Please ensure Javascript is enabled for purposes of website accessibility
Vol. 39 Núm. 1 (2024): 115, enero-abril
Artículos

Aglomeración y coaglomeración de las empresas de base tecnológica en el espacio intrametropolitano de Toluca, 2010-2020

José Antonio Cabrera Pereyra
El Colegio Mexiquense, A.C.
Biografía
José Antonio Álvarez Lobato
El Colegio Mexiquense, A.C.
Biografía
Carlos Garrocho
El Colegio Mexiquense, A.C.
Biografía

Publicado 2024-02-28

Palabras clave

  • aglomeración,
  • coaglomeración,
  • empresas de base tecnológica,
  • funciones M, m,
  • métodos multiescalares,
  • análisis de patrones de puntos,
  • geografía económica
  • ...Más
    Menos

Cómo citar

Cabrera Pereyra, J. A., Álvarez Lobato, J. A., & Garrocho, C. (2024). Aglomeración y coaglomeración de las empresas de base tecnológica en el espacio intrametropolitano de Toluca, 2010-2020. Estudios Demográficos Y Urbanos, 39(1). https://doi.org/10.24201/edu.v39i1.2156
Metrics
Vistas/Descargas
  • Resumen
    321
  • PDF
    342
  • En línea
    12
  • EPUB
    5
  • Kindle
    7
  • Audio
    4

Descargas

Los datos de descargas todavía no están disponibles.

Métrica

Resumen

En este estudio se develan, localizan y delimitan aglomeraciones y coaglomeraciones de empresas de base tecnológica (EBT) en la Zona Metropolitana de Toluca (ZMT), para 2010 y 2020. Se utilizan las funciones espaciales M y m, métodos multiescalares de análisis de patrones de puntos que apenas se aplican en el mundo y no se han utilizado en México. Estas funciones operan en espacios continuos y evitan el grave problema de la unidad espacial modificable, que afecta a numerosos análisis geoeconómicos. En la ZMT la industria del transporte es el eje articulador de procesos de aglomeración y coaglomeración de EBT. Por último, Se perfilan líneas de política y una agenda estratégica de investigación.

