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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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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