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Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis
8 references·3 citing·41 second hop
Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis (2019)Spatiality Preservable Factored Poisson R…Representing Urban Functions through Zone Embedding with Human Mobility Patterns (2018)Representing Urban Functions …Deep ROI-Based Modeling for Urban Human Mobility Prediction (2018)Deep ROI-Based Modeling for U…Context-aware real-time population estimation for metropolis (2016)Context-aware real-time popul…Forecasting urban dynamics with mobility logs by bilinear Poisson regression (2015)Forecasting urban dynamics wi…Extracting Land-Use Patterns using Location Data from Smartphones (2014)Extracting Land-Use Patterns …Discovering regions of different functions in a city using human mobility and POIs (2012)Discovering regions of differ…PFlow: Reconstructing People Flow Recycling Large-Scale Social Survey Data (2011)PFlow: Reconstructing People …Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network (2009)Eigenplaces: Analysing Cities…Early crowd forecasting away from stations by geographically complemented regression using transit search and mobility logs (2025)Early crowd forecasting away …Estimating the Number of Tourists in Kyoto Based on GPS Traces and Aggregate Mobile Statistics (2023)Estimating the Number of Tour…Simultaneous Multiple POI Population Pattern Analysis System with HDP Mixture Regression (2021)Simultaneous Multiple POI Pop…Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis (2006)Transportation mode detection using mobile phones and GIS information (2011)Analysis of smart card data for understanding spatial changes in consumption-oriented human flows (2016)Revealing travel patterns and city structure with taxi trip dataCellular Census: Explorations in Urban Data Collection (2007)Eigenbehaviors: identifying structure in routine (2009)Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks (2010)Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2010)Principal Component Analysis on Spatial Data: An Overview (2012)Reality mining: sensing complex social systems (2005)Uncovering individual and collective human dynamics from mobile phone records (2008)CityProphet (2016)The effect of Pokémon Go on the pulse of the city: a natural experiment (2017)Understanding the Interdependency of Land Use and Mobility for Urban Planning (2018)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)Friendship and mobility (2011)Estimation of Static and Dynamic Urban Populations with Mobile Network Metadata (2018)Estimation of urban crowd flux based on mobile phone location data: A case study of Beijing, China (2018)Using human mobility data to quantify experienced urban inequalities (2025)Modelling the scaling properties of human mobility (2010)Mining User Mobility Features for Next Place Prediction in Location-Based Services (2012)Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19 (2021)Transfer Urban Human Mobility via POI Embedding over Multiple Cities (2021)Understanding Urban Area Attractiveness Based on Private Car Trajectory Data Using a Deep Learning Approach (2021)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)Seasonal tourism spaces in Estonia: Case study with mobile positioning data (2006)Evaluating passive mobile positioning data for tourism surveys: An Estonian case study (2007)Fine-resolution population mapping using OpenStreetMap points-of-interest (2014)A study of crowding effects at the Hong Kong light rail transit stations (1999)Crowding cost estimation with large scale smart card and vehicle location data (2016)A survey on deep learning for human mobility (2023)Open PFLOW: Creation and evaluation of an open dataset for typical people mass movement in urban areas (2017)YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories (2024)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017)
this work references cited by second hop

References

  • 2012Discovering regions of different functions in a city using human mobility and POIs1,136 cited
  • 2009Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network262 cited
  • 2018Representing Urban Functions through Zone Embedding with Human Mobility Patterns119 cited
  • 2011PFlow: Reconstructing People Flow Recycling Large-Scale Social Survey Data92 cited
  • 2016Context-aware real-time population estimation for metropolis70 cited
  • 2015Forecasting urban dynamics with mobility logs by bilinear Poisson regression48 cited
  • 2018Deep ROI-Based Modeling for Urban Human Mobility Prediction48 cited
  • 2014Extracting Land-Use Patterns using Location Data from Smartphones13 cited

Cited by

  • 2023Estimating the Number of Tourists in Kyoto Based on GPS Traces and Aggregate Mobile Statistics5 cited
  • 2025Early crowd forecasting away from stations by geographically complemented regression using transit search and mobility logs3 cited
  • 2021Simultaneous Multiple POI Population Pattern Analysis System with HDP Mixture Regression3 cited

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