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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (8): 31-40    DOI: 10.11925/infotech.2096-3467.2017.1002
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The Study on the Temporal and Spatial Distribution of Event Tourism Based on Large-scale Tourism Early Warning Platform
Ling Wang,Qianjin Dai(),Xiaojun Wu
The Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China
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Abstract  

[Objective] This paper visualizes the big data of festival visitors, aiming to analyze their movement patterns and influencing factors. [Methods] We used the GIS tools to analyze the tourist flow data of 80 scenic spots during Shanghai Tourism Festival, and constructed metrological model to examine the influencing factors. [Results] We found that initiation tourism resources, which broke the obstacles facing event tourists, included the motivation of tourists gathering and rapid flows. The number of tourists declined from the multiple event centers to surrounding areas. The time distribution of tourist flow did not follow the classic “inverted U-shape”, and then led to more agglomeration effects. Tourism resource endowment, traffic conditions, competitiveness of tourism products, and tourism reception could all promote tourists gathering, while facilities (i.e. capacity) was no longer the key element in attracting visitors. [Limitations] More research is needed to discuss the dynamic path of tourist flow. [Conclusions] GIS and big data technology can be used to present the visitors’ flow.

Key wordsEvent Tourism      Passenger Flow      Time and Space Distribution      Agglomeration Effect      Influence Factor     
Received: 09 October 2017      Published: 08 September 2018

Cite this article:

Ling Wang,Qianjin Dai,Xiaojun Wu. The Study on the Temporal and Spatial Distribution of Event Tourism Based on Large-scale Tourism Early Warning Platform. Data Analysis and Knowledge Discovery, 2018, 2(8): 31-40.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1002     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I8/31

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