Referencias

  1. Álvarez-Lobato, J. A. (2020). Distribución espacial del acceso de alimentos en las metrópolis mexicanas. En A. G. Aguilar y I. Escamilla-Herrera (coords.), Expresiones de la segregación residencial y de la pobreza en contextos urbanos y metropolitanos (pp. 355-388). Ciudad de México: UNAM, Instituto de Geografía.
  2. Andersson, M., Larsson, J. P. y Wernberg, J. (2019). The economic microgeography of diversity and specialization externalities – firm-level evidence from Swedish cities. Research Policy, 48(6), 1385-1398. https://doi.org/10.1016/j.respol.2019.02.003 DOI: https://doi.org/10.1016/j.respol.2019.02.003
  3. Arbia, G., Espa, G. y Giuliani, D. (2021). Spatial microeconometrics. Routledge. https://doi.org/10.4324/9781315735276 DOI: https://doi.org/10.4324/9781315735276
  4. Ascani, A., Faggian, A. y Montresor, S. (2021). The geography of COVID‐19 and the structure of local economies: The case of Italy. Journal of Regional Science, 61(2), 407-441. https://doi.org/10.1111/jors.12510 DOI: https://doi.org/10.1111/jors.12510
  5. Asheim, B. T., Boschma, R. y Cooke, P. (2011). Constructing regional advantage: Platform policies based on related variety and differentiated knowledge bases. Regional Studies, 45(7), 893-904. https://doi.org/10.1080/09654313.2011.573127 DOI: https://doi.org/10.1080/00343404.2010.543126
  6. Ayala Espinosa, C. (2021). Gobierno del Estado de México genera plan para sector automotriz. El Economista, 14 de junio. https://www.eleconomista.com.mx/estados/Gobierno-del-Estado-de-Mexico-genera-plan-para-sector-automotriz-20210614-0120.html
  7. Aydalot, P. y Keeble, D. (eds.). (2018 [1988]). High technology industry and innovative environments: The European experience. Vol. 3. Routledge. https://doi.org/10.4324/9781315149769 DOI: https://doi.org/10.4324/9781315149769
  8. Barefoot, K., Curtis, D., Jolliff, W., Nicholson, J. R. y Omohundro, R. (2018). Defining and measuring the digital economy. Washington, DC: US Department of Commerce Bureau of Economic Analysis. https://www.bea.gov/sites/default/files/papers/defining-and-measuring-the-digital-economy.pdf
  9. Barthelemy, M. (2020). Revisiting urban economics for understanding urban data. En D. Pumain (ed.), Theories and models of urbanization (pp. 121-131). Cham: Springer. https://doi.org/10.1007/978-3-030-36656-8_7 DOI: https://doi.org/10.1007/978-3-030-36656-8_7
  10. Behrens, K. (2016). Agglomeration and clusters: Tools and insights from coagglomeration patterns. Canadian Journal of Economics / Revue Canadienne d'Économique, 49(4), 1293-1339. https://doi.org/10.1111/caje.12235 DOI: https://doi.org/10.1111/caje.12235
  11. Breznitz, D. (2021), Innovation in real places: Strategies for prosperity in an unforgiving world. Oxford University Press. https://doi.org/10.1093/oso/9780197508114.001.0001 DOI: https://doi.org/10.1093/oso/9780197508114.001.0001
  12. Buzard, K., Carlino, G. A., Hunt, R. M., Carr, J. K. y Smith, T. E. (2020). Localized knowledge spillovers: Evidence from the spatial clustering of R & D labs and patent citations. Regional Science and Urban Economics, 81, 103490. https://doi.org/10.1016/j.regsciurbeco.2019.103490 DOI: https://doi.org/10.1016/j.regsciurbeco.2019.103490
  13. Carlino, G. y Kerr, W. R. (2015). Chapter 6. Agglomeration and innovation. En G. Duranton, V. Henderson y W. Strange (eds.), Handbook of Regional and Urban Economics, 5, 349-404. https://doi.org/10.1016/B978-0-444-59517-1.00006-4 DOI: https://doi.org/10.1016/B978-0-444-59517-1.00006-4
  14. Castillo, G. H. (2005). Marco empírico histórico de la dimensión física del proceso de urbanización de las ciudades de México y Toluca. Quivera. Revista de Estudios Territoriales, 7(2), 42-74. http://www.redalyc.org/articulo.oa?id=40170202
