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School of Planning and Geography

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Student Number (s): c1113835

Module Code: CP0314

Title of Degree: BSc City and Regional Planning

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Investigating the Nature and Potential Problems of

Public Participation for UK Smart Cities.

Research Project

Module Code: CP0314

Student Number: c1113835

School of Geography and Planning Cardiff University

11th May 2015

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Acknowledgements

I would like to begin by thanking Neil Harris for his support in the inception of this research

project. My thanks also goes out to the 50 participants who provided data and the 4 people who

helped me preliminarily test my questionnaires. Biggest thanks goes to Brian Webb for his excellent

guidance and motivation.

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Abstract

This research project explores the potential for inclusion in the public participation process of

smart cities. It creates an analytical framework of public participation and outlines the nature of smart

cities. Issues surrounding passive data, the Digital Divide, data ethics and the utilization of e-

governance are outlined. 50 questionnaires were completed in the centre of Bath to identify public

behaviour and attitudes on internet access, local development, passive data collection, and

infringement of digital privacy. The results identified a somewhat positive potential for increased

public inclusion through the utilisation of passive data. Yet, there were feelings of scepticism towards

data-gathering, the government and especially the involvement of private corporations.

Recommendations of transparency and optional engagement in passive data collection were made to

pacify these public concerns.

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Contents

1 Introduction: The need for smart cities 6 1.1 Importance & interest 7

1.2 Aim & objectives 8

1.3 Structure 8

2 Literature Review: Public participation 9

2.1 Smart cities 17

2.2 Data & representativeness 20

2.3 Ethics & transparency 24

2.4 E-governance 25

2.5 Summary 25

3 Methodology: Research Strategy 26 3.1 Research method 26

3.2 Fieldwork location 29

3.3 Ethical issues 33

4 Results & Discussion: Overview 34 4.1 Passive data and the Digital Divide 36

4.2 Attitudes towards data ethics and privacy 40

4.3 Utilization of e-goverance 45

5 Conclusions: Discussion summary 51 5.1 Recommendations 52

5.2 Further research 52

6 References 53

7 Appendices 56 7.1 Appendix I: Ethical Approval Form 56

7.2 Appendix II: Questionnaire 63

7.3 Appendix I: Ethical Approval Form 67

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1 Introduction: The need for smart cities

As of 2013, 82% of the UK’s population – 52.5 million people – live in urban environments (The

World Bank, 2013). The Department for Business, Innovation & Skills (BIS, 2013) identifies that UK

cities face a variety of social, economic and environmental challenges at global and local scales (shown

in Table 1):

Table 1: Challenges that UK cities face

Challenge Description

Urban infrastructure Systems and services are struggling to cope with current population growth pressure. Worse, the ONS (2011) projects a 0.6% annual population increase to 73.2 million by 2035.

Economic growth The UK must progress economic recovery to raise employment levels and

make cities globally competitive. Climate change Pollution must be reduced by lowering emissions and improving energy

efficiency. Girardet (1999) found that most resource use and coastal pollution is attributed to cities.

Online connectivity Consumerism and services are shifting in accordance with new delivery

methods that may change the current urban fabric. Public finance The UK is subject to many national finance cuts while Local Authority (LA)

budgets are on average down 12-15% since 2010. Aging population The medium age will increase by 2.3 years to 42.2 by 2035 (ONS, 2011). An

aging population will amplify socio-economic pressures through increased service demand.

Source: BIS (2013), ONS (2011), Girardet (1999).

BIS (2013) identifies a need to “seek innovative solutions” to these challenges. This involves

efficient, cost-effective urban management through the enhancement of public information access.

The government believe that by opening data, new, integrated and online services that can be

outsourced or used by citizens to make informed decisions, thereby reducing pressure on service

demand. These solutions are encompassed by the ‘smart city’ concept – explored in section 2.1.

Essentially this is a city which uses Information Computer Technologies (ICTs) to meet the challenges

above (BIS, 2013) However, to achieve these solutions, smart cities require a change in the process of

public participation. The nature and problems associated with this change are the crux of this project.

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1.1 Importance & interest

Towards smart cities

Arup estimated that the smart city industry will be worth $400 billion by 2020, suggesting that

the UK could secure 10% of this market (BIS, 2013). According to the British Standards Institution (BSI,

2015), both public and private sectors recognise the need to work fast in capitalising on this emerging

market. Smarter urban approaches are seen as the key to improving living conditions and attracting

business. The international economic potential and increased service efficiency necessitates

investment in smart cities.

The government has invested £95m and created the Smart Cities Forum to ensure that the UK

does not miss smart city opportunities. A further £50m has been dedicated to the Future Cities

Catapult centre – a public-private-partnership technology centre specializing in smart solutions. In

addition, IBM, Intel and other private companies are collaborating with the government to advance

smart city research (Misco, 2013). This evidence illustrates that UK planning is headed towards the

smart city paradigm.

Public participation & smart city inclusion

Planning in the UK requires LAs to encourage public involvement. The public can lodge

concerns for/against planning applications and can also attend/contribute to planning inquiries and

appeals (Townsend and Tully, 2004). Developers meanwhile, must hold public consultations. They use

closed data and independent investigations to assess project viability – public participation is further

detailed in section 2.

These processes require active citizen engagement where only citizens who choose to engage

in the planning process can affect development. Instead, smart cities rely upon data for decision-

making – data is covered in section 2.2. Data acquisition systems including sensors, phones, and social

networks provide passive data (Chourabi et al. 2012). Passive data is involuntarily created, for example

citizen movement recorded by phone networks. Passive data can capture the opinions and actions of

citizens who affect and are affected by development but choose not to engage in the planning process.

Smart cities therefore can potentially make better, data-informed decisions.

The problem is the form of this data; smart cities are built on the foundation of digital

infrastructure (Idowu and Bari, 2012). Citizens that do not have monitored technologies risk being

excluded from data gathering and by extension, decision-making. Since smart city initiatives are

collaboratively spearheaded by public and private companies, passive data will be accessible to

corporations (Viitanen and Kingston, 2014). Citizens may consider passive data as private and may

actively block data gathering, thereby removing their input to the decision-making process. This will

create data-gaps that may result in less effective planning. These contextual aspects validate the

necessity for this project. Section 1.2 outlines how this project will investigate the issue of smart city

public participation.

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1.2 Aims & objectives

The aim of this project is:

To investigate the potential for inclusion in the public

participation process of UK smart cities.

The following objectives dissect the aim by focusing on the most prevalent concerns of

participation in smart cities. Each objective (a-c) features research questions (i-ii). Data gathered shall

supply answers to these queries, in turn fulfilling the objectives, thereby satisfying the aim. The

objectives are:

a) To investigate the digital divide and the potential of passive data i) Who has access to/uses technologies that allow them to be included in passive

data? Who does not and as a result is excluded?

ii) What is the public potential for passive data to effect development?

b) To examine public attitudes towards data ethics and privacy. i) Are the public happy to share information with the government and private

sector?

ii) Would people actively block data gathering and what gaps will this create?

c) To identify the current utilization of e-governance. i) Who accesses and uses e-governance?

ii) What is the scope for smart city e-governance to serve as the main outlet for

public participation?

1.3 Structure

The project features 5 chapters, each consisting of several sections. Literature on public

participation and smart cities is reviewed in chapter 2; Chapter 3 outlines and justifies the research

techniques for each objective. In chapter 4, the results are presented and the research questions are

discussed. Chapter 5 connects the implications identified by the questions and concludes with the key

findings. Recommendations are then made for mitigating data-gaps. Finally, advice is provided for

future research.

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2 Literature Review: Public participation

This chapter begins by reviewing the methods of public participation evaluation. It then

outlines the smart city and investigates current participatory phenomena surrounding data-gathering,

ethics and e-governance. It concludes by identifying the academic gaps this project aims to fill.

Definition & section structure

Public participation involves engaging the public in the formulation/implementation of

governmental processes (Parry et al., 1992 cited in Townsend and Tully, 2004). Since its conception,

there has been on-going debate on what constitutes ‘good’ public participation. To investigate the

potential for public inclusion, this project requires an evaluative framework that categorically assesses

the changes associated with the smart city approach. Thus, section 2 justifies the necessity of

participation while identifying and critiquing the key methods of evaluation. These methods are

employed to outline the history and problems of participation, summarising the current issues that

smart cities may effect.

Function & necessity

Sewell and Coppock (1977, cited in Petts and Leach, 2000) state that citizens have the right to

be informed/consulted on decisions affecting them. Participation of the governed in their government

is the “cornerstone of democracy” (Arnstein, 1969). Participation is a process of power distribution

where officials give/share power with citizens, allowing citizens to influence decisions for their benefit.

It has three main functions (Petts and Leach, 2000):

Legitimation of decision-making;

Enhancement of democracy; and

Enlargement of citizenship.

The aims and methods of these functions are detailed in Table 2. IEAM (2000, citied in Petts

and Leach. 2000) suggested that good practice involves all relevant stakeholders and conducting

participation in a transparent, interactive and timely fashion. Achieving this requires comprehension

of public interests including development proximity, cultural values and economic effects.

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Table 2: Aims and method of public participation. Aims Method

To satisfy statutory requirements to consult

Education and information provision. Information feedback.

To resolve conflicting views Extended involvement.

To increase transparency Education and information provision.

Information feedback.

Involvement and consultation.

Extended involvement.

To increase defensibility Information feedback.

Involvement and consultation.

Extended involvement.

To change people's views about an issue through education

Education and information provision. Extended involvement.

To improve services Information feedback.

Involvement and consultation.

To determine needs and desires Information feedback.

Involvement and consultation.

Extended involvement.

To empower citizens Education and information provision. Extended involvement.

To enable social learning Education and information provision. Extended involvement.

Source: adapted from Petts and Leach (2000).

Townsend and Tully (2004) categorise five types of current participation (Table 3). Public

involvement challenges professionalism, where officials limit their considerations to input from

experts (Petts and Leach, 2000). By exclusively consulting professional input, officials alienate public

opinion. Forester (1989 cited in Townsend and Tully, 2004) argues against “heavy investment” in

experts in favour of inspiring participation. He finds that this results in a bottom-up process –

characterised by the aims in Table 2 – that provides numerous advantages.

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Table 3: Current types of conventional public participation

Types Method of undertaking

Traditional Consultation documents and public meetings

Consumerist Surveys and questionnaires

Forums Dialogue with interest groups

Consultative innovations Citizen panels, juries and online interaction

Deliberative innovations Community based planning and visioning techniques

Source: adapted from Townsend and Tully (2004).

The public can promote creativity and become a “quality assurance method”. Slovic (1986,

cited in Petts and Leach, 2000) found that public perceptions are rationally-based on accessible

information and local knowledge. Public input may prompt officials and experts to consider alternative

solutions that might never have emerged from a professional perspective. By exercising the four

methods in Table 2, transparency, scheme comprehensibility and conflict compromise are improved,

reducing official-citizen friction in planning (Townsend and Tully, 2004). Table 4 further outlines

strengths and opportunities while introducing some problems.

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Table 4: SWOT analysis of public participation.

Source: adapted from Petts and Leach (2000).

Methods of evaluating effectiveness

Participation is evaluated by specific criteria, rather than greatest total satisfaction. Webler

(1995, cited in Petts and Leach, 2000) considers ‘fairness’ – access and opportunities to influence

development – and ‘competency’ – the ability to impart and use information – as key criteria. Barnes

(1999, cited in Petts and Leach, 2000) categorises these as concerns of inclusivity, focus, openness,

responsiveness and appropriateness. The central method to evaluate the effectiveness of

participation is Arstein’s (1969) ladder of citizen engagement (Collins and Ison, 2006). The ladder

(Table 5) hierarchically orders eight levels of participation in terms of effectiveness where 1

(manipulation) is the lowest and 8 (citizen control) is the highest.

