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Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows

1
School of Humanities, Southeast University, Nanjing 210096, China
2
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
5
Key Lab of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350108, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(7), 314; https://doi.org/10.3390/ijgi8070314
Received: 10 June 2019 / Revised: 11 July 2019 / Accepted: 20 July 2019 / Published: 23 July 2019
(This article belongs to the Special Issue Smart Cartography for Big Data Solutions)
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Abstract

At present, population mobility for the purpose of tourism has become a popular phenomenon. As it becomes easier to capture big data on the tourist digital footprint, it is possible to analyze the respective regional features and driving forces for both tourism sources and destination regions at a macro level. Based on the data of tourist flows to Nanjing on five short-period national holidays in China, this study first calculated the travel rate of tourist source regions (315 cities) and the geographical concentration index of the visited attractions (51 scenic spots). Then, the spatial autocorrelation metrics index was used to analyze the global autocorrelation of the travel rates of tourist source regions and the geographical concentration index of the tourist destinations on five short-term national holidays. Finally, a heuristic unsupervised machine-learning method was used to analyze and map tourist sources and visited attractions by adopting the travel rate and the geographical concentration index accordingly as regionalized variables. The results indicate that both source and sink regions expressed distinctive regional differentiation patterns in the corresponding regional variables. This study method provides a practical tool for analyzing regionalization of big data in tourist flows, and it can also be applied to other origin-destination (OD) studies. View Full-Text
Keywords: geographical regionalization; tourist flow data; cartographic generalization; geospatial interaction; regional analysis; geographic concentration index of scenic spots geographical regionalization; tourist flow data; cartographic generalization; geospatial interaction; regional analysis; geographic concentration index of scenic spots
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Gu, Q.; Zhang, H.; Chen, M.; Chen, C. Regionalization Analysis and Mapping for the Source and Sink of Tourist Flows. ISPRS Int. J. Geo-Inf. 2019, 8, 314.

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