sis 18: e25

Research Article

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

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  • @ARTICLE{10.4108/eai.26-10-2020.166767,
        author={Ravinder Singh and Yanchun Zhang and Hua Wang and Yuan Miao and Khandakar Ahmed},
        title={Investigation of Social Behaviour Patterns using Location-Based Data -- A Melbourne Case Study},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={10},
        keywords={Behaviour Patterns, Social Media, Spatio-Temporal, Mobility Patterns, Swarmapp, Twitter, Sentiment Analysis},
        doi={10.4108/eai.26-10-2020.166767}
    }
    
  • Ravinder Singh
    Yanchun Zhang
    Hua Wang
    Yuan Miao
    Khandakar Ahmed
    Year: 2020
    Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study
    SIS
    EAI
    DOI: 10.4108/eai.26-10-2020.166767
Ravinder Singh1,*, Yanchun Zhang1, Hua Wang1, Yuan Miao1, Khandakar Ahmed1
  • 1: Institute for Sustainable Industries and Liveable Cites, Victoria University, Melbourne, Australia
*Contact email: Ravinder.singh@vu.edu.au

Abstract

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 spatio-temporal, 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.