Abstract
Location based social networks such as Swarm provide a rich source of information on urban functions and city dynamics. Users voluntarily check-in at places they visit using a mobile application. Analysis of data created by check-ins can give insight into user’s mobility patterns. This study uses location-sharing data from Swarm to explore spatio-temporal and geo-temporal patterns within Melbourne city. Descriptive statistical analyses using SPSS on check-in data were performed to reveal meaningful trends and to attain a deeper understanding of human mobility patterns in the city. The results showed that mobility patterns vary based on gender and venue category. Furthermore, the patterns are different during different days of a week as well as at different times of a day but are not necessarily influenced by weather.
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Singh, R., Zhang, Y., Wang, H. (2018). Exploring Human Mobility Patterns in Melbourne Using Social Media Data. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_28
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DOI: https://doi.org/10.1007/978-3-319-92013-9_28
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