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

What is a Smart City?

2  

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

9

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

18  

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)?

19

!

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