A geospatial and big data exploration of tourism destinations: A case study of Busan, Republic of Korea

Seieun Kim, Aura Lydia Riswanto, Angellie Williady, Reza Asriandi Ekaputra and Hak-Seon Kim ()
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Seieun Kim: Kyungsung University
Aura Lydia Riswanto: Kyungsung University
Angellie Williady: Kyungsung University
Reza Asriandi Ekaputra: Kyungsung University
Hak-Seon Kim: Kyungsung University

Electronic Markets, 2025, vol. 35, issue 1, No 46, 17 pages

Abstract: Abstract Busan is known as one of the most popular tourist destinations in South Korea, with its combination of natural beauty, cultural heritage, and modern urban attractions attracting millions of visitors annually. However, understanding what drives visitor satisfaction remains challenging due to the complex interplay between spatial accessibility, attraction quality, and service availability. This study aims to bridge this gap by integrating GIS with big data analytics from online reviews to analyze the spatial dimensions of visitor satisfaction in Busan. The finding reveals that proximity has the strongest positive impact on customer satisfaction, followed by visitor perception and attraction site, while hospitality shows no significant effect. By integrating GIS with online reviews, this study identifies key attractions such as Gamcheon Culture Village and the Busan National Science Museum and assesses how transportation accessibility and hospitality services shape the visitor experience. The results highlight that proximity and accessibility play a crucial role in shaping visitor satisfaction, with well-connected transportation networks significantly enhance the overall tourism experience. By integrating big data analytics with spatial analysis, this study provides new insights into how urban infrastructure, visitor perceptions, and attraction distribution interact, offering practical implications for improving tourism planning and destination management.

Keywords: Tourism destinations; Geographic information systems; Big data analytics; Customer satisfaction; Online review; Kernel density estimation (KDE) (search for similar items in EconPapers)
JEL-codes: Z32 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12525-025-00791-x

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