A Hybrid Visualization Model for Knowledge Mapping: Scientometrics, SAOM, and SAO
Guangnian Xiao, Liu Chen, Xinqiang Chen, Chenming Jiang, Anning Ni, Chunqin Zhang, Fang Zong
Abstract
Predicting the crowd flow in various areas of the city is of strategic importance for traffic control and public safety. In recent years, crowd flow prediction based on spatio-temporal data are gaining more and more attention. In order to better understand the current status of spatio-temporal crowd flow prediction research and global cooperation, we use scientometric methods, social network analysis, and Stochastic Actor-oriented Model (SAOM) to visualize and analyze the source journals, hotspot co-occurrence networks, and national cooperation networks based on the relevant literature included in the Web of Science database, so as to explore the current status and characteristics of related academic research. In addition, this paper constructs a technical framework based on Subject–Action–Object (SAO) structural information, and draws a technical roadmap containing five levels: data, technology, influence factors, objectives, and applications. The visual mapping of the evolutionary path of technology topics based on SAO structure information can assist in analyzing the evolutionary path of technology topics and their development trends. This study contributes to the existing knowledge system of spatio-temporal crowd flow prediction by proposing a new, integrated, and holistic knowledge map.