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.
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
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
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.
2.1 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
IoTBD 2016 - International Conference on Internet of Things and Big Data
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
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
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.
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
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
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
Associated Tasks Provenance Information Possible Analytics Approaches
Survey and
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
Investigation of evidence,
assessment of alternatives,
identification of the cause of
Capture analysis techniques, capture
evidence details, stakeholders’ values,
Different data analytics techniques along
with value-based analysis, perception
Decision making
Negotiation among
stakeholders, citizens
involvement, decision
making based on
Stakeholders’ perception, citizens opinion,
capture evidences used in decision
making, policy success indicators
Opinion analysis, conflict resolution,
social learning capabilities
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
IoTBD 2016 - International Conference on Internet of Things and Big Data
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.
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.
Bakici, T. et al., (2013). A Smart City Initiative: The Case
of Barcelona. Journal of the Knowledge Economy.
Batty, M. et al., (2012). Smart cities of the future.
European Physical Journal Special Topics, pp. 481–
Bristol Is Open project. Available at:
www.bristolisopen.com [Accessed 4 March, 2016].
British Standard Institute, (2014). PAS 181: 2014 Smart
city framework – Guide to establishing strategies for
smart cities and communities. United Kingdom.
Available at: http://www.bsigroup.com/en-GB/smart-
cities/Smart- Cities-Standards-and-Publication/PAS-
Carata, L. et al., (2014). A Primer on Provenance.
Commun. ACM.
Ceolin, D. et al., (2013). Reliability Analyses of Open
Government Data. In URSW, pp. 34–39.
Chourabi, H. et al., (2012). Understanding Smart Cities:
An Integrative Framework. The 45th Hawaii
International Conference on System Sciences, pp.
2289 - 2297.
Coglianese, C. et al., (2008). Transparency and Public
Participation in the Rulemaking Process. A
Nonpartisan Presidential Transition Task Force
Daniell, K. A. et al., (2015). Policy analysis and policy
analytics. Annals of Operations Research.,
d'Aquin, M. et al., (2014). Dealing with Diversity in a
Smart-City Datahub. In Fifth Workshop on Semantics
for Smarter Cities. pp. 68–82.
A Provenance Framework for Policy Analytics in Smart Cities
De Marchi, G. et al., (2012). From Evidence Based Policy
Making to Policy Analytics. Cahier du LAMSADE
319. Université Paris Dauphine, Paris.
Edwards, P. et al., (2009). esocial science and evidence-
based policy assessment: challenges and solutions.
Social Science Computer Review. vol. 27(4), pp. 553–
Emaldi et al., (2013). To trust, or not to trust: Highlighting
the need for data provenance in mobile apps for smart
cities. International Workshop on Semantic Sensor
Networks (SSN).
FUPOL. Available at: http://www.fupol.eu/en [Accessed 4
March, 2016]
Geiger, C. P and Lucke, J. V., (2011). Open Government
Data. In CeDEM11. Conference for E-Democracy and
Open Government. pp. 183–194.
Horelli, L. and Wallin, S., (2013). New Approaches to
Urban Planning Insights from Participatory
Communities. Aalto University Publication series
Aalto-ST 10/2013, pp.11-16.
Huynh, T. D., et al., (2013). Interpretation of
crowdsourced activities using provenance network
analysis. In: First AAAI Conference on Human
Computation and Crowdsourcing.
Jeannine, E. R. and Sabharwal, M., (2009). Perceptions of
Transparency of Government Policymaking: A Cross-
National Study. Government Information Quarterly
26(1): pp.148-157.
Kaschesky, M. et al., (2011). Opinion mining in social
media: modeling, simulating, and visualizing political
opinion formation in the web. In: International
Conference on Digital Government Research.
Khan, Z. et al., (2014). ICT enabled participatory urban
planning and policy development: The UrbanAPI
project. Transforming Government: People, Process
and Policy, 8 (2). pp. 205-229. ISSN 1750-6166.
Liu, Q. et al., (2013). Data Provenance and Data
Management Systems. In Data Provenance and Data
Management in eScience, Springer Berlin Heidelberg.
Lopez-de-Opina, D. et al., (2013). Citizen-centric Linked
Data Apps for Smart Cities. Lecture Notes in
Computer Science, Springer Publishers. pp. 70-77.
Lotzmann, U. and Wimmer, M., (2012). Provenance and
Traceability in Agent-based Policy Simulation. In
Proceedings of 26th European Simulation and
Modelling Conference - ESM'2012.
Luzeaux, D. and Ruault, J. R., (2010). Systems of
Systems. ISTE Ltd and John Wiley & Sons Inc.
Margo, D and Smogor, R., (2010) Using provenance to
extract semantic file attributes. In: Proceedings of the
2nd conference on Theory and practice of provenance.
McClatchey, R. et al., (2014). Provenance Support for
Medical Research. In 5th International Provenance
and Annotation Workshop (IPAW2014).
Packer, H. et al., (2014). Semantics and Provenance for
Accountable Smart City Applications. Semantic Web –
Interoperability, Usability, Applicability an IOS Press
Ram, S. and Liu, J., (2009). A new perspective on
Semantics of Data Provenance. First International
Workshop on the role of Semantic Web in Provenance
Management (SWPM).
Scherer, S. et al. (2015). Evidence Based and Conceptual
Model Driven Approach for Agent-Based Policy
Modelling. Journal of Artificial Societies and Social
Shamdasani, J. et al. (2014). CRISTAL-ISE : Provenance
Applied in Industry. Proceedings of the 16
International Conference on Enterprise Information
Systems (ICEIS).
Simmhan, Y. L. et al., (2005). A Survey of Data
Provenance Techniques. In Technical Report TR-618:
Computer Science Department, Indiana University.
Smarticipate, (2016). Smart services for calculated impact
assessment in open governance. EC H2020 Project
start February 2016.
Tsoukiàs, A. et al., (2013). Policy analytics: An agenda for
research and practice. EURO Journal on Decision
Processes, 1, 115–134.
Urban API, (2014). Interactive Analysis, Simulation and
Visualisation Tools for Urban Agile Policy
Implementation. Available at: http://www.urbanapi.eu/
[Accessed 5 March, 2016]
World Health Organization, (2016). Climate change and
human health. Available at: http://www.who.int/
globalchange/ecosystems/urbanization/en/ [Accessed 5
March, 2016].
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