Skip to main content
SpringerLink
Log in

The Identification of Industrial Clusters and their Spatial Characteristics Based on Natural Semantics

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

Cluster identification based on input–output tables has long been limited in its effectiveness due to slow updates and issues of mutual exclusion. This study presents a novel method that leverages enterprise big data and semantic similarity to identify industrial clusters. Using the electronic information industry cluster in the Pearl River Delta (PRD) as an empirical example, we demonstrate the efficacy of our approach. Our analysis reveals that the PRD's electronic-information industry cluster comprises 27 industries, aligning closely with the results obtained from the input–output table calculations. Building on this cluster identification, our study further investigates the industrial association and spatial collaborative distribution characteristics among cluster enterprises. This study proposes a method to rapidly identify industrial clusters, and quantitatively evaluate industrial linkages and the spatial coordination of industrial clusters from the perspective of individual enterprises. The proposed method has significant implications for urban planners and policy makers in terms of helping them understand the context, relationship, and spatial synergy of industrial clusters.

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

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available on request from the corresponding author, [initials]，upon reasonable request.

References

  • Alcácer, J., & Zhao, M. (2016). Zooming in: A practical manual for identifying geographic clusters. Strategic Management Journal, 37(1), 10–21.

    Article  Google Scholar 

  • Alonso-Villar*, O., Chamorro-Rivas, J.-M., & González-Cerdeira, X. (2004). Agglomeration economies in manufacturing industries: the case of Spain. Applied economics, 36(18), 2103-2116

  • Anderson, G. (1994). Industry clustering for economic development. Economic Development Review, 12, 26–26.

    Google Scholar 

  • Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.

    Article  Google Scholar 

  • Barrios, S., Bertinelli, L., Strobl, E., & Teixeira, A. C. (2009). Spatial distribution of manufacturing activity and its determinants: A comparison of three small European countries. Regional Studies, 43(5), 721–738.

    Article  Google Scholar 

  • Blien, U., & Maier, G. (2008). The economics of regional clusters: networks, technology and policy. Edward Elgar Publishing.

    Google Scholar 

  • Boix, R., Hervás-Oliver, J. L., & De Miguel-Molina, B. (2015). Micro-geographies of creative industries clusters in E urope: From hot spots to assemblages. Papers in Regional Science, 94(4), 753–772.

    Article  Google Scholar 

  • Boschma, R. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61–74.

    Article  Google Scholar 

  • Brachert, M., Titze, M., & Kubis, A. (2011). Identifying industrial clusters from a multidimensional perspective: Methodical aspects with an application to Germany. Papers in Regional Science, 90(2), 419–439.

    Article  Google Scholar 

  • Brenner, T. (2006). Identification of local industrial clusters in Germany. Regional Studies, 40(9), 991–1004.

    Article  Google Scholar 

  • Carroll, M. C., Reid, N., & Smith, B. W. (2008). Location quotients versus spatial autocorrelation in identifying potential cluster regions. The Annals of Regional Science, 42, 449–463.

    Article  Google Scholar 

  • China, M. o. (2022, 30 Nov). List of advanced manufacturing clusters in 45 countries. Retrieved 04 May, 2023 from https://www.miit.gov.cn/jgsj/ghs/gzdt/art/2022/art_fa5bd57e9f364b65ae48de37a319046f.html

  • Crawley, A., Beynon, M., & Munday, M. (2013). Making location quotients more relevant as a policy aid in regional spatial analysis. Urban Studies, 50(9), 1854–1869.

    Article  Google Scholar 

  • Czamanski, S., & de Ablas, L. A. Q. (1979). Identification of industrial clusters and complexes: A comparison of methods and findings. Urban Studies, 16(1), 61–80.

    Article  Google Scholar 

  • De Propris, L. (2005). Mapping local production systems in the UK: Methodology and application. Regional Studies, 39(2), 197–211.

    Article  Google Scholar 

  • Delgado, M., Porter, M. E., & Stern, S. (2016). Defining clusters of related industries. Journal of Economic Geography, 16(1), 1–38.

    Article  Google Scholar 

  • Dong, K.-X., Hou, W.-H., Zhen, J., & Wang, P.-P. (2016). The Innovation of Electronic Information Industry Cluster Based on System Dynamic. Soft Science[In Chinese], 30(09), 5–10.

    Google Scholar 

  • Duranton, G., & Overman, H. G. (2005). Testing for localization using micro-geographic data. The Review of Economic Studies, 72(4), 1077–1106.

