Open Data initiatives are increasingly considered as defining elements of emerging smart cities. However, few studies have attempted to provide a better understanding of the nature of this convergence and the impact on both domains. This talk examines the challenges and trends with open data initiatives using a socio-technical perspective of smart cities. The talk presents findings from a detailed study of 18 open data initiatives across five smart cities to identify emerging best practice. Three distinct waves of open data innovation for smart cities are discussed. The talk details the specific impacts of open data innovation on the different smart cities domains, governance of the cities, and the nature of datasets available in the open data ecosystem within smart cities.

1. Open Data Innovation in Smart
Cities: Challenges and Trends
Edward Curry & Adegboyega Ojo
Insight / LERO
ed.curry@insight-centre.org
www.edwardcurry.org

2. Some Background
Multi-year research on
state of research and
practice of smart cities to
inform Next Generation
Smart City Design and
Policy
Part
of
an
Interna+onal
Smart
Ci+es
Research/Prac+ce
Consor+um
composed
of
interna+onal
research
teams
from
the
US,
Canada,
Mexico,
Colombia,
China
and
Ireland.

3. Agenda
n What is a Smart City?
n Technology Adoption
n Organisation and Policy Trends
n Technology Challenges: Open Data Platforms
n Conclusions and Future Work
3

4. What is a Smart City?
4

5. What is a Smart City?
Several definitions emerged in last few years describing the concept. One
definition attempting to capture emerging dimensions of the concept is :
A city in which investments in human and social capital and modern
ICT infrastructure and e-services fuel sustainable growth and quality
of life, enabled by a wise management of natural resources and
through participative government [Caragliu et al., 2009]

6. What is a Smart City?
A smart city is a socio-technical system of systems
Nam et al. in conceptualizes a “Smart City” as an
interplay among technological innovation,
organizational innovation, and policy innovation.
n Continuing Lifecycle
n Socio-technical system
n Collaborative system
n Industrialised system
n Rapid innovation
n Infrastructure Services
n Personal Data

7. Open Data as Urban Innovation
o Open data central to open innovations in cities
o Open data is powering a new civic movement that is changing the way citizens
experience cities (http://www.data.gov/cities/)
http://www.dublindashboard.ie/pages/index http://amsterdamsmartcity.com/projects/detail/id/
68/slug/smart-citysdk

8. Limitations of National Open Data
Efforts
n European Public Sector Information (PSI) directive
¨ Most EU member states have open data initiatives
¨ over 8,000 datasets available on the EU Open Data Portal
n Anticipated impacts far from being realized
¨ limited access and use by citizens and 3rd parties
¨ limited resource of gov. agencies to publish high value data
¨ weak legislative framework to enable reuse of available data

9. Why ?
n Examine
broader context
to ensure we
maximise the
impacts of
Smart City Open
Data Initiatives
n A Technology
only perspective
is not enough S. Alawadhi, A. Aldama-nalda, H. Chourabi, J. R. Gil-garcia, S. Leung, S.
Mellouli, T. Nam, T. A. Pardo, H. J. Scholl, and S. Walker, “Building
Understanding of Smart City Initiatives,” pp. 40– 53.

10. 10
Technology Adoption

11. Technology Adoption Lifecycle
Rogers, Everett M. (1962). Diffusion of Innovations. Glencoe: Free Press. ISBN
0-612-62843-4.

12. Technology Adoption Lifecycle
12
Innovators Late majority LaggardsEarly majorityEarly adopters
Central interest
Pleasure of
exploring the
new device
properties
Buy new product
concept very
early
Not technologists
First to get the
new stuff
Strong sense of
practicality
Wait until
something has
become an
established
standard
Not comfortable
with technology
Don’t want
anything to do
with new
technology
Technology
enthusiast
Pragmatists
ConservativesVisionaries

14. Characteristics Successful Adoption of
Innovation
n Relative Advantage: enabling better functioning city and city
life. (impact of the initiative on the different smart city
domains)
n Compatibility: degree to which a smart city initiative is
consistent with existing city stakeholder values, or interests,
and city context
n Complexity: the degree of difficulty involved in implementing
the initiative and communicating benefits to stakeholders.
n Trialability: degree to which experimentation is possible in
initiative
n Cost Efficiency and Feasibility: with respect to existing
comparable practice
n Evidence: availability of research evidence and practice
efficacy
n Risk: level of risk associated with the implementation and
adoption
J. P. Wisdom, K. H. B. Chor, K. E. Hoagwood, and S. M. Horwitz, “Innovation Adoption:
A Review of Theories and Constructs.,” Adm. Policy Ment. Health, Apr. 2013.

