Mining co-location patterns of manufacturing firms using Q statistic and additive color mixing
Copyright: © 2024 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
The agglomeration effect significantly influences firms' site selection. Manufacturing firms often exhibit intricate spatial co-location patterns that are indicative of agglomerations due to their reliance on material input and product output across various subdivisions of manufacture. In this study, we present an analytical approach employing the Q statistic and additive color mixing visualization to assess co-location patterns of manufacturing firms. We identified frequent pairs and triplets of manufacturing divisions, mapping them to reveal distinct categories: labor-intensive clusters, upstream/downstream industrial chains, and technology-spillover clusters. These agglomeration categories concentrate in different regions of the city. Policy implications are proposed to promote the upgrade of labor-intensive divisions, enhance the operational efficiency of upstream/downstream industrial chains, and reinforce the spillover effects of technology-intensive divisions.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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| Enthalten in: |
PloS one - 19(2024), 3 vom: 06., Seite e0299046 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Song, Yi [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 08.03.2024 Date Revised 08.03.2024 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0299046 |
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| 520 | |a The agglomeration effect significantly influences firms' site selection. Manufacturing firms often exhibit intricate spatial co-location patterns that are indicative of agglomerations due to their reliance on material input and product output across various subdivisions of manufacture. In this study, we present an analytical approach employing the Q statistic and additive color mixing visualization to assess co-location patterns of manufacturing firms. We identified frequent pairs and triplets of manufacturing divisions, mapping them to reveal distinct categories: labor-intensive clusters, upstream/downstream industrial chains, and technology-spillover clusters. These agglomeration categories concentrate in different regions of the city. Policy implications are proposed to promote the upgrade of labor-intensive divisions, enhance the operational efficiency of upstream/downstream industrial chains, and reinforce the spillover effects of technology-intensive divisions | ||
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| 700 | 1 | |a Wang, Yiheng |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Chang |e verfasserin |4 aut | |
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