Open Data Innovation in Smart Cities: Challenges and Trends

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<ol><li> 1. Open Data Innovation in Smart Cities: Challenges and Trends Edward Curry &amp; Adegboyega Ojo Insight / LERO </li><li> 2. Some Background Multi-year research on state of research and practice of smart cities to inform Next Generation Smart City Design and Policy PartofanInterna+onalSmartCi+es Research/Prac+ceConsor+um composedofinterna+onalresearch teamsfromtheUS,Canada,Mexico, Colombia,ChinaandIreland. </li><li> 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 </li><li> 4. What is a Smart City? 4 </li><li> 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] </li><li> 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 </li><li> 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 ( 68/slug/smart-citysdk </li><li> 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 </li><li> 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. </li><li> 10. 10 Technology Adoption </li><li> 11. Technology Adoption Lifecycle Rogers, Everett M. (1962). Diffusion of Innovations. Glencoe: Free Press. ISBN 0-612-62843-4. </li><li> 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 Dont want anything to do with new technology Technology enthusiast Pragmatists ConservativesVisionaries </li><li> 13. 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. </li><li> 14. 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 </li><li> 15. 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 </li><li> 16. ORGANISATION &amp; POLICY TRENDS: WAVES OF INNOVATION </li><li> 17. ! 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 </li><li> 18. 10 Smart City Cases Selected Smart Cities initiatives which were considered as good practices in different policy domains </li><li> 19. 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 </li><li> 20. Wave 1 Exemplar Dutch Open Hackathon n Available datasets including airport shuttle bus events, job data, flight data, supermarket, order etc. http:// www.dutchopenhack </li><li> 21. 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: </li><li> 22. 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 residents </li><li> 23. 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] </li><li> 24. Open Data as a Smart City Initiative Ojo,A.,Curry,E.,andSanaz-Ahmadi,F.2015.ATaleofOpenDataInnova+onsinFiveSmart Ci+es,in48thAnnualHawaiiInterna+onalConferenceonSystemSciences(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) </li><li> 25. 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 </li><li> 26. Open Data Powering Smart Cities Economy Energy Environment Education Health &amp; Wellbeing Tourism Mobility Grovenance </li><li> 27. What smart city domains are impacted by open data initiatives? Governance and Economic Domains standout Ojo,A.,Curry,E.,andSanaz-Ahmadi,F.2015.ATaleofOpenDataInnova+onsinFiveSmart Ci+es,in48thAnnualHawaiiInterna+onalConferenceonSystemSciences(HICSS-48) </li><li> 28. Key Governance Mechanisms Five governance mechanisms are discernible 1) Collaboration: enabling collaboration between city &amp; stakeholders Collaboration between city, developers, SMEs and residents Collaboration among smart cities initiatives. Collaboration between cities. </li><li> 29. 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 </li><li> 30. 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 </li><li> 31. 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 </li><li> 32. Smart City Focus of Talk 33 Technology Organisation Policy </li><li> 33. TECHNOLOGY CHALLENGES: OPEN DATA PLATFORMS </li><li> 34. Role of Open Data Portals in Smart Cities </li><li> 35. 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 </li><li> 36. 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 </li><li> 37. Elements provided by data publishing software 7-11July2014, 38 Technical Assessment of Open Data Platforms for National Statistical Organisations, World Bank Group </li><li> 38. 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. </li><li> 39. 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. </li><li> 40. 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. </li><li> 41. Platform Survey </li><li> 42. 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. </li><li> 43. Data Attributes Perceived Barriers </li><li> 44. Stakeholder Desired Features for Next Generation Open Data Platforms </li><li> 45. 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 </li><li> 46. 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 </li><li> 47. 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 </li><li> 48. CONCLUSION AND FUTURE WORK </li><li> 49. 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 </li><li> 50. 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 </li></ol>


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