    Article  MathSciNet  Google Scholar 

  • Duranton, G., & Overman, H. G. (2008). Exploring the detailed location patterns of UK manufacturing industries using microgeographic data. Journal of Regional Science, 48(1), 213–243.

    Article  Google Scholar 

  • Ellison, G., & Glaeser, E. L. (1997). Geographic concentration in US manufacturing industries: A dartboard approach. Journal of Political Economy, 105(5), 889–927.

    Article  Google Scholar 

  • Feng, P., Growe, A., & Shen, Y. (2022). The Middle-aged and Knowledge Workers: Demographic and Economic Changes in the Pearl River Delta, China. Chinese Geographical Science, 1–17.

  • Feser, E., Sweeney, S., & Renski, H. (2005). A descriptive analysis of discrete US industrial complexes. Journal of Regional Science, 45(2), 395–419.

    Article  Google Scholar 

  • Gardner, M. W., & Dorling, S. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14–15), 2627–2636.

    Article  CAS  ADS  Google Scholar 

  • Glasmeier, A. (2018). High-tech policy, high-tech realities: The spatial distribution of high-tech industry in America 1. In Growth policy in the age of high technology (pp. 67–96). Routledge.

  • Guo, J., Lao, X., & Shen, T. (2019). Location-based method to identify industrial clusters in Beijing-Tianjin-Hebei area in China. Journal of Urban Planning and Development, 145(2), 04019001.

    Article  Google Scholar 

  • Han, S. S., & Qin, B. (2009). The spatial distribution of producer services in Shanghai. Urban Studies, 46(4), 877–896.

    Article  Google Scholar 

  • He, C., Liang, J., & Zhang, H. (2005). Identification of Regional Manufacturing Clusters — A Case Study of Beijing Manufacturing Industry. Geographical Science (05), 11–18

  • Hendry, C., & Brown, J. (2006). Dynamics of clustering and performance in the UK opto-electronics industry. Regional Studies, 40(7), 707–725.

    Article  Google Scholar 

  • Hill, E. W., & Brennan, J. F. (2000). A methodology for identifying the drivers of industrial clusters: The foundation of regional competitive advantage. Economic Development Quarterly, 14(1), 65–96.

    Article  Google Scholar 

  • Hou-kai, W. (2009). The Development Strategy of Industrial Cluster in China. Journal of Henan University.

  • Isaksen, A. (1996). Towards increased regional specialization? The quantitative importance of new industrial spaces in Norway, 1970–1990. Norsk Geogr. Tidsskr, 50(2), 113–123.

    Article  Google Scholar 

  • Lang, G., Marcon, E., & Puech, F. (2020). Distance-based measures of spatial concentration: Introducing a relative density function. The Annals of Regional Science, 64, 243–265.

    Article  Google Scholar 

  • Leslie, T. F., & Kronenfeld, B. J. (2011). The Colocation Quotient: A New Measure of Spatial Association Between Categorical Subsets of Points. Geographical Analysis, 43(3), 306–326.

    Article  Google Scholar 

  • Li, P. (2021). An Improved TF-IDF Method for Calculating Text Feature Weight. International Core Journal of Engineering, 7(9), 244–248.

    Google Scholar 

  • Li, C., Wu, K., & Gao, X. (2020). Manufacturing industry agglomeration and spatial clustering: Evidence from Hebei Province, China. Environment, Development and Sustainability, 22, 2941–2965.

    Article  Google Scholar 

  • Liu, X., Sun, T., & Li, G. (2012). Spatial analysis of industry clusters based on local spatial statistics: A case study of Beijing manufacturing industry clusters. Sci. Geogr. Sin., 32(5), 530–535.

    CAS  Google Scholar 

  • Liu, Z., Chen, X., Xu, W., Chen, Y., & Li, X. (2021a). Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China. Environment Planning b: Urban Analytics City Science, 48(9), 2827–2841.

    Google Scholar 

  • Liu, Z., Chen, X., Xu, W., Chen, Y., & Li, X. (2021b). Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China. Environment Planning b: Urban Analytics and City Science, 48(9), 2827–2841.

    Google Scholar 

  • Liu, W., Zhan, J., Zhao, F., Wang, C., Zhang, F., Teng, Y., . . . Kumi, M. A. (2022). Spatio-temporal variations of ecosystem services and their drivers in the Pearl River Delta, China. Journal of Cleaner Production, 130466.

  • Lv, W., & Chen, W. (2009). Study on the characteristics of spatial agglomeration of Manufacturing industrial cluster in Jiangsu Province. Economic Geography, 29(10), 1677–1684.

