A Provenance Framework for Policy Analytics in Smart Cities

Barkha Javed, Richard McClatchey, Zaheer Khan and Jetendr Shamdasani

Faculty of Environment and Technology, University of the West of England, Frenchay Campus, Bristol ,U.K.

Keywords: Smart Cities, Policy Making, Provenance, Policy Analytics.

Abstract: Sustainable urban environments require appropriate policy management. However, such policies are

established as a result of underlying, potentially complex and long-term policy making processes.

Consequently, better policies require improved and verifiable planning processes. In order to assess and

evaluate the planning process, transparency of the system is pivotal which can be achieved by tracking the

provenance of policy making process. However, at present no system is available that can track the

complete cycle of urban planning and decision making. We propose to capture the complete process of

policy making and to investigate the role of Internet of Things (IoT) provenance to support design-making

for policy analytics and implementation. The environment in which this research will be demonstrated is

that of Smart Cities whose requirements will drive the research process.

1 INTRODUCTION

Unprecedented rapid urbanisation has been observed

in recent years; according to the World Health

Organization (2016), today’s urban population

accounts for more than 50% of the total global

population and is expected to further increase to

70% by 2050 (British Standard Institute, 2014). The

consequent growing population is placing pressure

on social, environmental and other resources

including the wider city infrastructure. To meet

these challenges, the notion of Smart Cities has

emerged in recent years. Smart Cities are often

referred to as the use of Information and

Communication Technology (ICT) to improve the

quality of life and provide sustainable living for

citizens (Bakici et al., 2013). However, the vision of

Smart Cities is only possible with new and better

approaches to urban planning and decision making

(Chourabi et al., 2012; British Standard Institute,

2014). Nevertheless, urban planning and decision

making is a challenging task as it entails diverse

information, complex processes, and involves

various stakeholders.

The policy making process consists of different

stages including problem identification, agenda

setting, analysis, negotiation and decision making,

implementation, and evaluation (Khan, 2014). Each

stage has further associated tasks; for example, the

problem identification stage may encompass the

acquisition of quantitative and qualitative

information, potentially through the use of the

Internet of Things (IoT), city databases, etc. The

data analysis may involve the investigation of data

and evidence, the assessment of alternative

scenarios, and the identification of the cause of the

issue(s): for example, the identification of the cause

of air pollution in a city using information gathered

from IoT sensors. Similarly, other planning phases

also consist of further tasks. In addition, various

stakeholders are involved at each phase of the

process. New emerging trends in policy require

processes to adopt more transformational approaches

(e.g. bottom-up initiatives) to enable collaborative

decision making. A generic planning cycle should

support both top-down and bottom-up planning

initiatives. In recent years, use of new ICT solutions

for public participation (i.e. participatory sensing)

has transformed planning processes in smart cities

(Batty et al., 2012).

The effectiveness of urban policies is largely

dependent on evidence employed and decisions

taken during the process. Tracking of the complete

lifecycle of policy making is required in the

planning process for evaluation of decisions and the

evidence used in policy making. This helps to

achieve more informed policy decisions and to make

the system more transparent, legitimate and

accountable (Coglianese et al., 2008; Jeannine and

Sabharwal, 2009). Recent literature indicates that

Javed, B., McClatchey, R., Khan, Z. and Shamdasani, J.

A Provenance Framework for Policy Analytics in Smart Cities.

DOI: 10.5220/0005931504290434

In Proceedings of the International Conference on Internet of Things and Big Data (IoTBD 2016), pages 429-434

ISBN: 978-989-758-183-0

429

local governments are realising the potential of co-

creativity and co-production through multi-

stakeholder participation in planning processes and

hence are looking for open governance models

where transparency in these processes plays an

important role.

In order to track the processes and decision-

making in the policy cycle, extensive provenance

information should be collected (Ram and Liu,

2009). This provenance includes information about

how, when, by whom and why data has been

gathered, analysed and used in policy making. Such

information is essential in order to track the

complete policy cycle and to provide data

integration and accountability at each decision

making stage. Provenance for smart cities can

provide useful information such as how and when

data was collected, the ownership of that data, how

collected information is being processed, evidence

considered during planning, potential stakeholders in

the planning, how citizens’ feedback were

accommodated, decisions made during planning,

alternatives considered during decision making, and

the outcome of decisions (Lopez-de-Opina et al.,

2013). Therefore the first motivation for this

research is to employ provenance information to

track all the phases of the smart city policy cycle

where each phase may be considered as an

individual system having associated inputs, outputs,

sets of tasks, and different stakeholders’

involvement. Tracking of each phase will provide

substantial information for subsequent phases which

will guide further planning process.