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Table 5: Arnstein's ladder of participation.

Extent of citizen power Levels of participation Description

Degrees of citizen power

8. Citizen control Citizens have full responsibility for planning. There are no intermediaries between them and fund sources.

7. Delegated power

Citizens have the majority of committee seats and are delegated powers to make decisions. The public holds accountability of decisions.

6. Partnership Power distribution is negotiated by citizens and officials. Planning responsibilities and accountability are shared.

Degrees of tokenism 5. Placation

"Worthy" citizens are hand-picked into committee positions by officials. These citizens can advise officials but do not have decision-making powers or planning responsibilities. This is the foundation of a two-way information flow.

4. Consultation

Citizens are consulted through meetings, enquiries and surveys. However, citizens have no committee positions or power. As such, their opinions can be fully ignored.

3. Informing A one-way flow of information presenting little opportunity for citizens to influence decisions.

Non-participation 2. Therapy Group education which constitutes "curing the pathology" of citizens to pacify their concerns.

1. Manipulation No consultation. Distortion of participation into a "public relations vehicle".

Source: adapted from Arnstein (1969) and Wilcox (1994).

Collins and Ison (2006) label the ladder as a power struggle between citizens climbing the

ladder and officials controlling citizen ascension. Table 5 describes the levels, relating each to one of

three extents of citizen power; “non-participation” (red), “tokenism” (purple) and “citizen control”

(blue). Non-participation is composed of manipulation (dark-red) and therapy (medium-red), it

centres on the assumption that officials choose the best option and citizens are manipulated to accept

this.

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Tokenism legitimises participation; citizens are informed (dark-purple), consulted (medium-

purple) and placated (light-purple). However, information flows tend to be one-way – from the

officials to the citizens (Arnstein, 1969). At the placation level, citizens are allowed advisory roles in

power-holding committees, yet they have no decision-making power. As such, officials can dismiss

public input, resulting in public mistrust of the participation process and calls for transparency.

Citizen power embodies the ability of citizens to have control, responsibility and accountability

for decision-making. Arnstein (1969) classifies three levels; partnership (dark-blue), where citizens

share power with officials, delegated power (medium-blue), where citizens are delegated decision-

making powers and citizen control (light-red), where citizens have full planning authority. Despite its

domination as a method of evaluation, the ladder is widely criticised.

Arnstein (1969) recognises her simple power distribution of citizens and officials, where

citizens are trivialised as powerless and lacking expertise while officials are expert monolithic power-

holders. The criticisms focus on citizen education, representation and context. Table 6 is White’s

(1996, cited in Brodie, et al. 2009) ‘typology of participation’, it adds an overview of the contextual

reasons for participation.

Table 6: White's typology of participation Form: Participation type

Top-Down: Benefits for the officials Bottom-up: Citizen benefits

Function: Purpose

Nominal - effectively non-participation.

Legitimation - to pacify public concerns

Inclusion - to try to access benefits

Display

Instrumental - effectively tokenism.

Efficiency - to draw on public input to make projects more cost-effective

Cost - to reduce project cost, positive effect on local economy

Means - for achieving cost-effectiveness

Representative - between tokenism and citizen power.

Sustainability - to avoid creating citizen dependency

Leverage - to influence project benefits for citizen

Voice - to give citizens a say in development affecting them

Transformative - effectively citizen power.

Empowerment - to enable citizens to make their own decisions, dispersing responsibility and accountability

Empowerment - to make their own decisions

Means and end - continuing dynamic

Source: adapted from Norad (2013).

In Philadelphia, building agencies placated citizens by choosing certain public members to

become committee advisers. Their lack of education on rights and planning functions made them

amiable subjects (Arnstein, 1969). Conversely, Petts and Leach (2000) found that citizens who

represented generic community interests (e.g. health and business) were invited to join committees.

Thus demonstrating the individual power variation of citizens and the selective nature of officials.

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Citizens chosen to join committees are meant to represent local stakeholder interests, an

issue the ladder fails to address is the representativeness of citizens-in-power. Representation is

undertaken by group/self-elected citizens where popularity of interest rather than range of interests

are taken into account, therefore unrepresented interests are dismissed (Petts and Leach, 2000).

Finally, Collins and Ison (2006) criticise the implications that higher levels of citizen control produces

better outcomes and argue against citizen control as the “goal of participation”. They also found that

some citizens may not wish to be engaged.

History of UK participation and problems

Participation was conceived in a tokenistic form under the 1947 Town and Country Planning

Act. The 1969 Skeffington report called for increased public engagement but this never advanced past

placation (Bloomfield, et al. 2001). The Conservatives regarded participation as dampener and delay

to development in the 1980’s, cutting participatory resources. This descended participation to the

consultation stage, resulting in cynicism and protest against the planning process. In the 1990’s, New

Labour published the 1998 White Paper: Modern Local Governments which set a precedence of raising

participation by recognising the value of creativity through citizen input (Petts and Leach 2000).

Current challenges and smart city potential of UK participation

Twitchen and Adams (2011) describe a “reality of declining engagement in a digital age” where

public empowerment is a government objective. There is an aspiration for planning/governance to

“embrace a more collaborative ethos”. Gorden, et al. (2011, cited in Twitchen and Adams, 2011)

branded the current and widely-utilized traditional public meetings as tokenism. Despite this, the

Localism Bill 2011 failed to consider smart city approaches to participation. While Petts and Leach

(2000) suggest additional cost as a barrier for participation, Gallent (2008 cited in Twitchen and

Adams, 2011) found that “streamlining” current planning types cost the public opportunities for

engagement. Smart cities are seen as an alternative that can balance these issues (BIS, 2013).

Pratchett (1999 cited in Petts and Leach, 2000) claimed that current public engagement types

contributed few positive outcomes. Smart cities however would undertake participation of a different

nature; whereas traditional participation types involve quantitative surveys or subjective debate (Tully

and Townsend, 2004), smart city data encompasses empirically objective and subjective, qualitative

and quantitative methods. In addition, online and automated data-gathering systems have the

advantage of eliminating conventional service costs for venue/refreshment provision.

Townsend and Tully (2004) discovered that representatives in a number of LAs were typically

white, middle-class and well-educated. They and the officials found participants who lacked power to

be “ill-informed and demanding”. Furthermore, understanding the views of ‘hard-to-access’ groups

provides unique insight to problems and helps guide the decision-making process to reflect people’s

“needs and wishes” (Cabinet Office, 1998 cited in Petts and Leach, 2000). Table 7 categorises the

‘hard-to-access’ groups.

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Table 7: 'Hard-to-access' groups

Group Reason for difficulty of accessibility

Elderly Mobility access - tend to be more immobile and are less able to access public meeting venues

Children Under the radar - children under 16 are not included on the electoral register, resulting in consultation exclusion. Also the right of children to be involved in decision-making has no status in UK law.

Disabled Mobility access

Socially/culturally isolated Community position - these groups avoid attendance due to feeling a lack of communal belonging, such peoples include ethic/class minorities.

Geographically isolated Insignificant infrastructure - reaching these groups is physically challenging

Those mistrusting of the government Active avoidance - Citizen panels, juries and online interaction

Those who choose not to participate Active avoidance -Community based planning and visioning techniques

Those with many commitments Active avoidance - Little spare time to engage in public participation

Source: adapted from Petts and Leach (2000).

If representativeness in UK planning continues to favour the socially privileged, confidence in

governance cannot be increased (Townsend and Tully, 2004). The data-gathering of smart cities may

eliminate this bias while including ‘hard-to-access’ groups. Finally, Meadowcroft (1997, cited in Petts

and Leach, 2000) considers participation to be a “messy” process that promotes conflict, delays and

diminishes expert value. Some smart data-gathering processes remove active input from participation

(see section 2.2). Though beneficial in reducing “messiness”, this may lower the UK’s position on the

ladder of participation.

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Summary

Planning faces multiple participation challenges; its current traditional approach is resulting

in decreased engagement while failing to capitalise on the digital age (Twitchen and Adams, 2011).

Ultimately, the success of participation depends upon demonstrating that public input results in

physical development/policy changes in-line with the needs and desires of the public.

2.1 Smart cities

Conception & section structure

There is no universal definition for smart cities. However, by amalgamating private and public

sector perspectives, Goodspeed (2014) identifies the common goal as the “pursuit of effective service

provision and city efficiency through real-time control”. The multi-sectored and data-centric nature of

smart cities presents challenges and opportunities to threaten and enhance public participation. The

subsequent sections highlight the concerns of participation in smart cities.

Following the justification for smart cities in the introduction, section 2.1 expands upon the

origins and definition of the concept. Section 2.2 then outlines the importance of data, raising the

issue of representativeness while transparency and engagement are addressed in sections 2.3 and 2.4

(which cover ethics and e-governance respectively). To conclude, section 2.5 summarises the issues

surrounding inclusion, thereby informing the design of the methodology.

Conception & definition

The use of data-informed decisions was conceived in the 1970’s as “urban cybernetics”. As

engine conditions can be stabilized by automated adjustments, it was argued that urban complexities

could also be automated – if they were understood (Goodspeed, 2014). The US discovered that

inadequate data limited their urban comprehension. However, in 2000 researchers undertook a

project named “Smart City” where “censors, networks and decision-making algorithms” created a

“green and efficient” environment (Hall, 2000, citied in Goodspeed, 2014). Whereas urban cybernetics

were only concerned with technical solutions, smart cities introduced a social angle.

Rittel and Webber (1973, cited in Goodspeed, 2014) classified planning challenges as “wicked

problems” whose unique contexts are unsolvable by value-based solutions. While technical processes

focus on data collection, problem-framing/solving is conducted through social contexts, thus urban

problems are sociotechnical. Greenfield (2013) mirrors this thought, but declares that smart cities

cannot cater for human imperfection, surmising that smart infrastructures are ill-equip to frame/solve

sociotechnical problems.

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Kitchin (2014) identifies two smart city meanings; firstly, the presence of ‘everyware’ – data-

gathering digital devices, integrated into the urban fabric which allow cities to become “knowable and

controllable” – and the development of a ‘knowledge economy’, where ICT-proficient citizens are

integrated to help improve governance and the economy. He indicates a sectorial discrepancy of

interpretation; the public sector is concerned with governance while the private sector prioritises

selling marketable technology. Indeed, Kitchin (2014) finds that since smart cities require utilization

of private sector technologies, corporations are being “increasingly vocal” on promoting smart cities.

Karadağ (2013) classifies the term ‘smart city’ as multiple ‘smart approaches’ (Table 9) to the

core domains of a city (Table 8). Smart cities therefore, are those which adopt smart approaches to

the ‘core (and associated sub) domains’ by combining the public and private sectors to implement

ICTs that utilize data to solve sociotechnical problems.

Table 8: The core and sub-domains of a smart city Core domains SMART ECONOMY: Competition SMART GOVERNANCE: Participation

Sub domains Innovation Participation in decision-making

Entrepreneurial support Services facilitating participation

Image and trademarks Transparency

Productivity Political strategies

Labour market flexibility Government and public co-operation

International market connection

Economic adaptability

Core domains SMART MOBILITY: Transport & ICT SMART ENVIRONMENT: Natural resources

Sub domains Accessibility Attractiveness of natural condition

Availability of ICT infrastructure Pollution levels

Sustainable transportation system Environmental protection

Sustainable resource management

Core domains SMART PEOPLE: Social capital SMART LIVING: Quality of life

Sub domains Education levels and qualifications Cultural facilities

Life-long learning Health conditions

Social interaction Individual safety

Flexibility Housing quality

Creativity Education facilities

Cosmopolitan nature Tourist attraction

Community integration Living costs

Global interconnectivity Social cohesion

Source: adapted from Vienna University of Technology (2007, cited in Karadağ, 2013).