    Google Scholar 

  • Mantaeva, E. I., & Goldenova, V. S. (2017). On the Role of Industrial Cluster in Regional Economy Development. Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Seriia 3, Ėkonomika, Ėkologiia, 19(1 (38)).

  • Marcon, E., & Puech, F. (2010). Measures of the geographic concentration of industries: Improving distance-based methods. Journal of Economic Geography, 10(5), 745–762.

    Article  Google Scholar 

  • Marcon, E., & Puech, F. (2017). A typology of distance-based measures of spatial concentration. Regional Science and Urban Economics, 62, 56–67.

    Article  Google Scholar 

  • Maurel, F., & Sédillot, B. (1999). A measure of the geographic concentration in French manufacturing industries. Regional Science Urban Economics, 29(5), 575–604.

    Article  Google Scholar 

  • Me, P. (1998). Clusters and the new economics of competition. Harvard Business Review, 76(6), 77–90.

    Google Scholar 

  • Moral, S. S. (2009). Industrial clusters and new firm creation in the manufacturing sector of Madrid’s metropolitan region. Regional Studies, 43(7), 949–965.

    Article  Google Scholar 

  • Niyimbanira, F., Eggink, M. E., & Nishimwe-Niyimbanira, R. (2020). The identification of the key sub-industries among coastal metropolitan cities of South Africa: An application of the location quotient technique. International Journal of Economics Finance Studies, 12(1), 50–70.

    Article  Google Scholar 

  • Oakey, R. P., Thwaites, A. T., & Nash, P. (1980). The regional distribution of innovative manufacturing establishments in Britain. Regional Studies, 14(3), 235–253.

    Article  Google Scholar 

  • Pandit, N. R., Cook, G. A., & Swann, P. (2001). The dynamics of industrial clustering in British financial services. Service Industries Journal, 21(4), 33–61.

    Article  Google Scholar 

  • Poter, M. E. (1998). Clusters and the new economics of competition. Harvard Business Review, 11, 77–90.

    Google Scholar 

  • Roepke, H., Adams, D., & Wiseman, R. (1974). A new approach to the identification of industrial complexes using input-output data. Journal of Regional Science, 14(1), 15–29.

    Article  Google Scholar 

  • Rosenfeld, S. A. (1997). Bringing business clusters into the mainstream of economic development. European Planning Studies, 5(1), 3–23.

    Article  Google Scholar 

  • Scholl, T., & Brenner, T. (2016). Detecting spatial clustering using a firm-level cluster index. Regional Studies, 50(6), 1054–1068.

    Article  Google Scholar 

  • Scott, A. (2002). A new map of Hollywood: The production and distribution of American motion pictures. Regional Studies, 36(9), 957–975.

    Article  Google Scholar 

  • Sohn, J. (2014). Industry classification considering spatial distribution of manufacturing activities. Area, 46(1), 101–110.

    Article  Google Scholar 

  • Stejskal, J., & Hajek, P. (2012). Competitive advantage analysis: A novel method for industrial clusters identification. Journal of Business Economics Management, 13(2), 344–365.

    Google Scholar 

  • Strcit, M. (1969). Spatial associations and economic linkages between industries. Journal of Regional Science, 9(2), 177–188.

    Article  Google Scholar 

  • Titze, M., Brachert, M., & Kubis, A. (2011). The identification of regional industrial clusters using qualitative input–output analysis (QIOA). Regional Studies, 45(1), 89–102.

    Article  Google Scholar 

  • Verheyen, J., & Franck, P. (2012). Etude de faisabilité d’un Pôle Média sur le site Reyers. https://perspective.brussels/sites/default/files/documents/IdeaConsult_ADT_Pole_media%20_Rapport_13022013.pdf.

  • Vom Hofe, R., & Chen, K. (2006). Whither or not industrial cluster: conclusions or confusions? Industrial Geographer, 4(1).

  • Wang, J.-C. (2002). Strategy of Local Industrial Clusters [J]. China Industrial Economy, 3, 47–54.

    ADS  Google Scholar 

  • Wang, S., Cui, Z., Lin, J., Xie, J., & Su, K. (2022). The coupling relationship between urbanization and ecological resilience in the Pearl River Delta. Journal of Geographical Sciences, 32(1), 44–64.

    Article  Google Scholar 

  • Yang, F. F., & Yeh, A. G. J. E. (2013). Spatial development of producer services in the Chinese urban system. Environment and Planning A, 45(1), 159–179.

    Article  Google Scholar 

  • Yang, Z., Liang, J., & Cai, J. (2014). Urban economic cluster template and its dynamics of Beijing. China. Chinese Geographical Science, 24(6), 740–750.

