Journal of Geo-information Science >
Spatiotemporal Analysis of the Trajectories of Guqin Celebrities based on Crowdsourcing Data
Received date: 2018-11-12
Request revised date: 2019-03-20
Online published: 2019-06-15
Supported by
National Natural Science Foundation of China, No.41771477
Innovation Project of State Key Laboratory of Resources and Environment Information System, No.O88RA20BYA
Copyright
Guqin is the most classical Chinese musical instrument. In its more than 3000-year history, guqin has developed many genres with specific characteristics in different regions of China, with each genre having its representative players. If the lifeline trajectories of guqin celebrities in history can be collected, the spatiotemporal distribution of each genre can be analyzed which will help to know the development and evolution of this ancient art. Due to the lack of specialized historic literature of guqin and that the information of guqin are scattered in other literature, it is difficult to collect all the information efficiently. With the rapid development of network technology and the increasing number of internet users, more and more volunteers on the Internet are willing to participate in crowdsourcing. A spatiotemporal trajectory collection and retrieval system of guqin celebrities was built by combining Chinese guqin art, crowdsourcing, and GIS. Representations and the databases of the trajectory data and knowledge data were presented in this study. There are three modules of the system, a data collection module, a knowledge base module, and a spatial retrieval and visualization module. The data collection module collects crowdsourcing input data. The knowledge base module is used to store and retrieve knowledge of guqin. There are complex relations between genres, places, and people, so the graph database Neo4j is used to represent guqin knowledge and the rich relationships among guqin players. The spatial retrieval and visualization module displays trajectories in 2 or 3 dimensions. With the collected trajectories, the spatiotemporal distribution of locations on the trajectories was analyzed. Results show that the trajectories of guqin celebrities were consistent with the trend of population migration in China's history, and that guqin celebrities tended to stay in historically famous cities and landscapes, which were conducive to spreading the guqin culture and creating guqin music.
Key words: guqin; crowdsourcing; GIS; visualization; spatiotemporal trajectory; knowledge graph; kernel density
LIU Ju , CHEN Can , XU Jun . Spatiotemporal Analysis of the Trajectories of Guqin Celebrities based on Crowdsourcing Data[J]. Journal of Geo-information Science, 2019 , 21(6) : 844 -853 . DOI: 10.12082/dqxxkx.2019.180575
Fig. 1 Conceptual diagram of a spatio-temporal trajectory图1 古琴名人时空轨迹概念图 |
Fig. 2 Model of the guqin knowledge graph图2 古琴知识图谱模型 |
Fig. 3 Framework of the guqin spatio-temporal information system图3 古琴时空信息采集和检索系统架构 |
Fig. 4 Structure of the trajectory database of guqin celebrities图4 古琴名人轨迹数据库结构 |
Fig. 5 Data input of a guqin celebrity's trajectory图5 古琴名人轨迹数据输入 |
Fig. 6 Text parsing of a guqin celebrity’s trajectory图6 古琴名人轨迹文本解析 |
Fig. 7 Combination of user-input trajectory and system-archived trajectory图7 将用户输入轨迹与系统中轨迹重组后的结果 |
Fig. 8 Flowchart of crowdsourcing collection of guqin celebrities’ trajectories图8 古琴名人轨迹数据众包流程 |
Fig. 9 Query result of guqin knowledge graph图9 古琴名人关系图谱查询结果 |
Fig. 10 Display of 3-D trajectory图10 三维轨迹显示 |
Fig. 11 Knowledge graph图11 知识图谱 |
Tab. 1 Statistics of the guqin celebrities’ trajectories表1 古琴名人轨迹数据统计 |
历史朝代 | 轨迹数量/条 | 古琴名人 |
---|---|---|
春秋战国 | 19 | 伯牙、孔子、师旷、师文 |
西汉 | 50 | 司马相如 |
东汉 | 8 | 蔡邕 |
三国 | 16 | 蔡文姬、嵇康、阮籍、嵇康 |
唐朝 | 2 | 董庭兰 |
宋朝 | 62 | 苏轼、范仲淹、欧阳修、王安石 |
明朝 | 52 | 邝露、严天池、朱权、徐青山等14人 |
清朝 | 34 | 张孔山、查阜西、王露、金陶等14人 |
Fig. 12 Kernel density analysis of the guqin celebrities' trajectory data in different dynasties图12 不同朝代古琴名人轨迹点的核密度分析 |
The authors have declared that no competing interests exist.
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