IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v48y2021i9p2827-2841.html

Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China

Author

Listed:
  • Zihui Liu
  • Xinyue Chen
  • Weipan Xu
  • Yimin Chen
  • Xun Li

Abstract

Industry cluster development is important to stimulate regional economy. Conventional spatial methods for detecting industry clusters use a pairwise manner to infer the co-location relationships of multiple industrial types or instances, which increases the difficulty of interpreting the results. This study proposes to use co-location patterns (CPs) mining method to directly capture the co-location of multiple industrial types from the bottom up without any conditions of data relations defined a priori. The method is applied in Dongguan, China, to investigate the industry cluster patterns at an intra-urban scale. At the city level, the results show prevalent CPs of information communication and technology industry and other associated sectors. At the sub-regional level, however, approximately 41% of the industrial CPs are different from those obtained at the city level. The local features of sub-regional industry clusters are associated with productions of, for instance, sporting goods and toys, digital instrument and office equipment, machine parts and woodware, and textile-related products.

Suggested Citation

  • Zihui Liu & Xinyue Chen & Weipan Xu & Yimin Chen & Xun Li, 2021. "Detecting industry clusters from the bottom up based on co-location patterns mining: A case study in Dongguan, China," Environment and Planning B, , vol. 48(9), pages 2827-2841, November.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:9:p:2827-2841
    DOI: 10.1177/2399808321991542
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808321991542
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2399808321991542?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sun, Yifan & Ma, Anbing & Su, Haorui & Su, Shiliang & Chen, Fei & Wang, Wen & Weng, Min, 2020. "Does the establishment of development zones really improve industrial land use efficiency? Implications for China’s high-quality development policy," Land Use Policy, Elsevier, vol. 90(C).
    2. Michael Porter, 2003. "The Economic Performance of Regions," Regional Studies, Taylor & Francis Journals, vol. 37(6-7), pages 549-578.
    3. Marcon, Eric & Puech, Florence, 2017. "A typology of distance-based measures of spatial concentration," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 56-67.
    4. Diodato, Dario & Neffke, Frank & O’Clery, Neave, 2018. "Why do industries coagglomerate? How Marshallian externalities differ by industry and have evolved over time," Journal of Urban Economics, Elsevier, vol. 106(C), pages 1-26.
    5. Marlen Komorowski, 2020. "Identifying industry clusters: a critical analysis of the most commonly used methods," Regional Studies, Regional Science, Taylor & Francis Journals, vol. 7(1), pages 92-100, January.
    6. Gilles Duranton & Henry G. Overman, 2005. "Testing for Localization Using Micro-Geographic Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1077-1106.
    7. Nilsson, Isabelle M. & Smirnov, Oleg A., 2016. "Measuring the effect of transportation infrastructure on retail firm co-location patterns," Journal of Transport Geography, Elsevier, vol. 51(C), pages 110-118.
    8. Tobias Scholl & Thomas Brenner, 2016. "Detecting Spatial Clustering Using a Firm-Level Cluster Index," Regional Studies, Taylor & Francis Journals, vol. 50(6), pages 1054-1068, June.
    9. Jean Dubé & Cédric Brunelle, 2014. "Dots to dots: a general methodology to build local indicators using spatial micro-data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 245-272, August.
    10. Goschin Zizi, 2019. "Specialisation Vs Diversification. Which One Better Upholds Regional Resilience to Economic Crises?," Journal of Social and Economic Statistics, Sciendo, vol. 8(2), pages 11-23, December.
    11. Manuel Ruiz & Fernando López & Antonio Páez, 2010. "Testing for spatial association of qualitative data using symbolic dynamics," Journal of Geographical Systems, Springer, vol. 12(3), pages 281-309, September.
    12. Mori, Tomoya & Smith, Tony E., 2015. "On the spatial scale of industrial agglomerations," Journal of Urban Economics, Elsevier, vol. 89(C), pages 1-20.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matias Nehuen Iglesias, 2021. "The Overlooked Insights from Correlation Structures in Economic Geography," Papers in Evolutionary Economic Geography (PEEG) 2105, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2021.
