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

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.

Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China

“…The analytical methods for, and the studies conducted on human activities from, mobile data are discussed in various works [42][43][44]. The use of LBSNs was investigated by Lindqvist et al [45], followed by a number of studies on human activity patterns based on LBSN data [16,[46][47][48]. Zhang and Chow [49] presented personalized geo-social recommendations based on LBSNs by using two different datasets (Foursquare and Gowalla), and observed similar patterns in both datasets.…”

Examining the population flow network in China and its implications for epidemic control based on Baidu migration data

“…For example, the frequency data of trains (Wang, 2018) and flights (Guimera et al, 2005) were used to understand the trends in population movement between cities. Geo-tagged social media data have also been used to explore the characteristics of human mobility at a much finer temporary and spatial scale (Khan et al, 2020;Hawelka et al, 2014;Naaman et al, 2012;Kamath et al, 2012).…”

Whom to Follow? A Comparison of Walking Routes Computed Based on Social Media Photos from Different Types of Contributors

“…[13], for example, used a deep neural network using TensorFlow to classify visitors' behaviour to six groups in Honk-Kong based on geo-located data of the Weibo social check-in platform, receiving an accuracy of close to 90%. In the study of [4], the authors used Weibo data for comparing the activity of locals and tourists in the Shanghai region, showing that the activities of tourists are significantly different from those of locals in terms of their spatiotemporal patterns.…”

“…Two main areas were analysed in Manhattan, New York. Close to 500,000 Flickr geotagged photos were downloaded using Flickr API 4 and shared on GitHub 5 . The first area is in Midtown Manhattan; this area is very famous and includes cultural, entertainment and leisure attractions, such as: shopping areas, Times Square, The New York Public Library and more.…”

“…Furthermore, tourists are more active and travel more over short periods and would prefer to explore new places by different modes of travel -rather than walking -to maximize their travel experience [3]. Moreover, the frequency in which tourists use social media platforms to document famous attractions exceeds that of locals [4].…”