Detection of geographic concentration of economic activities at different spatial scales has long been of interest to researchers from spatial economics, regional science and economic geography. Using a unique dataset from the first industrial land use survey of its kind in China, this research is the first effort attempting to explore spatial distribution particularly geographic concentration of industries in China using firm-level data. Distance-based functions and spatial cluster analysis are employed to detect the spatial scales as well as the geographic locations of industrial concentration. The results indicate that four of the five selected industries are in general concentrated in southern Jiangsu at small spatial scales (less than 5 km), while the chemical industry demonstrates an overall spatial dispersion pattern relative to the distribution of all other industries. Most industrial clusters have a radius of less than 2.5 km containing 20–60% of enterprises and 60–86% of employees from each selected industry, with larger clusters showing relatively weaker concentration. This research demonstrates the connections and complementarity of different approaches, complementing previous studies that use distance-based functions with spatial scan statistics.
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Generally, searching local clusters for large datasets with spatial scan statistics can be computationally expensive. For instance, a typical spatial cluster analysis using the SaTScan software involving 250,000 observations requires a computer memory of 128 GB (Kulldorff 2018). For the dataset used in this research which contains about 223,000 enterprises, it took about 20–80 min for running 1000 simulations of the M function, 18–22 h for the m function and nearly 4 h to identify local clusters for each industry using a desktop with the Intel Xeon Processor E5email@example.com GHz and 256 GB memory.
Arbia G (2001) The role of spatial effects in the empirical analysis of regional concentration. J Geogr Syst 3(3):271–281
Arbia G, Espa G, Giuliani D, Mazzitelli A (2010) Detecting the existence of space–time clustering of firms. Reg Sci Urban Econ 40(5):311–323
Becattini G (1990) The industrial district as a creative milieu. In: Benko G, Dunford M (eds) Industrial change and regional development. Belhaven Press, London, pp 102–116
Boschma R, Frenken K (2011) The emerging empirics of evolutionary economic geography. J Econ Geogr 11(2):295–307
Brakman S, Garretsen H, Zhao Z (2017) Spatial concentration of manufacturing firms in China. Papers Region Sci 96:S179–S205
Bureau of Statistics of Jiangsu (2017) Jiangsu statistical yearbook 2017. China Statistics Press, Beijing (in Chinese)
Coe NM, Kelly PF, Yeung HW (2013) Economic geography: a contemporary introduction, 2nd edn. Wiley, Hoboken
Combes PP, Mayer T, Thisse JF (2008) Economic geography: the integration of regions and nations. Princeton University Press, Princeton
Diggle PJ (2003) Statistical analysis of spatial point patterns, 2nd edn. Edward Arnold, London
Diggle PJ, Chetwynd AG (1991) Second-order analysis of spatial clustering for inhomogeneous populations. Biometrics 1155–1163
Duranton G, Overman HG (2005) Testing for localization using micro-geographic data. Rev Econ Stud 72(4):1077–1106
Ellison G, Glaeser EL (1997) Geographic concentration in US manufacturing industries: a dartboard approach. J Polit Econ 105(5):889–927
Fan CC, Scott AJ (2003) Industrial agglomeration and development: a survey of spatial economic issues in East Asia and a statistical analysis of Chinese regions. Econ Geogr 79(3):295–319
Fujita M, Krugman PR, Venables AJ (1999) The spatial economy: cities, regions, and international trade. MIT Press, Cambridge
Glaeser EL, Kallal HD, Scheinkman JA, Shleifer A (1992) Growth in cities. J Polit Econ 100(6):1126–1152
Guillain R, Le Gallo J (2010) Agglomeration and dispersion of economic activities in and around Paris: an exploratory spatial data analysis. Environ Plan B Plan Des 37(6):961–981
He C, Wei YD, Pan F (2007) Geographical concentration of manufacturing industries in China: the importance of spatial and industrial scales. Eur Geogr Econ 48(5):603–625
He C, Wei YD, Xie X (2008) Globalization, institutional change, and industrial location: economic transition and industrial concentration in China. Region Stud 42(7):923–945
Henderson JV (2003) Marshall's scale economies. J Urb Econ 53(1):1–28
Hayter R (1997) The dynamics of industrial location: the factory, the firm and the production system. Wiley, Chichester
Hoover EM (1936) The measurement of industrial localization. Rev Econ Stat 18:162–171
Isard W (1959) Industrial complex analysis and regional development: a case study of refinery-petrochemical-synthetic fiber complexes and Puerto Rico. MIT Press, Cambridge
