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

Ravinder Singh,Hua Wang,Yuan Miao,Khandakar Ahmed,Yanchun Zhang

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.

Highlights

Intra-urban mobility has long been a topic of interest across research communities, including urban planners, computer scientists, physicists and geographers [1]
We propose a methodology to conduct largescale mobility and human behaviour studies by utilizing location-based social media data
Social media generates an unprecedented amount of data and has led to new ways to discover urban functions and human behaviour [11]

Summary

Introduction

Intra-urban mobility has long been a topic of interest across research communities, including urban planners, computer scientists, physicists and geographers [1]. 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 This pilot study proves the effectiveness of our proposed methodology for conducting large-scale mobility and behaviour studies by utilizing location-based social media data and machine learning techniques for analyses. It is difficult to extract the emotions, perceptions, and experiences of people using this approach [34, 35] Even though such data has been used extensively in the past to study urban functions, urban planning, and geographical information systems, it has limitations in regard to the time and effort required to collect, process and analyze it. This may include the type of art people buy, the theatres they visit and the fashion they prefer [39]

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