The provenance tracked during policy making

process can be used for further analysis. For

example, provenance can provide a reference for

future planning. Collected evidence can be exploited

to explore the success or failure factors of previously

devised policy which can provide guidance for

devising other/future policies. It can also be used to

analyse the impact of policy decisions on other

associated city operations. The analysis of

provenance can thus improve future decisions.

Therefore, the second motivation of this study is to

investigate the role of provenance to support policy

analytics in smart cities environment.

Urban planning entails diverse and city-wide

information (such as transportation, air quality

monitoring, health, waste management etc). A

flexible system is required to capture the diverse

information associated with urban planning and to

track the complete history of changes. In this regard,

model-driven engineering (MDE) can be applied to

develop a provenance framework to support policy

analytics. The provenance framework will also

investigate the description driven approach such as

that of the CRISTAL ( McClatchey et al., 2014) to

capture provenance in the smart cities domain. This

research will evaluate the suitability of model driven

approaches to support provenance information

gathering for smart cities by experimentation.

Furthermore, we are aware of the issues related to

security of provenance data. However, this is out of

scope of this research.

This paper presents the needs and benefits for

tracking the full lifecycle of the planning process for

smart cities. The overall aim of this research is to

investigate the extent to which it is possible to make

effective use of provenance for evidence based

policy analytics. The next section outlines related

work and a provenance framework for policy

analytics is presented thereafter before conclusions

and future work are subsequently outlined.

2 LITERATURE REVIEW

2.1 A Planning Model for Smart Cities

The functioning of a city is a reflection of its

underlying planning and decision making.

Traditional approaches to urban planning can benefit

from new ICT solutions to address the growing

challenges of recent rapid urbanization. To address

the issues related to traditional approaches, smart

cities modelling considers using new operating

models for the planning of cities (Horelli and

Wallin, 2013; British Standard Institute, 2014).

Smart cities planning encourage an open and

transparent governance process and facilitate public

participation in a planning process.

Open Government (Geiger and Lucke, 2011) is a

mechanism employed in recent years for

government accountability and public scrutiny. This

approach provides transparency in the governance

system by enabling the availability of government-

held information to the public; furthermore, this

system also encourages citizens’ participation.

Different projects to support the Open Government

approach have been initiated in the past few years.

Of which Urban API (2014), FUPOL (2016) and

Smarticipate (2016) are suitable examples.

Open government provides transparency but may

not necessarily ensure reliability and trust in a

system (Ceolin et al., 2013). This can be achieved by

employing provenance for tracking the planning

process. The suitability of provenance for tracking

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urban planning and decision making is further

discussed in the subsequent section.

2.2 Provenance for Tracking Planning

Processes in Smart Cities

Provenance is often employed to trace the audit trail

and usage of data, to estimate data quality and

reliability and accuracy, to verify the validity of

information, integrity, authenticity, replication and

repetition of data and processes, to validate the

attribution of data, and to establish transparency and

trust in the system (Carata et al., 2014; Simmhan et

al., 2005).

The significant dual challenges of gathering and

storing provenance data in complex Smart Cities has

motivated a number of research efforts in recent

years. d'Aquin et al., (2014) addresses the

management of diverse datasets produced by

different objects in Smart Cities. Provenance is

employed in (Lopez-de-Opina et al., 2013; Emaldi et

al., 2013) for addressing validation and trust issues

related to open data in Smart Cities. Provenance is

employed in (Packer et al., 2014) for transparency

and accountability of sharing services in smart cities.

The literature demonstrates the potential use of

provenance in Smart City environments. However,

utilising provenance information and data emerging

from IoT sensor nets to capture the processes needed

in the planning process in smart cities has not yet

been investigated. Nevertheless, the suitability of

provenance for urban planning has been discussed

by Edwards et al., (2009). Furthermore,

eSocialScience tools and techniques have been

proposed to support social scientists involved in

policy-related research. Evidence-based policy

simulation is a focus of the OCOPOMO project

(Lotzmann and Wimmer, 2012) which enables

policy formulation using a set of ICT tools. The

tools facilitate policy makers in modelling policies

and in communicating them to other stakeholders for

feedback. Scherer (2015) extends the OCOPOMO

project by using a model-driven approach in the

project.

