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
从海量农业空间数据中提取有价值的信息,对于农业空间信息管理水平的提升非常重要。本文在空间数据挖掘理论和应用研究的基础上,首先介绍了农业空间数据挖掘(Agricultural Spatial Data Mining,简称ASDM)的基础理论、方法和一般处理过程...从海量农业空间数据中提取有价值的信息,对于农业空间信息管理水平的提升非常重要。本文在空间数据挖掘理论和应用研究的基础上,首先介绍了农业空间数据挖掘(Agricultural Spatial Data Mining,简称ASDM)的基础理论、方法和一般处理过程,给出了ASDM系统开发的框架结构,并对基于GIS的ASDM系统开发进行了实例阐述,以期为农业空间数据挖掘的发展提供理论指导。展开更多
A geodemographic classification aims to describe the most salient characteristics of a small area zonal geography.However,such representations are influenced by the methodological choices made during their constructio...A geodemographic classification aims to describe the most salient characteristics of a small area zonal geography.However,such representations are influenced by the methodological choices made during their construction.Of particular debate are the choice and specification of input variables,with the objective of identifying inputs that add value but also aim for model parsimony.Within this context,our paper introduces a principal component analysis(PCA)-based automated variable selection methodology that has the objective of identifying candidate inputs to a geodemographic classification from a collection of variables.The proposed methodology is exemplified in the context of variables from the UK 2011 Census,and its output compared to the Office for National Statistics 2011 Output Area Classification(2011 OAC).Through the implementation of the proposed methodology,the quality of the cluster assignment was improved relative to 2011 OAC,manifested by a lower total withincluster sum of square score.Across the UK,more than 70.2%of the Output Areas(OAs)occupied by the newly created classification(i.e.AVS-OAC)outperform the 2011 OAC,with particularly strong performance within Scotland and Wales.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.
文摘从海量农业空间数据中提取有价值的信息,对于农业空间信息管理水平的提升非常重要。本文在空间数据挖掘理论和应用研究的基础上,首先介绍了农业空间数据挖掘(Agricultural Spatial Data Mining,简称ASDM)的基础理论、方法和一般处理过程,给出了ASDM系统开发的框架结构,并对基于GIS的ASDM系统开发进行了实例阐述,以期为农业空间数据挖掘的发展提供理论指导。
文摘A geodemographic classification aims to describe the most salient characteristics of a small area zonal geography.However,such representations are influenced by the methodological choices made during their construction.Of particular debate are the choice and specification of input variables,with the objective of identifying inputs that add value but also aim for model parsimony.Within this context,our paper introduces a principal component analysis(PCA)-based automated variable selection methodology that has the objective of identifying candidate inputs to a geodemographic classification from a collection of variables.The proposed methodology is exemplified in the context of variables from the UK 2011 Census,and its output compared to the Office for National Statistics 2011 Output Area Classification(2011 OAC).Through the implementation of the proposed methodology,the quality of the cluster assignment was improved relative to 2011 OAC,manifested by a lower total withincluster sum of square score.Across the UK,more than 70.2%of the Output Areas(OAs)occupied by the newly created classification(i.e.AVS-OAC)outperform the 2011 OAC,with particularly strong performance within Scotland and Wales.
