Creating Smarter Cities 2011 - 02 - Nicos Komninos - What makes cities smart?


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Smart cities are expected to deal with major contemporary city challenges of competiveness within a knowledge economy, employment for social cohesion, and environmental sustainability, less greenhouse emissions and energy efficiency. The presentation discusses different trajectories and organisational settings that make cities more intelligent, and how collective intelligence, people-driven innovation, and future Internet solutions advance the efficiency, performance, and governance of cities.

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Creating Smarter Cities 2011 - 02 - Nicos Komninos - What makes cities smart?

  1. 1. WHAT MAKES CITIES SMART ? Nicos Komninos URENIO Research, Aristotle University SC Conference, Edinburgh 30 June 2011
  2. 2. Contents 1. City challenges and spatial intelligence of cities 2. Variable geometries of spatial intelligence of cities 3. Planning intelligent cities at URENIO
  3. 3. Major contemporary city challengesCities in developed countriesA public consultation on the priorities of European urban andregional policy has identified three major urban and regionalobjectives for the coming years (European Commission 2008). Competitiveness will continue to be at the heart of Europeanregional policy, sustained by research, innovation and upgradingof skills, which altogether drive towards a knowledge economy. Active labour markets which sustain employment and reducethe risk of poverty are also a high priority; to a large degreepoverty is a consequence of job losses. The third objective is environmental sustainability; coupledwith the need to save energy, use alternative energy sources,ensure lower CO2 emissions, reduce the carbon footprint of citiesand buildings, and sustain living ecosystems. 3
  4. 4. Major contemporary city challenges Cities in developing countries  Rapid urbanization and city growth. Increasing demand  Shortage of infrastructure. Do more with less  Poverty  Health and mortality  Sustainable development, CO2 reduction, energy and water saving Source: Millennium Goals, UN
  5. 5. Smart cities addressing city challengesSmart / intelligent cities are expected to contribute to thesechallenges and provide more efficient solutions:-Sustain the knowledge economy in developed countries-Offer solutions to rapid urbanisation in developing countriesThe questions are: How they do it? Which resources smart cities mobilize to address city challenges? Which processes enable the intelligence of cities to emerge?It becomes urgent to understand the sources and drivers of cityintelligence that assure a higher efficiency in addressing wickedproblems of contemporary urban agglomerations. 5
  6. 6. Key concept: Spatial intelligence of citiesSpatial intelligence of cities refers to informational and cognitiveprocesses - such as information collection and processing, real-time alert, forecasting, learning, collective intelligence,distributed problem solving - which characterize "intelligent" or"smart" cities. The concept allows unifying those of intelligent city and smart cityunder a common field of study focusing on their fundamental cognitiveprocesses. Emphasis on the "spatial" dimension denotes that urban space andthe agglomeration are preconditions of this form of intelligence. The concept refers also to the combined deployment of ICTs,institutional settings for knowledge and innovation, and physicalinfrastructure of cities to increase the problem-solving capability of acommunity. 6