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Table 9: The five smart approaches of a smart city

Smart approaches Description

Secure, open access data

Provision of access to open data sources such as big data that does not compromise personal privacy.

Citizen-centric service delivery

A recognition that services are improved when centred on public need. Sharing management information so the public can make more informed recommendations for improvement.

Intelligent digital infrastructure

Developing smart infrastructure to enable service providers to utilize data allowing more effective service delivery and strategic urban planning.

Learning Willingness to experiment with new data-informed approaches. Transparency of process and results

Enable citizens to understand the planning process and comprehend the justification of planning decisions

Source: adapted from BIS (2013).

BIS (2013) outlines five interconnected ‘smart approaches’ central to the smart city (Table

9); citizen-centric service delivery is provided through smart infrastructure which requires open

access data. In turn, data-informed decisions may promote transparency and prompt new urban

approaches. Yet, BIS finds that the most important aspect of the smart city is synonymous with that

of public participation; using public input to change the physical environment in-line with public

needs and desires.

Administration & management

Data-informed decision-making is operated by two administrative types; automation, where

ICTs analyse data and implement a supposedly Pareto-efficient solution and human control, where

human operators observe and respond to real-time data monitoring (Karadağ, 2013). Greenfield

(2013) criticises automation explaining that “Pareto-efficient” automated decisions will be based on

subjective programming. Both Kitchin (2014) and Greenfield (2013) deem human control to be top-

down, technocratic and authoritarian since the decision-makers are only required to engage with data

and not a democratic-style public debate.

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The threats to public participation

The managerial threat is one of dismantling the democratic process of participation, reducing

active public input and instead making authoritarian decisions on passive data. However, smart cities

do address this problem with e-governance. But to engage actively or passively, participation demands

that citizens become a consumer of private technologies.

Reliance on such technologies will increase private sector power and threaten public privacy

(Viitanen and Kingston, 2014). Technologically-equipped citizens will be increasingly subject to

involuntary digital inclusion while those digitally excluded are threatened to become invisible to smart

cities participation. Digital inclusion is the provision of (typically-online) service connectivity and digital

exclusion is the lack of service access (BSI, 2014).

2.2 Data & representativeness

Data characteristics

The public and private sectors, plus academics state that data is essential to smart cities (BIS,

2013; Arup, 2013; Kitchin, 2014). Smart city data has two central characteristics, it is open and big.

Open data is free to use data (BSI, 2014). Implementation of open data is seen as the key to smart

cities (Arup, 2013). As detailed in The Open Data White Paper (BIS, 2013), increasing data accessibility

allows the public to comprehend how data informs decisions, increasing the transparency of

governance.

Big data is a variety of “high volume, high velocity” information assets. It is created in/near

real-time and records both structured and unstructured data on artificial and human activities. The

scope of big data extends from the minuet scale up to the city-wide scale (Kitchin 2014). The current

forms of analysis are not yet able to infer relevant information from big data (BSI, 2014). Furthermore,

big data exposes cities to a variety of problems. Even so, both public and private sectors consider the

socio-economic advantages to out-weigh the disadvantages. Table 11 lists the pros and cons identified

by Kitchin (2014).

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Table 11: The advantages and disadvantages of big data. Subject Advantages Disadvantages

Information Real-time information on services and developments for citizens.

Information provision could increase public vulnerability and harm privacy.

Transparency Greater transparency of decision-

making process. Increased accountability for services.

Given that decisions are considered in-line with identified public needs, the public has more accountability for poor decision-making.

Participation Greater opportunity for citizens to

influence decision-making and service delivery. Stimulate and utilize citizen creativity.

May involuntarily include those who wish to be excluded from passive participation

Service delivery Patterns in data can reveal better

models for service delivery. Self-reinforcement of service delivery aimed at digitally included population.

Nature & neutrality

Decision-making can be positively informed natural truths revealed by data. Data-framed problems are essentially politically benign.

Data inflected by social values of the digitally included.

Governance Evidence-based decision-making.

Online debating and use of simulations can stimulate creativity and reveal solutions to planning issues.

Presumes all urban aspects can be recorded as meaningful data. Technocratic algorithmic approach to governance. Fails to consider urban issues as sociotechnical. Technological solutions can only respond to impacts, not the causes of issues.

Corporatisation More even power distribution

between private and public sectors.

Ethical concern that governance is being shaped by the private sector resulting in; marketization of public services and the "locking-in" of private companies as technical problem-solvers.

Hackable city Places that are dependent on software are

vulnerable to hacking and system crashes that have the potential to cripple city systems.

Panoptic city Increase of surveillance and

dataveillance could be used for crime prevention

Profiling and the ability to track individuals is an ethical breach. It can also be a step towards population and freedom of speech control.

Source: adapted from Kitchin (2014).

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There are three types of data source types; ‘directed’ (orange) – traditional surveillance by a

human operator, ‘automated’ (green) – automatic data collection by ICTs and ‘volunteered’ (blue) –

data “gifted by users” that is usually categorized by social media (Kitchin, 2014). Table 12 classifies the

full list of data sources identified in the literature. The next section addresses the issue of

representativeness by introducing the ‘Digital Divide’.

Table 12: Types and sources of big data.

Types Sources Description

Directed Surveillance Human operated surveillance of urban phenomena.

Automated

Vehicular counters

Meters that measure quantity and type of transportation movement, identifying mode transport patterns.

Sensors/Smart meters

Santander in Spain deployed 12,000 sensors that provide data on physical space including measurements of air quality, parking spaces and lighting.

Smart phones Serve as sensing devices and can relay information from the data network to the citizen and vice versa through smart applications.

Transactional Using transactional data from purchases to establish consumer and service requests.

Travel cards In London 85% of public transport users have an Oyster card. Such smart cards can be used to map journeys and see where services need to be improved.

Internet of Things

State where any object (from the environment to clothing) contains and communicates information with other objects. Thereby, societal integration is achieved through the internet.

Volunteered

E-governance ICT platform which facilitates online public-to-citizen and public-to-private interaction.

Social media 340 million tweets (dialogue posts on Twitter) are made each day. Social media makes feelings about a place explicit and reveals spatial attitudes.

Crowdsourcing Users generate data and contribute it to an information system. Google Earth and OpenStreetMap are examples of this.

Source: adapted from Arup (2013), Batty (2013), BSI (2014), Idowu and Bari (2012), Kitchin (2014) and Kolsaker and Lee-Kelly (2008).

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The Digital Divide

Digital inclusion is vital to smart city democracy. However, participation is affected by

availability of technology and personal behaviour (Viitanen and Kingston, 2014). The social

implications of unequal access to ICTs and ICT-related skills is defined as the ‘Digital Divide’ (Forester,

2000, cited in Partridge, 2004). Whereas contemporary participation forms seemingly exclude ‘hard-

to-access’ groups while being typically attended by a single socio-economic demographic, smart city

data-sources should include the widest range citizen participation.

The ONS (2014) reported that 13% of the UK population is digitally excluded from the internet.

Smart cities therefore have the potential for 87% public inclusivity, increasing the ‘fairness’ and

‘competence’ of participation (Welber, 1995, cited in Petts and Leach, 2000). Unfortunately, this figure

is solely based off the technological accessibility of those over the age of 16. In addition, the ONS fails

to define the frequency of internet usage. Thus, the true potential for public inclusivity maybe much

lower. Despite this, Figure 1 does show a continual decline of ‘internet non-users’.

Figure 1: Internet non-users.

Source: ONS (2014).

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Section 2 uncovered that ‘good’ participation prioritised representativeness over

representation. Smart city participation should discover a greater range of interests through larger

public input. However, those digitally excluded could become a new ‘hard-to-access’ group, whose

needs go unheard. Previous studies deemed the primary factors affecting the Digital Divide to be

economically-related. Yet Partridge (2004) highlights an ongoing divide despite falling

technology/service prices, pointing to personal behaviour as a primary factor of the Digital Divide.

There are two types of divide; ‘access’ – based upon cost and technological accessibility and

‘social’ – a culmination of psychological, social and cultural factors. The issue of representativeness is

prominent since US and Australian studies discovered that “interlocking” socio-economic factors

including ethnicity, location, disability and education resulted in poor and vulnerable groups being

susceptible to digital exclusion. This is counterintuitive as participation should allow “those with a

weak voice” to have influence in decision-making (Healey, 1997, cited in Petts and Leach, 2000).

Partridge (2004) identifies self-efficacy – “people’s judgements of their capabilities” as critical

to sealing the divide. Educating citizens on the use and benefits of technology should increase their

self-efficacy, thereby reducing the divide. BIS (2013) recognized this, citing the successful ICT

education program in Chicago as a model for UK cities to adopt. Additional behavioural factors

affecting the divide surround ethical issues, discussed in the following section.

2.3 Ethics & transparency

Role of private sector

Viitanen and Kingston (2014) believe the privatisation of smart cities to be problematic. Due

to the lack of technological expertise in the public sector, smart city implementation and operation

will be outsourced to private companies. Data collected on citizens will be gathered by the private

sector and this exposes citizen privacy.

Transparency

Good participation features high transparency, currently, smart city transparency has received

little attention. Citizen-centric data is mostly involuntarily produced without public consent (Viitanen

and Kingston, 2014). Decision-making based on involuntary data may result in development that

reinforces social commonalities, reducing citizen choice and potentially satisfaction. Therefore smart

city data may have negative consequences, unintended/unwanted by the public.

The private ownership of data features risks of unauthorised use or criminal access (BIS, 2013).

Firstly, the private sector can trade information for commercial purposes (Viitanen and Kingston,

2014) and secondly, criminals may use data to oppress and cause harm to citizens. Thus, citizens and

specifically the ‘hard-to-access’ groups (who choose not to participate and those mistrusting of the

government) may feel threatened and choose to block data-gathering.

The success of smart city data-gathering necessitates an increase of transparency by installing

public trust concerning data use (BIS 2013). This includes guaranteeing security, communicating how

data is used and providing the option to opt out (Arup, 2013). To do this, the government must interact

with citizens. In smart cities, the central platform for interaction is e-governance.

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2.4 E-governance

Definition & function

Governance is the governing of policy/decision-making across organisations (BSI, 2014). E-

governance is an ICT platform which facilitates this function through online public-to-citizen and

public-to-private interaction (Kolsaker and Lee-Kelly, 2008). The Council of Europe (2007, cited in

Kolsaker and Lee-Kelly, 2008) states that e-governance improves the three functions of participation

by increasing service availability, efficiency and inclusion.

Uptake of e-governance in the UK

Uptake in the UK is low despite its near top ranking of service provision in Europe (Viitanen,

2011). The Take-up study (DCLG, 2007, Viitanen, 2011) measured public engagement by form of

contact including the internet, telephones and person-to-person. Phone calls constituted 54% of

service/information enquiries while the internet made up 46%. Furthermore, surveys distributed by

the European Commission (2007, Kolsaker and Lee-Kelly, 2008) found that 22% used the internet for

downloading information and 7% utilized a service delivery function. Overall, information requests

were made online while service requests and democratic engagement were typically conducted over

the phone/in person. Citizen satisfaction was lowest online, they felt like customers rather than

democratic participants, yet they considered the internet to be the most transparent governance

platform.

