Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
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Transcript of Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
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EUROPEAN JOINT
COORDINATION
LOCAL WEAK
COORDINATION
Gibbons/NovotnyMode 1
Mode 2
Implications for Urban Policy
University
Industry Government
Learning
University
Industry Government
Knowledge
Market
Helix Spider
We believe a city to be smart wheninvestments in human and social capitaland traditional (transport) and modern(ICT) communication infrastructure fuelsustainable economic growth and a highquality of life, with a wise management ofnatural resources, through a participatedgovernance. (Caragliu, Del Bo and Nijkamp,2011).
9 cities (Bremerhaven, Edinburgh, Karlstad, Kristiansand, Lillesand, Groningen, Kortrijk, Osterholz, and Norfolk county)
Domains:• e-gov and ICTs• GDP and income• Population and density• Employment and Human Capital• Infrastructure• Business• Local Government• Tourism and cultural heritage• Leisure and recreation
Urban Audit
Collection of data: direct contact with city officials and statisticians
A special issue on the Journal of Urban Technology (“Smart cities”)
A book chapter (A.Caragliu, M.Deakin, C.Del Bo, S.Giordano, K.Kourtit, P.Lombardi, P. Nijkamp, “An advanced triple-helix network model for Smart Cities performance”, IGI Global)
A special issue on Innovation
Baseline data to used to calculate the Knowledge Economy Indicator for the 9 Smart Cities include:• The Economic Incentive and Institutional
Regime• Education and Human Resources• The Innovation System• ICTs
We then normalized the indicators according to the formula in the next slide
1) The actual data (u) is collected from urban datasets
2) Ranks are allocated to cities based on the absolute values (actual data) that describe each and every one of 6 variables (rank u). Cities with the same performance are allocated the same rank. Therefore, the rank equals 1 for a city that performs the best among those in our sample on a particular variable (that is, it has the highest score), the rank equals to 2 for a city that performs second best, and so on
3) The number of cities with higher rank (Nh) is calculated for the whole sample
4) The following formula is used in order to normalize the scores for every city on every variable according to their ranking and in relation to the total number of cities in the sample (Nc) with available data :Normalized (u) = 10*(1-Nh/Nc)
5) The above formula allocates a normalized score from 0 to 10 for each city
0
1
2
3
4
5
6
7
8
9
10
Knowledge Economy Indicator
Smart cities KEI
0.0
10.0
20.0
30.0
40.0
50.0
60.0University
Learning
Industry
Market
Government
Knowledge
EU27
Smart Cities
But, if we de-construct the average Smart Cities value
and zoom in on each of the nine cities, we obtain
markedly different results:
-2,0-1,5-1,0-0,50,00,51,01,52,02,53,0University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Bremerhaven
Edinburgh
Karlstad
Kristiansand
Lillesand
Groningen
Kortrijk
Osterholz
Norfolk
EU27
SCRAN
Results are rich and difficult to
compare; a more detailed
analysis is needed.
-1,5
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2,5
University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Bremerhaven
EU27
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
2,5University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator Edinburgh
EU27
-1,0
-0,5
0,0
0,5
1,0
1,5
2,0
University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Karlstad
EU27
-0,8-0,6-0,4-0,20,00,20,40,60,81,0University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Kristiansand
EU27
-1,5-1,0-0,50,00,51,01,52,02,53,0University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Lillesand
EU27
-2,0-1,5-1,0-0,50,00,51,01,52,02,5University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Groningen
EU27
-2,0
-1,0
0,0
1,0
2,0University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator Kortrijk
EU27
-1,5-1,0-0,50,00,51,01,52,02,5University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Osterholz-Scharmbeck
EU27
-2,0-1,5-1,0-0,50,00,51,01,52,02,5University
i2010
Learning
Intellectual property
Industry
RTD
Market
ICT-related employment
Government
e-services
Knowledge
Knowledge Economy Indicator
Norfolk
EU27
Variable Measure Notes
UniversityUniversity (% people aged 20-24 enrolledin tertiary education)
LearningLearning ( labour force with ISCED 5 and6 education)
IndustryIndustry (Number of companies per1,000 pop.)