  7. 7. Spatial intelligence of cities involves all layers /dimensions of a city:The digital space and the artificialintelligence embedded into the e-Intelligence e-Technologiesphysical environment of the city. Thepublic broadband communication Digital / Smart environmentsinfrastructure, wired and wireless, plusdigital technologies and applicationssustaining e-services. e-Innovation e-MarketsThe institutional space of cities, the Universities /social capital and collective intelligence Technology Transfer Organisations Tech Parks, Tech Networks, Brokers, Consultants Research Institutes Public R&Dof a city’s population, the institutional Laboratories CLUSTERS Group of companies in co- Private R&D Innovation system and institutions operation Departments Vertical / Horizontalmechanisms for knowledge and Centresdevelopment and co-operation in Innovation Financing Banks, Business Angels, Venture Capital, Regional Incentives Technology Information System Patents, Standards, Technical Publications, Emerging Markets, Foresightlearning and innovation.The physical space of cities and thepeople in the city; the intelligence, sbaL .vinU .grO .veDinventiveness and creativity of kraP hceT .tsnI .seR retsulC retsulC Knowledge-based agglomeration / Clustersindividuals who live and work in thecity. 7
  8. 8. 2. Variable geometries of spatial intelligence of cities
  9. 9. Multiple trajectories of spatial intelligence ASIA - AUSTRALIA USA - CANADA EUROPE 2001 -Bario, Malaysia -LaGrange, Georgia, US -Ennis, Ireland * Singapore -Nevada, Missouri, US -New York, US 2002 -Bangalore, India * Calgary, Alberta, CA -Sunderland, UK * Seoul, S. Korea -Florida, high tech corridor, US -Singapore - LaGrange, Georgia, US 2003-04 * Taipei, Taiwan -Spokane, Washington, US * Glasgow, UK -Victoria, Australia -Western Valley, N. Scotia, CA - Sunderland, Tyne & Wear, UK -Yokosuka, Japan 2005 * Mitaka, Japan -Pirai, Brazil -Issy-les-Moulineux, France -Tianjin, China -Toronto, Ontario, CA Sunderland, Tyne & Wear, UK -Singapore 2006 - Taipei, Taiwan -Cleveland, Ohio, US -Manchester, UK - Tianjin, China *Waterloo, Ontario, CA -Gagnam District Seoul -Ichikawa, Japan 2007 - Gangnam District -Ottawa-Gatineau, Ontario, CA -Dundee, Scotland, UK -Sunderland, Tyne & Wear, UK - Issy-les-Moulineaux, FR - Waterloo, Ontario, CA --Tallinn, Estonia 2008 *Gagnam District Seoul -Fredericton, New Brunswick, CA -Dundee, Scotland, UK -Northeast Ohio, US -Tallinn, Estonia -Westchester, New York, US -Winston-Salem, N. Carolina, US 2009 -Bristol, Virginia, US -Eindhoven, Netherlands - Fredericton, New Brunswick - Issy-les-Moulineaux - Moncton, New Brunswick, CA * Stockholm, Sweden - Tallinn 2010 * Suwon, South Korea - Arlington County, VA - Dundee, Scotland - Dublin, Ohio, US -Eindhoven - Ottawa, Ontario, CA - Tallinn, Estonia
  10. 10. I. Orchestration intelligence: Organized innovationworkflow within a community
  11. 11. Bletchley Park: Oldest orchestration intelligence
  12. 12. Bletchley Park regeneration Business ParkMuseum Open Site
  13. 13. Enigma machine Rotors: 26x26x26= 17.576 3 rotors= 6 positions, 17.576 x 6= 105.456 5 rotors= 60 positions, 17.576 x 60= 1.054.560III V IV GAH Wiring: Each rotor conditionCX AZ DV KT HU LW GP EY MR FQ 26! / 7! x 12! 27 = 1.305.093.289.500 connections In total: 1,3718 possible connections
  14. 14. Code breaking: Organized community workflow Community Collective intelligence Networks & Machines 1939 relocation of Mission: Find the dailyGovernment Code & settings of the Enigmas.Cipher School 100.000 Enigmas. Code breaking Collect all messagesexperts, Cambridge of the day / analyseMathematicians, A. them comparativelyTuring, military Represent the entirepersonnel, civilians, GA classification, maps,women. acronyms Personnel selection by Make assumptionscompetition (cribs) about meanings Send cribs to From 50 to 10.000 machines. Test solutionspeople Decoding, analysis, Bletchley Park + close intelligence reports,towns dissemination
  15. 15. Orchestration intelligence: Network-based innovation workflow of people and machines within a communityNetwork architecture: Nodes and connections: horizontal and verticalNodes: Human skills or clusters of skills Machines, expert systems, agentsConnections: Operations, processes Workflows linking nodes, people,clusters, and machinesRules: Administration, rights Conflict resolution, sustainability modelsResults: Distributed problem solving, KPIs
  16. 16. II. Amplification intelligence: Strengthening thecomponents of an innovation ecosystem Innovation Κόμβοι του δικτύου ecosystem
  17. 17. Cyberport Hong Kong 17
  18. 18. 18
