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Mining Individual Similarity by Assessing Interactions with Personally Significant Places from GPS Trajectories
16 references·5 citing·51 second hop
Mining Individual Similarity by Assessing Interactions with Personally Significant Places from GPS Trajectories (2018)Mining Individual Similarity by Assessing…Extracting urban functional regions from points of interest and human activities on location‐based social networks (2017)Extracting urban functional r…Mining human mobility patterns from social geo-tagged data (2016)Mining human mobility pattern…A framework for identifying activity groups from individual space-time profiles (2016)A framework for identifying a…Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data (2015)Twitter as an indicator for w…Social Sensing: A New Approach to Understanding Our Socioeconomic Environments (2015)Social Sensing: A New Approac…Predicting human mobility with activity changes (2015)Predicting human mobility wit…Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks (2015)Exploration of spatiotemporal…Mining user similarity based on routine activities (2013)Mining user similarity based …Measuring similarity of mobile phone user trajectories– a Spatio-temporal Edit Distance method (2013)Measuring similarity of mobil…From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns (2013)From Tweets to Semantic Traje…Inferring social ties between users with human location history (2012)Inferring social ties between…What you are is when you are (2011)What you are is when you areModelling the scaling properties of human mobility (2010)Modelling the scaling propert…Analyzing and geo-visualizing individual human mobility patterns using mobile call records (2010)Analyzing and geo-visualizing…Mining Individual Life Pattern Based on Location History (2009)Mining Individual Life Patter…Understanding individual human mobility patterns (2008)Understanding individual huma…Metagraph-Based Life Pattern Clustering With Big Human Mobility Data (2022)Metagraph-Based Life Pattern …Effective Metagraph-based Life Pattern Clustering with Big Human Mobility Data (2021)Effective Metagraph-based Lif…Mining Human Mobility Data to Discover Locations and Habits (2020)Mining Human Mobility Data to…From mobility data to habits and common pathways (2020)From mobility data to habits …Discovering Common Pathways Across Users’ Habits in Mobility Data (2019)Discovering Common Pathways A…Reality mining: sensing complex social systems (2005)Understanding mobility based on GPS data (2008)Inferring friendship network structure by using mobile phone data (2009)Tutorial on Location-Based Social Networks (2012)Methodologies for Cross-Domain Data Fusion: An Overview (2015)Detecting and visualizing cohesive activity-travel patterns: A network analysis approach (2017)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Friendship and mobility (2011)A universal model for mobility and migration patterns (2012)Features of similarity. 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(1978)Crowdsourcing geographic information for disaster response: a research frontier (2010)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Measuring human perceptions of a large-scale urban region using machine learning (2018)GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond (2019)Limits of Predictability in Human Mobility (2010)A survey of results on mobile phone datasets analysis (2015)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Human mobility: Models and applications (2018)The Modifiable Areal Unit Problem in Multivariate Statistical Analysis (1991)Local Indicators of Spatial Association—LISA (1995)Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (2002)Crowdsourcing a Collective Sense of Place (2016)Editorial: human dynamics in the mobile and big data era (2016)Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment (2017)The Analysis of Spatial Association by Use of Distance Statistics (1992)Local Spatial Autocorrelation Statistics: Distributional Issues and an Application (1995)Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data (2017)Social media for enhanced understanding of disaster resilience during Hurricane Florence (2020)Discovering regions of different functions in a city using human mobility and POIs (2012)Natural Scales in Geographical Patterns (2017)Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs (2018)Portraying the spatial dynamics of urban vibrancy using multisource urban big data (2019)Understanding house price appreciation using multi-source big geo-data and machine learning (2020)
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References

  • 2008Understanding individual human mobility patterns6,071 cited
  • 2010Modelling the scaling properties of human mobility1,286 cited
  • 2015Social Sensing: A New Approach to Understanding Our Socioeconomic Environments804 cited
  • 2017Extracting urban functional regions from points of interest and human activities on location‐based social networks470 cited
  • 2009Mining Individual Life Pattern Based on Location History281 cited
  • 2015Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data159 cited
  • 2012Inferring social ties between users with human location history148 cited
  • 2015Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks137 cited
  • 2011What you are is when you are109 cited
  • 2013Measuring similarity of mobile phone user trajectories– a Spatio-temporal Edit Distance method94 cited
  • 2010Analyzing and geo-visualizing individual human mobility patterns using mobile call records85 cited
  • 2013From Tweets to Semantic Trajectories: Mining Anomalous Urban Mobility Patterns70 cited
  • 2016Mining human mobility patterns from social geo-tagged data66 cited
  • 2013Mining user similarity based on routine activities65 cited
  • 2015

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Predicting human mobility with activity changes
60 cited
  • 2016A framework for identifying activity groups from individual space-time profiles58 cited
  • Cited by

    • 2022Metagraph-Based Life Pattern Clustering With Big Human Mobility Data18 cited
    • 2020From mobility data to habits and common pathways11 cited
    • 2020Mining Human Mobility Data to Discover Locations and Habits10 cited
    • 2019Discovering Common Pathways Across Users’ Habits in Mobility Data4 cited
    • 2021Effective Metagraph-based Life Pattern Clustering with Big Human Mobility Data4 cited