Spatial location patterns of Mexican manufacturing: Analysis using the technique of spatial points patterns

  • José M. Albert Ortiz Universidad Jaume I
  • Francisco M. Gasca Sánchez Tecnológico de Monterrey
  • Miguel A. Flores Segovia Tecnológico de Monterrey
Keywords: Ripley’s K function, Mexi-can manufacturing, Distance-based methods, Spatial agglomerations
JEL Classification: C15, C40, C60, R12


This paper explores the spatial location patterns of firms in different sectors of the Mexican manufacturing industry. The analysis is carried out using a continuous spatial statistic approach by employing a K-function for each sector that is then compared to a Complete Spatial Randomness (CSR) distribution and other relevant benchmarks. We show that Mexican manufacturing follows a bimodal distribution and significant spatial concentrations are present for all manufacturing sec- tors at different distances. However, using the spatial distribution of the complete set of manufactures as a point of reference, variations in the spatial distribution are also found to exist.


Albert, J.M., J. Mateu y V. Orts. 2007. Distribución espacial de la actividad económica en la Unión Europea, Instituto Valenciano de Investigaciones Económicas, WP-EC 2007-02.

Albert, J.M., M. Casanova, V. Orts. 2012. Spatial locations patterns of Spanish manufacturing firms, Papers in Regional Science, 9(1): 107-137.

Andersson, R., J. Quigley y M. Wilhelmsson. 2005. Agglomeration and the spatial distribution of creativity, Papers in Regional Science, 84(3): 445- 464.

Anselin, L. 1995. Local indicators of spatial Association-LISA, Geographical Analysis, 27(2): 93-115.

Anselin, L., I. Syabri y Y. Kho. 2004. GeoDa: An introduction to spatial data analysis, Geographical Analysis, 38(1): 5-22.

Arbia, G. 2001. Modelling the geography of economic activities on a continuous space, Papers in Regional Science, 80(4): 411-424.

Arbia, G., G. Espa, D. Giuliani y A. Mazzitelli. 2012. Clusters of firms in an inhomogeneous space: The high-tech industries in Milan, Economic Modelling, 29(1): 3-11.

Austin, S. et al. 2005. Clustering of fast food restaurants around Schools: A novel application of spatial to the study of food enviroments, Research and Practice, 12(9): 1575-1581.

Baddeley, A. y R. Turner. 2005. Spatstat: An R package for analyzing spatial points patterns, Journal of Statistical Software, 12(6): 1-42.

Barff, R. 1987. Industrial clustering and the organization of production: A point pattern analysis of manufacturing in Cincinnati, Ohio, Annals of the Association of American Geographers, 77(1): 89-103.

Barkley, D., L. Roger, A. Dahlgran y S.M. Smith. 1988. High-technology manufacturing in the nonmetropolitan west: Gold or just glitter, American Journal of Agricultural Economics, 70(3): 560-571.

Billings, S. y E. Johnson. 2012. A nonparametric test for industrial specialization, Journal of Urban Economics, 71(3): 312-331.

Bivand, R., S. Edzer, J. Pebezma y V. Gómez-Rubio. 2008. Applied Spatial Data Analysis with R, Springer Science.

Bresnahan, T. y A. Gambardella. 2004. Building High-Tech Clusters: Silicon Valley and Beyond, Cambridge University

Brülhart, M. 2001. Evolving geographical concentration of European manufacturing industries, Review of World Economics, 132(2): 215-243.

Buck et al. 2013. Clustering of unhealthy food around German schools and its influence on dietary behavior in school children: A pilot study, International Journal of Behavioral Nutrition and Physical Activity, 10(65).

Casanova, M., O. Vicente y J.M. Albert. 2017. Sectoral scope and colocalisation of Spanish manufacturing industries, Journal of Geographical Systems, 19(1): 65-92.

Cuthbert, A. y W. Andersson. 2002. Using spatial statistics to examine the pattern of urban land development in Halifax-Darthmouth, The Professional Geographer, 54(4): 521-532.

Dixon, P.M. 2002. Ripley’s K function, Encyclopedia of Environmetrics, 3: 1796-1803.

Duncan, R.P. 1993. Testing for life historical changes in spatial patterns of four tropical tree species in Westland, New Zealand, Journal of Ecology, 81: 403-416.

Duranton, G. y H.G. Overman. 2005. Testing for localization using microgeographic data, Review of Economic Studies, 72(4): 1077-1106.

Ellison, G. y E.L. Glaeser. 1997. Geographic concentration in U.S. manufacturing industries: A dartboard approach, Journal of Political Economy, 105(5): 889-927.

Frenkel, A. 2001. Why high technology firms choose to locate or near metropolitan areas, Urban Studies, 38(7): 1083-1101.