  19. 19. TECHNOLOGY ZONE – CY 1, 2, 3, 4 19
  20. 20. Open Platforms – Digital media learning
  21. 21. Open Platforms – Digital media learningThe Digital Entertainment Incubation and Training, is a platform having asobjective to build and promote entrepreneurship and skills in the digitalentertainment industry, focusing on business skills, games, animation and digitalentertainment, and enhance networking with industry.Digital Media Centre, is a unique state-of-the-art digital multimedia creationfacility, having as objective to offer software and hardware support to contentdevelopers, multimedia professionals, small and medium enterprises.The iResource Centre, is a digital content storage platform, which serves as atrusted marketplace and clearing house for the aggregation, protection, licenseissuance and distribution of digital content.The Testing and Certification of Wireless Communication Platform is a centrethat provides continuous mobile communication service and coverage of mobilephone signal (3G, GSM, CDMA and PCS).The Cyberport Institute was established by the University of Hong Kong tointroduce and , run IT courses for talented people and support various ITdevelopment and related businesses in Hong Kong.
  22. 22. COMMERCIAL 22
  23. 23. CENTRAL PARK
  24. 24. HOUSING COMPLEX 24
  26. 26. III. Instrumentation intelligence Future Internet + Embedded Spaces Πηγή: Linked Data and Search: Thomas Steiner 26 (Google Inc, Germany)
  27. 27. IBM instrumentation intelligenceThe IBM smart cities concept: (1)interconnected, (2) instrumented, and (3)intelligent. Interconnection means that different partsof a core system can be joined andcommunicate with each other, turning datainto information. Instrumentation of a city’s system meansthat the workings of that system are turnedinto data points and the system is mademeasurable with instruments and smartmeters. Intelligence refers to the ability to use theinformation to model patterns of behaviour,develop predictive models of likely outcomesand translate them into real knowledge,allowing better decision making and informedactions
  28. 28. Accenture: Intelligent city infrastructure 28
  29. 29. Smart city Amsterdam
  30. 30. 3. Planning intelligent cities at URENIO
  31. 31. City and Districts Transport hubs Industry clusters and Housing Districts sectors Port CBD University Science Parks and Incubators City and Districts 31
  32. 32. Intelligent City Districts Layer 3: APPS + EMBEDDED SYSTEMS + SOCIAL MEDIA 4 TYPES OF APPLICATIONS INTELLIGENCE E-LEARNING CO-CREATION MARKETPLACE Layer 2: INNOVATION ECOSYSTEMS OF DISTRICTS 4 FUNDAMENTAL PROCESSES: WATCH – LEARN– INNOVATE - MARKET University Campus Transport Central Business Industry Hub Technology District Port DistrictLayer 1: TYPICAL CITY DISTRICTS People, Activities, Infrastructure
  33. 33. Transport hubsIndustry Housing Districtsclusters and sectors Planning intelligent cities 1. L1- City: Description of the city or Port CBD district – CHALLENGES or PROBLEMS TO University ADDRESS 2. L2-Innovation ecosystem: Information and knowledge processes related to Science Parks and Incubators districts and challenges City and Districts 3. L3- Digital spaces, smart environments: web, web 2.0, crowdsourcing, social media, cloud, mobile apps suitable for L1 and L2 4. L1-L2-L3 integration: Knowledge services - Spatial intelligence- Solution to challenges 5. Measurement: KP Indicators for L1, L2, L3 and new services assessment 6. Business models for new services sustainability
  34. 34. Intelligent City Platforms integrating L1-L2-L3
  35. 35. Intelligent City Platforms integrating L1-L2-L3 e-Intelligence e-Technologies Digital / Smart environments e-Innovation e-Markets Universities / Technology Transfer Research Organisations Institutes Tech Parks, Tech Networks, Brokers, Consultants Public R&D Laboratories CLUSTERS Group of companies in co- Private R&D Innovation system and institutions operation Departments Vertical / Horizontal and Centres Innovation Financing Technology Information System Banks, Business Angels, Patents, Standards, Technical Venture Capital, Regional Publications, Emerging Markets, Incentives Foresight Layers sbaL .vinU .grO .veD kraP hceT retsulC .tsnI .seR retsulC Knowledge-based agglomeration / Clusters PlatformsLayers are spatialities (P-I-D)Platforms are knowledge functions atP-I-D space 35
