Journal of Geo-information Science >
A New Approach for Tourists' Visual Behavior Patterns and Perception Evaluation based on Multi-source Data
Received date: 2021-12-30
Revised date: 2022-02-28
Online published: 2022-12-25
Supported by
National Natural Science Foundation of China(42171219)
Fujian Natural Science Foundation Project(2020J01011)
User Generated Content (UGC), as a new type of geographic big data for perceiving the physical space of tourism destination, depicts the objective environment of tourism destination from the perspective of users, which is an important way to explore the perception of tourism destination. However, the traditional tourism research has limited ability to deal with travel photos. The development of deep learning image semantic segmentation technology provides strong support for mining tourists' visual behavior patterns and exploring tourism destination environmental perception. This study proposes a framework for tourists' visual behavior model and perception evaluation, which integrates the big data of online travel photos and small data of questionnaire survey, and applies it to the case of Gulangyu Island. Firstly, 744 tourism trajectories are clustered into six types of visual behavior patterns, and visualized and spatiotemporal analysis is carried out; Secondly, based on the full convolution network algorithm, the semantics of 22 507 travel photos are quantified to explore the spatial differentiation of the elements concerned by tourists with different visual modes; Finally, through the correlation analysis of photo semantics and scene perception questionnaire and the multiple linear regression model, the overall visual perception satisfaction of tourism destination is evaluated, and the corresponding spatial optimization suggestions are put forward. The results show that: (1) the visual behavior patterns of tourists on Gulangyu Island are clustered into six categories: single point tour, island scenery tour, around the island tour, street and lane space tour, heritage building tour, and whole island tour; (2) Tourists with different visual behavior patterns have spatial agglomeration in their visual interest areas, and the transfer of visual space follows the geographical proximity effect; (3) The results of correlation analysis and model show that tourists prefer areas with high spatial openness, and the areas with lower perceived satisfaction have less photography behavior, which is the focus of environmental improvement; (4) Maximizing travel time and cost efficiency, built environment, psychological environment, and social environment are the main factors affecting tourists' visual perception. This study extends the application of artificial intelligence technology in the study of tourists' visual perception, and provides a reference for tourism destination spatial optimization.
LI Yuan , GUO Jing , CHEN Yiping . A New Approach for Tourists' Visual Behavior Patterns and Perception Evaluation based on Multi-source Data[J]. Journal of Geo-information Science, 2022 , 24(10) : 2004 -2020 . DOI: 10.12082/dqxxkx.2022.210840
图4 鼓浪屿风貌分区与旅游者拍摄点、街景采样点GPS分布Fig. 4 GPS distribution map of Gulangyu Island landscape zoning, tourist photography points and street view sampling points |
表1 鼓浪屿风貌分区Tab. 1 Landscape zoning of Gulangyu Island |
风貌区编号 | 风貌区 | 空间单元 |
---|---|---|
A | 旅游商业区 | A1、A2、A3、A4、A5 |
B | 滨海风景区 | B1、B2、B3 |
C | 北部森林区 | C |
D | 内厝澳社区 | D1、D2、D3 |
E | 遗产建筑区 | E1、E2、E3、E4、E5、E6 |
表2 数据来源与类型描述表Tab. 2 Data source and type description |
数据类型 | 数据来源 | 数据格式 | 初始样本/个 | 处理后样本/个 | 有效率/% | 数据时间 |
---|---|---|---|---|---|---|
大数据 | 两步路、六只脚 | 照片 | 26 422 | 22 507 | 85.18 | 2007年11月—2021年9月 |
GPS轨迹 | 984 | 744 | 75.61 | |||
小数据 | 街景采样 | 照片 | 793 | 777 | 97.98 | 2021年9月 |
问卷调查 | 量表 | 360 | 354 | 98.33 | 2021年9月 |
表3 视觉行为模式聚类统计表Tab. 3 Clustering statistics of photographic behavior chain types (个) |
视觉行为 模式 | 摄影轨迹链 数量 | 照片 数量 | 单条轨迹平均 照片数量 |
---|---|---|---|
单点游 | 177 | 845 | 4.77 |
海岛风光游 | 209 | 3116 | 14.91 |
环岛游 | 76 | 788 | 10.37 |
街巷空间游 | 123 | 5125 | 41.67 |
遗产建筑游 | 60 | 2868 | 47.80 |
全岛游 | 99 | 9657 | 97.55 |
表4 旅游者视觉感知要素的多重线性回归分析Tab. 4 Multiple linear regression analysis of tourists' visual perception factors |
因变量 | 自变量 | 标准系数 | t | 累积R² | 方差膨胀因子 | 容忍度 |
---|---|---|---|---|---|---|
视觉感知满意度 | 地板 | -0.814 | -7.265*** | 0.731 | 1.028 | 0.972 |
建筑 | -0.429 | -3.824** | 0.939 | 1.028 | 0.972 |
注:显著性*p<0.1,**p<0.05,***p<0.01。 |
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