Investigating economic activity concentration patterns of co-agglomerations through association rule mining

  • Alket CecajEmail author
  • Marco Mamei
Original Research


Economic activity tends to concentrate in particular geographic areas forming agglomerations and co-locations of firms. These agglomerations bring benefits for the firms themselves by increasing productivity, access to human resources, labor pooling, innovation, knowledge spillovers and regional growth. In this paper, we present a method for the discovery and analysis of such agglomerations. The method allows to spot patterns of co-locations in the composition of the agglomerations. Those patterns identify important relationships between the firms compounding the agglomerations thus describing the dynamics that exists inside the agglomeration itself.


Data mining Economic activity concentration Ateco Association rules 


  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, San Francisco, pp 487–499Google Scholar
  2. Ahlfeldt GM, Redding S, Sturm DM, Wolf N (2015) The economics of density: evidence from the Berlin wall. LSE research online documents on economics 61152, London School of Economics and Political Science, LSE LibraryGoogle Scholar
  3. Ahrend R, Farchy E, Kaplanis I, Lembcke AC (2015) What makes cities more productive? Agglomeration economies and the role of urban governance: evidence from 5 OECD countries. LSE research online documents on economics 64619, London School of Economics and Political Science, LSE LibraryGoogle Scholar
  4. Albert JM, Casanova MR, Orts V (2012) Spatial location patterns of Spanish manufacturing firms. Pap Reg Sci 91(1):107–136CrossRefGoogle Scholar
  5. Albert JM, Casanova MR, Mateu J, Orts V (2013) Distance-based methods: an improvement of Ripley’s k function vs. the K density function. Tech. Rep. 2013/07, Economics Department, Universitat Jaume I, CastellonGoogle Scholar
  6. Almazan A, de Motta A, Titman S (2003) Firm location and the creation and utilization of human capital. NBER working papers 10106, National Bureau of Economic Research Inc, CambridgeGoogle Scholar
  7. Andersson F, Burgess S, Lane J (2004) Cities, matching and the productivity gains of agglomeration. CEPR discussion papers 4598Google Scholar
  8. Andini M, de Blasio G, Duranton G, Strange WC (2013) Marshallian labour market pooling: evidence from Italy. Reg Sci Urban Econ 43(6):1008–1022CrossRefGoogle Scholar
  9. Appice A, Ceci M, Lanza A, Lisi FA, Malerba D (2003) Discovery of spatial association rules in geo-referenced census data: a relational mining approach. Intell Data Anal 7(6):541–566CrossRefGoogle Scholar
  10. Arbia G, Espa G, Quah D (2008) A class of spatial econometric methods in the empirical analysis of clusters of firms in the space. Empir Econ 34(1):81–103CrossRefzbMATHGoogle Scholar
  11. Arbia G, Espa G, Giuliani D, Mazzitelli A (2012) Clusters of firms in an inhomogeneous space: the high-tech industries in Milan. Econ Model 29(1):3–11CrossRefGoogle Scholar
  12. Barrios S, Bertinelli L, Strobl E, Teixeira AC (2003) Agglomeration economies and the location of industries: a comparison of three small european countries. CORE discussion papers 2003067. Center for Operations Research and Econometrics (CORE), Universit catholique de Louvain, BelgiumGoogle Scholar
  13. Bayardo RJ Jr, Agrawal R (1999) Mining the most interesting rules. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’99, ACM, New York, pp 145–154Google Scholar
  14. Berliant M, Wang P (2004) Dynamic urban models: agglomeration and growth. Urban/Regional 0404006, EconWPAGoogle Scholar
  15. Breinlich H, Ottaviano GIP, Temple JRW (2013) Regional growth and regional decline. LSE research online documents on economics 51575, London School of Economics and Political Science, LSE LibraryGoogle Scholar
  16. Cainelli G, Iacobucci D, Morganti E (2004) Spatial agglomeration and business groups: new evidence from Italian industrial districts. ERSA conference papers ersa04p402, European Regional Science Association, AzoresGoogle Scholar
  17. Carlino GA, Kerr WR (2014) Agglomeration and innovation. Working papers 14–26. Federal Reserve Bank of Philadelphia, PhiladelphiaGoogle Scholar
  18. Chen Y (2009) Agglomeration and location of foreign direct investment: the case of China. China Econ Rev 20(3):549–557CrossRefGoogle Scholar
  19. Chertov O, Aleksandrova M (2013) Using association rules for searching levers of influence in census data. Proced Soc Behav Sci 73:475 – 478. In: Proceedings of the 2nd international conference on integrated information (IC-ININFO 2012), Budapest, 30 Aug–3 Sept 2012Google Scholar
  20. Duranton G, Overman HG (2005) Testing for localization using micro-geographic data. Rev Econ Stud 72(4):1077–1106MathSciNetCrossRefzbMATHGoogle Scholar
  21. Duranton G, Overman HG (2006) Exploring the detailed location patters of UK manufacturing industries using microgeographic data. Tech. Rep. 5858, CEPR discussion papersGoogle Scholar
  22. Ehrl P (2013) Agglomeration economies with consistent productivity estimates. Reg Sci Urban Econ 43(5):751–763CrossRefGoogle Scholar
  23. Fisher AGB (1939) Production, primary, secondary and tertiary. Econ Rec 15(1):24–38CrossRefGoogle Scholar
  24. Heaton J (2017) Comparing dataset characteristics that favor the Apriori, Eclat or FP-growth frequent itemset mining algorithms. arXiv:1701.09042
  25. Krugman P (2010) The new economic geography, now middle-agedGoogle Scholar
  26. Marcon E, Puech F (2003) Evaluating the geographic concentration of industries using distance-based methods. J Econ Geogr 3(4):409–428CrossRefGoogle Scholar
  27. Mare D, Timmins J (2007) Geographic concentration and firm productivity. Occasional papers 07/1, Ministry of Economic Development, New ZealandGoogle Scholar
  28. Fujita M, Thisse JF (1996) Economics of agglomeration. J Jpn Int Econ 10(4):339–378CrossRefGoogle Scholar
  29. McDonald F, Vertova G (2001) Geographical concentration and competitiveness in the European Union. Eur Bus Rev 13(3):157–165CrossRefGoogle Scholar
  30. Overman HG, Puga D (2008) Labour pooling as a source of agglomeration: an empirical investigation. Working papers 2008-05. Instituto Madrileo de Estudios Avanzados (IMDEA) Ciencias Sociales, MadridGoogle Scholar
  31. Puga D (2009) The magnitude and causes of agglomeration economies. Working papers 2009-09, Instituto Madrileo de Estudios Avanzados (IMDEA) Ciencias Sociales, MadridGoogle Scholar
  32. Strange W, Hejazi W, Tang J (2006) The uncertain city: competitive instability, skills, innovation and the strategy of agglomeration. J Urban Econ 59(3):331–351CrossRefGoogle Scholar
  33. Tan PN, Kumar V, Srivastava J (2004) Selecting the right objective measure for association analysis. Inf Syst 29(4):293–313. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.University of Modena and Reggio EmiliaReggio EmiliaItaly

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