15. Key Message
n Non-technical factors are critical to adoption of
innovation
n We need to consider the context beyond technology
to maximise the impact of the technology

16. Key Questions
1. What are best practices in organisation/policy to
ensure adoption of Open Data in Smart Cities?
2. What are the key challenges and missing features
from the technology to reduce barriers to adoption
(i.e Open Data Platforms)?
16

17. ORGANISATION & POLICY TRENDS:
WAVES OF INNOVATION

18. !
Smart City Initiative Design Framework
Ojo, A., Curry, E., and Janowski, T. 2014. “Designing Next Generation Smart City
Initiatives - Harnessing Findings And Lessons From A Study Of Ten Smart City
Programs,” in 22nd European Conference on Information Systems (ECIS 2014)
n Developed from the studies of smart city programs in 10 countries.
n Links Smart City initiatives to concrete city domains and associated
stakeholders

19. 10 Smart City Cases
Selected Smart Cities initiatives which were considered as good
practices in different policy domains

20. Waves of Open Data Innovation
Networks
of Civic
Innovation
Offices
Need-
driven
Programs
Hack
Events
“Direct” engagement of residents, city managers, other stakeholders
Freedom for bottom up innovation, techno-centric with “token”-level
participation of city management and residents
+t

21. Wave 1 Exemplar – Dutch Open
Hackathon
n Available datasets including airport shuttle bus
events, job data, flight data, supermarket, order etc.
http://
www.dutchopenhack
athon.com

22. Wave 2 Exemplar –
Summer of Smart in San Francisco
• Engage mayoral
candidates in San Francisco
(2011) on solutions by
Hack Teams to pressing
problems in areas
including
1. Community
Development
2. Buildings.
Transportation and
Sustainability
3. Public Health, Food and
Nutrition
• Focus is on real needs and
involvement of major
stakeholders in solutions
Source: http://www.summerofsmart.org/home/

23. Wave 3 Example :
New Urban Mechanics
Boston
UtahPhilly
A Network of civic innovation
offices in Boston, Philadelphia
and Utah.
Each of the innovation offices
serve as the in-house research
and development group for the
respective mayors.
They build partnerships
between internal agencies and
outside entrepreneurs to pilot
projects that address the needs
of residentshttp://newurbanmechanics.org

24. Key (Open) Challenges
o Bottom up open innovation activities generate relatively low
number of commercially viable and sustainable solution
o How to scale civic city innovation initiatives like Code for
America, Code for Europe etc.
o How to continue to pursue “out of the box” bottom up
innovation while directly addressing concrete needs of city
residents?
o There are limited codified patterns of good practices with
respect of open Innovations in Smart Cities.
o Poor understanding of how open data programs are shaped
by the smart city context and the kinds of innovations
enabled by open data in cities.
[Source: Townsend 2013]

25. Open Data as a Smart City Initiative
Ojo,
A.,
Curry,
E.,
and
Sanaz-­‐Ahmadi,
F.
2015.
“A
Tale
of
Open
Data
Innova+ons
in
Five
Smart
Ci+es,”
in
48th
Annual
Hawaii
Interna+onal
Conference
on
System
Sciences
(HICSS-­‐48)
How does open data
program impact the
smart city context?
Smart City
Program
Open Data
Program
• Impact domains
• Open innovation and engagement
• Governance
How does smart
city program shape
open data
initiatives?
• Specialized (big) datasets
• Ecosystem Dynamics (Actors)

26. Open Data as a Smart City Initiative:
Methodology
Case selection
1) Well-developed smart city
program
2) City strongly promotes
OD initiatives as SCs
initiatives
3) Availability of significant
information on OD
initiatives
¨ 18 Open Data initiatives
across the 5 cities

27. Open Data Powering Smart Cities
Economy Energy Environment Education
Health &
Wellbeing
Tourism Mobility Grovenance

28. What smart city domains are impacted
by open data initiatives?
Governance and Economic Domains standout …
Ojo,
A.,
Curry,
E.,
and
Sanaz-­‐Ahmadi,
F.
2015.
“A
Tale
of
Open
Data
Innova+ons
in
Five
Smart
Ci+es,”
in
48th
Annual
Hawaii
Interna+onal
Conference
on
System
Sciences
(HICSS-­‐48)

29. Key Governance Mechanisms
Five governance mechanisms are discernible
1) Collaboration: enabling collaboration between
city & stakeholders
¨ Collaboration between city, developers, SMEs and residents
¨ Collaboration among smart cities initiatives.
¨ Collaboration between cities.

