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Understanding Urban Dynamics via State-sharing Hidden Markov Model
9 references·2 citing·42 second hop
Understanding Urban Dynamics via State-sharing Hidden Markov Model (2019)Understanding Urban Dynamics via State-sh…DeepDPM: Dynamic Population Mapping via Deep Neural Network (2019)DeepDPM: Dynamic Population M…Smartphone App Usage Prediction Using Points of Interest (2018)Smartphone App Usage Predicti…Representing Urban Functions through Zone Embedding with Human Mobility Patterns (2018)Representing Urban Functions …Detecting Popular Temporal Modes in Population-scale Unlabelled Trajectory Data (2018)Detecting Popular Temporal Mo…Regions, Periods, Activities (2017)Regions, Periods, ActivitiesGMove (2016)GMoveTheories of Urban Dynamics (2014)Theories of Urban DynamicsDiscovering Urban Functional Zones Using Latent Activity Trajectories (2014)Discovering Urban Functional …Predicting future locations with hidden Markov models (2012)Predicting future locations w…Activity-aware urban area embedding with contrastive learning for intelligent transportation systems applications (2025)Activity-aware urban area emb…Understanding Urban Dynamics via State-Sharing Hidden Markov Model (2020)Understanding Urban Dynamics …Understanding individual human mobility patterns (2008)Understanding mobility based on GPS data (2008)Next place prediction using mobility Markov chains (2012)Big Data Driven Hidden Markov Model Based Individual Mobility Prediction at Points of Interest (2016)Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States! (2020)The Causes of Metropolitan Suburbanization (1993)A Changing Demographic Regime and Evolving Poly centric Urban Regions: Consequences for the Size, Composition and Distribution of City Populations (2001)The trajectories of European cities, 1960–2005 (2007)Promoted Urbanization of the Countryside: The Case of Santiago’s Periphery, Chile (1980–2017) (2020)Introduction: A Broad Perspective on the Concepts of Urban Dynamics, Environment, and Health (2023)Map projections: A working manual (1987)Discovering regions of different functions in a city using human mobility and POIs (2012)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Regularity and Conformity (2015)The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City (2021)Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach (2021)Limits of Predictability in Human Mobility (2010)Friendship and mobility (2011)Trajectory-User Linking via Variational AutoEncoder (2018)Learning to Simulate Human Mobility (2020)Learning Urban Community Structures (2018)Habit2vec: Trajectory Semantic Embedding for Living Pattern Recognition in Population (2019)Exploring Millions of Footprints in Location Sharing Services (2021)Geospatial Artificial Intelligence (GeoAI) (2021)Habits make smartphone use more pervasive (2011)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Strategies and limitations in app usage and human mobility (2019)User profiling from their use of smartphone applications: A survey (2019)Characterizing Internet Card User Portraits for Efficient Churn Prediction Model Design (2023)The measurement of urban travel demand (1974)Bowling Alone: The Collapse and Revival of American Community (2000)Adversarial Substructured Representation Learning for Mobile User Profiling (2019)Efficient Region Embedding with Multi-View Spatial Networks: A Perspective of Locality-Constrained Spatial Autocorrelations (2019)Multi-Graph Fusion Networks for Urban Region Embedding (2022)Human mobility modeling at metropolitan scales (2012)Context-aware real-time population estimation for metropolis (2016)DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data (2019)Multi-level Inter-regional Migrant Population Estimation Using Multi-source Spatiotemporal Big Data: A Case Study of Migrants in Hubei Province During the Outbreak of COVID-19 in Wuhan (2021)Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (2018)Revisitation in Urban Space vs. Online (2018)Revealing Urban Dynamics by Learning Online and Offline Behaviours Together (2019)Demographics of mobile app usage: long-term analysis of mobile app usage (2021)
this work references cited by second hop

References

  • 2014Discovering Urban Functional Zones Using Latent Activity Trajectories413 cited
  • 2012Predicting future locations with hidden Markov models384 cited
  • 2016GMove158 cited
  • 2017Regions, Periods, Activities142 cited
  • 2018Representing Urban Functions through Zone Embedding with Human Mobility Patterns119 cited
  • 2018Smartphone App Usage Prediction Using Points of Interest101 cited
  • 2018Detecting Popular Temporal Modes in Population-scale Unlabelled Trajectory Data37 cited
  • 2019DeepDPM: Dynamic Population Mapping via Deep Neural Network25 cited
  • 2014Theories of Urban Dynamics15 cited

Cited by

  • 2025Activity-aware urban area embedding with contrastive learning for intelligent transportation systems applications8 cited
  • 2020Understanding Urban Dynamics via State-Sharing Hidden Markov Model8 cited

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