dynamic networks of city magnetism

Dynamic networks of city magnetism

Dynamic networks of city magnetismMiruna Mazurencu -marinescuDaniel traian peleKarima kourtitPeter nijkampRegional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'

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Agenda

Introduction;Literature review;Data and methodology;Main findings;Conclusions and recommendations;Limitations and further research.

Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'

BackgroundCities in a modern world:RivalsAllies

2 questions: 1. Who is the best? (competitiveness) Who are the magnets?Competition among cities is like riding a bicycle: if you dont pedal, youll fall off (Mirn, Urban Land Institute).

2. What can they learn from each other? Network contagion in a regional context like EU, commercial zones etc.

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Literature review

In recent years, various attempts have been made to develop a classification or ranking of world cities based on their actual performance or their perceived success (see e.g. Taylor et al. 2009, Grosveld 2002, Arribas-Bel et al., 2011; Kourtit et al. 2011b, Suzuki et al. 2011).

One of the most detailed databases on world cities can be found in a recent study on the Global Power City Index (GPCI) undertaken by the Institute for Urban Strategies with the Mori Memorial Foundation in Tokyo for the year 2009-2015.

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Other studiesGrosveld, H., The Leading Cities of the World and Their Competitive Advantages, PhD dissertation, University of Amsterdam, 2002. Institute for Urban Strategies, Global Power City Index, The Mori Memorial Foundation, Tokyo, 2010 (http://www.morimfoundation.or.jp/english/research/project/6/pdf/GPCI2009_English.pdf). Kourtit, K., P. Nijkamp and R. Stough (eds), Drivers of Innovation, Entrepreneurship and Regional Dynamics, Springer-Verlag, Berlin, 2011. Kourtit, K., P. Nijkamp and D. Arribas, Smart Cities Perspective a Comparative European Study by Means of Self-Organizing Maps, Journal Innovation, 2012a (forthcoming). Kourtit, K., D. Arribas and P. Nijkamp, Benchmarking of World Cities through Self-organizing Maps, Cities, 2012b (forthcoming).Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'

Data and methodology;

dataThis database contains extensive information in numerical form of many world cities which are evaluated and ranked according to their magnetism, e.g. their competitive power to attract creative people and business enterprises from all over the world. This open access database is carefully validated through field visits and in-depth reports. It contains a wealth of multi-annual data on major critical indicators and a very detailed list of sub-indicators for economic strength of the relevant cities contained in the database. Studied period: 2009-2015, 2008 did not have complete information.At present, this data system has accurate information on 40 world cities ranging from New York to Istanbul, and from Tokyo to Geneva.For each individual city, 6 main classes of functions were carefully mapped out and numerically assessed: economy, research and development, cultural interaction, livability, environment and accessibility.In addition, the importance of these indicators was carefully assessed by 5 distinct groups of stakeholder: managers, researchers, artists, visitors and residents.


An overview of the main categories of performance indicators used in GPCI -2009 - 2015 The comprehensive performance scores and rankings of these global cities in the GPCI-data set are based on six main categories, namely: "Economy", "Research & Development", "Cultural Interaction", "Liveability", "Ecology & Natural Environment", and "Accessibility"This operational framework of empirical information is used in our analysis in order to explore and represent in a comparative sense cluster performance of these global cities in terms their socio-economic achievement

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Methodology

We have applied the K-Nearest Neighbor (KNN) classification algorithm implemented through the PROC MODECLUS in SAS for both: the city functions/dimensions (economy, research and development, cultural interaction, livability, environment and accessibility ) and for the actors perceptions (managers, researchers, artists, visitors and residents). Proc MODECLUS usesnonparametric density estimation. Given a sufficiently large sample, nonparametric clustering methods are capable of detecting clusters of unequal size and dispersion and with highly irregular shapes.We have obtained anyearly dataset and thenused UCINET and its option NETDRAW to visualise the clusters based on the smallest Euclidean distance from both perspectives: functions/dimensions and actors.

Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'Proc modeclus usesnonparametric density estimation. Given a sufficiently large sample, nonparametric clustering methods are capable of detecting clusters of unequal size and dispersion and with highly irregular shapes.