Fujita, M. y J.F Thisse. 1996. Economics of agglomeration, Journal of the Japanese and International Economies, 10: 339-378.

Fujita, M., P. Krugman y A.J. Venables. 1999. The Spatial Economy: Cities, Regions and International Trade, MIT press.

Fujita, M. y P. Krugman. 2004. The new geography: Past, present and the future, Papers in Regional Science, 83(1): 139-164.

Garrrocho, C., J. A. Álvarez y T. Chávez. 2012. Aglomeración espacial de establecimientos comerciales en un centro tradicional de negocios: un análisis basado en las funciones K, Economía Mexicana, 21(1): 93-131.

Gatrell, A.C. et al. 1996. Spatial point pattern analysis and its application in geographical epidemiology, Transactions of the Institute of British Geogra- phers, 21(1): 256-274.

Giuliani, D., G. Arbia y G. Espa. 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.

Goreaud F. y R. Pélissier. 1999. On explicit formulas of edge effect correction for Ripley’s K-function, Journal of Vegetation Science, 10(3): 433-438.

INEGI. 2012. Directorio estadístico nacional de unidades económicas, DENUE <>.

Krugman, P. 1991a. Geography and Trade, The MIT Press.

Krugman, P. 1991b. Increasing returns and economic geography, Journal of Political Economy, 9(3): 483-499.

Krugman, P. 1991c. History versus expectations, Quarterly Journal of Economics, 106(2): 651-667.

Malecki, E. J. 1979. Locational trends in R&D by large U.S. corporations 1965-1977, Economic Geography, 55: 309-323.

Marshall, A. 1920 [1890]. Principles of Economics, London, McMillan.

Marcon, E. y F. Puech. 2003. Evaluating the geographic concentration of industries using distance-base methods, Journal of Economic Geography, 3(4): 409-428.

Marcon, E., F. Puech y S. Traissac. 2012. Characterizing the relative spatial structure of points patterns, International Journal of Ecology, 2012: 1-11.

Munier, F. 2006. Firm size, technological intensity of sector and relational competencies to innovate: Evidence from French industrial innovating firms, Economics of Innovation and New Technology, 15(4-5): 493-505.

Nakajima, K., Y. Saito y L. Uesugi. 2010. Measuring economic localization: Evidence from Japanese firm-level data, RIETI Discussion Paper Series 10- E-030.

Nijkamp, P. 1988. Information center policy in a spatial development perspective, Economic Development and Cultural Change, 37(1): 173-193.

OECD. 2011. ISIC, Rev. 3, technology intensity definition. Clasification of manufacturing industries into categories based on R&D intensities, (consultado marzo de 2015).

Porter, M. 1998. On Competition, Harvard Business School Press.

Ripley, B. D. 1976. The second-order analysis of stationary point processes, Journal of Applied Probability, 13(2): 255-266.

Ripley, B. D. 1977. Modelling spatial patterns, Journal of the Royal Statistical Society. Series B (Methodological), 39(2): 172-212.

Schneiderm, M.R., C. Schulze-Bentrop y M. Paunescu. 2010. Mapping the institutional capital of high-tech firms: A fuzzy-set analysis of capitalist variety and export performance, Journal of International Business Studies, 41(2): 246-266.

Scholls, T. y T. Brenner. 2012. Testing for clustering industries evidence from micro geographic data, (mimeo).

Sohn, J. 2014. Industry classification considering spatial spatial distribution of manufacturing activities, AREA, 46(1): 101-110.

Stuart, T. y O. Sorenson. 2003. The geography of opportunity: Spatial heterogeneity in founding rates and the performance of biotechnology firms, Research Policy, 32: 229-253.

Sweeney, S.H. y E.J. Feser. 1998. Plant size and clustering of manufacturing activity, Geographical Analysis, 30(1): 45-64.

Venables, A. 2008. New economic geography, en, S.N. Darlauf y L.E. Blume (comps.), The New Palgrave Dictionary of Economics, second edition, Palgrave Macmillan.

Villarreal, A., E. Mack y M. Flores. 2017. Industrial Complexes in Mexico: Implications for Regional Industrial Policy Based on Related Variety and Smart Specialization, Regional Studies, 51(4): 537-547

Vinay L.S. y S. Chakravorty. 2005. Industrial location and spatial inequiality: Theory and evidence from India, Review of Development Economics, 9(1): 47-68.

Vitali, S., M. Napoletano y G. Fagiolo. 2013. Spatial localization in manufacturing: A cross-country analysis, Regional Studies, 47(9): 1534-1554.

Weterings, A. y O. Marsili. 2015. Spatial concentration of industries and new firm exits: Does this relationship differ between exits by closure and by M&A? Regional Studies, 49(1): 44-58.