Where and Why Travelers Visit? Classifying Coastal Tourism Activities Using Geotagged Image Content from Social Media Data

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space.

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Publication Details

Journal
ISPRS International Journal of Geo-Information
Published
2024-10-07
DOI
https://doi.org/10.3390/ijgi13100355
Citations
4
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
2.73

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article

Where and Why Travelers Visit? Classifying Coastal Tourism Activities Using Geotagged Image Content from Social Media Data

Woo‐Kyun Lee, Gang Sun Kim, Choong-Ki Kim
4 citations
ISPRS International Journal of Geo-Information
Human Mobility and Location-Based Analysis
2.73
article

Where and Why Travelers Visit? Classifying Coastal Tourism Activities Using Geotagged Image Content from Social Media Data

Woo‐Kyun Lee, Gang Sun Kim, Choong-Ki Kim
article en
4 citations

Abstract

Accurate information regarding the size, activity, and distribution of coastal tourists is essential for the effective management and planning of coastal tourism. In this study, geotagged photos uploaded to social network services were classified to identify coastal tourism activities. These activities were linked with spatial-scale data on tourist numbers estimated from social media data. To classify the activities, which included recreation, appreciation, education, and other activities, an image-supervised classification model was trained using 12,229 images, and the test accuracy was found to be 0.7244. On the Flickr platform, 43% of the image data located in the coastal land of South Korea are other activities, 39% are appreciation activities, and 18% are recreation and education activities. Other activities are mainly located in urban areas with a high population density and are spatially concentrated, while appreciation activities are mainly located in the natural environment and tend to be spatially spread out. Data on tourist activity categorization through content classification, combined with traditional tourist volume estimates, can help us understand previously overlooked information and context about a space.

ISPRS International Journal of Geo-InformationVol. 13(10)
Korea University (KR), Korea Environment Institute (KR)
Korea University, Korea Environment Institute, Korea Coast Guard
Life below water
Openalex Percentile: Top 10%
Human Mobility and Location-Based Analysis
2.73
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Where and Why Travelers Visit? Classifying Coastal Tourism Activities Using Geotagged Image Content from Social Media Data — Woo‐Kyun Lee, Gang Sun Kim, et al. · ISPRS International Journal of Geo-Information (2024) | TGRS Research Map | TGRS