The Annals of Regional Science

, Volume 48, Issue 2, pp 501–527 | Cite as

High performers in complex spatial systems: a self-organizing mapping approach with reference to The Netherlands

  • Karima Kourtit
  • Daniel Arribas-Bel
  • Peter Nijkamp
Open Access
Special Issue Paper

Abstract

This paper addresses the performance of creative firms from the perspective of complex spatial systems. Based on an extensive high-dimensional database on both the attributes of individual creative firms in the Netherlands and a series of detailed regional facilitating and driving factors related, inter alia, to talent, innovation, skills, networks, accessibility and hardware, a new methodology called self-organizing mapping is applied to identify and explain in virtual topological space, the relative differences between these firms and their business performance in various regions. It turns out that there are significant differences in the spatial and functional profile of large firms vis-à-vis SMEs across distinct geographical areas in the country.

JEL Classification

M1 M19 M2 M20 M21 Q5 Q56 R1 R10 R11 R12 R15 R30 

References

  1. Acs, ZJ, Groot, HLF, Nijkamp, P (eds) (2002) The emergence of the knowledge economy. Springer, BerlinGoogle Scholar
  2. Agarwal, P, Skupin, A (eds) (2007) Self-organizing maps: applications in geographic information science. Wiley, ChichesterGoogle Scholar
  3. Alecke B, Alsleben C, Scharr F, Untiedt G (2006) Are there really high-tech clusters? The geographic concentration of German manufacturing industries and its determinants. Ann Reg Sci 40: 19–42CrossRefGoogle Scholar
  4. Andersson A (1985) Creativity and regional development. Pap Reg Sci Assoc 56: 5–20CrossRefGoogle Scholar
  5. Arribas-Bel D, Nijkamp P, Scholten H (2011) Multidimensional urban sprawl in Europe: a self-organizing map approach. Comput Environ Urb Syst 35(4): 263–275CrossRefGoogle Scholar
  6. Bayliss D (2007) The rise of the creative city: culture and creativity in Copenhagen. Eur Plan Stud 15: 889–903CrossRefGoogle Scholar
  7. Bellini E, Ottaviano GIP, Pinelli D, Prarolo G (2008) Cultural diversity and economic performance: evidence from European Regions. In: Paper presented at the 7th European urban and regional studies conference ‘Diverse Europe: urban and regional openings, connections and exclusions’, Istanbul, 18–21 September 2008Google Scholar
  8. Berry MMJ, Taggart JH (1996) Combining technology and corporate strategy in small high tech firms, strathclyde international business unit, Department of marketing. University of Strathclyde, GlasgowGoogle Scholar
  9. Bommer M, Jalajas D (2002) The innovation work environment of high-tech SMEs in the USA and Canada. R D Manag 32(5): 379–386CrossRefGoogle Scholar
  10. Bruinsma FR, Kourtit K, Nijkamp P (2009) An agent-based decision support model for the development of e-services in the tourist sector. Research memorandum 2009–32. Faculteit der Economische Wetenschappen en Bedrijfskunde, AmsterdamGoogle Scholar
  11. Capello R (2002) Entrepreneurship and spatial externalities: theory and measurement. Ann Reg Sci 36: 387–402CrossRefGoogle Scholar
  12. Caragliu A, Del Bo C, Kourtit K, Nijkamp P, Suzuki S (2011) A search for incredible cities by means of super-efficiency data envelopment analysis. Stud Reg Sci (forthcoming)Google Scholar
  13. Charnes A, Cooper W, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2: 429–444CrossRefGoogle Scholar
  14. Cooper SY, Park JS (2008) The impact of ’Incubator’ organizations on opportunity recognition and technology innovation in new, entrepreneurial high-technology ventures. Int Small Bus J 26(1): 27–56CrossRefGoogle Scholar
  15. de Waal AA (2007) Strategic performance management, a managerial and behavioural approach. Palgrave MacMillan, LondonGoogle Scholar
  16. de Waal AA, Kourtit K, Nijkamp P (2009) The relationship between the level of completeness of a strategic performance management system and perceived advantages and disadvantages. Int J Oper Prod Manag 29(12): 1242–1265CrossRefGoogle Scholar
