Research Article
Investigation of Social Behaviour Patterns using Location-Based Data – A Melbourne Case Study
@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}, volume={8}, number={31}, 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
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.
Copyright © 2020 Ravinder Singh et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
