Mining co-location patterns of manufacturing firms using Q statistic and additive color mixing

被引:0
|
作者
Song, Yi [1 ,2 ]
Li, Guanglei [3 ]
Wang, Yihan [4 ]
Wang, Yiheng [4 ,5 ]
Ren, Chang [6 ]
机构
[1] Minist Nat Resources, Key Lab Urban Land Resources Monitoring & Simulat, Shenzhen, Guangdong, Peoples R China
[2] Dev Res Ctr Nat Resource & Real Estate Assessment, Shenzhen, Guangdong, Peoples R China
[3] Peking Univ, Sch Urban Planning & Design, Shenzhen, Guangdong, Peoples R China
[4] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China
[5] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing, Peoples R China
[6] Civil Aviat Flight Univ China, Coll Air Traff Management, Guanghan, Sichuan, Peoples R China
来源
PLOS ONE | 2024年 / 19卷 / 03期
关键词
EXPLORATORY ANALYSIS; AGGLOMERATION; CLUSTERS; LOCALIZATION; COMPETITION; ECONOMIES; GERMAN; RETAIL;
D O I
10.1371/journal.pone.0299046
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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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收藏
页数:21
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