smart cities and open data platforms
TRANSCRIPT
Smart Cities and Open Data Platforms Edward Curry & Adegboyega Ojo [email protected] www.edwardcurry.org
What is a Smart City?
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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]
Open Data Powering Smart Cities
Economy Energy Environment Education
Health & Wellbeing
Tourism Mobility Grovenance
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
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
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
Technology Adoption Lifecycle
Rogers, Everett M. (1962). Diffusion of Innovations. Glencoe: Free Press. ISBN 0-612-62843-4.
Technology Adoption Lifecycle
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Innovators Late majority Laggards Early majority Early 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
Conservatives Visionaries
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.
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
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)
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.
The Socio-technical System Stack
http://csis.pace.edu/~marchese/SE616_New/L10/L10_new.htm
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
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.
Smart City
Focus of Talk
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Technology
Organisation Policy
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)?
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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
10 Smart City Cases
Selected Smart Cities initiatives which were considered as good practices in different policy domains
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)
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
ORGANISATION/ POLICY: WAVES OF INNOVATION
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
Wave 1 Exemplar – Dutch Open Hackathon
n Available datasets including airport shuttle bus events, job data, flight data, supermarket, order etc.
http://www.dutchopenhackathon.com
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/
Wave 3 Example : New Urban Mechanics
Boston
Utah Philly
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 residents http://newurbanmechanics.org
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]
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.
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.
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)
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
TECHNOLOGY: OPEN DATA PLATFORMS
Portal Role as Infrastructure
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
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
Elements provided by data publishing software
7-‐11 July 2014, Rhodes, Greece
38 Technical Assessment of Open Data Platforms for National Statistical Organisations, World Bank Group
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.
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.
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.
Platform Survey
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.
Data Attributes Perceived Barriers
Stakeholder Desired Features for Next Generation Open Data Platforms
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
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
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
CONCLUSION
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