Delineation of an urban agglomeration boundary based on Sina Weibo microblog ‘check-in’ data: A case study of the Yangtze River Delta

Zhu, Feng; Yang, Chi-Ming; Qin, Xiao; Wang, Bo

doi:10.1016/j.cities.2016.08.014

Cited by 123 publications

(56 citation statements)

References 32 publications

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“…The primary inspiration in the use of LBSN is to share interests and activities, and thereby build new and close social relationships, enabling researchers to discover patterns in users' activities and preferences from the big data generated by the LBSN. The data source for this research is Weibo, regarded as one of the biggest and most popular microblogs in China, which allows the users to 'check-in' from any location using their mobile devices [19]. According to Weibo Data Center [49], the total number of monthly active users in China reached 462 million by December 2018, which is about one third of its entire population.…”

Section: Data Acquisition and Preparationmentioning

confidence: 99%

“…It is difficult to find the exact related data about the tourist in China (e.g., [17]). However, several previous studies (e.g., [18,19]) used Weibo, which is one of the most popular microblogs and online networking platform in China, to analyze the tourists' behaviors. Therefore, the current study attempts to use check-in data from Weibo to differentiate tourists and residents in Shanghai and perform the spatiotemporal analysis for both groups in the city.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Khan

Wan

2020

IJGI

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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.

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Section: Data Acquisition and Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Khan

Wan

2020

IJGI

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“…Many researchers [51][52][53][54][55] have concentrated on human mobility patterns, venue tagging, and check-in behavior toward using location-based social networks. Automatic venue tagging is one of the new concepts to observe spatial differences in many applications [56,57].…”

Section: Related Workmentioning

confidence: 99%

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Muhammad

Wan

Cervantes

et al. 2018

IJGI

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Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as "Weibo") in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

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“…While several studies (e.g. Wang et al, 2016;Zhen, Cao, Qin, & Wang, 2017) have used the Weibo platformi.e. one of the most popular online social microblogging and networking platforms in Chinato analyse Chinese tourists' spatio-temporal behaviours in mainland Chinese cities, this study attempted to use Weibo check-in data to differentiate mainland Chinese tourists (MCTs) and residents in Hong Kong and analyse their spatio-temporal behaviour patterns in this city.…”

Section: Introductionmentioning

confidence: 99%

Analysing trends in the spatio-temporal behaviour patterns of mainland Chinese tourists and residents in Hong Kong based on Weibo data

Xing

Spierings

Dijst

et al. 2019

Current Issues in Tourism

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Visiting tourists and residents of a city interact at various locations at various times. Previous studies paid little attention to comparing the spatio-temporal behaviours of tourists and residents from a long-term perspective. The aim of the present study was to identify and compare the spatio-temporal behaviours of mainland Chinese tourists and residents in Hong Kong over a period of five years. Their behaviours were compared by means of kernel density analysis and temporal statistical analysis, using Weibo geotagged check-in data and geographic information systems (GIS). The results show that the spatial behaviours of mainland Chinese tourists (MCT) are more concentrated than those of residents, especially in central urban areas, whereas residents also visit suburban and exurban areas. Simultaneously, MCTs' temporal behaviours varied significantly, whereas those of residents were relatively stable. From a long-term perspective, we found that in the central urban area, MCTs' preferences for locations tended to be relatively fixed, but their activity in local neighbourhoods and residential quarters decreased. However, residents' interest in typical tourism highlights decreased reflecting potential strategies of avoidance in those areas whereas their activities in other tourism hotspots were more stable. These findings can be applied in urban planning, destination management and sustainable development.

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Delineation of an urban agglomeration boundary based on Sina Weibo microblog ‘check-in’ data: A case study of the Yangtze River Delta

Cited by 123 publications

References 32 publications

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

RETRACTED: Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

Analysing trends in the spatio-temporal behaviour patterns of mainland Chinese tourists and residents in Hong Kong based on Weibo data

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