Abstract
The aim of this study is to analyze and compare the patterns of behavior of tourists and residents from Location-Based Social Network (LBSN) data in Shanghai, China using various spatiotemporal analysis techniques at different venue categories. The paper presents the applications of location-based social network’s data by exploring the patterns in check-ins over a period of six months. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data is translated into the Geographical Information Systems (GIS) format, and compared with the help of temporal statistical analysis and kernel density estimation. The venue classification is done by using information regarding the nature of physical locations. The findings reveal that the spatial activities of tourists are more concentrated as compared to those of residents, particularly in downtown, while the residents also visited suburban areas and the temporal activities of tourists varied significantly while the residents’ activities showed relatively stable behavior. These results can be applied in destination management, urban planning, and smart city development.
Highlights
Mining patterns and gaining useful insights from spatiotemporal data has been an important research topic in recent years
Application areas like urban tourism are associated with reviving the urban texture and cultural development, as well as improving the local economy and urban vitality [1,2]
Most of the previous research has been carried out using data from Location-Based Social Network (LBSN) like Foursquare, Twitter etc., to find different patterns including human mobility, activity, urban planning, and venue classification etc
Summary
Mining patterns and gaining useful insights from spatiotemporal data has been an important research topic in recent years. Interactions between the tourists and residents can be better studied by combining and comparing the spatiotemporal patterns of both groups, which provides useful insights to better understand their behavior, improve attractions, transport, services, and marketing strategies of a city, based on the actual facts from users’ data [12]. By exploring patterns for when and where tourists and residents have encountered at various venues, along with the nature of the venues, it can potentially change the competition for urban areas between both groups and improve avoidance behaviors and crowd management Such types of analysis can be beneficial by indicating the patterns in the preferences of tourists and residents, and providing us with the potential insights that are crucial in achieving more sustainable cities, and for marketing, managing, and planning tourism activities and attractions.
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