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Mobile Phone Data as Source to Discover Spatial Activity and Motion Patterns
6 references·6 citing·43 second hop
Mobile Phone Data as Source to Discover Spatial Activity and Motion Patterns (2012)Mobile Phone Data as Source to Discover S…Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti (2011)Improved Response to Disaster…Mobility profiler: A framework for discovering mobility profiles of cell phone users (2010)Mobility profiler: A framewor…Understanding individual human mobility patterns (2008)Understanding individual huma…Digital Footprinting: Uncovering Tourists with User-Generated Content (2008)Digital Footprinting: Uncover…Mobile Landscapes: Graz in Real Time (2007)Mobile Landscapes: Graz in Re…Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Mobile Landscapes: Using Loca…Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method (2020)Fine-Scale Dasymetric Populat…From Mobility Analysis to Mobility Hubs Discovery: A Concept Based on Using CDR Data of the Mobile Networks (2018)From Mobility Analysis to Mob…Dynamic, Interactive and Visual Analysis of Population Distribution and Mobility Dynamics in an Urban Environment Using the Mobility Explorer Framework (2017)Dynamic, Interactive and Visu…Towards dynamic exposure-based schedule for hazardous material trains (2016)Towards dynamic exposure-base…Uncovering the Directional Heterogeneity of an Aggregated Mobile Phone Network (2014)Uncovering the Directional He…From the Guest Editors: Mobility, Communication, and Urban Space (2014)From the Guest Editors: Mobil…Smart mobs: the next social revolution (2003)Smart Mobs: The Next Social Revolution (2003)Sensing and using social context (2008)Discovering urban and country dynamics from mobile phone data with spatial correlation patterns (2014)Measure of Landmark Semantic Salience through Geosocial Data Streams (2014)Reality mining: sensing complex social systems (2005)Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system (2010)User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software (2014)Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore (2016)A Measurement Theory for Time Geography (2004)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Data Quality of Travel Behavior Studies: Factors Influencing the Reporting Rate of Self-Reported and GPS-Recorded Trips in Persons with Disabilities (2018)Measuring visitor flows using mobile positioning data in three Hungarian second-tier cities (2023)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Limits of Predictability in Human Mobility (2010)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)Using GPS to learn significant locations and predict movement across multiple users (2003)Breaking the habit: Measuring and predicting departures from routine in individual human mobility (2013)A Mobility Analytical Framework for Big Mobile Data in Densely Populated Area (2016)Inferring fine-grained transport modes from mobile phone cellular signaling data (2019)Measuring the information society (1989)Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake (2010)Predictability of population displacement after the 2010 Haiti earthquake (2012)Applications of artificial intelligence for disaster management (2020)Social physics (2022)Me++ (2003)Me++: The Cyborg Self and the Networked City (2003)A survey of results on mobile phone datasets analysis (2015)Human mobility: Models and applications (2018)Catastrophe Avoidance Models for Hazardous Materials Route Planning (2000)Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina (2000)Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation (2001)Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010)Human mobility characterization from cellular network data (2013)Exploring a New Model for Mobile Positioning Based on CDR Data of The Cellular Networks (2019)Mainstreaming Sustainable Urban Mobility – The Mieri-Mobil Project (2023)Using passive Wi-Fi for community crowd sensing during the COVID-19 pandemic (2023)Geographically Weighted Regression (1998)Dynamic population mapping using mobile phone data (2014)A Transport Mode Detection Framework Based on Mobile Phone Signaling Data Combined with Bus GPS Data (2024)Assessing urban-scale spatiotemporal heterogeneous metro station coverage using multi-source mobility data (2024)
this work references cited by second hop

References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2006Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis865 cited
  • 2011Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti634 cited
  • 2008Digital Footprinting: Uncovering Tourists with User-Generated Content470 cited
  • 2010Mobility profiler: A framework for discovering mobility profiles of cell phone users101 cited
  • 2007Mobile Landscapes: Graz in Real Time94 cited

Cited by

  • 2020Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method19 cited
  • 2018From Mobility Analysis to Mobility Hubs Discovery: A Concept Based on Using CDR Data of the Mobile Networks10 cited
  • 2016Towards dynamic exposure-based schedule for hazardous material trains9 cited
  • 2014From the Guest Editors: Mobility, Communication, and Urban Space8 cited
  • 2017Dynamic, Interactive and Visual Analysis of Population Distribution and Mobility Dynamics in an Urban Environment Using the Mobility Explorer Framework5 cited
  • 2014Uncovering the Directional Heterogeneity of an Aggregated Mobile Phone Network4 cited

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