Mining co-location patterns of manufacturing firms using Q statistic and additive color mixing
Yi Song, Guanglei Li, Yihan Wang, Yiheng Wang, Chang Ren
TL;DR
This paper uses statistical and visual methods to analyze how manufacturing firms cluster together in cities, revealing patterns linked to labor, supply chains, and technology.
Contribution
The novel approach combines the Q statistic with additive color mixing to identify and visualize co-location patterns of manufacturing firms.
Findings
Frequent co-location patterns include labor-intensive clusters, upstream/downstream industrial chains, and technology-spillover clusters.
These agglomeration categories are spatially concentrated in different urban regions.
Policy recommendations aim to improve efficiency and promote upgrades in manufacturing divisions.
Abstract
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…
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Taxonomy
TopicsRegional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Regional Economic and Spatial Analysis