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Generating the Users Geographic Map Using Mobile Phone Data
12 references·1 citing·40 second hop
Generating the Users Geographic Map Using Mobile Phone Data (2022)Generating the Users Geographic Map Using…Detecting home locations from CDR data: introducing spatial uncertainty to the state-of-the-art (2018)Detecting home locations from…How friends share urban space: An exploratory spatiotemporal analysis using mobile phone data (2017)How friends share urban space…Extracting regular mobility patterns from sparse CDR data without a priori assumptions (2017)Extracting regular mobility p…Exploring Home and Work Locations in a City from Mobile Phone Data (2017)Exploring Home and Work Locat…Exploring Georeferenced Mobile Phone Datasets – A Survey and Reference Framework (2016)Exploring Georeferenced Mobil…Automatic identification of relevant places from cellular network data (2016)Automatic identification of r…From Mobile Phone Data to Transport Network -- Gaining Insight about Human Mobility (2015)From Mobile Phone Data to Tra…Identifying Significant Places Using Multi-day Call Detail Records (2014)Identifying Significant Place…Approaching the Limit of Predictability in Human Mobility (2013)Approaching the Limit of Pred…Are call detail records biased for sampling human mobility? (2012)Are call detail records biase…Identifying Important Places in People’s Lives from Cellular Network Data (2011)Identifying Important Places …Understanding individual human mobility patterns (2008)Understanding individual huma…Profile segmentation: Clustering approach based on behavioral patterns extracted from mobile phone data (2025)Profile segmentation: Cluster…Line generalisation by repeated elimination of points (1993)Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010)Friendship and mobility (2011)Using GPS to learn significant locations and predict movement across multiple users (2003)Limits of Predictability in Human Mobility (2010)The Mobile Data Challenge: Big Data for Mobile Computing Research (2012)Measuring Large-Scale Social Networks with High Resolution (2014)Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014)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)Understanding mobility based on GPS data (2008)Combining smart card data and household travel survey to analyze jobs–housing relationships in Beijing (2015)Understanding the tourist mobility using GPS: Where is the next place? (2016)Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh (2016)A new insight into land use classification based on aggregated mobile phone data (2014)CellTradeMap: Delineating Trade Areas for Urban Commercial Districts with Cellular Networks (2019)CellTrans (2019)The Use of Big Mobile Data to Gain Multilayered Insights for Syrian Refugee Crisis (2019)Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti (2011)Development of origin–destination matrices using mobile phone call data (2014)Analysis of tourist classification from cellular network data (2018)Eliciting cities points of interest from people movements and suggesting effective itineraries (2020)Using mobile phone data to map evacuation and displacement: a case study of the central Italy earthquake (2023)Citizens as sensors: the world of volunteered geography (2007)Assessing Refugees’ Integration via Spatio-Temporal Similarities of Mobility and Calling Behaviors (2019)A Mobility Model for Synthetic Travel Demand From Sparse Traces (2022)Epidemiological-survey-based multidimensional modeling for understanding daily mobility during the COVID-19 pandemic across urban-rural gradient in the Chinese mainland (2023)Urban computing with taxicabs (2011)Unravelling daily human mobility motifs (2013)Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region (2021)Sensing Mobility and Routine Locations through Mobile Phone and Crowdsourced Data: Analyzing Travel and Behavior during COVID-19 (2023)Activity type detection of mobile phone data based on self-training: Application of the teacher–student cycling model (2024)Multi-scale spatio-temporal analysis of human mobility (2017)Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data (2019)Measuring objective and subjective well-being: dimensions and data sources (2020)Effects of Data Preprocessing Methods on Addressing Location Uncertainty in Mobile Signaling Data (2020)Tourism Geography through the Lens of Time Use: A Computational Framework Using Fine-Grained Mobile Phone Data (2020)Beyond Distance Decay: Discover Homophily in Spatially Embedded Social Networks (2021)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2011Identifying Important Places in People’s Lives from Cellular Network Data401 cited
  • 2013Approaching the Limit of Predictability in Human Mobility381 cited
  • 2012Are call detail records biased for sampling human mobility?124 cited
  • 2017How friends share urban space: An exploratory spatiotemporal analysis using mobile phone data51 cited
  • 2014Identifying Significant Places Using Multi-day Call Detail Records22 cited
  • 2017Extracting regular mobility patterns from sparse CDR data without a priori assumptions20 cited
  • 2016Automatic identification of relevant places from cellular network data17 cited
  • 2017Exploring Home and Work Locations in a City from Mobile Phone Data14 cited
  • 2015From Mobile Phone Data to Transport Network -- Gaining Insight about Human Mobility6 cited
  • 2016Exploring Georeferenced Mobile Phone Datasets – A Survey and Reference Framework6 cited
  • 2018Detecting home locations from CDR data: introducing spatial uncertainty to the state-of-the-art6 cited

Cited by

  • 2025Profile segmentation: Clustering approach based on behavioral patterns extracted from mobile phone data3 cited

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