Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs

Yong Gao, Chao Ye, Xiang Zhong, Lun Wu and Yu Liu
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Yong Gao: Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Chao Ye: Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Xiang Zhong: Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Lun Wu: Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Yu Liu: Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China

Sustainability, 2019, vol. 11, issue 13, 1-18

Abstract: Spatial patterns of tourist mobility are important for tourism management and planning. A large number of traveler-generated content accumulated on the internet provide a unique opportunity for revealing comprehensive spatial patterns of tourist movements. Instead of concentrating on a single city or attraction in previous research, this work investigates the intercity travel flows extracted from the online travel blogs in China from 2012 to 2016. The descriptive statistics of travel flows are first analyzed. The distribution of travel volume is found to satisfy the power-law distribution. Based on the intercity travel flows, a network structure is then constructed to investigate tourism interactions between cities. After four communities and 14 sub-communities being detected from the network, a tourism spatial layout with regional agglomeration effects are recognized. This research concludes that distance is essential in determining tourist movements based on a spatial interaction model. Intercity travel flows decline with distance under a power-law function. These results reveal the spatial patterns of tourist movements at an intercity scale. It will be helpful for arranging tourism resources, predicting tourist flows, and maintaining sustainable tourism.

Keywords: spatial pattern; tourist movement; complex network; spatial interaction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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