Exploring Human Mobility Patterns in Melbourne Using Social Media Data

Singh, Ravinder; Zhang, Yanchun; Wang, Hua

doi:10.1007/978-3-319-92013-9_28

Cited by 8 publications

(14 citation statements)

References 7 publications

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“…Due to the popularity of Location-Based Social Networks (LBSNs) in the modern era, the availability of huge amounts of information generated by users has become invaluable, especially from a practical point of view. The information extracted from this data can be used in many application areas, such as analysis of public transit flows, location recommendations, population density estimation, route planning, disaster management, and many more [1]. Online services encourage different users to share their activities and interests with their social friends, and moreover generate enormous amounts of data, enabling researchers to understand users' activities, patterns, and preferences more accurately.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Wan

2020

IJGI

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The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated.

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Section: Introductionmentioning

confidence: 99%

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

Khan

Wan

2020

IJGI

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“…Our results suggest that activities in Melbourne differ over the course of the day and also of a week. We identified several meaningful patterns that are closely related to human activity from a spatial and temporal point of view [7]. Figure 3 shows how the activities in the city increase as the day passes.…”

Section: Spatio-temporal User Activity Patternsmentioning

confidence: 99%

“…The figure also shows an interesting finding in that 4 of 5 check-ins made in bars are by males. On the contrary, 4 in 5 check-ins made in shopping centres are by females [7]. One would imagine that men need to go to shopping centres to buy clothes, shoes, etc.…”

Section: Gender-based Activity Analysismentioning

confidence: 99%

“…A salon is more of a private place and people may not like others to know that they are having some sort of treatment to make them look better. According to Foursquare [24], most of its users are middle-aged individuals who may not feel that it is appropriate to be associated with religion, which may explain why a lot of people do not check-in when they visit these types of venues [7]. Activity in each venue category varies depending on the time of day.…”

Section: Geo-temporal Patternsmentioning

confidence: 99%

“…These intra-urban mobility patterns can give insight into the behavioural traits of a group of people in a city or at a particular area in a city [5,6]. The type of places people visit and the time of the day they visit these places tells a lot about their behavioural traits and mobility patterns [7]. The aim of this research is to find new ways to explore human behaviour in today's world of big data and machine learning using Melbourne as a case study.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study

Singh¹,

Zhang²,

Wang³

et al. 2018

ICST Transactions on Scalable Information Systems

Self Cite

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Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user's mobility and behaviour patterns. In this study, we used location-sharing data from Swarm to explore spatiotemporal, geo-temporal and behaviour patterns within the city of Melbourne. Moreover, we used several tools for different datasets. We used the MeaningCloud tool for sentiment analysis and the LIWC15 tool for psychometric analysis. Also, we employed SPSS software for the descriptive statistical analysis on check-in data to reveal meaningful trends and attain a deeper understanding of human behaviour patterns in the city. The results show that most people do not express strong negative or positive emotions in relation to the places they visit. Behaviour patterns vary based on gender. Furthermore, mobility patterns are different on different days of the week as well as at different times of a day but are not necessarily influenced by the weather.

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A Framework for Early Detection of Antisocial Behavior on Twitter Using Natural Language Processing

Singh

Zhang

et al. 2019

Advances in Intelligent Systems and Computing

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Exploring Human Mobility Patterns in Melbourne Using Social Media Data

Cited by 8 publications

References 7 publications

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

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

Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study

A Framework for Early Detection of Antisocial Behavior on Twitter Using Natural Language Processing

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