What is required is a holistic approach to

managing the full lifecycle of policy making for

smart cities. This will necessitate the use of a

process oriented approach to identify socio-technical

activities and exchange of data among actors in a

policy cycle. This approach will deal with the

integration of heterogeneous data in a common

conceptual model (potentially description-driven, as

in the CRISTAL software) and the gathering,

curation and analysis of data emerging from smart

city sensing devices plus tracking the provenance

and processing of those data and how they may

influence decision making, policy implementation

and its evaluation in a city-wide environment.

The existing work (Edwards et al., 2009;

Lotzmann and Wimmer, 2012; Scherer et al., 2015)

shows the potential role of provenance in urban

planning. However, the current systems do not track

all activities of the policy cycle and are not in the

context of smart cities. Citizens’ participation is

important for smart cities planning ( BristolisOpen,

2015). Therefore, provenance gathering will also

need to capture how their suggestions were

accommodated in the policy process. Provenance

tracking of smart cities’ planning will provide a rich

source of information regarding the policy making

process. This information can be used to support

policy analytics in smart cities which is discussed in

section 2.3.

2.3 Using Provenance to Support

Policy Analytics in Smart Cities

Policy analytics in the past couple of years has

attracted the attention of many researchers (De

Marchi et al., 2012; Tsoukiàs et al., 2013; Daniell et

al., 2015). Opinion mining has been employed by

Kaschesky et al., (2011) to track and analyse the

citizens’ participation in policy making process.

Similarly, possible use of preference learning, text

mining, value-driven analysis, prospective analysis,

and data mining for policy analytics has been

specified by a number of researchers (Tsoukiàs et

al., 2013; Daniell et al., 2015).

The planning process requires both data and

value-driven decision making (Tsoukiàs et al.,

2013). Therefore, in order to enable policy analytics

and to aid in decision making, tracking of both data

and values is required. Provenance of the policy

making process will provide an integrated platform

and will provide rich information regarding the

process such as the evidence used (in the case of

smart cities, data from IoT sensors is pertinent),

public engagement, and decisions of policy makers.

Such information can be used for analysis and to

inform current and future decision making.

Provenance can be employed to find useful

information and can be used for the purpose of

learning and knowledge discovery (Liu et al., 2013).

Huynh et al., (2013) used provenance analytics to

assess the quality of the crowd-generated data.

Margo and Smogor (2010) employed machine

learning classification techniques in order to classify

files using their provenance. Huynh and Margo

A Provenance Framework for Policy Analytics in Smart Cities

431

show the possible use of provenance to support data

analytics. Provenance can also be considered to

support policy analytics in the smart cities planning

process. However, the potential benefits and use of

provenance for policy analytics need to be explored

by further study and experimentation.

3 PROVENANCE FRAMEWORK

TO SUPPORT POLICY

ANALYTICS

Figure 1 (Khan, 2014) depicts a typical policy

making process and aim is to capture provenance

through all stages of this process.

Figure 1: Policy Cycle (Khan, 2014).

Each phase of the planning process has further

associated processes and tasks. This suggests that

each stage can be considered as an individual system

and points to a system-of-systems (Luzeaux and

Ruault, 2010) approach to modelling the policy

cycle. This will be investigated in the current

research.

Our research will assess the extent to which

provenance of planning processes are effective for

policy making and analytics in smart cities and the

framework needed to support these analytics. In

order to clarify the provenance support for policy

analytics, table 1 shows the provenance captured at

each phase of the smart cities policy cycle and

possible analytics that can be applied using

provenance. However, this will be improved on

further research.

3.1 Case Study: Air Quality

Monitoring

For further clarification, let us consider the scenario

of Air Quality Monitoring. Assume a large

concentration of Carbon Monoxide (CO) and

Nitrogen Oxide (NO) has been recorded in ‘City A’

which is mostly contributed by transportation fumes.