  15. Cissé, I., Dubé, J. y Brunelle, C. (2020). New business location: How local characteristics influence individual location decision? The Annals of Regional Science, 64(1), 185-214. https://doi.org/10.1007/s00168-019-00968-1 DOI: https://doi.org/10.1007/s00168-019-00968-1
  16. Coll‐Martínez, E., Moreno‐Monroy, A. I. y Arauzo‐Carod, J. M. (2019). Agglomeration of creative industries: An intra‐metropolitan analysis for Barcelona. Papers in Regional Science, 98(1), 409-431. https://doi.org/10.1111/pirs.12330 DOI: https://doi.org/10.1111/pirs.12330
  17. Combes, P. P. y Gobillon, L. (2015). Chapter 5. The empirics of agglomeration economies. En G. Duranton, V. Henderson y W. Strange (eds.), Handbook of Regional and Urban Economics, 5, 247-348. https://doi.org/10.1016/B978-0-444-59517-1.00005-2 DOI: https://doi.org/10.1016/B978-0-444-59517-1.00005-2
  18. Conapo (2018). Delimitación de las zonas metropolitanas de México 2015. Ciudad de México: Consejo Nacional de Población. https://www.gob.mx/conapo/documentos/delimitacion-de-las-zonas-metropolitanas-de-mexico-2015
  19. Cottineau, C. y Arcaute, E. (2020). The nested structure of urban business clusters. Applied Network Science, 5(1), 1-20. https://doi.org/10.1007/s41109-019-0246-9 DOI: https://doi.org/10.1007/s41109-019-0246-9
  20. Cruz, F. y Garza, G. (2014). Configuración microespacial de la industria en la Ciudad de México a inicios del siglo XXI. Estudios Demográficos y Urbanos, 29(1), 9-52. https://doi.org/10.24201/edu.v29i1.1454 DOI: https://doi.org/10.24201/edu.v29i1.1454
  21. Davis, D. R. y Dingel, J. I. (2019). A spatial knowledge economy. American Economic Review, 109(1), 153-70. https://doi.org/10.1257/aer.20130249 DOI: https://doi.org/10.1257/aer.20130249
  22. Davis, D. R. y Dingel, J. I. (2020). The comparative advantage of cities. Journal of International Economics, 123. https://doi.org/10.1016/j.jinteco.2020.103291 DOI: https://doi.org/10.1016/j.jinteco.2020.103291
  23. De Groot, H. L., Poot, J. y Smit, M. J. (2016). Which agglomeration externalities matter most and why? Journal of Economic Surveys, 30(4), 756-782. https://doi.org/10.1111/joes.12112 DOI: https://doi.org/10.1111/joes.12112
  24. Dirzu, Madalina-Stefania (2012). A conceptual approach to economic agglomerations. (Documento de Trabajo, vol. 4, núm. 3). Centre for European Studies. https://www.ceeol.com/search/article-detail?id=110903
  25. Du, J. y Vanino, E. (2021). Agglomeration externalities of fast-growth firms. Regional Studies, 55(2), 167-181. https://doi.org/10.1080/00343404.2020.1760234 DOI: https://doi.org/10.1080/00343404.2020.1760234
  26. Duranton, G. (2015). Growing through cities in developing countries. The World Bank Research Observer, 30(1), 39-73. https://doi.org/10.1093/wbro/lku006 DOI: https://doi.org/10.1093/wbro/lku006
  27. Duranton, G. y Overman, H. G. (2005). Testing for localization using micro-geographic data. The Review of Economic Studies, 72(4), 1077-1106. https://doi.org/10.1111/0034-6527.00362 DOI: https://doi.org/10.1111/0034-6527.00362
  28. Duranton, G. y Puga, D. (2004). Micro-foundations of urban agglomeration economies. Handbook of Regional and Urban Economics, 4, 2063-2117. https://doi.org/10.1016/S1574-0080(04)80005-1 DOI: https://doi.org/10.1016/S1574-0080(04)80005-1
  29. Duranton, G. y Puga, D. (2020). The economics of urban density. Journal of Economic Perspectives, 34(3), 3-26. https://doi.org/10.1257/jep.34.3.3 DOI: https://doi.org/10.1257/jep.34.3.3
  30. Ellison, G. y Glaeser, E. L. (1997). Geographic concentration in US manufacturing industries: A dartboard approach. Journal of Political Economy, 105(5), 889-927. https://doi.org/10.1086/262098 DOI: https://doi.org/10.1086/262098
  31. Ellison, G., Glaeser, E. L. y Kerr, W. R. (2010). What causes industry agglomeration? Evidence from coagglomeration patterns. American Economic Review, 100(3), 1195-1213. https://doi.org/10.1257/aer.100.3.1195 DOI: https://doi.org/10.1257/aer.100.3.1195
