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Multi-scale population and mobility estimation with geo-tagged Tweets

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References

2005The origin of bursts and heavy tails in human dynamics2,260 cited
2012A universal model for mobility and migration patterns1,509 cited
1946The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons1,033 cited
2014Geo-located Twitter as proxy for global mobility patterns675 cited
2013A review of urban computing for mobile phone traces254 cited
2013Happiness and the Patterns of Life: A Study of Geolocated Tweets156 cited
2014Inferring human mobility using communication patterns93 cited

Cited by

2021Understanding collective human movement dynamics during large-scale events using big geosocial data analytics19 cited
2019Capturing and Characterizing Human Activities Using Building Locations in America16 cited
2017The use of public spaces in a medium-sized city: from Twitter data to mobility patterns16 cited
2016Tracking Language Mobility in the Twitter Landscape11 cited
2016Contribution of geotagged Twitter data in the study of a social group’s activity space11 cited
2021DeepFlowGen: Intention-Aware Fine Grained Crowd Flow Generation via Deep Neural Networks10 cited
2015Where Chicagoans tweet the most10 cited
2017

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Mobility in cities: Comparative analysis of mobility models using Geo-tagged tweets in Australia

9 cited

2024Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway4 cited