samir Menon and Stan Curtis gave us several examples of more general QoL indices that have been

developed:

Genuine Progress Indicator (GPI) The GPI indicator one of a number of metrics developed, often

by economists ‐ in the late 20th century as alternatives to Gross Domestic Product (GDP). These

often incorporate factors indicating that GDP growth is associated with some negative impacts.

The GPI is based on the concept of sustainable income, presented by economist John Hicks

(1948). The sustainable income is the amount a person or an economy can consume during one

period without decreasing his or her consumption during the next period. In the same manner,

GPI depicts the state of welfare in the society by taking into account the ability to maintain

welfare on at least the same level in the future. GPI considers the following sum of factors (see

reference 42):

o GPI = A + B ‐ C ‐ D + I

o A is income weighted private consumption

o B is value of non‐market services generating welfare

o C is private defensive cost of natural deterioration

o D is cost of deterioration of nature and natural resources

o I is increase in capital stock and balance of international trade

Social Progress Index (SPI) The SPI is an example of a genuine progress indicator that has been

developed and assessed over some 50 countries by Michael Porter. “Social progress is defined

as the capacity of a society to meet the basic human needs of its citizens, establish the building

blocks that allow citizens and communities to enhance and sustain the quality of their lives, and

create the conditions for all individuals to reach their full potential.” The index has three

dimensions that are assessed annual for some fifty countries (see reference 43):

o Basic Human Needs

o Foundations of Well‐Being

o Opportunity

Human Development Index (HDI) The HDI was created by the Pakistani economist

MahbubulHaq and the Indian economist Amartya Sen in 1990 and was published by the United

Nations Development Programme. It is supported by the World Bank. Its factors are

(definitions taken from reference 44):

o Life expectancy at birth, as an index of population health and longevity

o Knowledge and education as measured by the adult literacy rate and functions of school

enrollment

o Standard of living measured as a logarithmic function of GDP, adjusted to purchasing

power parity.

Gross National Happiness (GNH) GND was conceived in 1972 by the king of Bhutan and

developed by the Centre for Bhutan Studies. The four pillars of GNH are the promotion of


sustainable development, preservation and promotion of cultural values, conservation of the

natural environment, and establishment of good governance. A second generation version of

the GNH index was developed in 2006 as an index function of the total average per capita of the

following factors (definitions taken from reference 45):

o Economic Wellness: Indicated via direct survey and statistical measurement of economic

metrics such as consumer debt, average income to consumer price index ratio and

income distribution

o Environmental Wellness: Indicated via direct survey and statistical measurement of

environmental metrics such as pollution, noise and traffic

o Physical Wellness: Indicated via statistical measurement of physical health metrics such

as severe illnesses

o Mental Wellness: Indicated via direct survey and statistical measurement of mental

health metrics such as usage of antidepressants and rise or decline of psychotherapy

patients

o Workplace Wellness: Indicated via direct survey and statistical measurement of labor

metrics such as jobless claims, job change, workplace complaints and lawsuits

o Social Wellness: Indicated via direct survey and statistical measurement of social metrics

such as discrimination, safety, divorce rates, complaints of domestic conflicts and family

lawsuits, public lawsuits, crime rates

o Political Wellness: Indicated via direct survey and statistical measurement of political

metrics such as the quality of local democracy, individual freedom, and foreign conflicts.

Beyond GDP This originated with a conference of the European Commission, the Club of Rome,

the OECD, and the World Wildlife Fund and was taken up by Joseph Stiglitz. It is based on five

factors (definitions taken from reference 46):

o GDP “The Gross Domestic Product is the sum of the market value of all final goods and

services produced in a country in a given period. GDP per capita has traditionally been

used to illustrate a country’s material standard of living, but today its usage is meeting

increased criticism.”

o Enlarged GDP “Enlarged GDP indicators start from GDP but adjust for some of its

limitations to deliver a more comprehensive overview of a country’s wealth or well‐

being.”

o Social “Social indicators give insights into a broad range of social issues, concerns and

trends such as life expectancy, poverty rates, unemployment rates, disposable income,

and education levels, etc. They are also used to give insights into broader notions of

social progress.”

o Environment “Environmental indicators cast light over the state and development of

issues such as natural resources, environmental pollution and waste, as well as related

issues such as human health.”

o Well‐being “Well‐being indicators are used to broadly illustrate people’s general

satisfaction with life, or give a more nuanced picture of well‐being in relation to their

jobs, family life, health conditions, and standards of living.”


Better Life Index (BLI) The BLI was developed by OECD and published in 2011, building in part

on Stiglitz’ work on “Beyond GDP”. It includes the following factors (definitions taken from

reference 47):

o Housing: housing conditions and spendings (e.g. real estate pricing)

o Income: household income and financial wealth

o Jobs: earnings, job security and unemployment

o Community: quality of social support network

o Education: education and what you get out of it

o Environment: quality of environment (e.g. Environmental health)

o Governance: involvement in democracy

o Health

o Life Satisfaction: level of happiness

o Safety: murder and assault rates

o Work‐life balance

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