  17. Duranton G, Puga D (2003) Micro-foundations of urban agglomeration economies, NBER Working Paper 9931. National Bureau of Economic Research Washington, DCGoogle Scholar
  18. Felsenstein D (2002) Do high technology agglomerations encourage urban sprawl?.   Ann Reg Sci 36(4): 663–682CrossRefGoogle Scholar
  19. Fischer MM (2001) Computational neural networks: tools for spatial data analysis. In: Fischer MM, Leung Y (eds) Geocomputational modelling: techniques and applications. Springer, Heidelberg, pp 79–102Google Scholar
  20. Florida R (2002) The rise of the creative class. Basic Books, New YorkGoogle Scholar
  21. Florida R (2003) Entrepreneurship, creativity and regional economic growth. In: Hart DM (eds) The emergency of entrepreneurship policy. Cambridge University Press, Cambridge, pp 15–43Google Scholar
  22. Florida R (2004) The flight of the creative class. Basic Books, New YorkGoogle Scholar
  23. Forte F, Fusco Girard L, Nijkamp P (2006) Smart policy, creative strategy and urban development. Stud Reg Sci 35(4): 947–963CrossRefGoogle Scholar
  24. Glaeser E (2004) Review of Richard Florida’s The Rise of the Creative Class, unpublished monograph, Harvard University, post.economics.harvard.edu/f aculty/glaeser/papers/Review_Florida.pdfGoogle Scholar
  25. Jacobs J (1969) The economy of cities. Vintage, New YorkGoogle Scholar
  26. Jones-Evans D, Klofsten M (1997) Universities and local economic development: the case of Linkoping. Eur Plan Stud 5(1): 77–94CrossRefGoogle Scholar
  27. Kohonen T (2001) Self-organizing maps. Springer, BerlinCrossRefGoogle Scholar
  28. Kourtit K, de Waal AA (2009) Strategic performance management in practice: advantages, disadvantages and reasons for use. Paper presented at the 2009 performance measurement association conference. University of Otago’s School of Business, DunedinGoogle Scholar
  29. Kourtit K, Nijkamp P (2011) Creativity and diversity: strategic performance management of high-tech SMEs in Dutch urban areas. In: Kourtit K, Nijkamp P, Stough RR (eds) Drivers of innovation, entrepreneurship and regional dynamics. Springer, Berlin, pp 45–64CrossRefGoogle Scholar
  30. Kourtit, K, Nijkamp, P, Stough, R (eds) (2011a) Drivers of innovation, entrepreneurship and regional dynamics. Springer, BerlinGoogle Scholar
  31. Kourtit K, Nijkamp P, Arribas D (2011b) Smart cities perspective—a comparative European study by means of self-organizing maps. J Innov (forthcoming)Google Scholar
  32. Kourtit K, Nijkamp P, Lowik S, van Vught F, Vulto P (2011c) From Islands of innovation to creative hotspots. J Reg Sci Policy Pract 3(3):145–161Google Scholar
  33. Kohonen T, Honkela T (2007) Kohonen network. Scholarpedia 2(1): 1568CrossRefGoogle Scholar
  34. Krugman PR (1996) The self-organizing economy. Blackwell, CambridgeGoogle Scholar
  35. Kumar M, Bowen WM, Kaufman M (2007) Urban spatial pattern as self-organizing system: an empirical evaluation of firm location decisions in Cleveland-Akron PMSA, Ohio. Ann Reg Sci 41(2): 297–314CrossRefGoogle Scholar
  36. Landry C (2000) The creative city: a toolkit for urban innovators. Earthscan, LondonGoogle Scholar
  37. Lee SY, Florida R, Acs ZJ (2004) Creativity and entrepreneurship: a regional analysis of new firm formation. Reg Stud 38: 879–891CrossRefGoogle Scholar
  38. Lung Y (1988) Complexity and spatial dynamics modelling. From catastrophe theory to self-organizing process: a review of the literature. Ann Reg Sci 22(2): 81–111CrossRefGoogle Scholar
  39. Matheson B (2006) A culture of creativity: design education and the creative industries. J Manag Dev 25(1): 55–64CrossRefGoogle Scholar
  40. Mcgranahan M, Timothy W (2007) Recasting the creative class to examine growth processes in rural and urban countries. Reg Stud 41(2): 197–216CrossRefGoogle Scholar
  41. Moreno R, Paci R, Usai S (2005) Geographical and sectoral clusters of innovation in Europe. Ann Reg Sci 39(4): 715–739CrossRefGoogle Scholar
  42. Muñiz ASG, Raya AM, Carvajal CR (2010) Spanish and European innovation diffusion: a structural hole approach in the input-output field. Ann Reg Sci 44(1): 147–165CrossRefGoogle Scholar
