Clustering Analysis of Visited Places in the City based on Social media data: The Case of Daejeon in South Korea

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City visitors travel for a variety of purposes and information about a particular city. These cities’ elements that attract visitors are scattered throughout the urban space according to their specific purpose and form. Visitors are known to visit a city based on the physical distance between the attractions and other factors such as the purpose of visit, personal preferences, and nature of the place. In addition, the acquisition path of these factors is increasingly virtualized, and platforms such as social media are used. Visitors decide where to visit through social media and share images and emotions. Therefore, the behavior of visitors must be understood in terms of the relationship between the various attractions. This study aims to develop a methodological framework for detecting the spatiotemporal behavior of visitors from social media data, analyze the clustering of visited places using this information, and extract a new spatial classification. This study is conducted in four phases to achieve this purpose, targeting Daejeon, a city in Korea. The first is a spatial analysis of Daejeon through general information such as population, weather, and the number of visitors. We also collect transportation information to analyze physical proximity. Second, we collect all regions mentioned in the Daejeon on Instagram and define POIs. The POI is given a new ID, and the posting date, number of likes, related hashtags, and posting images are collected. It creates a table by POI and organizes the database. Third, we analyze the network based on the collected data and detect the communities. It analyzes the top six communities using modularity in graph theory. Finally, spatial characteristics analysis is performed through the extracted communities, and images of each community are classified. It shows the nature of a community’s physical space and the perceptions people have of the place. By targeting Daejeon, one of the most visited cities in Korea, it is possible to understand visitors’ purpose and behavior patterns in a new direction. However, this result has limitations in focusing only on the Daejeon. Since visits are influenced by more diverse factors, starting with this study, we can analyze more diverse factors in the future. Furthermore, we believe that the new division of urban space will significantly contribute to urban planning, provision of tourism facilities, and construction of scenic areas.
Publisher
Intelligent Human Systems Integration (IHSI)
Issue Date
2022-02-23
Language
English
Citation

The 5th International Conference on Intelligent Human Systems Integration

URI
http://hdl.handle.net/10203/296627
Appears in Collection
GCT-Conference Papers(학술회의논문)
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