Implementing successful e-governance

There two identified methods to increase uptake, the first is an issue of self-efficacy; Davis

(1989, cited in Kolsaker and Lee-Kelly, 2008) found that ongoing utilization of e-governance services

increases online competency, resulting in higher utilization. The government can increase competency

and therefore utilization with education. Secondly, the government must enhance the democratic

engagement of e-governance to create a two-way information flow. Currently, a power imbalance

(created by superior expertise of officials) means that debate is characterised by a one-way

information flow, typical of placation (Kolsaker and Lee-Kelly, 2008).

2.5 Summary

There is significant and mostly up-to-date literature on the aspects this project aims to

investigate. However, there has been little attempt to use public participation as a framework to

evaluate how smart cities will change participation. The literature identifies that smart cities mainly

focus on passive data-gathering and highlights a lack of information in particular areas (such as the

investigation of citizen behaviour in response to privacy concerns of passive data-gathering). The

distinguishing claims and voids surrounding data-gathering, ethics and e-governance will be utilized

to inform the methodology in the next chapter.

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3 Methodology: Research Strategy

To satisfy objectives a-c, this project employs a positivist epistemology. Positivism is a

philosophical approach to methodology conception which centres on the belief that social reality can

be studied using natural science methods. The research method for this project investigates the

theoretical smart city claims identified in the literature review, therefore the research exercises a

deductive approach that tests these claims. Deductive approaches are typified by quantitative data,

as such, the research method must be an empirically measurable form (Bryman, 2008).

This research dictates a need to employ the case study approach. Though typically associated

with qualitative research, case studies are central to exploring social phenomena through empirical

events while retaining the holistic characteristics of real-life (Schell, 1992) – for this project this

involves investigating social (sociotechnical, socio-economic and socio-demographic) phenomena

using quantitative research within a representative contemporary technologically-interconnected

urban context.

Though the research questions are “who” and “what”-type questions which aim to identify

citizen inclusion in smart cities, recommendations for improving inclusion requires examining “how”

and “why” citizens participate. Case studies are best suited for these question types (Yin, 2009). There

are several dangers of case studies; the integration of bias, an inability to be scientifically generalised

due to the specific context and time constraint. Yin argues these can be allayed and as such these

concerns are addressed throughout the chapter. The subsequent sections identify and justify the

research method (3.1), the fieldwork location (3.2) and ethical considerations (3.3).

3.1 Research method

Schell (1992) clarifies that surveys are suited for “who”, “what”, “how”-type questions with a

limited capacity to investigate “why” question types. Flowerdue and Martin (1997) created a six-part

framework for research method creation (Table 13), which is utilized for justifying questionnaires as

the chosen research method. The review of literature and aim development has satisfied the first

stage. To use primary data effectively, data-gathering must address the research questions, consider

the previous studies and have an analytical plan in mind (Flowerdue and Martin, 1997).

In relation to the objectives, data-gathering is centred on demographics, current participation,

the digital divide, ethics, e-governance and socio-economics. This allows for the illustration of

contemporary participation to be compared with the potential for smart city inclusion while linking

the findings to demographic and socio-economic factors. By examining potential relationships

between these factors, the nature and problems of participation may be unveiled.

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Table 13: Stages of methodology creation.

Stage General activity Specific task

1 Initial research idea (refine and develop analytic design)

Developing aims and research objectives Reviewing literature Identifying what is already known 2 Design of research Hypothesis formation Consideration of dependent, independent and controlled variables Choice of methodology Drafting questionnaire Identifying sampling technique and biases

3 Refinement of research instrument

Pilot work Post-pilot revision of questionnaire Sampling refinement 4 Main fieldwork Interviewer briefing (if appropriate) Data collection 5 Processing/data

analysis Data organisation

Statistical analysis 6 Results Hypothesis testing (to inform discussion) Demonstrating statistical significance Source: adapted from Flowerdue and Martin (1997).

Questionnaire: Justification

Two of the studies on public participation – identified in the literature – used questionnaires

for data collection (Partridge, 2004, Kolsaker and Lee-Kelley, 2008). The questionnaire is highly

applicable for addressing the desired research question types. The following characteristics justify its

selection as the primary data acquisition tool: its ability to investigate three data types, including

‘classification’ – which categorises respondent’s contextual attributes, ‘behavioural’ – which profiles

the “what”, “how” and “who” of respondent’s actions and ‘attitudinal’ – which probes the “why”

behind behavioural data.

Questionnaires are descriptive and/or analytical, able to establish relationships between

dependent variables (current participation, the digital divide, ethics and e-governance) and

independent variables (demographics and socio-economics) as well as enquiring into the causes of

data patterns. Furthermore, the empirical nature of questionnaires facilitates fast data-collection from

a large variety of respondents. This creates directly observable indicators which can be statistically

analysed (Flowerdue and Martin, 1997).

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Questionnaire: Design

Stage 2, creating a successful questionnaire requires the pinpointing and countering of

limitations. Flowerdue and Martin, (1997) emphasise two error categories; sampling errors and non-

sampling errors. The latter is introduced through questionnaire design, it features two types; response

errors and non-response errors. In terms of response errors, questionnaires are highly structured and

this restricts attitudinal data-collection, losing the depth of a qualitative response. Where respondents

felt their answers where restricted, they were prompted to add information onto the questionnaire.

Data quality can be harmed by ‘fatigue’ bias resulting from poor wording (use of jargon that

alienates the respondents), poor formatting (where respondents are immediately confronted with

complex questions), boring engagement (resulting from a lack of question types) and excessive length

(in terms of question number and time taken by the respondent to complete the survey). In addition,

questions must not force attitudes on respondents through manipulative wording (Flowerdue and

Martin, 1997).

The questionnaire counters these issues through appropriate application of wording, question

sequencing (opening with easy ‘warm-up’ questions) and using a variety of question types.

Furthermore, the process of refinement (detailed later) acted as a failsafe to ensure these principles

were upheld. The non-response errors are characterised by a refusal to participate in the

questionnaire. Particular population subsets could be misrepresented. To counter this, section 3.2

details a strategy to obtain participant diversity.

Sampling

As surveying a whole population is unfeasible, sampling is utilized to create a selection of units

that are representative of the population (Flowerdue and Martin, 1997). For this project the ‘units’

are citizens and the ‘population’ are inhabitants of a city with average online infrastructure. This

sample is defined by three boundaries; geographical (the survey catchment area described in section

3.2), temporal and demographic.

To achieve a representative sample of the urban adult population, a random sampling of

citizens aged 18+ will be conducted from 9am-6pm over three days (including a weekend). The random

sampling aims to reduce bias with chance-oriented demographically-neutral participant selection,

while the temporal boundary seeks to increase representation by engaging citizens who are available

on weekends but have weekday commitments and vice versa. Conversely, these boundaries also limit

the sample representation; for example, the geographical boundary excludes potential participants

who are outside of the fieldwork site location.

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Refinement, fieldwork and results

Stage 3, (refinement) involves reducing poor design through the piloting and revision of

questionnaires in response to feedback. The questionnaire has been submitted for supervisor

inspection and preliminarily tested on four citizens. The refined questionnaire can be found in

Appendix I. With this process complete, stage 4 (data-collection) commenced. Questionnaires were

undertaken on a face-to-face basis as Flowerdue and Martin (1997) found that face-to-face interaction

yielded the best response rate. Alternatives such as postal/internet administrated surveys are less

effective; Kolsaker and Lee-Kelley (2008) recorded a 10% return rate from postal questionnaires and

given that this project investigates internet access, posting the survey online would digitally exclude

those without internet access.

The final stages concern results and analysis, the nature of which is explored in chapter 4. That

said, Flowerdue and Martin (1997) state that for most statistical tests, a minimum of 30 respondents

is required. Questions based on level of measurement were implemented to enable statistical tests

(Trochim, 2006). These included numerically coded dichotomous “yes/no” and categorical questions

that created nominal data, plus ordinal data was created using Likert scale questions, allowing

respondents to rate their answer on a scale.

3.2 Fieldwork location

National justification

To make the method scientifically generalizable to UK cities, Bath has been chosen as the

location to conduct fieldwork. There are multiple factors contributing to this decision. Firstly, Map 1

(ONS, 2014) shows that the area containing Bath – in the red circle – is within the average UK internet

user category (85-89.9%) as defined by the mean (87.2%). Smart cities require online infrastructure

and it can be inferred from Map 1 that the area’s online infrastructure is illustrative of the UK

midpoint, thus providing a more representative image of the scope for public inclusion in smart city

participation. Secondly, Map 2 (Oxford Internet Institute, 2013) shows that Bath is the closest city to

Cardiff (where the project is being undertaken) within the average user category of Map 1.

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Map 1: Internet users and non-users by unitary authority as of 2014.

Source: ONS (2014).

Bath’s proximity is a manageable constraint on resources including time, accommodation and

funding. Personal experience of the city is beneficial for identifying questionnaire distribution sites.

Furthermore, the city is compact (covering 35-40km²) and its population is relatively dense (B&NES,

2013). This is advantageous for surveying a diverse population over a smaller area. In summary, Bath

is nationally representative of average online infrastructure while being highly accommodating of

project resources.

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Map 2: Internet use in Britain as of 2013.

Source: Oxford Internet Institute (2013).

Bath

Bath (see

Map 3)

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Local justification

To acquire a socio-economic diversity of participants, areas proximate to supermarkets were

targeted as questionnaire distribution points (Sites 1 and 2 on Map 3). City centres tend to contain the

greatest commercial and service outlets for local inhabitants, thereby inviting customer diversity. The

supermarket offer of utilitarian convenience products also attracts a diversity of customers, thus city

centre supermarkets meet the requirement for random sampling of demographically and socio-

economically diverse participants. Site 1 is situated opposite a Waitrose and Site 2 is by a Sainsbury’s.

These supermarkets were chosen by considering the availability of store selection in the city centre

and the rankings of market share measured by Statistia (2015) – where Sainsbury’s is third and

Waitrose is sixth (out of ten). Supermarkets were deemed a better alternative to door-to-door

questionnaire distribution due to the elimination of systematic neighbourhood selection and safety

concerns of data-collection in wards with a higher crime rate.

Map 3: Bath city centre & questionnaire distribution sites.

Source: Ordinance Survey (2015).

Site 1

Site 2

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3.3 Ethical issues

Bryman (2008) states that the primary ethical concerns are those of informed consent, right

to withdraw, harm to participants and protection of privacy. In accordance with supervisor advice,

consent forms were not deemed necessary for questionnaires. Participants were verbally informed of

their right to withdraw and that they, as information sources were anonymised. Per SREC

requirements, an Ethical Approval Form (in Appendix II) was completed and checked by the supervisor

to ensure this project abides university policy on research projects. In addition, the questionnaire is

designed to deter any forms of harm (for example stress) with the inclusion of opt-out options. No

locational ethics were required since the questionnaires were distributed in public space.

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4 Results & Discussion: Overview

50 questionnaires were collected and all the data has been entered into tables located in the

appendices. The questions will be labelled in the analysis to identify the source of discussed

information. A diverse respondent selection was achieved; there were 21 females to 26 males – 3

respondents wished to remain as unspecified (Question 2). Figure 2 illustrates age range (Question 1),

Figure 3 shows household income (Question 20) and Figure 4 shows education levels (Question 21).

Respondent age range is a relatively even split, however the largest group (35-44) is the same size of

the two smallest groups combined (65-74 and 75+).

Household income appears unbalanced, a third of respondents are within the £45,000+

category. This is due to many participants inhabiting multiple-person occupancies and combining their

income with the other inhabitants. Each education type has been matched to levels established in the

census, allowing it to become ranked data. The most prominent levels are 3 (A-levels/BTEC), 4

(Bachelor degree) and 6 (Doctorate degree), which make up for 78% of respondents. The “retired”,

“prefer not to say” and “other” categories of Figures 3 and 4 are not able to be ranked, limiting their

usefulness as empirical measures.