  32. Faggio, G., Silva, O. y Strange, W. C. (2020). Tales of the city: What do agglomeration cases tell us about agglomeration in general? Journal of Economic Geography, 20(5), 1117-1143. https://doi.org/10.1093/jeg/lbaa007 DOI: https://doi.org/10.1093/jeg/lbaa007
  33. Fitjar, R. D. y Rodríguez‐Pose, A. (2017). Nothing is in the air. Growth and Change, 48(1), 22-39. https://doi.org/10.1111/grow.12161 DOI: https://doi.org/10.1111/grow.12161
  34. Frenkel, A. (2012). Intra-metropolitan competition for attracting high-technology firms. Regional Studies, 46(6), 723-740. https://doi.org/10.1080/00343404.2010.529120 DOI: https://doi.org/10.1080/00343404.2010.529120
  35. Fritsch, M. y Meschede, M. (2001). Product innovation, process innovation, and size. Review of Industrial Organization, 19(3), 335-350. https://doi.org/10.1023/A:1011856020135 DOI: https://doi.org/10.1023/A:1011856020135
  36. Galindo-Rueda, F. y Verger, F. (2016), OECD taxonomy of economic activities based on RyD Intensity. (Documento de Trabajo, 2016/04). París: OECD. http://dx.doi.org/10.1787/5jlv73sqqp8r-en DOI: https://doi.org/10.1787/5jlv73sqqp8r-en
  37. Garrocho-Rangel, C., Álvarez-Lobato, J. A. y Chávez, T. (2013). Calculating intraurban agglomeration of economic units with planar and network K-functions: A comparative analysis. Urban Geography, 34(2), 261-286. https://doi.org/10.1080/02723638.2013.778655 DOI: https://doi.org/10.1080/02723638.2013.778655
  38. George, G., Lakhani, K. R. y Puranam, P. (2020). What has changed? The impact of Covid pandemic on the technology and innovation management research agenda. Journal of Management Studies, 57(8), 1754. https://doi.org/10.1111/joms.12634 DOI: https://doi.org/10.1111/joms.12634
  39. Gertler, M. S. (2003). Tacit knowledge and the economic geography of context, or the undefinable tacitness of being (there). Journal of Economic Geography, 3(1), 75-99. https://doi.org/10.1093/jeg/3.1.75 DOI: https://doi.org/10.1093/jeg/3.1.75
  40. Giuliano, G., Kang, S., y Yuan, Q. (2019). Agglomeration economies and evolving urban form. The Annals of Regional Science, 63(3), 377-398. https://doi.org/10.1007/s00168-019-00957-4 DOI: https://doi.org/10.1007/s00168-019-00957-4
  41. Glaeser, E. L., Kallal, H. D., Scheinkman, J. A. y Shleifer, A. (1992). Growth in cities. Journal of Political Economy, 100(6), 1126-1152. https://doi.org/10.1086/261856 DOI: https://doi.org/10.1086/261856
  42. Gómez-Antonio, M. y Alañón-Pardo, Á. (2020). Point pattern methods for analyzing industrial location. Investigación Económica, 79(314), 51-74. https://doi.org/10.22201/fe.01851667p.2020.314.75474 DOI: https://doi.org/10.22201/fe.01851667p.2020.314.75474
  43. Growe, A. (2019a). Buzz at workplaces in knowledge-intensive service production: Spatial settings of temporary spatial proximity. European and Regional Studies, 26(4), 434-448. https://doi.org/10.1177%2F0969776418784999 DOI: https://doi.org/10.1177/0969776418784999
  44. Growe, A. (2019b). Developing trust in face-to-face interaction of knowledge-intensive business services (KIBS). Regional Studies, 53(5), 720-730. https://doi.org/10.1080/00343404.2018.1473567 DOI: https://doi.org/10.1080/00343404.2018.1473567
  45. Hatzichronoglou, T. (1997). Revision of the high-technology sector and product classification. (Documento de Trabajo, 1997/02). París: OECD. https://doi.org/10.1787/050148678127 DOI: https://doi.org/10.1787/050148678127
  46. Henderson, J. V. (2007). Understanding knowledge spillovers. Regional Science and Urban Economics, 37(4), 497-508. https://doi.org/10.1016/j.regsciurbeco.2006.11.010 DOI: https://doi.org/10.1016/j.regsciurbeco.2006.11.010
  47. INEGI (2009). Censos Económicos 2009. Aguascalientes, México: Instituto Nacional de Estadística y Geografía, Sistema Automatizado de Información Censal (SAIC). https://www.inegi.org.mx/app/saic/
