Keynote from Edward Curry on "Smart Cities and Open Data Platforms" in the 1st Summer School on Smart Cities and Linked Open Data (LD4SC 2015).

1. Smart Cities and Open Data
Platforms
Edward Curry & Adegboyega Ojo
ed.curry@insight-centre.org
www.edwardcurry.org

2. What is a Smart City?
2

3. 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]

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

5. 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

6. 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

7. Why ?
n We need to examine a broader context to ensure
we maximise the impacts of Smart City Open Data
Initiatives
n A Technology only perspective is not enough

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

9. Technology Adoption Lifecycle
9
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

11. 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.

12. 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

13. Definitions
System: a set of interacting or interdependent
components forming an integrated whole (Wikipedia)
Socio-technical system: the interaction between
society's complex infrastructures and human
behaviour (Wikipedia)

14. Socio-Technical Model
Lyytinen, Kalle, and Mike Newman. "Explaining information systems change: a
punctuated socio-technical change model." European Journal of Information
Systems 17.6 (2008): 589-613.

15. The Socio-technical System Stack
http://csis.pace.edu/~marchese/SE616_New/L10/L10_new.htm

16. What is a Smart City?
A smart city is a socio-technical system of systems
Nam et al. in [9] 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

17. Smart City Initiatives Framework
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.

18. Smart City
Focus of Talk
18
Technology
Organisation Policy

19. Key Questions
1. What are best practices in organisation/policy to
ensure adoption of Open Data in Smart Cities?
2. What are the key missing features from the
technology to reduce barriers to adoption (i.e
Open Data Platforms)?
19

20. !
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

21. 10 Smart City Cases
Selected Smart Cities initiatives which were considered as good
practices in different policy domains

22. Open Data as a Smart City Initiative
Ojo,
A.,
Curry,
E.,
and
Sanaz-­‐Ahmadi,
F.
2015.
“A
Tale
of
Open
Data
InnovaFons
in
Five
Smart
CiFes,”
in
48th
Annual
Hawaii
InternaFonal
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)

23. Open Data as a Smart City Initiative:
Methodology
n Case selection – 3 criteria used to select the cases
1) It must have a well-developed smart city program
2) The city strongly promotes OD initiatives as SCs
initiatives
3) Availability of significant information on OD initiatives
n The five cities selected are: Chicago, Helsinki, Amsterdam,
Barcelona, and Manchester
n Carried out between Feb – May 2014, 18 initiatives selected
after careful analysis of initiatives

24. ORGANISATION/ POLICY:
WAVES OF INNOVATION

25. 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

26. 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

27. 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/

28. 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

29. 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 need 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]

30. Governance Mechanisms (Key
Findings)
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.

31. Governance Mechanisms (Key
Findings)
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.

32. 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
Governance Mechanisms (Key
Findings)

33. Major Findings
n Two significant findings from this study:
1) Emerging 2nd generation open data based 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) There are still huge potentials and gaps in how
open data can impact smart cities
¨ need driven, stakeholder-led data driven innovation
programs are still relatively few

34. TECHNOLOGY: OPEN DATA PLATFORMS

35. Portal Role as Infrastructure

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 of information from 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
1. 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
2. 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

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