14%

16%

20%14%

16%

10%

10%

Figure 2: Age range of respondents

18-24 years old

25-34 years old

35-44 years old

45-54 years old

55-64 years old

65-74 years old

75+ years old

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These demographic categories, along with internet access, make up the independent variables

against which information on internet use, passive data and public participation can be measured.

This chapter will be divided into 3 sections; one for objective, with the research questions becoming

sub-sections. The nature and problems with passive data will inform a critique of smart city public

participation, thereby uncovering the potential for inclusion in a digital urban realm.

18%

24%

8%

34%

10%

6%

Figure 3: Household income of respondents

Under £15,000

£15,000-£29,999

£30,000-£44,999

£45,000+

Retired

Prefer not to say

8%

26%

28%

8%

24%

6%

Figure 4: Education level of respondents

Level 1-2 (GCSE/O-levels)

Level 3 (A-levels/BTEC)

Level 4 (Bachelor's degree)

Level 5 (Master's degree)

Level 6 (Doctorate degree)

Other

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4.1 Passive data and the Digital Divide

Who has access to/uses technologies that allow them to be included in

passive data?

46 out of 50 respondents had access to the internet (Question 3). The population sample

indicated that internet access was 5% higher than the UK mean – 92% compared to 87% (ONS, 2014).

Access was very frequent; 88% accessed the internet 2 or more times a day, the remaining 4%

accessing once every few days (Question 6). Figure 5 shows that the 46 internet users utilized 202

uses, an average of 4.4 out of 7, with communication and research/business being the mode (Question

7). Some of these uses can provide passive data (dependent on ethical considerations). 60% of

respondents use social media, 68% shop and 52% access services. This data may have the potential to

inform the government on development and service demand in the local area.

Figure 6 shows that respondents owned 156 devices capable of passive data collection, an

average of 3.4 out 7 per person (Question 8). Smart phones, computers and tablets were the most

popular. More specialised devices, particularly smart travel cards are employed situationally and since

Bath public transport has not implemented such a device, it fails to deliver passive data within the

defined geographical context. Overall, high internet access and technology ownership paints a positive

picture for the potential of capturing passive data.

30 30

34

40 39

26

3

0

5

10

15

20

25

30

35

40

45

Nu

mb

er o

f re

spo

nd

ent

use

s

Internet uses

Figure 5: Internet uses of all respondents

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In terms of demographic variation there are three areas of significance; age, income and

education. Respondents without internet access occupied the two eldest age groups; one fifth of the

65-74s and three fifths of the 75+s. In all cases, respondents had no desire for internet access and this

was due to a lack of motivation/reasons (Questions 4 and 5). Average number of devices owned

increases with household income and education: respondents in the “under £15,000” category

typically owned 2.6 devices versus 3.8 for the “£45,000+” category. Respondents with level 1-2

education tend to own 2.5 devices compared to those with level 5 education who own 4.

These are issues that typify both the ‘social’ and ‘access’ Digital Divides highlighted by

Partridge (2004). The findings could suggest that citizens with less income and education have less

technological opportunity to produce passive data. However, this is dependent on how much each

technology was utilized, a topic not covered in this investigation. Therefore, the elderly, those with

lower level education and income may characterise new ‘hard-to-access’ groups in a digital society.

Respondents were made aware of the fact that the technologies in Figure 6 involuntarily

collect passive data on their activities (Question 9). 80% were already aware of this fact and 60% would

consider disabling their internet connection to halt this process (Question 10). There was no significant

derivation of responses in demographic terms. A respondent annotated the related dichotomous

question with a “maybe” option, this reflected the comments of multiple respondents who voiced

that their actions would depend on the purpose of data collection. A possible reason for the high

percentage of respondents who would consider disabling the internet connection is the threat of

ethical breaches which characterise a panoptic city (Kitchin, 2014).

35

18

42

31

14

8 8

0

5

10

15

20

25

30

35

40

45

Smart phone Portable mediaplayer

Computer Tablet Smart travelcard

Meters Other

Nu

mb

er o

f d

evic

es o

wn

ed

Devices

Figure 6: Device ownership of all respondents

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34% of respondents had been actively involved with local development (Question 12). The

major cause for concern is that 30% of them were in the two eldest age groups that were subject to

digital exclusion. Yet, if passive data based on internet access was utilized, a maximum of 92% public

participation maybe possible. Overall, this would increase the socio-economic diversity of public

participation.

The respondents were informed of the government wish to invest in passive data utilization,

they were then asked to rate a series of statements using a Likert scale consisting of 5 categories,

ranging from “strongly agree” to “strongly disagree”. Statements are presented by an independent

variable when significant. Significance is defined by the presence of a mean statement range greater

than 1.2, since that gap represents 1.5 opinion positions and demonstrates that a vast difference of

opinion is related to a specific independent variable. Mean statements are the average of respondent

opinion positions. For example in Figure 8, the mean statement of two age groups features a gap of

2.8, ranging from 2 (agree) to 4.8 (almost strongly disagree).

Figure 7 shows an even distribution between “agree” and “strongly disagree” to the statement

“I voice my opinions about local issues on social networks/blogs” (Question 15i). The mean response

was between “neither agree nor disagree” and “disagree”. This statement investigates the potential

to use social media as passive data. The mean indicates that respondents tended to not voice their

opinions on local issues online. However, as Figure 8 demonstrates, the younger generations appear

to voice their opinions online more often.

2

14

1011

13

0

2

4

6

8

10

12

14

16

Strongly agree Agree Neither agree nordisagree

Disagree Strongly disagree

Freq

uen

cy o

f o

pin

ion

po

siti

on

Opinion position

Figure 7: Respondent agreement with "I voice my opinions about local issues on social networks."

(Question 15i)

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Therefore, from a social media perspective, they have greater passive data input than older

generations. In summary, this sub-section has quantified respondent ownership and utilization of

technology and practices from which passive data can be derived. 92% of respondents have

technology that supports passive data-gathering. The social Digital Divide accounts for the remaining

8% who have no internet access; such citizens are characterised by old age and lack of technological

interest.

What is the public potential for passive data to effect development?

Although the practical potential of passive data use is still being researched, its public

participation potential can be quantified by citizen opinion. The respondents tended to have a neutral

opinion towards passive data to affecting development (Question 15ii). Demographically, young

people were keener in wanting passive data on their activities to affect development, as shown by

Figure 9. Those with the highest and lowest education levels seem to be opposed to passive data

collection. While the younger generation’s enthusiasm may be linked to a better comprehension of

technology, there is no theory that links opposition with the polar ends of education level.

Respondents were nonplussed by whether their input would improve development (Question

15iii). However, the youngest and oldest groups, plus those with an income of under £15,000 agreed

that passive data would be beneficial. Since all of the 75+s are retired and 86% of 18-24 year olds have

a household income under £15,000, it appears that those with less financial security believe that data

on their activities will improve the local development. 88% of all respondents either agreed or strongly

agreed that they wanted to know how passive data would affect local development (Question 15iv).

This is a demand of information flow similar to the ‘informing’ stage of tokenism (Arnstein, 1969).

2

3.12.9

3

4

4.8 4.8

1.0

1.8

2.6

3.4

4.2

5.0

18-24 25-34 35-44 45-54 55-64 65-74 75+

Mean opinion position.

Strongly agree = 1 -1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Age group

Figure 8: Mean respondent agreement by age for question 15i

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By helping citizens to understand the development process, decision-making should become

more legitimized, allowing passive data to fulfil one of the 3 public participation functions (Petts and

Leach, 2000). The second function; enlargement of citizenship, was addressed by Question 15v:

participants were asked if they would feel more responsible for development if decision-making

considered data on their activities. 78% agreed/strongly agreed, suggesting a perceived increase of

citizenship enlargement, despite the statement not mentioning any power distribution change. In

summary, there is apathy towards passive participation. Yet this indifference faded when the

questionnaire implied that citizens could be informed on how passive data may contribute to

development, resulting in respondents stating that to some extent, they would feel responsible for

local development.

4.2 Attitudes towards data ethics and privacy

Are the public happy to share information with the government and private

sector?

Respondents were informed that harnessing passive data would require the government to

enter into business partnerships with corporations. 56% of respondents did not trust the government

to anonymise the data compared to 30% – mainly composed of the lowest household income group

– who did (Question 16i). Respondents were marginally more accepting of the government collecting

passive data (Question 16ii). However, 72% were uncomfortable with private corporations collecting

passive data (Question 16iii). Figure 10 illustrates this comparison. The government must increase

trust if passive data-gathering is to be accepted by the public. This requires identifying trust by

demographic areas and the causes for concern.

2

2.9

2.6 2.6

3.63.4

3

1.0

1.8

2.6

3.4

4.2

5.0

18-24 25-34 35-44 45-54 55-64 65-74 75+

Mean opinion position.

Strongly agree = 1 -1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Age group

Figure 9: Mean respondant agreement by age for question 15ii

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2

13

10

17

8

0

3

11

20

16

0

5

10

15

20

25

Strongly agree Agree Neither agreenor disagree

Disagree Strongly disagree

Freq

uen

cy o

f o

pin

ion

po

siti

on

Opinion position

Figure 10: Respondent agreement with "I feel comfortable with the government/private sector collecting passive data." (Questions 16ii and 16iv)

Government

Private sector

2.7

3.4

3.7

2.7

3.5

4

3

3.7

3

4

3.6

4.1

4.44.6

1

1.8

2.6

3.4

4.2

5

18-24 25-34 35-44 45-54 55-64 65-74 75+

Mean opinion position.

Strongly agree = 1 -1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Age group

Figure 11: Mean respondent agreement by age for questions 16ii and 16iii

Government

Private sector

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Figure 11 shows the mean opinion position of each age group, concerning the statement “I

feel comfortable with the government/private corporations collecting passive data about me”. In

terms of government data-gathering, 4 age groups occupy the “disagree” opinion section; the 25-34s,

the 35-44’s, the 55-64’s and the 65-74s. There is no discernible explanation for this pattern, but the

cause could be related to the disadvantages of big data (Kitchin, 2014). Foremost is the threat of

population control through the creation of a panoptic city. The involuntary nature of passive data,

combined with information that the government is investing in passive data may have caused the

respondents to negatively perceive smart city public participation as a mandatory imposition, resulting

in a more pessimistic answer.

Private corporations are even more distrusted than the government; only the 25-34 age group

occupied the “neither agree nor disagree” opinion position, the rest either disagreed/strongly

disagreed. Private companies also present the threats above, however these are amplified by

corporatisation, where the private sector tries to increase its power by shaping governance (Kitchin,

2014). Still, the main respondent concern appears to be the profit-oriented nature of corporations as

indicated by the responses to Question 16iv.

Figure 12 shows a more positive categorisation of public opinion. It appears that those on a

low household income have more trust for government data-gathering while the two wealthier groups

are uncomfortable with data collection. Figure 13 once again portrays that the polar ends of the

educations levels have similar negative opinions concerning passive data collection. Both Figures 12

and 13 display a universal displeasure with corporations being involved in data collection.

2.4

3.1

4.3

3.4

3.73.9

4.8

3.7

1

1.8

2.6

3.4

4.2

5Mean opinion position.