  36. 36. PLATFORM 1: Strategic intelligenceA strategic information system based on (i) people of a community,(ii) rules for information management, and (iii) business intelligence tools Community + Space Rules - Agreements BI tool / e-services Population of the Rules concerning thecommunity collection of information Geographic area of Sources of informationreference / Physical space and validation procedures Social group of reference Rules concerning the/ type of cluster community of dissemination Human network ofinformation gathering and Users’ rights andelaboration privileges Data from sensors Information analysis – Knowledge model Network-based Sustainability ofinformation collection, information servicesdissemination , feed back
  37. 37. PLATFORM 2: Technology learning / absorption A technology transfer system based on (i) a community of technology providers, (ii) institutions of technology management, και (iii) intellectual property management tools and e-services Community Institutions Digital space A community of IPR management rulestechnology providers Technology transfer / University Labs licensing agreements R&D valorization and Research fields commercialization Technology district agreements Spin-offs Network of technology Technology disseminationproviders rules Network of technology Technologyrecipients demonstration
  38. 38. PLATFORM 3: Collaborative innovation Living labs for people-driven innovation based on (i) a community of users, (ii) institutions for collaborative innovation, and (iii) crowdsourcing applications and e-tools Community Working rules Digital space Innovation community Co-Creation, bringing together technology push and Living Lab application pull Exploration, engaging all R&D providers stakeholders, especially user Global technology communities, at the earlierproviders stage of the co-creation process, Government institutions Experimentation, implementing the proper level End users – large of technological artfacts tonumber experience live scenarios with a large number of users, and Citizens Evaluation of new ideas, innovative products, technological artfacts in real Real life environments life situations. 38
  39. 39. PLATFORM 4: Dissemination / Promotion Marketplaces based on (i) physical spaces and a community of vendors, (ii) market operation rules, and (iii) online marketplaces and social media Community + Space Institutions Digital Marketplaces Commercial community Information dissemination Local vendors CBD marketplaces Promotion of products and services Peripheral marketplaces End users / consumers Promotion rules Trade associations Marketing plans Citizens Marketing alliances Accessibility facilities Global supply chains Environmentalconditions Innovation diplomacy 39 39
  40. 40. Intelligent City Platforms at any city district PLATFORMS – KNOWLEDGE FUNCTIONS 1. Strategic intelligence – OUTPUTS Foresight • GDP District • EmploymentΧ Sector 2. Technology transfer - Learning 3. Innovation in R • Sales/ Exports • Resource saving • Skills • R&D • IP • New products collaboration 4. Dissemination – Global markets Measurement Scoreboard
  41. 41. Intel cities: 4 core processes at 3 spatial levels amplifying all the ecosystems of citiesIntelligence (global) Living Lab Marketplace (local) (global) Technology supply (global)
  42. 42. Related publicationsKomninos, N. (2011) "Intelligent cities: Variable geometries of spatialintelligence", From Intelligent to Smart Cities, Mark Deakin and Husam AlWaer (eds), Journal of Intelligent Building International, Vol. 3, pp. 1-17.Komninos, N. (2009) “Intelligent cities: Towards interactive and globalinnovation environments” International Journal of Innovation and RegionalDevelopment, Vol. 1, No. 4, 337–355.Komninos N. (2008) Intelligent Cities and Globalisation of InnovationNetworks, London and New York: Routledge. 42