30. 2) Participation: enabling participation of
residents and developers
¨ Inspire participation of residents, developers in creating apps
and new services
¨ Promote idea sharing among residents.
3) Communication: enable better policy outcomes
through publication of relevant data
¨ Increased communication between city and residents and other
stakeholders
¨ Designing communication plans.
Key Governance Mechanisms

31. 4) Data exchange: enabling data sharing among city
authorities and network of cities
¨ Data exchange between government, residents and other
stakeholders for purpose of city development.
¨ Data exchange among city authorities (CA)
¨ Data exchange among CA and developers.
¨ Data exchange between sensor infrastructure and CA.
¨ Data exchange among cities.
5) Service and application integration: to provide
software development tools
¨ e.g. CitySDK to build OD-based applications
Key Governance Mechanisms

32. Major Findings
1) Emerging 2nd generation open data smart
city initiatives are redefining the respective
cities as “Open Innovation Economies”
¨ Significantly different from the emphasis of first
generation initiatives with are strongly linked to
physical environment and infrastructure
2) Huge potential and gaps in how open data
can impact smart cities
¨ Need driven, stakeholder-led data driven
innovation programs are still relatively few

33. Smart City
Focus of Talk
33
Technology
Organisation Policy

34. TECHNOLOGY CHALLENGES:
OPEN DATA PLATFORMS

35. Role of Open Data Portals in Smart
Cities

36. Open Data Platform
n Various data and software components form part of
an overall open data platform
Technical Assessment of Open Data Platforms for
National Statistical Organisations, World Bank Group

37. Open Data Platforms for National
Statistical Organizations (NSOs)
n Two key concerns related to data
dissemination products are addressed:
¨ Can such products designed primarily for NSOs
satisfy requirements for an open data initiative?
¨ Can such products designed primarily for open
data satisfy the requirements of NSOs?
n Adoption Characteristics
Cost Efficiency and Feasibility: with respect to existing
comparable practice
Technical Assessment of Open Data Platforms for
National Statistical Organisations, World Bank Group

38. Elements provided by data publishing
software
7-­‐11
July
2014,
38
Technical Assessment of Open Data Platforms for
National Statistical Organisations, World Bank Group

39. Stakeholder Survey of Open Data
Platforms
Availability of features that enables Public Authorities
and other city data providers publish high quality
datasets
n Accessibility, usability, understandability,
informativeness and auditability, as well as social
interaction and collaboration on datasets
Adoption Characteristics
n Compatibility: degree to which a smart city initiative is
consistent with existing city stakeholder values, or
interests, and city context
n Complexity: the degree of difficulty involved in
implementing the initiative and communicating benefits
to stakeholders.

40. Stakeholder Survey of Open Data
Platforms
Analysis
n Review of literature, survey of eleven state-of-the-
art open data platforms, stakeholder interviews,
and stakeholder workshops in Dublin and Prato
The platforms reviewed and evaluated include:
n CKAN, DKAN, Socrata, PublishMyData, Information
Workbench, Enigma, Junar, DataTank,
OpenDataSoft, Callimachus, DataTank and Semantic
MediaWiki.

41. Dimensions of the Survey
n These criteria include availability of:
1. Metadata, Data and File Format Standards and Schemas
2. Flexible search facility for datasets
3. Social Media, Collaboration and Social Sharing tools
4. Dataset Publishing workshop
5. Harvesting, Federation and Cataloguing
6. Data Analysis tools
7. Visualisation tools
8. Personalisation tools
9. Customisation tools
10. Dataset licensing service
11. Accessibility
12. Extensibility mechanisms.

42. Platform Survey

43. Perceived Barriers to Use and Adoption
Open Data Platforms
Top Barrier: Perceived
poor quality of open data
available on the platforms
n poor metadata
n failure to use the right
format for different
audience
n difficulty in locating data
of interest
Other barriers:
n non-relevancy of
available datasets
n usability of platforms
n data available
n lack of example of prior
use of available
datasets.

44. Data Attributes Perceived Barriers

45. Stakeholder Desired Features for
Next Generation Open Data Platforms

46. Stakeholder Desired Features for
Next Generation Open Data Platforms
Social and Collaboration
¨ Dataset rating and feedback on datasets
¨ Wall style feedback
¨ Collaborative curation of datasets
¨ Prioritization and voting on dataset requests
¨ Reward system and gamification

47. Stakeholder Desired Features for
Next Generation Open Data Platforms
Understandability, Usability, and Decision
making needs
¨ Customisable dashboards
¨ Data mining tools and custom visualization
tools
¨ Support for linked data and map based search
¨ Question and Answering features

48. Technology - Conclusions
Few state-of-the-art open data platforms exist and
significant challenges must be tackled
¨ Perceived poor quality of datasets published on these
platforms
¨ New features needed for social collaboration
understandability, usability, and decision making needs
Open and extensible technology platforms are
available as basis for next generation open data
platform
¨ CKAN, DKAN and Semantic MediaWiki are candidate
platforms
¨ Have vibrant developer community could support further
development

49. CONCLUSION AND FUTURE WORK

50. Conclusions
Organisation/Policy
n Huge potentials and gaps in
how open data can impact
smart cities
n Needs driven, stakeholder-
led data driven innovation
programs are relatively few
Technology
n Perceived poor quality of
datasets published on open
data needs to be addressed
n Social collaboration and
features to support
Understandability, Usability,
and Decision making are
needed

51. Future work
o De-construction of Smart cities and Open data
programs and applying strategic alignment model
to exploit the opportunities.
o Similar to the strategic alignment approach used in
Organization-IT alignment