Strongly agree = 1 -1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Household income group

Figure 12: Mean respondent agreement by income for questions 16ii and 16iii

Government

Private sector

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Figure 14 shows three main data abuse concerns. Interestingly, the threat of personal

freedom reduction and criminal access is considered less of a concern than possible marketing

purposes behind smart city integration (Questions 16iv, 16v and 16vi). This further demonstrates the

respondent’s disapproval of private sector integration. On the whole, this sub-section indicates that

respondents are not particularly comfortable with the government collecting passive data – with the

exception of a single demographic group – and they are uncomfortable with the involvement of

private companies. As outlined by Petts and Leach (2000) in section 2, increasing transparency can

reduce scepticism surrounding public participation. The methods of delivery include education and

information provision. However, such provision must target a diversity of socio-economic groups.

4.5

33.1

2.8

4

4.8

3.9

3.63.5

3.8

1

1.8

2.6

3.4

4.2

5Mean opinion position.

Strongly agree = 1 -1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Education level group

Figure 13: Mean respondent agreement by education level for questions 16ii and 16iii

Government

Private sector

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Would people actively block data gathering and what gaps will this create?

Section 4.1 identified that 60% of respondents “would consider” disabling their internet

access to halt passive data-gathering. Yet, given the modern necessity of the internet, it is likely that

they would only temporarily disable access in particular circumstances. BIS (2013) declared that

ethical considerations had to be established before opening public data access. This would involve

providing security and an opt-out option. The mean statement opinion for wanting “the ability to opt-

out of data collection” was “strongly agree” (Question 16vii), this was universal across all demographic

variables.

Figure 15 compares the respondent’s desire to opt-out of data-gathering depending on the

level of security provided (Questions 16viii and 16ix). Low data security would result in a substantial

82% of respondents opting out. 54% strongly agreed that they would opt-out, the other 28% just

agreeing. Conversely, if security was high, 26% would still strongly agree/agree to opt-out while only

24% would not. 50% of respondents did not agree or disagree. This is a poor population

representation, the absence of so many information sources would also reduce representativeness.

Given that the majority of respondents could not decide, it may be prudent to automatically opt

people in and give them the opportunity to opt out, rather than asking citizens if they should want to

opt in.

21 21

5

21

18

16

9

6

1

15

18

9

6

2

0

5

10

15

20

25

Strongly agree Agree Neither agreenor disagree

Disagree Stronglydisagree

Freq

uen

cy o

f o

pin

ion

po

siti

on

Opinion position

Figure 14: Respondent agreement with "I am concerned that data collected about me may be used

for marketing purposes/criminal access/risk my personal freedom." (Questions 16iv, 16v and 16vi)

Marketing purposes

Criminal access

Personal freedom risk

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Given high data security, there are two demographic variables of significance; age and income.

The three eldest age groups are most likely to opt-out. Their opinions positions range between 2-2.2

(“agree”). In addition to the reasons given in the previous sub-section, the 55+ population maybe less

comfortable within a digitally operated city. The only evidence of this it the 4 non-internet users (who

were for opting out) and that the 55+ age groups on average have half the smart devices per person

that 18-54 year olds (Question 8). Overall this research question finds that a minimum of 26% and a

maximum of 82% of respondents would actively block data gathering (this would be a maximum of

96% if those occupying the “neither agree not disagree” position opted out).

4.3 Utilization of e-governance

Who accesses and uses e-governance?

Bath has a limited capacity of e-governance – there is no forum representative of a committee,

only “e-planning” functions, which essentially are information and service delivery tools. To

investigate the potential for future participation, the current e-governance utilization must be

established. 34% of respondents had engaged in local development (Question 12). This was defined

as commenting on development applications and/or attending planning-related debates (such as

committees and public consultations). No 45-54 year old respondents and only 14% of 18-24 year olds

had been involved in planning in comparison with 80% of 65-74 year olds. The remaining age group’s

engagement ranges between 37.5%-50%.

27

14

7

2

0

76

25

10

2

0

5

10

15

20

25

30

Strongly agree Agree Neither agreenor disagree

Disagree Stronglydisagree

Freq

uen

cy o

f o

pin

ion

po

siti

on

Opinion position

Figure 15: Respondent agreement with "I will opt-out if data security is low/high." (Questions 16viii-16ix)

Low security

High security

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In terms of household income, there is a positive relationship between increased income and

percentage of planning participants. Figure 16 illustrates the reasons why 66% of respondents have

not engaged in public participation. The most prominent reason (35%) is that respondents were

unaware of the opportunities to engage. While the other reasons are related to choice or physical

constraint, not being informed of the engagement opportunities is a failure of current public

participation outreach.

Conversely, those who engaged in the public participation borderline disagreed that their

views were taken into account by local government (Question 14i). On average respondents stated

that their input did not result in design alterations, nor were the local government effective at

communicating the planning process (Questions 14ii and 14iii). No demographic variable significantly

discerns any patterns. The reasons for lack of engagement and the general dissatisfaction with current

planning engagement appears to be universal. 42% of respondents have used planning services

(Question 17) – this is differentiated from question 12 as it does not specifically concern public

engagement with a local development. Planning services consist of application, research and public

participation. 60% or more of the three eldest age groups have used planning services in comparison

to 40% and less of the younger groups.

35%

18%

17%

2%

17%

11%

Figure 16: Reasons for lack of public engagement

You were unaware of theopportunities to be involved

You could not spare the time

You were unavailable at thescheduled time of meetings

You were physically unable to attenddue to mobility issues

You did not wish to participate

You believe you lack theunderstanding necessary to engage inpublic meetings

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In relation to Viitanen’s (2011) documenting of the best platform for contacting local

government, respondents were asked to rate four communication platforms from 1-4 in order of their

communicative efficiency for local government contact (Question 18). The internet scored 2.38 in

comparison to 3.49 for post, 2.18 for the telephone and 1.98 for face-to-face contact. The internet is

considered the best option by the £30,000-£44,999 and £45,000+ income groups who score it as 1.5-

1.9. In contrast, it is considered the worst option by the 75+ age group, who rate it 3.8, though this is

likely due to the group’s lack of internet-accessible technology rather than poor provision of online

planning services.

Those who have accessed planning services rate face-to-face as the best platform (1.75), the

internet and phone as equal (2.4) and post as the worst (3.45). To summarise this section, the elderly

and wealthier typically tend to use planning services more, the latter preferring to use the internet.

Public engagement is under-utilized due poor community outreach and those who did engage in

planning were less than satisfied. Finally, the internet – and by extension, the use of e-governance –

excludes the very elderly while being viewed favourably by those with higher incomes, yet the internet

is considered inferior to face-to-face interaction by those who have accessed planning services.

What is the scope for smart city e-governance to serve as the main outlet for

public participation?

In its current form, this investigation finds that e-governance is not yet developed enough to

replace person-to-person democratic debate and become the primary outlet for public participation.

Nonetheless, its potential can be explored. Figure 17 identifies that on average, respondents agree

that the internet is the first platform they would utilize for planning services (Question 19i). Figure 18

however, again demonstrates a relationship between increasing age and resistance to the internet.

11

16

10

67

0

2

4

6

8

10

12

14

16

18

Strongly agree Agree Neither agreenor disagree

Disagree Stronglydisagree

Freq

uen

cy o

f o

pin

ion

po

siti

on

Opinion position

Figure 17: Respondent agreement with "The internet is the first platform I would use for planning services." (Question 19i)

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The positive relationship in Figure 18 can also be explained by planning system experience. As

identified earlier, with increasing age comes a higher percentage of respondents who have engaged

in planning. Thus, this is a rather disconcerting relationship that suggests citizens who are exposed to

planning tend to think less of the online service provision, implying that online services are currently

inferior to other forms of service access and democratic engagement.

1

1.8

2.6

3.4

4.2

5

18-24 25-34 35-44 45-54 55-64 65-74 75+

Mean opinion position.

Strongly agree = 1 - 1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Age group

Figure 18: Mean respondent agreement by age for question 19i

2

15

11

14

8

2

13

19

9

7

0

2

4

6

8

10

12

14

16

18

20

Strongly agree Agree Neither agreenor disagree

Disagree Stronglydisagree

Freq

uen

cy o

f o

pin

ion

po

siti

on

Opnion position

Figure 19: Respondent agreement with "I would attend free IT sessions." & "I would be more likely to

use IT for planning post-session." (Questions 19ii-19iii)

IT session attendence

Post-session IT use

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Figure 19 shows the relationship between respondent opinions on whether they would attend

free education sessions on how to use e-governance and their opinions on whether they would be

more likely to use e-governance after the sessions (Questions 19ii and 19iii). The mean statement for

IT session attendance and post-session IT use is 3.2 and 3.1 respectively (both are in the “neither agree

nor disagree” opinion position). Therefore, respondents collectively suggest a very minor benefit to

education. However, age groups are a significant variable affecting this outcome.

Figure 20 shows that targeting 45-54 and 55-64 year olds would yield the most effective

results, since that is where the gap between IT session attendance and post-session IT use is greatest.

Despite this, the margin for improvement is still minor. This contrasts with Kolsaker and Lee-Kelly’s

(2008) self-efficacy implication that increasing online competency of e-governance usage results in

higher utilization in the future. As such, one must question the legitimacy of the respondent’s answers

when comparing them to a real-life test.

Figure 21 illustrates a collective disagreement that online planning provision can replace

person-to-person meetings (Question 19v). Again, these opinions are defined by age group, displayed

by Figure 22. 25-34s agree that e-governance can be accomplished. 18-24s, 35-44s and 45-54s are

undecided and again, the three eldest groups oppose adopting e-governance as the primary planning

platform. To summarise, e-governance is still in its infancy. In a city representative of the average

digital infrastructure, it is lacking the function of facilitating online democratic debate. Still, there is

clearly a demand for online planning, as evidenced by figure 17 and despite a poor outlook from the

respondents, academics have demonstrated that e-governance utilization can be increased through

sessions to improve internet self-efficacy.

3

2.5

2.93

3.3

44.2

2.62.5

2.4

3.63.5

4 4

1

1.8

2.6

3.4

4.2

5

18-24 25-34 35-44 45-54 55-64 65-74 75+

Mean opinion position.

Strongly agree = 1 -1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Age group

Figure 20: Mean respondent agreement by age for questions 19ii and 19iii

IT session attendence

Post-session IT use

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1

10

12

16

11

0

2

4

6

8

10

12

14

16

18

Strongly agree Agree Neither agree nordisagree

Disagree Strongly disagree

Freq

uen

cy o

f o

pin

ion

po

siti

on

Opinion position

Figure 21: Respondent agreement with "I believe that online planning services can replace face-to-face

meetings." (Question 19v)

3.1

2.1

3.4

2.6

3.6

4.8

4.4

1

1.8

2.6

3.4

4.2

5

18-24 25-34 35-44 45-54 55-64 65-74 75+

Mean opinion position.

Strongly agree = 1 -1.8

Agree = 1.8 - 2.6

Neither agree nor disagree = 2.6 - 3.4

Disagree = 3.4 - 4.2

Strongly disagree = 4.2 - 5

Age group

Figure 22: Mean respondent agreement by age for question 19v

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5 Conclusions: Discussion summary

The aim of this project was:

To investigate the potential for inclusion in the public

participation process of UK smart cities.

UK smart cities are a work in progress. They are at the stage of information trials. This analysis

was based off a questionnaire that queried the theories of smart cities. There are two vital aspects

missing from both government, private corporation documents and as a result, this investigation; the

nature of smart city administration and the fate of traditional public participation. Policy from

public/private sectors are centrally concerned with the benefits of big data. Whether it is able to

replace the traditional planning system remains to be seen.

The analysis aimed to utilise an analytical framework of public participation, to see what

participation level smart cities would be categorised as. However, the structure of smart cities are in

flux, much like undefined advantages of big data. They are more tangible than the logarithmically

automated machines that Greenfield (2013) describes. While this analysis cannot place smart cities

on a particular rung of Arnstein’s (1969) ladder, it has answered the research questions and fulfilled

its objectives. As such multiple recommendations will be made in section 5.1.

