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Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway
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Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway (2024)Georeferenced X (formerly twitter) data a…Twitter reveals human mobility dynamics during the COVID-19 pandemic (2020)Twitter reveals human mobilit…Inferring dynamic origin-destination flows by transport mode using mobile phone data (2019)Inferring dynamic origin-dest…Big data in tourism research: A literature review (2018)Big data in tourism research:…The geography of Twitter topics in London (2016)The geography of Twitter topi…Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy (2016)Mapping Cilento: Using geotag…Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago (2016)Explore spatiotemporal and de…Understanding Human Mobility from Twitter (2015)Understanding Human Mobility …Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information (2015)Social Media and the City: Re…Rethinking big data: A review on the data quality and usage issues (2015)Rethinking big data: A review…Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Origin–destination trips by p…Review of GPS Travel Survey and GPS Data-Processing Methods (2014)Review of GPS Travel Survey a…Geo-located Twitter as proxy for global mobility patterns (2014)Geo-located Twitter as proxy …What do people study when they study Twitter? Classifying Twitter related academic papers (2013)What do people study when the…Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study (2010)Evaluating the feasibility of…Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands (2009)Deriving and validating trip …Household travel surveys: Where are we going? (2006)Household travel surveys: Whe…Power laws, Pareto distributions and Zipf's law (2005)Understanding individual human mobility patterns (2008)Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China (2018)Power-law distributions in empirical data (2018)Urban mobility and neighborhood isolation in America’s 50 largest cities (2018)Social physics (2022)Can Land-use Policy Really Affect Travel Behaviour? 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References

  • 2018Big data in tourism research: A literature review1,073 cited
  • 2014Geo-located Twitter as proxy for global mobility patterns675 cited
  • 2015Origin–destination trips by purpose and time of day inferred from mobile phone data637 cited
  • 2015Understanding Human Mobility from Twitter558 cited
  • 2009Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands501 cited
  • 2015Social Media and the City: Rethinking Urban Socio-Spatial Inequality Using User-Generated Geographic Information388 cited
  • 2014Review of GPS Travel Survey and GPS Data-Processing Methods350 cited
  • 2006Household travel surveys: Where are we going?317 cited
  • 2020Twitter reveals human mobility dynamics during the COVID-19 pandemic242 cited
  • 2015Rethinking big data: A review on the data quality and usage issues236 cited
  • 2016Explore spatiotemporal and demographic characteristics of human mobility via Twitter: A case study of Chicago225 cited
  • 2016Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy216 cited
  • 2013What do people study when they study Twitter? Classifying Twitter related academic papers213 cited
  • 2016The geography of Twitter topics in London211 cited

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  • 2010Evaluating the feasibility of a passive travel survey collection in a complex urban environment: Lessons learned from the New York City case study193 cited
  • 2019Inferring dynamic origin-destination flows by transport mode using mobile phone data183 cited
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