Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

Abstract

Mobile applications are really important nowadays due to providing the accurate check-in data for research. The primary goal of the study is to look into the impact of several forms of entertainment activities on the density dispersal of occupants in Shanghai, China, as well as prototypical check-in data from a location-based social network using a combination of temporal, spatial, and visualization techniques and categories of visitors’ check-ins. This article explores Weibo for big data assessment and its reliability in a variety of categories rather than physically obtained information by examining the link between time, frequency, place, class, and place of check-in based on geographic attributes and related implications. The data for this study came from Weibo, a popular Chinese microblog. It was preprocessed to extract the most important and associated results elements, then converted to geographical information systems format, appraised, and finally displayed using graphs, tables, and heat maps. For data significance, a linear regression model was used, and, for spatial analysis, kernel density estimation was utilized. As per results of hours-to-day usage patterns, enjoyment activities and frequency distribution are produced. Our findings are based on the check-in behaviour of users at amusement locations, the density of check-ins, rush periods for visiting amusement locations, and gender differences. Our data provide light on different elements of human behaviour patterns, the importance of entertainment venues, and their impact in Shanghai. So it can be used in pattern recognition, endorsement structures, and additional multimedia content for these collections.

Tourist Spatiotemporal Behavior in Cross-Border Tourism Area:

“…Tourist spatiotemporal behavior is a complex concept that includes various factors, such as mobility, spatial aspects, tourist activities, and time (Hall, 2005;Lau and McKercher, 2006;Lew and McKercher, 2006). Many studies have examined spatiotemporal behavior in the field of tourism, including (Caldeira and Kastenholz, 2020;Hou, Liu et al, 2021;Lee, Tussyadiah et al, 2010;Li, Guo et al, 2022;Tussydiah and Fesenmaier, 2007). However, the majority of them have focused on a specific region or country, and there is a lack of literature regarding tourist spatiotemporal behavior in the context of cross-border tourism.…”

“…The analysis of tourist spatiotemporal behavior has undergone significant development, and studies have employed various methods to achieve their objectives. One commonly used method is GPS, as evidenced by the works of Hou, Liu et al (2021); Liu, Wang et al (2022); Sisi (2019). Other methods such as photography-based study, computer deep learning models, smart card data, and simulation using social media data have also been employed, as documented in the works of Fang, Homma et al (2023) and Shi, Long et al (2022).…”

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care

“…Several studies have been conducted in an attempt to analyze and imitate human actions through geo-data. The latest research, for instance, leverages check-in records from globally prominent LBSNs such as Twitter, Facebook, and Foursquare to reveal connections and trends among consumers such as gender, expert or less skilled groups, and age groupings [ 1 – 3 ].…”