References

• Arbia, G. (2001) Modelling the geography of economic activities on a continuous space, Papers in Regional Science, 80, 411–424.10.1007/PL00013646
   Web of Science ®Google Scholar
• Arbia, G., Espa, G., Giuliani, D. & Mazzitelli, A. (2012). Clusters of firms in an inhomogeneous space: the high-tech industries in Milan, Economic Modelling, 29(1), 3–11.10.1016/j.econmod.2011.01.012
   Web of Science ®Google Scholar
• Baddeley, A. J., Moller, J. & Waagepetersen, R. (2000) Non-and semi-parametric estimation of interaction in inhomogeneous point patterns, Statistica Neerlandica, 54(3), 329–350.10.1111/1467-9574.00144
   Web of Science ®Google Scholar
• Berman, P. & Diggle, P. J. (1989) Estimating weighted integrals of the second-order intensity of a spatial point process, Journal of the Royal Statistical Society. Series B., 51, 81–92.
   Google Scholar
• Combes, P.-P., Mayer, T. & Thisse, J.-F. (2006) Chapitre 10. Mesurer la concentration spatiale [Chapter 10. Measuring spatial concentration]. Document Universite Paris 1.
   Google Scholar
• Combes, P.-P. & Overman, H. (2004) The spatial distribution of economic activities in the European Union. In J. Henderson, J. F. Thisse (eds.) Handbook of Urban and Regional Economics, pp. 2845–2909, Elsevier, Amsterdam.
   Google Scholar
• Diggle, P. J. & Chetwynd, A. G. (1991), Second-order analysis of spatial clustering for inhomogeneous populations, Biometrics, 47(3), 1155–1163.10.2307/2532668
   PubMed Web of Science ®Google Scholar
• Diggle, P. J., Gómez-Rubio, V., Brown, P. E., Chetwynd, A. G. & Gooding, S. (2007). Second-order analysis of inhomogeneous spatial point processes using case-control data, Biometrics, 63(2), 550–557.10.1111/j.1541-0420.2006.00683.x
   PubMed Web of Science ®Google Scholar
• Duranton, G. & Overman, H. G. (2005) Testing for localization using micro-geographic data, Review of Economic Studies, 72(4), 1077–1106.10.1111/0034-6527.00362
   Web of Science ®Google Scholar
• Espa, G., Arbia, G. & Giuliani, D. (2013). Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan, Journal of Geographical Systems, 15(1), 31–50.10.1007/s10109-012-0163-2
   Web of Science ®Google Scholar
• Giuliani, D., Arbia, G. & Espa, G. (2014) Weighting Ripley’s K-function to account for the firm dimension in the analysis of spatial concentration, International Regional Science Review, 37(3), 251–272.10.1177/0160017612461357
   Web of Science ®Google Scholar
• Illian, J., Illian, P, Stoyan, H. & Stoyan, D. (2008) Statistical Analysis and Modelling of Spatial Point Patterns, Wiley, Statistics in practice, New York.
   Google Scholar
• Marcon, E. & Puech, F. (2003) Evaluating the geographic concentration of industries using distance-based methods, Journal of Economic Geography, 3(4), 409–428.10.1093/jeg/lbg016
   Web of Science ®Google Scholar
• Marcon, E. & Puech, F. (2010) Measures of the geographic concentration of industries: improving distance-based methods, Journal of Economic Geography, 10(5), 745–762.10.1093/jeg/lbp056
   Web of Science ®Google Scholar
• Marcon, E., Puech, F. & Traissac, S. (2012). Characterizing the relative spatial structure of point patterns, International Journal of Ecology, 2012, 11 pages.
   Google Scholar
• Moller, J. & Waagepetersen, R.P. (2004) Statistical Inference and Simulation for Spatial Point Processes, vol. 100. Chapman & Hall CRC, London.
   Google Scholar
• Schlather, M. (2001) On the second-order characteristics of marked point processes, Bernoulli, 7(1), 99–117.10.2307/3318604
   Web of Science ®Google Scholar
• Stoyan, D. & Stoyan, H. (1995) Fractals, Random Shapes and Point Fields: Methods of Geometrical Statistics, John Wiley and Sons, New-York.
   Google Scholar
• Sweeney, S. H. & Feser, E. J. (1998). Plant size and clustering of manufacturing activity, Geographical Analysis, 30(1), 45–64.10.1111/j.1538-4632.1998.tb00388.x
   Web of Science ®Google Scholar