  48. INEGI (2010). Directorio Estadístico Nacional de Unidades Económicas (DENUE). Aguascalientes, México: Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/app/mapa/denue/default.aspx
  49. INEGI (2018). Sistema de Clasificación Industrial de América del Norte 2018 (SCIAN 2018). Aguascalientes, México: Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/app/scian/
  50. INEGI (2019). Censos Económicos 2019. Sistema Automatizado de Información Censal (SAIC). Aguascalientes, México: Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/app/saic/
  51. INEGI (2020). Directorio Estadístico Nacional de Unidades Económicas (DENUE). Aguascalientes, México: Instituto Nacional de Estadística y Geografía. https://www.inegi.org.mx/app/mapa/denue/default.aspx
  52. Juhász, S., Broekel, T. y Boschma, R. (2021). Explaining the dynamics of relatedness: The role of co‐location and complexity. Papers in Regional Science, 100(1), 3-21. https://doi.org/10.1111/pirs.12567 DOI: https://doi.org/10.1111/pirs.12567
  53. Kasmi, F. (2021). Milieu–Innovative Milieu: The strength of proximity ties. En D. Uzundis, F. Kasmi, y L. Adatto (eds.), Innovation Economics, Engineering and Management Handbook (vol. 2, pp. 195-200). Wiley. https://doi.org/10.1002/9781119832522.ch23 DOI: https://doi.org/10.1002/9781119832522.ch23
  54. Kerr, W. R. y Kominers, S. D. (2015). Agglomerative forces and cluster shapes. Review of Economics and Statistics, 97(4), 877-899. https://doi.org/10.1162/REST_a_00471. DOI: https://doi.org/10.1162/REST_a_00471
  55. Kerr, W. R. y Robert-Nicoud, F. (2020). Tech clusters. Journal of Economic Perspectives, 34(3), 50-76. https://doi.org/10.1257/jep.34.3.50 DOI: https://doi.org/10.1257/jep.34.3.50
  56. Lang, G., Marcon, E. y Puech, F. (2020). Distance-based measures of spatial concentration: Introducing a relative density function. The Annals of Regional Science, 64(2), 243-265. https://doi.org/10.1007/s00168-019-00946-7 DOI: https://doi.org/10.1007/s00168-019-00946-7
  57. Lavoratori, K. y Castellani, D. (2021). Too close for comfort? Micro‐geography of agglomeration economies in the United Kingdom. Journal of Regional Science, 61(5), 883-1139. https://onlinelibrary.wiley.com/doi/epdf/10.1111/jors.12531 DOI: https://doi.org/10.1111/jors.12531
  58. LeDuff, C. (2014). Detroit: An American autopsy. Penguin.
  59. Malecki, E. J. (1991). Technology and economic development: The dynamics of local, regional and national change. Harlow: Longman.
  60. Malmberg, A. y Maskell, P. (2006). Localized learning revisited. Growth and Change, 37(1), 1-18. https://doi.org/10.1111/j.1468-2257.2006.00302.x DOI: https://doi.org/10.1111/j.1468-2257.2006.00302.x
  61. Marcon, E. y Puech, F. (2017). A typology of distance-based measures of spatial concentration. Regional Science and Urban Economics, 62, 56-67. https://doi.org/10.1016/j.regsciurbeco.2016.10.004 DOI: https://doi.org/10.1016/j.regsciurbeco.2016.10.004
  62. Marcon, E., Traissac, S., Puech, F. y Lang, G. (2015). Tools to characterize point patterns: dbmss for R. Journal of Statistical Software, 67(3), 1-15. https://doi.org/10.18637/jss.v067.c03 DOI: https://doi.org/10.18637/jss.v067.c03
  63. Maskell, P. y Malmberg, A. (2007). Myopia, knowledge development and cluster evolution. Journal of Economic Geography, 7(5), 603-618. https://doi.org/10.1093/jeg/lbm020 DOI: https://doi.org/10.1093/jeg/lbm020
  64. Meijers, E. J. y Burger, M. J. (2017). Stretching the concept of ‘borrowed size’. Urban Studies, 54(1), 269-291. https://doi.org/10.1177%2F0042098015597642 DOI: https://doi.org/10.1177/0042098015597642
  65. Moreno Brid, J. C. (2016). Política macro e industrial para un cambio estructural y crecimiento: gran pendiente de la economía mexicana. Problemas del Desarrollo, 47(185), 57-78. https://www.probdes.iiec.unam.mx/index.php/pde/article/view/53919 DOI: https://doi.org/10.1016/j.rpd.2015.10.013
