Research on the Spatial-Time Evolution and Influence Mechanism of Logistics Network Structure in Zhengzhou Metropolitan Area

Abstract

Based on the modified gravity model and social network analysis method, the evolution characteristics and driving mechanism of logistics network spatial structure in Zhengzhou metropolitan area (ZMA ) from 2008 to 2019 are analyzed at the county (district) scale. The research finds that: (1) The distribution of logistics links in counties (districts) of ZMA has significant spatial heterogeneity, showing a spatial pattern of "sparse in the east and dense in the west", and the counties (districts) logistics links are concentrated in Zhengzhou. ( 2) The overall level of the logistics network density in ZMA is low, but it is fluctuating and rising. The overall realization is the logistics radiation effect. The core area of the logistics network layout is mainly concentrated in the main urban area of Zhengzhou and surrounding counties (districts), and the spatial scope is expanding and connecting into one piece. The contact density of counties (districts) in the core area is high, and the contact density between counties (districts) in the edge area is low. (3) The logistics connection intensity of counties (districts) in ZMA has a positive role in promoting the logistics connection intensity of surrounding areas, with obvious spatial correlation characteristics. (4) The upgrading of industrial structure (lnInd), the level of economic development (lnGDP), and the size of population (lnPop) are the main factors that promote the formation of the local logistics network structure. The upgrading of industrial structure (lnInd) and the level of economic development (lnGDP) restrain the logistics network structure and internal logistics network structure in the surrounding areas, while the population size (lnPop) and government intervention (lnGov) promote the formation of the external logistics network structure in the surrounding areas.INDEX TERMS Zhengzhou metropolitan area, logistics network structure, modified gravity model, Social network analysis, influential mechanism.