基于街景照片的城市旅游情感空间探测研究

doi:10.13959/j.issn.1003-2398.2023.03.018

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Abstract Under the paradigm of pictorial urbanism, street view photos have become a new type of big data technique for tourists to perceive the urban environment. However, due to technical limitations, tourism sentiment researches based on photos are still at the preliminary exploration stage. This paper introduces a deep learning model in order to measure the tourism emotions of street view photos by the computer vision. It empirically analyses the emotional spatial characteristics of typical tourist blocks in Xi'an. It shows the deep learning model can accurately detect the spatial characteristics of urban tourism emotions using street view photos as a data source; the positive emotions and negative emotions of typical tourist blocks in Xi'an show different types of agglomeration patterns; the tourism emotions inside typical tourist blocks in Xi'an show complex and diverse characteristics. It is found that although the Bell and Drum Tower region, Xiaozhai region and Qujiang region are three different types of tourism blocks, the tourism emotion space detection results have the following commonalities: 1) The positive emotional space is mainly concentrated around the main road, universities, shopping malls or scenic spots and their surrounding areas; 2) The positive emotional space is slightly different with different functional zones and different emotional types; 3) The distribution of negative emotional space is more obvious than that of the positive emotional space. From the results of emotion space exploration, it reflects the interaction and mutual influence of people, environment and emotion from the perspective of emotional geography.

Key words： street view photos deep learning tourism emotions space exploration

Received: 10 February 2022

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F590.1

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	Articles by authors
	LU Huan
	YANG Miao-tian
	ZHANG Yan-yan
	LI Jun-yi

Cite this article:

LU Huan,YANG Miao-tian,ZHANG Yan-yan等. URBAN TOURISM SENTIMENT SPATIAL DETECTION BASED ON STREET VIEW PHOTOS[J]. HUMAN GEOGRAPHY, 2023, 38(3): 164-172.

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http://rwdl.xisu.edu.cn/EN/10.13959/j.issn.1003-2398.2023.03.018 OR http://rwdl.xisu.edu.cn/EN/Y2023/V38/I3/164

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