Study on Tourism Flow Network Patterns on May Day Holiday

The development of tourism is based on tourism flow and studying a tourism flow network can help to elucidate its mechanism of operation. Transportation network is the path to realize the spatial displacement of tourism flow. This study used “Tencent migration” big data to explore the spatial distri...

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Main Authors: Shanshan Wu, Lucang Wang, Haiyang Liu
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/13/2/947
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spelling doaj-450531b53a4346ec82dc8817836a76b12021-01-19T00:04:53ZengMDPI AGSustainability2071-10502021-01-011394794710.3390/su13020947Study on Tourism Flow Network Patterns on May Day HolidayShanshan Wu0Lucang Wang1Haiyang Liu2College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, ChinaCollege of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, ChinaCollege of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, ChinaThe development of tourism is based on tourism flow and studying a tourism flow network can help to elucidate its mechanism of operation. Transportation network is the path to realize the spatial displacement of tourism flow. This study used “Tencent migration” big data to explore the spatial distribution characteristics and rules of tourism flow in China, providing suggestions for the development of tourism. The results demonstrate that the 361 cities studied can be divided into three types: destination-oriented, tourist-origin-oriented, and destination-oriented and tourist-origin-oriented. There are significant differences in the quantity of flow, the area of concentration, and the factors affecting the flow in the three types of cities. The larger the flow of tourism between cities, the higher the network level, and the wider the network range. The high-level nodes are closely related, while the peripheral nodes are more widely distributed, with weak attractiveness and inconvenient traffic, forming a “core-edge” structure. Different network patterns are established for different modes of transportation. The degree of response of different types of transportation to distance is the main factor influencing the network patterns of diverse paths. These findings have practical implications for the choice of appropriate travel destinations and transportation modes for tourists.https://www.mdpi.com/2071-1050/13/2/947“Tencent migration” big datatourism flowMay Daytourism networkChina
collection DOAJ
language English
format Article
sources DOAJ
author Shanshan Wu
Lucang Wang
Haiyang Liu
spellingShingle Shanshan Wu
Lucang Wang
Haiyang Liu
Study on Tourism Flow Network Patterns on May Day Holiday
Sustainability
“Tencent migration” big data
tourism flow
May Day
tourism network
China
author_facet Shanshan Wu
Lucang Wang
Haiyang Liu
author_sort Shanshan Wu
title Study on Tourism Flow Network Patterns on May Day Holiday
title_short Study on Tourism Flow Network Patterns on May Day Holiday
title_full Study on Tourism Flow Network Patterns on May Day Holiday
title_fullStr Study on Tourism Flow Network Patterns on May Day Holiday
title_full_unstemmed Study on Tourism Flow Network Patterns on May Day Holiday
title_sort study on tourism flow network patterns on may day holiday
publisher MDPI AG
series Sustainability
issn 2071-1050
publishDate 2021-01-01
description The development of tourism is based on tourism flow and studying a tourism flow network can help to elucidate its mechanism of operation. Transportation network is the path to realize the spatial displacement of tourism flow. This study used “Tencent migration” big data to explore the spatial distribution characteristics and rules of tourism flow in China, providing suggestions for the development of tourism. The results demonstrate that the 361 cities studied can be divided into three types: destination-oriented, tourist-origin-oriented, and destination-oriented and tourist-origin-oriented. There are significant differences in the quantity of flow, the area of concentration, and the factors affecting the flow in the three types of cities. The larger the flow of tourism between cities, the higher the network level, and the wider the network range. The high-level nodes are closely related, while the peripheral nodes are more widely distributed, with weak attractiveness and inconvenient traffic, forming a “core-edge” structure. Different network patterns are established for different modes of transportation. The degree of response of different types of transportation to distance is the main factor influencing the network patterns of diverse paths. These findings have practical implications for the choice of appropriate travel destinations and transportation modes for tourists.
topic “Tencent migration” big data
tourism flow
May Day
tourism network
China
url https://www.mdpi.com/2071-1050/13/2/947
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AT lucangwang studyontourismflownetworkpatternsonmaydayholiday
AT haiyangliu studyontourismflownetworkpatternsonmaydayholiday
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