Market Market (Per capita GDP)
Government
Government (% labour force ingovernment sector-L to Q: Publicadministration and community services;activities of households; extra-territorialorganizations )
KnowledgeKnowledge (Patent applications to theUSPTO per 1,000 inh.)
e-servicesPer capita number of administrativeforms available for download from officialweb site
ICT-related employmentNumber of local units manufacturing ICTproducts over total active companies
For the EU, % of GDP produced by theICT industry
Business R&D expenditure Business R&D expenditures (2006)Source: NUTS1/2 data from the RegionalInnovation Scoreboard 2009
Intellectual propertyNumber of patent applications to theUSPTO shared by at least one companyand one university since 1977.
Co-patenting between industry anduniversities
Knowledge Economy Indicator Average World Bank KEI scorehttp://info.worldbank.org/etools/kam2/KAM_page5.asp
i2020Municipal scores calculated by theEdimburgh team.
Indicators for the New Triple Helix
References
1. Caragliu, A; Del Bo, C. & Nijkamp, P (2011). “Smart cities in Europe”, Journal of Urban Technology, forthcoming
2. A. Caragliu, M. Deakin, C. Del Bo, S. Giordano, K. Kourtit, P. Lombardi, P. Nijkamp (2011). “An advanced Triple-Helix network model for smart cities performance”, in O. Yalciner Ercoskun (ed.), “Green and ecological technologies for urban planning: creating smart cities”, Hershey (PA): IGI Global
Performance: ratio between input and output
DEA: comparative analysis
Great variety in smart cities
Relevance of multiple helix
Meaning of performance analysis
Message:
reinforce strong points and address weak
points
Editors:
Karima Kourtit &
Peter Nijkamp
No. 4, 2011
Published by Taylor &
Francis (UK)
turn mass population movement
towards urban agglomerations
into new opportunities
manage production and investments to the benefit of sustainable economic
development of urban areas
develop a balanced
national (or supra-national)
strategy for emerging
connected city systems
develop an effective policy to ensure that
the benefits of agglomeration advantages are
higher than their social costs
satisfy the socio-economic demand of an increasingly
large share of urban population for high-quality
urban amenities
develop effective
measures for eco-friendly and climate-
neutral metropolitan
areas
manage sustainable
accessibility and mobility
of urban systems
through new logistic and infrastructural concepts
need for conflict management and pro-active inclusions strategies for less privileged groups in urban
areas
design of fit-for-purpose institutional mechanisms
and structures in a multi-layer dynamic system of
urban areas
design a spatially-integrated and
balanced urban land use strategy that is compatible with ecological sustainability
Improvement transport systems & infrastructure
New information technology
Climate change
Demographic transformation
Increased globalisation
Rising urbanization in Europe
Regional, national and international competition
push cities
Cities are in competition in a way that is similar to
competition between companies and products
3333
“Competition among cities is like riding a
bicycle: if you don’t pedal, you’ll fall off”.
However, globalization is making us
increasingly uniform, so we must
construct and promote our difference in
order to continue existing”
Mirón, Urban Land Institute
The Special Issue of Journal Innovation on ‘Smart Cities in the Innovation Age’:
Provides a unique forum for discussing worldwide urban challenges and developments
Addresses in particular the feasibility of smart cities concepts by presenting a series of applied studies on the success conditions and implications of smart city strategies and ideas
The papers on all aspects of European urban developments contribute to the improvement of social science knowledge and to the setting of a policy-focused European research agenda
Table of Contents
1. Smartness and European Urban Performance: Assessing the Local Impacts of
Smart Urban Attributes by Andrea Caragliu and Chiara Del Bo
2. Intelligent Cities as Smart Providers: CoPs as Organizations for Developing
Integrated Models of eGovernment Services by Mark Deakin
3. Modelling the Smart Cities Performances by Patrizia Lombardi, Silvia Giordano,
Hend Farouh and Wael Yousef
4. Is Innovation in Cities a Matter of Knowledge Intensive Services? An Empirical
Investigation by Roberta Capello, Andrea Caragliu and Camilla Lenzi
5. Smart Networked Cities? by Emmanouil Tranos and Drew Gertner
6. Open Innovation Among University Spin-off Firms: What is in it for Them, and
What Can Cities Do? by Marina van Geenhuizen
7. Bright Stars in the Urban Galaxy – The Efficiency of Ethnic Entrepreneurs in
the Urban Economy by Mediha Sahin, Alina Todiras, Peter Nijkamp and Soushi
Suzuki
8. Smart Cities in Perspective − A Comparative European Study by Means of
Self-Organizing Maps by Karima Kourtit, Peter Nijkamp and Daniel Arribas
1. Smartness and European Urban Performance: Assessing the Local Impacts of Smart Urban Attributes by Andrea Caragliu and Chiara Del Bo:
Provides a comparative benchmark analysis of the growth performance of various smart cites in Europe
Points in the direction of the critical importance of space specific characteristics in shaping the economic benefits of smart urban qualities, providing a justification for place-based public policies that account for local characteristics
Identifies different clusters with respect to the impacts of smartness on urban performance and wealth, highlighting the need for geographically-differentiated policy actions.
