An analysis on the spatiotemporal behavior of inbound tourists in Jiaodong Peninsula based on Flickr geotagged photos

• Published in: International journal of applied earth observation and geoinformation Vol. 120; p. 103349
• Main Authors: Liu, Lin, Zhang, Yongqi, Ma, Zepeng, Wang, Hengsheng
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2023; Elsevier
• Subjects: Aggregation of communities; Geotagged photos; K-core decomposition algorithm; Scale effect; Spatiotemporal behavior characteristics; STL time series analysis; Geotagged photos; STL time series analysis; K-core decomposition algorithm; Spatiotemporal behavior characteristics; Scale effect; Aggregation of communities
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2023.103349

•The time series model provides a fine-grained analysis that supplements official survey data.•Hotspot Status Index (HSI) provides a new perspective for extracting popular tourist attractions.•The k-core decomposition algorithm is introduced into the field of tourism research.•The multi-scale Fast Unfolding algorithm is used to detect the aggregation structure of the tourist flow network. Exploring the spatiotemporal behavior characteristics of inbound tourists is of great practical significance to the management and planning of attractions. Based on Flickr geotagged photos and metadata, the research analyzes the spatiotemporal behavior characteristics of inbound tourists in Jiaodong Peninsula from the perspective of tourist flow network. We decompose the time series characteristics of tourists with STL decomposition algorithm, and predict the inbound tourism trend through the time series model. We propose the Hot Status Index (HSI) based on network centrality, extract and analyze the distribution pattern of inbound tourism hotspots with the structural hole method. The k-core decomposition algorithm is introduced to analyze the hierarchical characteristics of the tourist flow network. And Fast Unfolding algorithm is adopted to analyze the characteristics of community aggregation under different scales, dividing the scenic area into four communities. The results show that the time series model can accurately estimate the trend of the number of tourists. And there is a “competition” effect among attractions in Jiaodong Peninsula. The attractions show a hierarchy effect, of which the core layer has obvious small-world characteristics, with a clustering coefficient of 0.768. The inter-city tourist flow in Jiaodong Peninsula reveals a closed “multi-triangle” distribution pattern, mainly in the marginal coastal cities. Qingdao old town presents community stability, and other urban communities have a scale effect, mainly comprising two tourism circles.