  43. Nijkamp P (2008) XXQ factors for sustainable urban development: a systems economics view. Romanian J Reg Sci 2(1): 325–342Google Scholar
  44. Nijkamp P (2009) Regional development as self-organized converging growth. In: Kochendörfer-Lucius G, Pleskovic BB (eds) Spatial disparities and development. The World Bank, Washington, pp 265–281Google Scholar
  45. Nijkamp P, Suzuki S (2009) A generalized goals-achievement model in data envelopment analysis: an application to efficiency improvement in local government finance in Japan. Spatial Econ Anal 4(3): 249–274CrossRefGoogle Scholar
  46. Oakey RP (2007) Clustering and the R&D management of high-technology small firms in theory and practice. J R and Manag 37(3): 237–248Google Scholar
  47. Olfert MR, Partridge MD (2011) Creating the cultural community: ethnic diversity versus agglomeration. Spatial Econ Anal 6(1): 25–55CrossRefGoogle Scholar
  48. Pavitt K (1990) What we know about the strategic management of technology. Calif Manag Rev Spring 17–26Google Scholar
  49. Peck J (2005) Struggling with the creative class. Int J Urban Reg Res 29(4): 740–770CrossRefGoogle Scholar
  50. Porter ME (1990) Competitive advantage of nations. Macmillan, London and BasingstokeGoogle Scholar
  51. Porter ME (2002) Regional foundations of competitiveness. Issues for wales. Paper presented at conference ‘Future competitiveness of wales: innovation, entrepreneurship and technology change’ on April 3, 2002Google Scholar
  52. Reggiani, A, Nijkamp, P (eds) (2009) Complexity and spatial networks: in search of simplicity. Springer, BerlinGoogle Scholar
  53. Shea C (2004) The road to riches? Boston Globe 29 Feb, D1. http://www.boston.com/news/globe/ideas/articles/2004/02/29/the_road_to_riches/
  54. Skupin A, Agarwal P (2007) Introduction: what is a self-organizing map?. In: Agarwal P, Skupin A (eds) Self-organizing maps: applications in geographic information science. Wiley, Chichester, pp 9–21Google Scholar
  55. Skupin A, Hagelman R (2005) Visualizing demographic trajectories with self-organizing maps. GeoInformatica 9(2): 159–179CrossRefGoogle Scholar
  56. Sonis M, Hewings GJD (1998) Economic complexity as network complication: multiregional input output structural path analysis. Ann Reg Sci 32: 407–436CrossRefGoogle Scholar
  57. Spielman S, Thill J (2008) Social area analysis, data mining, and GIS. Comput Environ Urban Syst 32(2): 110–122CrossRefGoogle Scholar
  58. Suzuki S, Nijkamp P (2011) A stepwise-projection data envelopment analysis for public transport operations in Japan. Lett Spatial Res Sci 4(2): 139–156CrossRefGoogle Scholar
  59. Suzuki S, Nijkamp P, Rietveld P, Pels E (2010) A distance friction minimization approach in data envelopment analysis: a comparative study on airport efficiency. Eur J Oper Res 207: 104–1115CrossRefGoogle Scholar
  60. Suzuki S, Nijkamp P, Rietveld P (2011) Regional efficiency improvement by means of data envelopment analysis through euclidean distance minimization including fixed input factors: an application to tourist regions in Italy. Pap Reg Sci 90(1): 67–89CrossRefGoogle Scholar
  61. Tornqvist G (1983) Creativity and the renewal of regional life. Lund Stud Geogr B Hum Geogr 50: 91–112Google Scholar
  62. van Geenhuizen M (2007) Modeling dynamics of knowledge networks and local connectedness: a case study of urban high tech companies in the Netherlands. Ann Reg Sci 41(4): 813–833CrossRefGoogle Scholar
  63. van den Berg GJ (2001) Duration models: specification, identification, and multiple durations. In: Heckman JJ, Leamer E (eds) Handbook of econometrics, vol V. North-Holland, Amsterdam, pp 123–147Google Scholar
  64. Yan J, Thill J (2009) Visual data mining in spatial interaction analysis with self-organizing maps. Environ Plan B Plan Des 36: 466–486CrossRefGoogle Scholar

Copyright information

© The Author(s) 2011

Authors and Affiliations

  • Karima Kourtit
    • 1
  • Daniel Arribas-Bel
    • 2
  • Peter Nijkamp
    • 1
  1. 1.Department of Spatial EconomicsFree UniversityAmsterdamThe Netherlands
  2. 2.GeoDa Center for Geospatial Analysis and ComputationArizona State UniversityTempeUSA

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