  66. Moreno‐Monroy, A. I. y García-Cruz, G. A. (2016). Intrametropolitan agglomeration of formal and informal manufacturing activity: Evidence from Cali, Colombia. Tijdschrift voor Economische en Sociale Geografie, 107(4), 389-406. https://doi.org/10.1111/tesg.12163 DOI: https://doi.org/10.1111/tesg.12163
  67. Niebuhr, A., Peters, J. C. y Schmidke, A. (2020). Spatial sorting of innovative firms and heterogeneous effects of agglomeration on innovation in Germany. The Journal of Technology Transfer, 45(5), 1343-1375. https://doi.org/10.1007/s10961-019-09755-8 DOI: https://doi.org/10.1007/s10961-019-09755-8
  68. OCDE. (2009). OECD reviews of innovation policy: Mexico. OECD. https://www.oecd.org/sti/inno/oecdreviewsofinnovationpolicymexico.htm
  69. OCDE. (2011). ISIC REV. 3 Technology intensity definition. Classification of manufacturing industries into categories based on RyD intensities. OCDE. https://www.oecd.org/sti/ind/48350231.pdf
  70. Openshaw, S. y Taylor, P. J. (1979). A million or so correlation coefficients. Three experiments on the modifiable areal unit problem. En N. Wrigley (ed.), Statistical applications in the spatial sciences (pp. 127-144). Londres: Pion.
  71. Perry, S., Wang, L. y Hernandez, T. (2020). The changing spatial organization of ethnic retailing: Chinese and South Asian grocery retailers in Toronto. Papers in Applied Geography,6(4), 687-305. https://doi.org/10.1080/23754931.2020.1763832 DOI: https://doi.org/10.1080/23754931.2020.1763832
  72. Porto-Gomez, I., Zabala-Iturriagagoitia, J. M. y Leydesdorff, L. (2019). Innovation systems in México: A matter of missing synergies. Technological Forecasting and Social Change, 148. https://doi.org/10.1016/j.techfore.2019.119721 DOI: https://doi.org/10.1016/j.techfore.2019.119721
  73. Potter, A. y Watts, H. D. (2014). Revisiting Marshall’s agglomeration economies: Technological relatedness and the evolution of the Sheffield metals cluster. Regional Studies, 48(4), 603-623. https://doi.org/10.1080/00343404.2012.667560 DOI: https://doi.org/10.1080/00343404.2012.667560
  74. Proost, S. y Thisse, J. F. (2019). What can be learned from spatial economics? Journal of Economic Literature, 57(3), 575-643. https://doi.org/10.1257/jel.20181414 DOI: https://doi.org/10.1257/jel.20181414
  75. R Core Team (2020). R: A language and environment for statistical computing. Viena: R Foundation for Statistical Computing. https://www.R-project.org/
  76. Rendón Rojas, L. y Godínez Enciso, J. A. (2016). Evolución y cambio industrial en las zonas metropolitanas del Valle de México y de Toluca, 1993-2008. Análisis Económico, 31(77), 115-146. http://www.analisiseconomico.azc.uam.mx/index.php/rae/article/view/53
  77. Rickard, S. J. (2020). Economic geography, politics, and policy. Annual Review of Political Science, 23, 187-202. https://doi.org/10.1146/annurev-polisci-050718-033649 DOI: https://doi.org/10.1146/annurev-polisci-050718-033649
  78. Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B (Methodological), 39(2), 172-192. https://doi.org/10.1111/j.2517-6161.1977.tb01615.x DOI: https://doi.org/10.1111/j.2517-6161.1977.tb01615.x
  79. Rosenthal, S. S. y Strange, W. C. (2020). How close is close? The spatial reach of agglomeration economies. Journal of Economic Perspectives, 34(3), 27-49. https://doi.org/10.1257/jep.34.3.27 DOI: https://doi.org/10.1257/jep.34.3.27
  80. Rotolo, D., Hicks, D. y Martin, B. R. (2015). What is an emerging technology? Research Policy, 44(10), 1827-1843. https://doi.org/10.1016/j.respol.2015.06.006 DOI: https://doi.org/10.1016/j.respol.2015.06.006
  81. Rudkin, S., He, M. y Chen, Y. (2020). Attraction or repulsion? Testing coagglomeration of innovation between firm and university. (Working Paper Series, 608). Asian Development Bank Economics. https://dx.doi.org/10.2139/ssrn.3590930 DOI: https://doi.org/10.22617/WPS200067-2
  82. Seo, I. y Sonn, J.W. (2019). Conflicting motivations and knowledge spillovers: Dynamics of the market across space. Geoforum, 105, 210-212. https://doi.org/10.1016/j.geoforum.2019.05.026 DOI: https://doi.org/10.1016/j.geoforum.2019.05.026
