Research on Measurement and Community Detection of Geographic Multiple Flow Based on Multi-layer Network Methods
Geographical flows are formed by the movement and exchange of substances,information,energy,and other elements between different locations. The Geographical Multiple Flow (GMF) is the combination of various geographical flows. Human activities such as international trade,population migration,and air transportation have formed multiple global scale GMF,which will provide new information for the study of global issues like international relation. GMF is composed of single geographic flows,containing various types of information. It can effectively compensate for the shortcomings of single geographic flows and reveal the patterns that cannot be reflected by them. Thus,how to extract information from it is an urgent problem. The Global Database of Events,Language,and Tone (GDELT) is a real-time updated global news database. It records all events reported by global news media since January 1st,1979,extracting key information including participants,locations,event types,and emotional tendencies through text analysis. The paper takes the international relation research using GDELT data as the application background,exploring the measurement and community detection methods of GMF. We use networked methods to analyze GMF. Multi-layer network methods are applied to measure the structure of GMF,and the feasibility is verified on the GDELT dataset. Firstly,we select data from GDELT Event Database to construct single geographic flow networks including media networks,legislature networks,and insurgent networks,and then uses them to construct GMF networks based on multiplex network. Secondly,we use multi-layer network methods for measurement and community. For calculating GMF networks,the corresponding single-layer network methods are also used to extract features of each layer. Finally,the temporal variation characteristics of the GMF networks are analyzed. We take one year as a cycle to analyze the similarities and differences of network features at different times and discover the evolution of things reflected in it. Results show that:① GMF network contains more information than single geographic flow network,which can reflect the characteristics of the system from a more comprehensive perspective;② The characteristics of GMF can reflect the combined effects of multiple elements;③ The multi-layer network methods can directly characterize the overall features of geographic multiple flows,avoiding errors caused by comprehensive analysis;④ Combining GMF networks and geographic flow networks can yield more comprehensive results from different scales. This research provides reference for the development of GMF spatiotemporal analysis methods and the exploration of spatiotemporal big data analysis methods for international relation.
NETL
NSTL
万方数据
