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On the use of instantaneous entropy to measure the momentary predictability of human mobility
9 references·1 citing·29 second hop
On the use of instantaneous entropy to measure the momentary predictability of human mobility (2013)On the use of instantaneous entropy to me…Interdependence and predictability of human mobility and social interactions (2013)Interdependence and predictab…The Mobile Data Challenge: Big Data for Mobile Computing Research (2012)The Mobile Data Challenge: Bi…Predictability of individuals' mobility with high-resolution positioning data (2012)Predictability of individuals…Exploring Periods of Low Predictability in Daily Life Mobility (2012)Exploring Periods of Low Pred…Discovering places of interest in everyday life from smartphone data (2012)Discovering places of interes…CRAWDAD dataset yonsei/lifemap (v. 2012-01-03) (2012)CRAWDAD dataset yonsei/lifema…NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011)NextPlace: A Spatio-temporal …Limits of Predictability in Human Mobility (2010)Limits of Predictability in H…Using GPS to learn significant locations and predict movement across multiple users (2003)Using GPS to learn significan…Adaptive sensor cooperation for predicting human mobility (2014)Adaptive sensor cooperation f…Understanding individual human mobility patterns (2008)Inferring friendship network structure by using mobile phone data (2009)Find me if you can (2010)Predicting future locations with hidden Markov models (2012)Measuring Large-Scale Social Networks with High Resolution (2014)A survey on deep learning for human mobility (2023)Understanding mobility based on GPS data (2008)Lab And/Or Field? Measuring Personality Processes and Their Social Consequences (2015)Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions (2018)Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research (2019)Reality mining: sensing complex social systems (2005)The origin of bursts and heavy tails in human dynamics (2005)Modelling the scaling properties of human mobility (2010)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)The Museum Wearable: real-time sensor-driven understanding of visitors' interests for personalized visually-augmented museum experiences (2002)Learning and inferring transportation routines (2007)The scaling laws of human travel (2006)Computational Social Science (2009)From big smartphone data to worldwide research: The Mobile Data Challenge (2013)Urban Computing Leveraging Location-Based Social Network Data (2019)Urban Human Mobility (2019)Improving Location Prediction by Exploring Spatial‐Temporal‐Social Ties (2014)Analysis of a fast LZ-based entropy estimator for mobility data (2015)A probabilistic kernel method for human mobility prediction with smartphones (2014)A refined limit on the predictability of human mobility (2014)On the properties of human mobility (2016)gSCorr (2012)Analyzing large-scale human mobility data: a survey of machine learning methods and applications (2018)
this work references cited by second hop

References

  • 2010Limits of Predictability in Human Mobility3,230 cited
  • 2003Using GPS to learn significant locations and predict movement across multiple users1,060 cited
  • 2012The Mobile Data Challenge: Big Data for Mobile Computing Research429 cited
  • 2011NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems277 cited
  • 2013Interdependence and predictability of human mobility and social interactions150 cited
  • 2012Discovering places of interest in everyday life from smartphone data137 cited
  • 2012Predictability of individuals' mobility with high-resolution positioning data90 cited
  • 2012Exploring Periods of Low Predictability in Daily Life Mobility17 cited
  • 2012CRAWDAD dataset yonsei/lifemap (v. 2012-01-03)15 cited

Cited by

  • 2014Adaptive sensor cooperation for predicting human mobility4 cited

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