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Beyond Sights: Large Scale Study of Tourists' Behavior Using Foursquare Data
7 references·6 citing·41 second hop
Beyond Sights: Large Scale Study of Tourists' Behavior Using Foursquare Data (2015)Beyond Sights: Large Scale Study of Touri…Exploring Millions of Footprints in Location Sharing Services (2021)Exploring Millions of Footpri…An Empirical Study of Geographic User Activity Patterns in Foursquare (2021)An Empirical Study of Geograp…Large-scale study of city dynamics and urban social behavior using participatory sensing (2014)Large-scale study of city dyn…Mining user behaviours (2013)Mining user behavioursDiscovering and predicting user routines by differential analysis of social network traces (2013)Discovering and predicting us…A Picture of Instagram is Worth More Than a Thousand Words: Workload Characterization and Application (2013)A Picture of Instagram is Wor…Understanding individual human mobility patterns (2008)Understanding individual huma…Modeling Interest Networks in Urban Areas: A Comparative Study of Google Places and Foursquare Across Countries (2025)Modeling Interest Networks in…Redes de Interesse: comparando o Google Places e Foursquare na captura da escolha de usuários por áreas urbanas (2024)Redes de Interesse: comparand…Profiling the Mobility of Tourists Exploring Social Sensing (2019)Profiling the Mobility of Tou…Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes (2019)Los datos geolocalizados de l…Identifying tourist-functional relations of urban places through Foursquare from Barcelona (2019)Identifying tourist-functiona…Classifying Venue Categories of Unlabeled Check-ins Using Mobility Patterns (2019)Classifying Venue Categories …Using GPS to learn significant locations and predict movement across multiple users (2003)Reality mining: sensing complex social systems (2005)Discovery of spatio-temporal patterns from location-based social networks (2015)Smart cities, urban sensing, and big data: mining geo-location in social networks (2017)Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability (2020)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)A universal model for mobility and migration patterns (2012)Limits of Predictability in Human Mobility (2010)Friendship and mobility (2011)SoCoMo marketing for travel and tourism: Empowering co-creation of value (2015)Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China (2016)Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play (2018)Mapping the world's photos (2009)Service Pricing Decision in Cyber-Physical Systems: Insights from Game Theory (2015)Identifying opportunity places for urban regeneration through LBSNs (2019)The 51 V's Of Big Data (2019)Ethnic Groups and Boundaries: The Social Organization of Culture Difference (1970)The Computer for the 21st Century (1991)Citizens as sensors: the world of volunteered geography (2007)How to Study the City on Instagram (2016)The effects of social media opinion leaders’ recommendations on followers’ intention to buy (2017)Urban Computing Leveraging Location-Based Social Network Data (2019)Geographical Research (1951)Digital Footprinting: Uncovering Tourists with User-Generated Content (2008)Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017)Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data (2020)Reading urban land use through spatio-temporal and content analysis of geotagged Twitter data (2021)Geospatial Spatiotemporal Analysis of Tourism Facility Attractiveness and Tourism Vitality in Historic Districts: A Case Study of Suzhou Old City (2025)Chapter 12: What does the Anthropocene hold for citizenship? (1905)CRITICAL QUESTIONS FOR BIG DATA (2012)The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry (2017)La contribución del Big Data al estudio de la sostenibilidad de la forma urbana (2022)Social dynamics in cities: analysis through LBSN data (2022)Inferring friendship network structure by using mobile phone data (2009)Exploring temporal effects for location recommendation on location-based social networks (2013)Measuring spatial accessibility to healthcare for populations with multiple transportation modes (2013)Social Media data: Challenges, opportunities and limitations in urban studies (2018)Geo-located Twitter as proxy for global mobility patterns (2014)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2021Exploring Millions of Footprints in Location Sharing Services685 cited
  • 2021An Empirical Study of Geographic User Activity Patterns in Foursquare490 cited
  • 2013A Picture of Instagram is Worth More Than a Thousand Words: Workload Characterization and Application119 cited
  • 2013Mining user behaviours93 cited
  • 2014Large-scale study of city dynamics and urban social behavior using participatory sensing80 cited
  • 2013Discovering and predicting user routines by differential analysis of social network traces32 cited

Cited by

  • 2019Identifying tourist-functional relations of urban places through Foursquare from Barcelona17 cited
  • 2019Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes9 cited
  • 2019Profiling the Mobility of Tourists Exploring Social Sensing8 cited
  • 2024Redes de Interesse: comparando o Google Places e Foursquare na captura da escolha de usuários por áreas urbanas3 cited
  • 2019Classifying Venue Categories of Unlabeled Check-ins Using Mobility Patterns3 cited
  • 2025Modeling Interest Networks in Urban Areas: A Comparative Study of Google Places and Foursquare Across Countries2 cited

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