  83. SGG EdoMéx. (2018). Zonas metropolitanas del Estado de México. Consejo Estatal de Población. http://coespo.edomex.gob.mx/zonas_metropolitanas
  84. Shearmur, R. (2012). The geography of intrametropolitan KIBS innovation: Distinguishing agglomeration economies from innovation dynamics. Urban Studies, 49(11), 2331-2356. https://doi.org/10.1177%2F0042098011431281 DOI: https://doi.org/10.1177/0042098011431281
  85. Shearmur, R., Garrocho, C., Álvarez-Lobato, J. A. y Chávez-Soto, T. (2015). Hacia una geografía de las actividades económicas en la Ciudad de México: métodos, conceptos, cultura y subjetividad. En C. Garrocho y G. Buzai (coords.), Geografía aplicada en Iberoamérica: avances, retos y perspectivas (pp. 431-472). El Colegio Mexiquense.
  86. Tuitjer, G. y Küpper, P. (2020). How knowledge-based local and global networks foster innovations in rural areas. Journal of Innovation Economics Management, 3, 9-29. https://doi.org/10.3917/jie.033.0009. DOI: https://doi.org/10.3917/jie.033.0009
  87. Van Meeteren, M., Boussauw, K., Derudder, B. y Witlox, F. (2016). Flemish Diamond or ABC-Axis? The spatial structure of the Belgian metropolitan area. European Planning Studies, 24(5), 974-995. https://doi.org/10.1080/09654313.2016.1139058 DOI: https://doi.org/10.1080/09654313.2016.1139058
  88. Vilchis, I., Chávez-Soto, T. y Garrocho, C. (2021a). Análisis espacio-sectorial del empleo en sectores intensivos en uso del conocimiento: Red-Bajío, México, 2015-2020. Estudios de Economía Aplicada, 40(1). https://shorturl.at/grSTZ DOI: https://doi.org/10.25115/eea.v40i1.5335
  89. Vilchis, I., Garrocho, C. y Chávez-Soto, T. (2021b). Dinámica del empleo en sectores intensivos en uso del conocimiento en la megalópolis del Valle de México, 2014-2018. Revista de Geografía Norte Grande, 79, 229-251. https://doi.org/10.4067/S0718-34022021000200229 DOI: https://doi.org/10.4067/S0718-34022021000200229
  90. Villarreal González, A., Flores Sánchez, S. M. y Flores Segovia, M. A. (2016a). Patrones de co-localización espacial de la industria aeroespacial en México. Estudios Económicos 31(1), 169-211. https://doi.org/10.24201/ee.v31i1.15 DOI: https://doi.org/10.24201/ee.v31i1.15
  91. Villarreal González, A., Gasca Sánchez, F. M. y Flores Segovia, M. A. (2016b). Patrones de aglomeración espacial de la industria creativa en el Área Metropolitana de Monterrey. Estudios Demográficos y Urbanos, 31(2), 331-383. https://doi.org/10.24201/edu.v31i2.1591 DOI: https://doi.org/10.24201/edu.v31i2.1591
  92. Villarreal Gonzalez, A., Mack, E. A. y Flores, M. (2017). Industrial complexes in Mexico: Implications for regional industrial policy based on related variety and smart specialization. Regional Studies, 51(4), 537-547. https://doi.org/10.1080/00343404.2015.1114174 DOI: https://doi.org/10.1080/00343404.2015.1114174
  93. Wu, Z., Cai, H., Zhao, R., Fan, Y., Di, Z. y Zhang, J. (2020). A topological analysis of trade distance: Evidence from the gravity model and complex flow networks. Sustainability, 12(9). https://doi.org/10.3390/su12093511 DOI: https://doi.org/10.3390/su12093511
  94. Ye, X. y Rogerson, P. (2021). The impacts of the modifiable areal unit problem (MAUP) on omission error. Geographical Analysis, 54, 32-57. https://doi.org/10.1111/gean.12269 DOI: https://doi.org/10.1111/gean.12269
  95. Zhang, X., Yao, J., Sila-Nowicka, K. y Song, C. (2020). Geographic concentration of industries in Jiangsu, China: A spatial point pattern analysis using micro-geographic data. The Annals of Regional Science, 66, 439-461. https://doi.org/10.1007/s00168-020-01026-x DOI: https://doi.org/10.1007/s00168-020-01026-x
  96. Zheng, S. y Tan, Z. (eds.). (2020). Toward urban economic vibrancy: Patterns and practices in Asia’s new cities. MIT School of Architecture and Planning