In order to minimise the concentration of identified

pollutants, a new policy is required to be put in

place. To devise a new policy, air quality data is

captured from air monitoring IoT sensors by analysts

who run statistical analysis according to the

thresholds set by the current air quality policy (for

example EU and national levels). Similarly, in order

to investigate the potential role of traffic in air

pollution, information regarding traffic and vehicles

has been collected from highway and vehicle

Table 1: Policy analytics using provenance.

Policy cycle

phases

Associated Tasks Provenance Information Possible Analytics Approaches

Survey and

Problem

identification

Acquisition of qualitative

and quantitative data (data

from sensors, surveys,

interviewees etc)

Verifiable source(s) of data (social

network, IoT, city databases, etc.),

perception/ views of different stakeholders

Range of analytic technique (such as data

mining, machine learning algorithms),

value-driven analysis

Agenda setting Priority Setting Domain experts’ views Value-based analysis

Analysis

Investigation of evidence,

assessment of alternatives,

identification of the cause of

issues

Capture analysis techniques, capture

evidence details, stakeholders’ values,

views/perceptions.

Different data analytics techniques along

with value-based analysis, perception

analysis

Decision making

Negotiation among

stakeholders, citizens

involvement, decision

making based on

policies/evidence

Stakeholders’ perception, citizens opinion,

capture evidences used in decision

making, policy success indicators

Opinion analysis, conflict resolution,

social learning capabilities

Implementation

Interagency cooperation

(some metrics to track

policy implementation)

Track the data used to assess

implementation compliance to original

policy specification

Evaluation Monitor the policy Track the matrices used for monitoring

Perception analysis, data analytics

techniques

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licensing agencies respectively. The collected data is

then analysed by the analyst and is communicated to

the concerned department. Provenance information

is gathered at the point of recording air quality,

traffic, and vehicle data; its analysis and its outputs

are recorded in order to facilitate linkage to the

subsequent decision making stage.

For our case study let us now assume the

analysis demonstrates the role of traffic in air

pollution. The issue is communicated to urban

planners, city administration, the environment

agency, and citizens via the recorded provenance

information in order to ensure the trustworthiness of

the analysis. Based on the feedback of stakeholders,

policy makers then propose a strategy to minimise

traffic congestion by devising alternative routes.

These decisions are also recorded alongside the

processed data in the provenance store. If traffic

exceeds a particular threshold then it is routed to

other available routes. The proposed threshold is

negotiated among policy makers. The strategy is

implemented and air quality is continuously

monitored to evaluate the policy based on some

evaluation criteria. Each stage in the process is

recorded in order to provide full traceability of the

policy cycle.

This case study demonstrates the complex

process of planning; the various evidence (data from

IoT sensors, traffic and vehicles data, data gathered

at each phase), decision choices (of policy makers

and stakeholders), evaluation criteria (set by air

quality policy in given example), and stakeholders

(urban planners, city administration, the

environment agency, and citizens) involvement in

the process. For transparency in the system,

provenance information is captured at each phase of

policy of the planning process. Let us suppose that

the devised policy is not successful. In order to

uncover the issue, provenance at each stage can be

carefully analysed (by using analytics techniques).

The identified issue is addressed by considering

options and therefore devising a new policy, driven

by the model-based holistic policy support

framework. Similarly, provenance can also provide

assistance in evaluating accountability, exploring the

benefits of public participation, evaluating decisions.

4 CONCLUSION AND FUTURE

WORK

Urban planning and decision making is a

challenging task as it entails complex processes, it

involves various actors, and uses data collected from

heterogeneous sources. To improve services and to

guide in future decision making, all planning

decisions are required to be maintained which

necessitates the capturing of provenance for smart

cities environments. This paper puts forward the

idea of tracking the full urban planning process and

considers each phase of the process as an individual

system. Furthermore, the idea of using provenance,

potentially using a description-driven, model-based

approach to supporting policy analytics is also

presented in this position paper.

Future work will consider the implementation

and evaluation of the proposed research study using

practical examples of data derived from IoT sensor

network. The aim is to explore what possible

analyses could be carried with Smart Cities

provenance data. Policy analytics is an area which is

still in its infancy as highlighted by the literature in

section 2 of this paper; therefore this study will

explore how policy analytics can be supported by

using provenance captured during urban planning.

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