Potential for inclusion

Question 19i captured the public desire to keep the traditional public participation. One key

demographic emerged time and again; the older age group’s resistance to big data-related

technologies. Figure 18 perfectly exemplified that technological acceptance is a gradual process which

is quickly adapted to by the young but slowly implemented or fully rejected by the older citizens.

To satisfy the aim it is necessary to address the limiting factors in turn. In comparison to the

34% who were included in traditional public participation, 92% of respondents owned technology

allowing for digital inclusion in public participation. As far as passive data is concerned, the remaining

8% are now a ‘hard-to-access’ group, abandoned due to the social Digital Divide. Respondents were

mainly apathetic to their personal input to passive data. Yet they declared a feeling of increased

responsibility which may enlarge citizenship. Crucially, participants wanted to know how their input

would affect development. This has been a recurring phenomenon throughout this project – proof of

public influence is vital to current active or future passive participation.

The next limiting factor to inclusion in smart city participation is the data ethics. Nearly all

respondents lacked trust of both the government and private corporations. They wanted an opt-out

option and dependent on the security, between 26%-96% of respondents stated an intent to opt out.

The final limiting factor was the insufficient e-governance provision. The software is not available to

fulfil the functions of traditional engagement.

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Overall, smart cities empirically reach a greater variety of citizens, increasing

representativeness. But those citizens who lack technology and have engaged in the traditional

participation risk becoming invisible. The best option for public inclusion is to supplement the current

system with passive data. That way passive data is captured and active data is provided through e-

governance and traditional participation. Of course, this may result in higher costs and cost reduction

was a primary objective smart cities aimed to achieve.

5.2 Recommendations

Most importantly, this project has illustrated that regardless of traditional or smart city

participation, public engagement has to be transparent. The public must be guided through how the

contribute and the outcomes this will have on development. Inviting members of the public for

consultation and then ignoring advice and not communicating sews deeper distrust than not inviting

them at all.

Secondly, if passive data is to be utilized, then the public concern over private corporations

must be pacified. There is no easy way to do this. But public exhibitions and demonstrations would

get the public involved and informed on the benefits of passive data. At the same time and opt-out

option must be provided. However as previously advised, apathetic people will tend to reframe from

acting whether they are opted in or not. The best option would be to give plenty of advertisement

prior to enacting an automatic opt-in. Finally, regardless of the respondent’s feedback, the benefit of

IT/e-governance sessions should still be explored. This project recommends running workshops to

ascertain the worth of citizen education.

5.3 Further research

There are several areas which require expanded research. Foremost is a qualitative approach

to this investigation. Comments prompted by the questionnaire suggested a need to investigate the

attitudes behind why members of the public were typically mistrustful of the government and private

corporations. It would also be very helpful for reasons of digital inclusion to conduct in-depth

interviews with the elderly to discuss the digitally reclusive nature identified in this project. Finally,

there was the odd phenomenon of level 1-2 and level 6 educated citizens having similar prejudices

towards the government and passive data. It could be just a coincidence, but the sample sizes ranged

between 4 and 12.

Word count: 10,025 (counted after the contents. Does not include tables, figures or captions).

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System [Online]. Available at: http://www-sre.wu-wien.ac.at/ersa/ersaconfs/ersa04/PDF/51.pdf

[Accessed: 31 March 2015].

Twitchen, C. and Adams, D. (2011). Increasing levels of public participation in planning using web 2.0

technology [Online]. Available at: www.bcu.ac.uk/Download/.../684ef8e9-3026-455b-9455-

39ca785fb45c [Accessed: 5 April 2015].

Viitanen, J. (2011). Citizenship in the Electronically Networked City [Online]. Available at:

https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-

scw:122625&datastreamId=FULL-TEXT.PDF [Accessed: 17 April 2015].

Viitanen, J. and Kingston, R. (2014). Smart cities and green growth: outsourcing democratic and

environmental resilience to the global technology sector. Environment and Planning A [Online] 46(4).

Available at: http://www.envplan.com/abstract.cgi?id=a46242 [Accessed: 1 April 2015].

Wilcox, D. (1994). 10 key ideas about participation [Online]. Available at:

http://www.partnerships.org.uk/guide/ideas.htm [Accessed: 5 April 2015].

Yin, R. (2009). Case Study Research: Design and Methods. 4th edition. London: Sage.

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7 Appendices

7.1 Appendix I: Questionnaire

When printed out, the questionnaire only covers four pages. Due to different margins of this

document and the insertion of the above titles, the questionnaire appears more spacious. The

following questions, form and font are taken from the questionnaire.

1. What is your age? (please tick the relevant box)

18-24 years old 55-64 years old

25-34 years old 65-74 years old

35-44 years old 75 years or more

45-54 years old

2. What is your gender?

Female Male

3. Do you have access to the internet?

Yes - Proceed to question 6 No - Proceed to question 4

4. Why are you unable to access the internet

No desire for internet access - Proceed to question 5

No internet provision in your area - Proceed to question 6

Cost of internet-accessible devices/internet service - Proceed to question 6

Other

5. Why do you desire no internet access?

Lack of education/skill on how to use the internet

Fear of exposure to crime/being monitored

Lack of motivation/reasons to use the internet

Other

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6. How frequently do you use the internet?

2 or more times every day Once every few days

Once a day Less than once every few days

7. What do you tend to use the internet for?

Social networking Research/business

Media Accessing services

Shopping Other

Communication

8. Which of these devices do you own? (Please tick all which you have)

Smart phone Smart travel card (e.g. an Oyster card)

Portable media player (e.g. iPod) Meters (that measure water/energy use)

Computer (any type) Other (e.g. GoPro)

Tablet/Personal digital assistant

These devices are all capable of collecting “passive data” about your activities.

Passive data is created involuntarily. It can be used to gain insight to understand

patterns of human behaviour and attitudes. Some examples of passive data are:

Movement patterns recorded by your smart phone or smart card

Comments posted on social networks like Facebook and Twitter

Energy/water use recorded by smart meters

9. Were you aware that your devices are possibly producing data on your activities

before you read the information above?

Yes No

10. Most devices have the ability to disable internet/Wi-Fi connection. Would you

consider turning off your Wi-Fi connection to stop data being collected about your

activities?

Yes No

The following section is concerned with your public input to building development in

the city.

11. Would you like to be involved with new building developments in the city?

Yes No

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12. Have you been involved in any local development – by submitting views for/against

development applications or by attending planning committees, public consultations

or other similar meetings?

Yes - Proceed to question 14 No - Proceed to question 13

13. Which of the following reasons explains why you have not been publicly involved in

previous local developments (you can choose multiple reasons)

You were unaware of the opportunities to be publicly involved

You could not spare the time

You were unavailable at the scheduled time of consultations/meetings/committees

You were physically unable to attend because of mobility problems

You did not wish to participate

You believe you lack the understanding/knowledge necessary to engage in public meetings

14. Only answer if you answered “Yes” for question 12. To what extent do you agree

with the following statements? (Please tick one box for each statement)

Strongly agree Agree

Neither agree nor disagree Disagree

Strongly disagree

I feel my views were taken into account by the local government

My input resulted in design

alterations to the development The local government

communicated how my/public

advice resulted in design

changes

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The government wishes to use passive data to make more informed decisions about

development in cities. They believe that using passive data will result in development

that is more beneficial to local people.

15. Having read the information above, to what extent do you agree with the following

statements?

Strongly agree Agree

Neither agree nor disagree Disagree

Strongly disagree

I voice my opinion about local issues on social networks/blogs

I would like passive data on my

activities to affect

developments

I believe that passive data

about myself/local inhabitants

will improve local development

I would like to know how data

collected about my activities

will affect local development

I will feel more responsible for

the design of local

development if my activities

and opinions have been

considered

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The government is currently investing in ways to make passive data publicly

available, but anonymous. To do this they are entering business partnerships with

private corporations like IBM and Intel who have technical expertise

16. In light of the information above, to what extent do you agree with the following

statements?

Strongly agree Agree

Neither agree nor disagree Disagree

Strongly disagree

I trust the government to

anonymise data collected

about me

I feel comfortable with the

government collecting passive

data about me

I feel comfortable with private

corporations collecting passive

data about me

I am concerned that data

collected about me may be

used for marketing purposes

I am concerned that data

collected about me may be

accessed by criminals

I am concerned that passive

data collection is a risk to my

personal freedom

I want the ability to opt-out of

data collection regardless of

the level of data security

I will opt-out if data security is

low

I will opt-out, even if data

security is high

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The following section is concerned with planning services. Planning services include

providing information on the local area, applying for planning permission and

commenting on developments.

17. Have you used planning services?

Yes No

Even if you have not yet used planning services please answer the following

questions.

18. What do you consider to be the most effective platform for contacting the council?

Please rank the following platforms from 1 to 4; 1 being the best and 4 being the

worst.

Online (the internet)

Telephone

Face-to-face

Post

19. To what extent do you agree with the following statements?

Strongly agree Agree

Neither agree nor disagree Disagree

Strongly disagree

The internet is the first platform I would use for planning services I would attend free education

sessions on how to use online

planning services

I would be more likely to use

online planning services

having attended education

sessions

I believe the internet is the

best platform to voice my

concerns

I believe that online planning

services which use the internet

can replace face-to-face

meetings with local

government

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20. What is your household income?

Under £15,000 £45,000 +

£15,000 – £29,999 Retired

£30,000 – £44,999 Prefer not to say

21. What education levels have you completed?

GCSE/O-levels Bachelor’s degree

A-levels Master’s degree

NVQ/BTEC Doctorate/Professional degree

Other Prefer not to disclose

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7.2 Appendix II: Ethical Approval Form

CARDIFF SCHOOL OF CITY AND REGIONAL PLANNING

Ethical Approval Form

Student Projects (Undergraduate & Taught Masters)

For those student projects which module leaders/supervisors feel require further

discussion by CPLAN’s Ethics Committee (SREC) this form must be completed and

submitted at least TWO WEEKS before a SREC meeting to: Ruth Leo, SREC Secretary /

email: LeoR@cardiff.ac.uk / Tel Ext: 74462 / Room 2.95 Glamorgan Building).

In the case of dissertations it is the responsibility of the student to submit the form, duly

signed by their supervisor, and secure ethical approval prior to any fieldwork

commencing.

A copy of the signed form should be included by all students with their final

dissertation.

Title of Project:

Investigating the Nature and Potential Problems of Public

Participation for UK Smart Cities.

Name of Student(s):

Alexander Henry Thomas Edge

Name of Supervisor/Module Leader:

Dr Brian Webb/Dr Neil Harris

Degree Programme and Level:

BSc City and Regional Planning

Date: 28/04/2015

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Recruitment Procedures:

Consent Procedures:

Yes No N/A

8 Will you tell participants that their

participation is voluntary?

✓

9 Will you obtain written consent for

participation?

✓

10 If the research is observational, will

you ask participants for their consent

to being observed?

✓

Yes No N/A

1 Does your project include children

under 16 years of age?

✓

2 Does your project include people with

learning or communication difficulties?

✓

3 Does your project include people in

custody?

✓

4 Is your project likely to include people

involved in illegal activities?

✓

5 Does project involve people belonging

to a vulnerable group, other than

those listed above?

✓

6 Does your project include people who

are, or are likely to become your

clients or clients of the department in

which you work?

✓

7 Does your project include people for

whom English / Welsh is not their first

language?

✓

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11 Will you tell participants that they

may withdraw from the research at

any time and for any reasons?

✓

12 Will you give potential participants a

significant period of time to consider

participation?