2. Intelligent Cities as Smart Providers: CoPs as Organizations for Developing Integrated Models of eGovernment Services by Mark Deakin
Analyses the learning aspects of smart cities
Interprets intelligent cities as facilitators and communities of practice for designing and implementing e-government services
Identifies how the growing interest in intelligent cities has led universities to explore the opportunities „communities of practice‟ (CoPs) offer to industry in order to become smart providers of online services
3. Modelling the Smart Cities Performances by Patrizia Lombardi, Silvia Giordano, Hend Farouh and Wael Yousef
Addresses the assessment and modelling of the performance of smart cities is an intriguing research challenge
Proposes a novel research agenda for the development of a testing exercise with the participation of main city stakeholders, offering a reflexive learning opportunity for cities to measure what options exist to improve their performances
4. Is Innovation in Cities a Matter of Knowledge Intensive Services? An Empirical Investigation by Roberta Capello, Andrea Caragliu and Camilla Lenzi
Raises the question whether a high innovation degree in cities is related to the local presence of knowledge-intensive services
Argues that the linkage between the presence of cities in the region and their innovative performance is mediated by the urban industrial structure
Argues that a positive correlation is likely to exist between the presence of large cities in a region and its innovative performance. Such a relationship could also depend on the presence of knowledge-intensive services, rather than on advanced manufacturing activities
5. Smart Networked Cities? by Emmanouil Tranos and Drew Gertner
Argues that cities are part of a broad national or global network, both
physical and virtual
Investigates conceptually and empirically the issue of smart networked cities
Argues that the local policy agenda – and more specifically smart city
initiatives – should be informed about and address the structure of the
transnational urban network, as this can affect the efficiency of such local
policies
6. Open Innovation Among University Spin-off Firms: What is in it for Them, and What
Can Cities Do? by Marina van Geenhuizen
Argues that smart cities are most likely well equipped with an advanced
knowledge infrastructure which may induce important benefits
Offers a new perspective on the open innovation potential provided by
university spin-off firms
Examines a particular category of high-tech firms, university spin-offs, and
highlights resources that are missing and the level of openness in learning
networks to gain these resources
Argues that the vitality of modern cities is nowadays strongly influenced by
cultural diversity
7. Bright Stars in the Urban Galaxy – The Efficiency of Ethnic Entrepreneurs in the Urban Economy by Mediha Sahin, Alina Todiras, Peter Nijkamp and Soushi Suzuki
Argues that the new urban entrepreneurs – usually coined ethnicentrepreneurs − play a prominent role
Presents findings on the efficiency profiles of ethnicentrepreneurs in Dutch cities.
Argues that the se entrepreneurs appear to move increasingly to high-skilled segments of urban business life, offering a boost to the local economy.
8. Smart Cities in Perspective − A Comparative European Study by Means of Self-Organizing Maps by Karima Kourtit, Peter Nijkamp and Daniel Arribas
Presents a study on the relative differences among smart cities by analysing a multi-dimensional set of urban attributes related to smart cities
Employs an analytical tool set which is based on self-organising mapping analysis
Points the idea that some cities (actually most of them) have 'converged', that is, they have become more similar over the observation period ,while others have become a bit of outliers in positions where they were not found before
This special issue offers new horizons on the innovation and knowledge drivers, the functioning and the positioning of smart cities
There is a need for a conceptual clarity of smart cities, that is evidence-based and appropriate for empirical measurement and comparison
For strategic policy support, an evidence-based monitoring and benchmarking system for smart cities has to be designed (urban compass)
It is also evident that strategic urban policy should exploit the knowledge-intensive and creative potential of smart cities: knowledge creation, access and use are critical parameters for the future of our cities