The shift in clusters dimensions perspective from 2009 to 2010


2011 to 2012


2013- 2014-2015


Main findings 1 - dimensionsBeijing and Shanghai are always in the same cluster;Taipei and Fukuoka are always in the same cluster, except for 2013;Vienna and Berlin are always in the same cluster, even more, in the last two years they form a separate cluster;Chicago and San Francisco are always in the same cluster except for 2009;London and New York were in the same cluster, except for 2010 and 2011 when London glued with Paris;Zurich and Geneva remained in the same cluster until 2015;Sao Paolo, Mumbai and Cairo tend to form a stable cluster except for 2010 and 2011;Amsterdam and Frankfurt tend to stay in the same cluster along all the studied period except for 2012;Madrid and Milan remained in the same cluster all along.Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'

Clusters shift from actors perspective 2009 to 2010


2011 to 2012


2013 2014- 2015


Main findings 2 actors perceptions

Sao Paolo, Mumbai and Cairo rest in the same cluster all along according to actors perceptions as well. As it can be seen from the table, the rank of the dimensions that glue them together is: livability, economy, cultural interaction, followed by accessibility, environment and R&D.Amsterdam, Vienna and Berlin glue together for all studied years;Moscow changes clusters from one year to the other;Boston and San Francisco used to be in the same cluster with Seoul at the beginning of the period but tend to remain only them in a individual cluster for the last years of study;Kuala Lumpur and Bangkok tend to cluster as well except for two years;Hong Kong and Singapore are in the same cluster except for 2010;Zurich and Geneva were in the same cluster at the beginnig of the period, than only in 2015 they fot back together after two years when they were forming clusters with other cities;London and New York were in the same cluster, except for 2010 and 2011 when London glued with Beijing and Shanghai (2010) and Paris (2011) while New York with Tokyo and Paris (2010) and Tokyo (2011).

Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'ScoreEconomyRDCILivEnvAccCairo54398.54.950.1202.683.1103.8Mumbai590.2111.77.847.6242.6105.475.1Sao Paulo671.2133.116.263.4219.5165.973.2CV15.26%60.92%15.82%9.06%36.27%20.40%

Conclusions

Although it was not part of any modelling input, in general, geographical proximity seems to be the main characteristic that is common for all clusters on both subjective (actors perceptions) and objective criteria (dimensions);Other communality: cultural factors (common or similar language, history, habits)The most striking cluster: Sao Paolo, Mumbai and Cairo: three continents, cultures etc. but still, on both perspective they were clustered together.Total score is not relevant for cluster formation, that is why we have proposed a new methodology;The clusters according to both perspectives do not coincide, so we recommend that in analysis one set or the other should be used;There are more than 8 clusters (up to 13), so there is diversity. The size of the clusters is not equal either;The diversity is increasing along the years;The extremes are well discriminated, the rest is not, as it can be seen from the graph below (2015):


The distance between clusters for 2015Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'

Policy Lessons and SuggestionsOur analysis has aimed to shed new light on the ranking of world cities;Clearly, cities have the possibility to increase their potential. As it can be concluded from the analysis, even within the same cluster, some cities are better positioned compared to others. For each function/dimension each city from a certain cluster can learn from the cluster above in order to improve its performance.In conclusion, our methodology is able to present a more realistic analysis and may thus provide a meaningful contribution to decision making and planning for the improvement of the relevant agents

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Who are the magnets?Dimensions impact on clusters formation (2015)Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'

Tokyo is the magnet for economy!New York is the magnet for R&D!


Paris is the magnet for accessibility! New York is the magnet for environment!

Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'Paris is a magnet for livability!!

Who are the magnets?Actors impact on clusters formation (2015)Regional Science Academy ABC (Advanced Brainstorm Carrefour) on 'The Science of the City'

London is the magnet for managers!Ney York is the magnet for researchers!


Paris is the magnet for artists!London is the magnet for visitors!


Paris is the magnet for residents!

Further researchCreate an authomatic procedure that can identify which dimension plays the most important role in cluster formation;Enrich the database with some other indicators like population size, indicators able to capture Hofstedes cultural dimensions, migrants by type: white collars, blue collars so further analysis would be more complex;Encompass the present knowledge in the network dynamics studies.


dynamic networks of city magnetism

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