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FGVis: visual analytics of human mobility patterns and urban areas based on F-GloVe
3 references·0 citing·15 second hop
FGVis: visual analytics of human mobility patterns and urban areas based on F-GloVe (2021)FGVis: visual analytics of human mobility…Location2vec: A Situation-Aware Representation for Visual Exploration of Urban Locations (2019)Location2vec: A Situation-Awa…POI2Vec: Geographical Latent Representation for Predicting Future Visitors (2017)POI2Vec: Geographical Latent …The xPOI Concept (2005)The xPOI ConceptFriendship and mobility (2011)LORE (2014)Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016)Trip2Vec: a deep embedding approach for clustering and profiling taxi trip purposes (2018)Predicting Human Mobility via Variational Attention (2019)Context-aware Deep Model for Joint Mobility and Time Prediction (2020)Toward a Theory of Situation Awareness in Dynamic Systems (1995)Discovering regions of different functions in a city using human mobility and POIs (2012)Discovering Urban Functional Zones Using Latent Activity Trajectories (2014)Pareto-Optimal Transit Route Planning With Multi-Objective Monte-Carlo Tree Search (2020)Toward travel pattern aware tourism region planning: a big data approach (2021)GeoBERT: Pre-Training Geospatial Representation Learning on Point-of-Interest (2022)An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification (2019)A Study on Spatial and Temporal Aggregation Patterns of Urban Population in Wuhan City based on Baidu Heat Map and POI Data (2020)Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China (2020)
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References

  • 2017POI2Vec: Geographical Latent Representation for Predicting Future Visitors224 cited
  • 2019Location2vec: A Situation-Aware Representation for Visual Exploration of Urban Locations50 cited
  • 2005The xPOI Concept23 cited

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