creating smarter cities 2011 - 13 - peter nijkamp - performance of smart cities

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3

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1

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


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


Industry

RTD

Market


Government

e-services

Knowledge


Bremerhaven

EU27

-1,0

-0,5

0,0

0,5

1,0

1,5

2,0

2,5University

i2010

Learning


Industry

RTD

Market


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


Industry

RTD

Market


Government

e-services

Knowledge


Karlstad

EU27

-0,8-0,6-0,4-0,20,00,20,40,60,81,0University

i2010

Learning


Industry

RTD

Market


Government

e-services

Knowledge


Kristiansand

EU27

-1,5-1,0-0,50,00,51,01,52,02,53,0University

i2010

Learning


Industry

RTD

Market


Government

e-services

Knowledge


Lillesand

EU27

-2,0-1,5-1,0-0,50,00,51,01,52,02,5University

i2010

Learning


Industry

RTD

Market


Government

e-services

Knowledge


Groningen

EU27

-2,0

-1,0

0,0

1,0

2,0University

i2010

Learning


Industry

RTD

Market


Government

e-services

Knowledge

Knowledge Economy Indicator Kortrijk

EU27

-1,5-1,0-0,50,00,51,01,52,02,5University

i2010

Learning


Industry

RTD

Market


Government

e-services

Knowledge


Osterholz-Scharmbeck

EU27

-2,0-1,5-1,0-0,50,00,51,01,52,02,5University

i2010

Learning


Industry

RTD

Market


Government

e-services

Knowledge


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