✓

Possible Harm to Participants:

Yes No N/A

13 Is there any realistic risk of any

participants experiencing either

physical or psychological distress or

discomfort?

✓

14 Is there any realistic risk of any

participants experiencing a detriment

to their interests as a result of

participation?

✓

If there are any risks to the participants you must explain in the box on page 5 how

you intend to minimise these risks

Data Protection:

Yes No N/A

15 Will any non-anonymised and/or

personalised data be generated

and/or stored?

✓

16 Will you have access to documents

containing sensitive1 data about

living individuals?

If “Yes” will you gain the consent of

the individuals concerned?

✓

✓

If you have checked any shaded area here please expand on page 5

1 Sensitive data are inter alia data that relates to racial or ethnic origin, political opinions, religious beliefs, trade

union membership, physical or mental health, sexual life, actual and alleged offences.

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If there are any other potential ethical issues that you think the Committee should

consider please explain them in the box on page 5. It is your obligation to bring to

the attention of the Committee any ethical issues not covered on this form.

If any of the shaded boxes have been ticked the supervisor/module

leader must explain below how the potential ethical issue will be

handled:

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7.3 Questionnaire results

Question 1. What is your age? Age Coding Frequency

18-24 1 7

25-34 2 8

35-44 3 10

45-54 4 7

55-64 5 8

65-74 6 5

75+ 7 5

TOTAL N/A 50

Question 2. What is your gender? Gender Coding Frequency

Female 0 21

Male 1 26

Unspecified 1 3

TOTAL N/A 50

Question 3. Do you have internet access? Answer Coding Frequency

Yes 0 46

No 1 4

TOTAL N/A 50

Question 4. Why are you unable to access the internet? Reasons Coding Frequency

No desire for internet access 1 4

No internet provision in your area 2 0

Cost of internet-accessible devices/internet service 3 0

Other 4 0

TOTAL N/A 4

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Question 5. Why do you desire no internet access? Reasons Coding Frequency

Lack of education/skill on how to use the internet 1 1

Fear of exposure to crime/being monitored 2 0

Lack of motivation/reasons to use the internet 3 4

Other 4 0

TOTAL N/A 5

Question 6. How frequently do you use the internet? Usage Coding Frequency

2 or more times a day 1 44

Once a day 2 0

Once every few days 3 2

Less than once every few days 4 0

TOTAL N/A 46

Question 7. What do you tend to use the internet for? Uses Coding Frequency

Social networking 1 30

Media 2 30

Shopping 3 34

Communication 4 40

Research/business 5 39

Accessing services 6 26

Other 7 3

TOTAL N/A 202

Question 8. Which of these devices do you own? Devices Coding Frequency

Smart phone 1 35

Portable media player 2 18

Computer 3 42

Tablet 4 31

Smart travel card 5 14

Meters 6 8

Other 7 8

TOTAL N/A 156

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Question 9. Were you aware that your devices are possibly producing data on your activities before you read the above information?

Answer Coding Frequency

Yes 0 40

No 1 10

TOTAL N/A 50

Question 10. Would you consider turning off you Wi-Fi connection to stop data being collected about your activities?

Answer Coding Frequency

Yes 0 30

No 1 20

TOTAL N/A 50

Question 11. Would you like to be involved with new building developments in the city?

Answer Coding Frequency

Yes 0 30

No 1 20

TOTAL N/A 50

Question 12. Have you been involved in any local development? Answer Coding Frequency

Yes 0 17

No 1 33

TOTAL N/A 50

Question 13. Which of the following reasons explains why you have not been publicly involved in previous local developments?

Reasons Coding Frequency

You were unaware of the opportunities to be involved 1 19

You could not spare the time 2 10

You were unavailable at the scheduled time of meetings 3 9

You were physically unable to attend due to mobility issues 4 1

You did not wish to participate 5 9 You believe you lack the understanding necessary to engage in public meetings 6 6

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Question 14i. I feel my views were taken into account by the local government

Position Coding Frequency

Strongly agree 1 1

Agree 2 2

Neither agree nor disagree 3 6

Disagree 4 5

Strongly disagree 5 3

TOTAL N/A 17

Mean statement N/A 3.4

Question 14ii. My input resulted in design alterations to the development

Position Coding Frequency

Strongly agree 1 1

Agree 2 1

Neither agree nor disagree 3 7

Disagree 4 5

Strongly disagree 5 3

TOTAL N/A 17

Mean statement N/A 3.5

Question 14iii. The local government communicated how my/public advice resulted in design changes

Position Coding Frequency

Strongly agree 1 0

Agree 2 2

Neither agree nor disagree 3 7

Disagree 4 4

Strongly disagree 5 4

TOTAL N/A 17

Mean statement N/A 3.6

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Question 15i. I voice my opinions about local issues on social networks/blogs

Position Coding Frequency

Strongly agree 1 2

Agree 2 14

Neither agree nor disagree 3 10

Disagree 4 11

Strongly disagree 5 13

TOTAL N/A 50

MEAN N/A 3.38

Question 15ii. I would like passive data on my activities to affect developments

Position Coding Frequency

Strongly agree 1 1

Agree 2 24

Neither agree nor disagree 3 12

Disagree 4 6

Strongly disagree 5 7

TOTAL N/A 50

MEAN N/A 2.88

Question 15iii. I believe that passive data about myself/local inhabitants will improve local development

Position Coding Frequency

Strongly agree 1 2

Agree 2 23

Neither agree nor disagree 3 14

Disagree 4 6

Strongly disagree 5 5

TOTAL N/A 50

MEAN N/A 2.78

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Question 15iv. I would like to know how data collected about my activities will affect local development

Position Coding Frequency

Strongly agree 1 18

Agree 2 26

Neither agree nor disagree 3 2

Disagree 4 2

Strongly disagree 5 2

TOTAL N/A 50

MEAN N/A 1.88

Question 15v. I will feel more responsible for the design of local development if my activities and opinions have been considered

Position Coding Frequency

Strongly agree 1 9

Agree 2 30

Neither agree nor disagree 3 7

Disagree 4 1

Strongly disagree 5 3

TOTAL N/A 50

MEAN N/A 2.18

Question 16i. I trust the government to anonymise data collected about me

Position Coding Frequency

Strongly agree 1 2

Agree 2 13

Neither agree nor disagree 3 7

Disagree 4 17

Strongly disagree 5 11

TOTAL N/A 50

Mean statement N/A

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Question 16ii. I feel comfortable with the government collecting passive data about me

Position Coding Frequency

Strongly agree 1 2

Agree 2 13

Neither agree nor disagree 3 10

Disagree 4 17

Strongly disagree 5 8

TOTAL N/A 50

Mean statement N/A

Question 16iii. I feel comfortable with private corporations collecting passive data about me

Position Coding Frequency

Strongly agree 1 0

Agree 2 3

Neither agree nor disagree 3 11

Disagree 4 20

Strongly disagree 5 16

TOTAL N/A 50

Mean statement N/A

Question 16iv. I am concerned that data collected about me may be used for marketing purposes

Position Coding Frequency

Strongly agree 1 21

Agree 2 21

Neither agree nor disagree 3 5

Disagree 4 2

Strongly disagree 5 1

TOTAL N/A 50

Mean statement N/A

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Question 16v. I am concerned that data collected about me may be accessed by criminals

Position Coding Frequency

Strongly agree 1 18

Agree 2 16

Neither agree nor disagree 3 9

Disagree 4 6

Strongly disagree 5 1

TOTAL N/A 50

Mean statement N/A

Question 16vi. I am concerned that passive data collection is a risk to my personal freedom

Position Coding Frequency

Strongly agree 1 15

Agree 2 18

Neither agree nor disagree 3 9

Disagree 4 6

Strongly disagree 5 2

TOTAL N/A 50

Mean statement N/A

Question 16vii. I want the ability to opt-out of data-collection regardless of the level of data security

Position Coding Frequency

Strongly agree 1 25

Agree 2 19

Neither agree nor disagree 3 5

Disagree 4 0

Strongly disagree 5 1

TOTAL N/A 50

Mean statement N/A

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Question 16viii. I will opt-out if data security is low

Position Coding Frequency

Strongly agree 1 27

Agree 2 14

Neither agree nor disagree 3 7

Disagree 4 2

Strongly disagree 5 0

TOTAL N/A 50

Mean statement N/A

Question 16ix. I will opt-out, even if data security is high

Position Coding Frequency

Strongly agree 1 7

Agree 2 6

Neither agree nor disagree 3 25

Disagree 4 10

Strongly disagree 5 2

TOTAL N/A 50

Mean statement N/A

Question 17. Have you used planning services?

Answer Coding Frequency

Yes 0 21

No 1 29

TOTAL N/A 50

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Question 18. What do you consider to be the most effective platform for contacting the council? (Rank from 1-4)

Respondent number

Platform #1

#2

#3

#4

#5

#6

#7

#8

#9

#10

#11

#12

#13

#14

#15

#16

Online (the internet) 1 3 2 4 4 3 1 2 2 2 3 1 2 2 3 4

Telephone 4 2 1 1 1 2 2 3 4 3 2 2 1 1 2 3

Face-to-face 2 1 3 3 2 1 3 1 1 1 1 3 3 4 1 1

Post 3 4 4 2 3 4 4 4 3 4 4 4 4 3 4 2

#17

#18

#19

#20

#21

#22

#23

#25

#26

#27

#28

#29

#30

#31

#32

#33

#34

4 3 1 1 1 3 3 4 3 2 3 1 2 3 1 4 4

2 1 2 3 2 2 2 3 1 1 2 4 1 1 3 2 1

1 2 3 2 4 1 1 2 2 4 1 2 3 2 2 1 2

3 4 4 4 3 4 4 1 4 3 4 3 4 4 4 3 3

#35

#36

#37

#38

#39

#40

#41

#42

#43

#44

#45

#46

#47

#48

#49

#50 Average

1 4 1 2 3 1 4 1 3 1 3 2 1 1 4 3 2.3877551

02

3 3 2 3 2 3 2 3 2 3 1 3 2 2 2 2 2.1428571

43

2 1 4 1 1 2 1 2 1 2 2 1 3 4 3 1 1.9795918

37

4 2 3 4 4 4 3 4 4 4 4 4 4 3 1 4 3.4897959

18

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Question 19i. The internet is the first platform I would use for planning services

Position Coding Frequency

Strongly agree 1 11

Agree 2 16

Neither agree nor disagree 3 10

Disagree 4 6

Strongly disagree 5 7

TOTAL N/A 50

Mean statement N/A

Question 19ii. I would attend free education sessions on how to use online planning services

Position Coding Frequency

Strongly agree 1 2

Agree 2 15 Neither agree nor disagree 3 11

Disagree 4 14

Strongly disagree 5 8

TOTAL N/A 50

Mean statement N/A

Question 19iii. I would be more likely to use online planning services having attended education sessions

Position Coding Frequency

Strongly agree 1 2

Agree 2 13 Neither agree nor disagree 3 19

Disagree 4 9

Strongly disagree 5 7

TOTAL N/A 50

Mean statement N/A

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Question 19iv. I believe the internet is the best platform to voice my concerns

Position Coding Frequency

Strongly agree 1 6

Agree 2 15

Neither agree nor disagree 3 13

Disagree 4 9

Strongly disagree 5 7

TOTAL N/A 50

Mean statement N/A

Question 19v. I believe that online planning services which use the internet can replace face-to-face meetings with local government

Position Coding Frequency

Strongly agree 1 1

Agree 2 10

Neither agree nor disagree 3 12

Disagree 4 16

Strongly disagree 5 11

TOTAL N/A 50

Mean statement N/A