Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

Abstract

The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network’s data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors’ spatial operations are more intense than residents’ spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists’ temporal behaviors vary significantly while citizens’ movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities.

Implementation of Cloud Computing Protocol in E-Learning for Future Wireless Systems

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

“…KDE has been used in studying the patterns of visitors in green parks [66,[69][70][71][72][73][74][75][76][77]. ese studies, mostly concerned with the visitors' number, are based on the assumption that people who visit a green park have similar behavior.…”

Retracted: Analyzing the Check‐In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”