Identifying the spatiotemporal interaction pattern of agricultural product circulation(APC)is crucial for agricultural resource adjustment and food security.Current studies are mostly based on static statistical data ...Identifying the spatiotemporal interaction pattern of agricultural product circulation(APC)is crucial for agricultural resource adjustment and food security.Current studies are mostly based on static statistical data over an entire year or a specific period,which cannot describe the spatial pattern of APC and its seasonal variation on a fine spatiotemporal scale.Thus,this study extracts an APC trip chain based on national truck trajectory data and constructs the flow network of the Beijing APC with the city as the spatial unit and the season as the temporal unit.The spatial interaction pattern and seasonal variation in APC are then analyzed from the network spatial form,city node function role,and transportation corridors.The results are as follows:(1)Compared with methods based on static statistical data,the proposed method provides a more complete and refined depiction of the spatiotemporal interaction pattern of APC.(2)The flow network of the Beijing APC involves 316 cities in China,of which 143 cities play a major role with typical seasonal characteristics.These cities can be divided into perennial core cities,perennial major cities,core cities in winter-spring,major cities in winter-spring,core cities in summer-autumn,and major cities in summer-autumn,contributing 2.6%-40.3%to the Beijing APC.(3)There are 6 transportation corridors for the Beijing APC.The Beijing-Tianjin-Hebei corridor and coastal corridor contribute 53.5%and 12.8%of the annual supply,respectively,with a balanced supply in all seasons.The Beijing-Kunming corridor and Beijing-Guangzhou corridor contribute 14.3%and 9.0%,respectively,with much higher supplies in winter and spring.The northeast and northwest corridors contribute 7.3%and 3.3%,respectively,mainly in the summer and autumn.These results help deepen the understanding of agricultural product supply patterns and provide a reference for the design and optimization of agricultural product transportation routes.展开更多
基金Innovation Project of LREIS,No.KPI003National Natural Science Foundation of China,No.42101423Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDA23010202。
文摘Identifying the spatiotemporal interaction pattern of agricultural product circulation(APC)is crucial for agricultural resource adjustment and food security.Current studies are mostly based on static statistical data over an entire year or a specific period,which cannot describe the spatial pattern of APC and its seasonal variation on a fine spatiotemporal scale.Thus,this study extracts an APC trip chain based on national truck trajectory data and constructs the flow network of the Beijing APC with the city as the spatial unit and the season as the temporal unit.The spatial interaction pattern and seasonal variation in APC are then analyzed from the network spatial form,city node function role,and transportation corridors.The results are as follows:(1)Compared with methods based on static statistical data,the proposed method provides a more complete and refined depiction of the spatiotemporal interaction pattern of APC.(2)The flow network of the Beijing APC involves 316 cities in China,of which 143 cities play a major role with typical seasonal characteristics.These cities can be divided into perennial core cities,perennial major cities,core cities in winter-spring,major cities in winter-spring,core cities in summer-autumn,and major cities in summer-autumn,contributing 2.6%-40.3%to the Beijing APC.(3)There are 6 transportation corridors for the Beijing APC.The Beijing-Tianjin-Hebei corridor and coastal corridor contribute 53.5%and 12.8%of the annual supply,respectively,with a balanced supply in all seasons.The Beijing-Kunming corridor and Beijing-Guangzhou corridor contribute 14.3%and 9.0%,respectively,with much higher supplies in winter and spring.The northeast and northwest corridors contribute 7.3%and 3.3%,respectively,mainly in the summer and autumn.These results help deepen the understanding of agricultural product supply patterns and provide a reference for the design and optimization of agricultural product transportation routes.