Jing N, Cai W (2010) Analysis on the spatial distribution of logistics industry in the developed East Coast Area in China. Ann Region Sci 45(2):331–350
Kopczewska K (2017) Distance-based measurement of agglomeration, concentration and specialisation. Measuring regional specialisation: a new approach. Springer, Cham, pp 173–216
Klepper S (2007) Disagreements, spinoffs, and the evolution of Detroit as the capital of the US automobile industry. Manage Sci 53(4):616–631
Krugman P (1991) Geography and trade. MIT Press, Cambridge
Kukuliač P, Horák J (2017) W function: a new distance-based measure of spatial distribution of economic activities. Geogr Anal 49(2):199–214
Kulldorff M (1997) A spatial scan statistic. Commun Stat Theory Methods 26(6):1481–1496
Kulldorff M (1999) Spatial scan statistics: models, calculations, and applications. In: Glaz J, Balakrishnan N (eds) Scan statistics and applications. Birkhäuser, Boston, pp 303–322
Kulldorff M (2018) SaTScan Users Guide for version 9.6. https://www.satscan.org/cgi-bin/satscan/register.pl/SaTScan_Users_Guide.pdf?todo=process_userguide_download. Accessed 7 June 2019
Lang G, Marcon E, Puech F (2014). Distance-based measures of spatial concentration: introducing a relative density function. Working Papers, HAL (version 1). https://hal-mnhn.archives-ouvertes.fr/INRA/hal-01082178v1. Accessed 29 Mar 2019
Liu Z (2014) Global and local: measuring geographical concentration of China's manufacturing industries. Prof Geogr 66(2):284–297
Lin HL, Li HY, Yang CH (2011) Agglomeration and productivity: firm-level evidence from China's textile industry China. Econ Rev 22(3):313–329
Lu J, Tao Z (2009) Trends and determinants of China's industrial agglomeration. J Urb Econ 65(2):167–180
Marcon E, Puech F (2010) Measures of the geographic concentration of industries: improving distance-based methods. J Econ Geogr 10(5):745–762
Marcon E, Puech F (2017) A typology of distance-based measures of spatial concentration. Region Sci Urb Econ 62:56–67
Marcon E, Traissac S, Puech F, Lang G (2015) Tools to characterize point patterns: dbmss for R. J Stat Softw 67(3):1–15
Marshall A (1920) Principles of economics. MacMillan, London
National Bureau of Statistics of China (2011) Industrial classification for national economic activities (GB/4754–2011). https://www.stats.gov.cn/tjsj/tjbz/hyflbz/2011/. Accessed 7 June 2019. (in Chinese)
National Bureau of Statistics of China (2017) China statistical yearbook 2017. China Statistics Press, Beijing (in Chinese)
Openshaw S (1984) The modifiable areal unit problem. Geo Books, Norwick
Penttinen A, Stoyan D, Henttonen HM (1992) Marked point processes in forest statistics. Forest Science 38(4):806–824
Porter ME (1990) The competitive advantage of nations. Free Press, New York
Porter ME (1998) Clusters and the new economics of competition. Harvard Bus Rev 76(6):77–90
Ripley BD (1976) The second-order analysis of stationary point processes. J Appl Prob 13(2):255–266
Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, London
Wang J (2001) Innovative spaces: enterprise clusters and regional development. Peking University Press, Beijing (in Chinese)
Wang CC, Lin GC, Li G (2010) Industrial clustering and technological innovation in China: new evidence from the ICT industry in Shenzhen. Environ Plan A 42(8):1987–2010
Wei YD (2010) Beyond new regionalism, beyond global production networks: remaking the Sunan model, China. Environ Plan C Gov Policy 28(1):72–96
Wei YD (2015) Zone fever, project fever: development policy, economic transition, and urban expansion in China. Geogr Rev 105(2):56–177
Wei YH, Lu Y, Chen W (2009) Globalizing regional development in Sunan, China: Does Suzhou Industrial Park fit a neo-Marshallian district model? Region Stud 43(3):409–427
Xinhua News Agency (2018) Promoting land saving and intensive use in Jiangsu. https://www.xinhuanet.com/2018-07/06/c_1123089318.htm. Accessed 20 June 2020. (in Chinese)
Yuan F, Gao J, Wang L, Cai Y (2017) Co-location of manufacturing and producer services in Nanjing, China. Cities 63:81–91
We are grateful to the Department of Natural Resources of Jiangsu for their provision of the industry land use survey data.
This study was supported by the Fundamental Research Funds for the Central Universities (Grant No. B200204029).
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The authors declare that they have no conflict of interest.
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Zhang, X., Yao, J., Sila-Nowicka, K. et al. Geographic concentration of industries in Jiangsu, China: a spatial point pattern analysis using micro-geographic data. Ann Reg Sci 66, 439–461 (2021). https://doi.org/10.1007/s00168-020-01026-x