Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. http://www.igi-global.com/dictionary/ateco-code/39228

  2. https://cran.r-project.org/web/packages/arules/index.html

References

  • 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 487–499

  • 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 Library

  • 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 Library

  • Albert JM, Casanova MR, Orts V (2012) Spatial location patterns of Spanish manufacturing firms. Pap Reg Sci 91(1):107–136

    Article  Google Scholar 

  • 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, Castellon

  • 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, Cambridge

  • Andersson F, Burgess S, Lane J (2004) Cities, matching and the productivity gains of agglomeration. CEPR discussion papers 4598

  • Andini M, de Blasio G, Duranton G, Strange WC (2013) Marshallian labour market pooling: evidence from Italy. Reg Sci Urban Econ 43(6):1008–1022

    Article  Google Scholar 

  • 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):541–566

    Article  Google Scholar 

  • 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):81–103

    Article  MATH  Google Scholar 

  • 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):3–11

    Article  Google Scholar 

  • 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, Belgium

  • 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 145–154

  • Berliant M, Wang P (2004) Dynamic urban models: agglomeration and growth. Urban/Regional 0404006, EconWPA

  • 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 Library

  • 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, Azores

  • Carlino GA, Kerr WR (2014) Agglomeration and innovation. Working papers 14–26. Federal Reserve Bank of Philadelphia, Philadelphia

  • Chen Y (2009) Agglomeration and location of foreign direct investment: the case of China. China Econ Rev 20(3):549–557

    Article  Google Scholar 

  • 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 2012

  • Duranton G, Overman HG (2005) Testing for localization using micro-geographic data. Rev Econ Stud 72(4):1077–1106

    Article  MathSciNet  MATH  Google Scholar 

  • Duranton G, Overman HG (2006) Exploring the detailed location patters of UK manufacturing industries using microgeographic data. Tech. Rep. 5858, CEPR discussion papers

  • Ehrl P (2013) Agglomeration economies with consistent productivity estimates. Reg Sci Urban Econ 43(5):751–763

    Article  Google Scholar 

  • Fisher AGB (1939) Production, primary, secondary and tertiary. Econ Rec 15(1):24–38

    Article  Google Scholar 

  • Heaton J (2017) Comparing dataset characteristics that favor the Apriori, Eclat or FP-growth frequent itemset mining algorithms. arXiv:1701.09042

  • Krugman P (2010) The new economic geography, now middle-aged

  • Marcon E, Puech F (2003) Evaluating the geographic concentration of industries using distance-based methods. J Econ Geogr 3(4):409–428

    Article  Google Scholar 

  • Mare D, Timmins J (2007) Geographic concentration and firm productivity. Occasional papers 07/1, Ministry of Economic Development, New Zealand

  • Fujita M, Thisse JF (1996) Economics of agglomeration. J Jpn Int Econ 10(4):339–378

    Article  Google Scholar 

  • McDonald F, Vertova G (2001) Geographical concentration and competitiveness in the European Union. Eur Bus Rev 13(3):157–165

    Article  Google Scholar 

  • 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, Madrid

  • Puga D (2009) The magnitude and causes of agglomeration economies. Working papers 2009-09, Instituto Madrileo de Estudios Avanzados (IMDEA) Ciencias Sociales, Madrid

  • Strange W, Hejazi W, Tang J (2006) The uncertain city: competitive instability, skills, innovation and the strategy of agglomeration. J Urban Econ 59(3):331–351

    Article  Google Scholar 

  • Tan PN, Kumar V, Srivastava J (2004) Selecting the right objective measure for association analysis. Inf Syst 29(4):293–313. https://doi.org/10.1016/S0306-4379(03)00072-3

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Modena and Reggio Emilia, Reggio Emilia, Italy

    Alket Cecaj & Marco Mamei

Authors
  1. Alket Cecaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marco Mamei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alket Cecaj.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cecaj, A., Mamei, M. Investigating economic activity concentration patterns of co-agglomerations through association rule mining. J Ambient Intell Human Comput 10, 463–476 (2019). https://doi.org/10.1007/s12652-017-0665-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-017-0665-3

Keywords

Search

Navigation