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Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data.
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Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data. (2018)Predicting the Spatio-Temporal Evolution …Associations between fast food and physical activity environments and adiposity in mid-life: cross-sectional, observational evidence from UK Biobank (2017)Associations between fast foo…Friendship and mobility (2011)Friendship and mobilityDGeye: Probabilistic Risk Perception and Prediction for Urban Dangerous Goods Management (2021)DGeye: Probabilistic Risk Per…Birds of a Feather: Homophily in Social Networks (2001)Power-Law Distributions in Empirical Data (2009)Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs (2014)Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts (2016)DeepMove (2018)Power-law distributions in empirical data (2018)Modern Epidemiology 3rd edition (1986)Gender differences in the associations between health and neighbourhood environment (2004)Measuring the Built Environment for Physical Activity (2009)Are exposures to ready-to-eat food environments associated with type 2 diabetes? A cross-sectional study of 347 551 UK Biobank adult participants (2018)Neighbourhood socioeconomic disadvantage, risk factors, and diabetes from childhood to middle age in the Young Finns Study: a cohort study (2018)Quantifying the Causal Effect of Individual Mobility on Health Status in Urban Space (2021)Discovering regions of different functions in a city using human mobility and POIs (2012)Discovering Urban Functional Zones Using Latent Activity Trajectories (2014)Urban activity pattern classification using topic models from online geo-location data (2014)
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References

  • 2011Friendship and mobility2,912 cited
  • 2017Associations between fast food and physical activity environments and adiposity in mid-life: cross-sectional, observational evidence from UK Biobank128 cited

Cited by

  • 2021DGeye: Probabilistic Risk Perception and Prediction for Urban Dangerous Goods Management15 cited

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