    2. Kalathil, Nikhil & Lanahan, Lauren & Feldman, Maryann & Fuchs, Erica R.H, 2025. "Varieties of agglomeration: Disentangling horizontal and vertical agglomeration within the manufacturing sector in the United States," Research Policy, Elsevier, vol. 54(7).
    3. Thomas Brenner, 2017. "Identification of Clusters - An Actor based Approach," Working Papers on Innovation and Space 2017-02, Philipps University Marburg, Department of Geography.
    4. Gabriel Lang & Eric Marcon & Florence Puech, 2020. "Distance-based measures of spatial concentration: introducing a relative density function," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 243-265, April.
    5. Youwei Tan & Zhihui Gu & Yu Chen & Jiayun Li, 2022. "Industry Linkage and Spatial Co-Evolution Characteristics of Industrial Clusters Based on Natural Semantics—Taking the Electronic Information Industry Cluster in the Pearl River Delta as an Example," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    6. Delgado, Mercedes & Porter, Michael E. & Stern, Scott, 2014. "Clusters, convergence, and economic performance," Research Policy, Elsevier, vol. 43(10), pages 1785-1799.
    7. Yang Li & Frank Neffke, 2022. "Relatedness in regional development: in search of the right specification," Papers in Evolutionary Economic Geography (PEEG) 2208, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Apr 2022.
    8. Frank Van Oort, 2013. "Agglomeration Economics Beyond the Specialisation-Diversity Controversy," Papers in Evolutionary Economic Geography (PEEG) 1313, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Aug 2013.
    9. Daniel Straulino & Juan C. Saldarriaga & Jairo A. G'omez & Juan C. Duque & Neave O'Clery, 2021. "Uncovering commercial activity in informal cities," Papers 2104.04545, arXiv.org.
    10. Robert Schweizog & Alan Collins, 2015. "A Simple Location Index Plus Some Maps and no Apologies: Back to Basics on the Development of Links Between Economic Integration and Spatial Concentration of Industries," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 106(1), pages 17-35, February.
    11. Henderson, J. Vernon & Thisse, Jacques-François, 2024. "Urban and spatial economics after 50 years," Journal of Urban Economics, Elsevier, vol. 144(C).
    12. Grashof, Nils, 2020. "Putting the watering can away Towards a targeted (problem-oriented) cluster policy framework," Papers in Innovation Studies 2020/4, Lund University, CIRCLE - Centre for Innovation Research.
    13. Bustos, Sebastián & Yıldırım, Muhammed A., 2022. "Production Ability and economic growth," Research Policy, Elsevier, vol. 51(8).
    14. Reinhold Kosfeld & Mirko Titze, 2014. "Benchmark Value Added Chains and Regional Clusters in German R&D Intensive Industries," MAGKS Papers on Economics 201437, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    15. Andrew M. Isserman & James Westervelt, 2006. "1.5 Million Missing Numbers: Overcoming Employment Suppression in County Business Patterns Data," International Regional Science Review, , vol. 29(3), pages 311-335, July.
    16. Xiaowen Wang & Nishang Tian & Shuting Wang, 2022. "The Impact of Information and Communication Technology Industrial Co-Agglomeration on Carbon Productivity with the Background of the Digital Economy: Empirical Evidence from China," IJERPH, MDPI, vol. 20(1), pages 1-21, December.
    17. Kristian Behrens & Rachel Guillain, 2025. "The heterogeneous determinants of coagglomeration: a differenced perspective," The Japanese Economic Review, Springer, vol. 76(4), pages 741-794, October.
    18. Xiaoxiang Zhang & Jing Yao & Katarzyna Sila-Nowicka & Chonghui Song, 2021. "Geographic concentration of industries in Jiangsu, China: a spatial point pattern analysis using micro-geographic data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(2), pages 439-461, April.
    19. Mare, David, 2005. "Concentration, Specialisation and Agglomeration of firms in New Zealand," Motu Working Papers 292889, Motu Economic and Public Policy Research.
    20. repec:elg:eechap:14395_12 is not listed on IDEAS
    21. Li, Yang & Neffke, Frank M.H., 2024. "Evaluating the principle of relatedness: Estimation, drivers and implications for policy," Research Policy, Elsevier, vol. 53(3).

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envirb:v:48:y:2021:i:9:p:2827-2841. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.