    Article  Google Scholar 

  • Yang, S., Shimou, Y., & Luocheng, Z. (2018). Spatial Expansion of Urban Network for the Three Coastal Agglomerations of China: A study Based on Integrated Traffic Information Network. Sci-Entia Geographica Sinica, 38(6), 827–837.

    Google Scholar 

  • Yu, Z., Zu, J., Xu, Y., Chen, Y., & Liu, X. (2022). Spatial and functional organizations of industrial agglomerations in China’s Greater Bay Area. Environment Planning b: Urban Analytics City Science, 49(7), 1995–2010.

    Google Scholar 

  • Yuan, F., Chen, W., & Song, Z. (2015). Identification and Description of Manufacturing Clusters: A Case Study of Yangtze River Delta Region. Journal of Earth Information Science [In Chinese], 17(12), 1511–1519.

    Google Scholar 

  • Zhang, X., Huang, P., Sun, L., & Wang, Z. (2013). Spatial evolution and locational determinants of high-tech industries in Beijing. Chinese Geographical Science, 23(2), 249–260.

    Article  ADS  Google Scholar 

  • Zhihua, L. (2014). Evaluation model for and empirical study on performance of regional science and technology synergy innovation. Chinese Journal of Management, 11(6), 861–868.

    Google Scholar 

  • Zhou, H. (2022). Research of Text Classification Based on TF-IDF and CNN-LSTM. Journal of Physics: Conference Series.

  • Zhu, H., Li, N., Zhuang, Y., & Jiang, Z. (2021). Resilience characteristics and influencing factors of electronic information industry clusters in the Yangtze River Delta under the impact of crisis. Geographical research [In Chinese], 40(12), 3420–3436.

    Google Scholar 

Download references

Funding

Financial support from the National Key R&D Program of China (2019YFB210310-3) is gratefully acknowledged.

Author information

Authors and Affiliations

  1. College of Architecture and Urban Planning, Shenzhen University, Shenzhen, China

    Youwei Tan, Zhihui Gu & Yu Chen

  2. Shenzhen Key Laboratory for Optimizing Design of Built Environment, Shenzhen, China

    Zhihui Gu

  3. Shenzhen Lay-Out Planning Consultants Company Limited, Shenzhen, China

    Jiayun Li

Authors
  1. Youwei Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhihui Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiayun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhihui Gu or Yu Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Type

Major industries

Medium industry

C Manufacturing industry

C24 Education, Industry, Sports and Entertainment Supplies Manufacturing

C241 Cultural and educational office supplies manufacturing

C29 Rubber and plastic products industry

C292 Plastic product industry

C33 Metal products industry

C335 Manufacturing of metal products for construction and safety

C339 Casting and other metal products manufacturing

C34 General equipment manufacturing industry

C347 Machinery manufacturing for culture and office

C35 Special equipment manufacturing

C356 Manufacture of special equipment for electronic and electrical machinery

C38 Electrical machinery and equipment manufacturing industry

C382 Power transmission, distribution and control equipment manufacturing

C383 Manufacture of wire, cable, optical cable and electrical equipment

C385 Manufacture of household electrical appliances

C387 Lighting equipment manufacturing

C389 Other electrical machinery and equipment manufacturing

C39 Computer, communications and other electronic equipment manufacturing

C391 Computer manufacturing

C392 Communication equipment manufacturing

C393 Radio and television equipment manufacturing

C395 Manufacture of non-professional audiovisual equipment

C396 Intelligent consumer equipment manufacturing

C397 electronic device manufacturing

C398 electronic components and electronic special materials manufacturing

C399 Other electronic equipment manufacturing

C40 Instrument manufacturing

C401 General Instrumentation Manufacturing

C402 special instrument manufacturing

F Wholesale and retail

F51 Wholesale business

F514 Culture, sports goods and equipment wholesale

F517 Wholesale of mechanical equipment, hardware and electronic products

F52 Retail business

F527 Specialized retail of household appliances and electronic products

I Information transmission, software and information technology services

I65 Software and information technology services

I652 Integrated Circuit Design

I656 Information technology consulting service

M the scientific study and technological service enterprise

M74 Professional, scientific and technical services

M749 Industrial and Professional Design and Other Professional Technical Services

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, Y., Gu, Z., Chen, Y. et al. The Identification of Industrial Clusters and their Spatial Characteristics Based on Natural Semantics. Appl. Spatial Analysis 17, 1–25 (2024). https://doi.org/10.1007/s12061-023-09528-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-023-09528-9

Keywords

Search

Navigation