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Mining hourly population dynamics by activity type based on decomposition of sequential snapshot data
16 references·0 citing·50 second hopsampled
Mining hourly population dynamics by activity type based on decomposition of sequential snapshot data (2022)Mining hourly population dynamics by acti…Uncovering temporal changes in Europe’s population density patterns using a data fusion approach (2020)Uncovering temporal changes i…Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities (2020)Recognizing urban functional …Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data (2020)Mining Daily Activity Chains …Mapping monthly population distribution and variation at 1-km resolution across China (2020)Mapping monthly population di…Comparison of the spatiotemporal mobility patterns among typical subgroups of the actual population with mobile phone data: A case study of Beijing (2020)Comparison of the spatiotempo…Identifying Building Functions from the Spatiotemporal Population Density and the Interactions of People among Buildings (2019)Identifying Building Function…A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data (2018)A framework for mixed-use dec…Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai (2017)Quantifying and visualizing j…Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data (2017)Mapping fine-scale population…Inferring spatial interaction patterns from sequential snapshots of spatial distributions (2017)Inferring spatial interaction…Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation (2017)Enhancing spatial accuracy of…Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns (2017)Coupling mobile phone and soc…Inferring trip purposes and uncovering travel patterns from taxi trajectory data (2015)Inferring trip purposes and u…Inferring individual daily activities from mobile phone traces: A Boston example (2015)Inferring individual daily ac…Analyzing Cell Phone Location Data for Urban Travel (2015)Analyzing Cell Phone Location…Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation (2012)Mapping populations at risk: …Discrete Choice Analysis: Theory and Application to Travel Demand. 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References

  • 2017Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns298 cited
  • 2015Inferring trip purposes and uncovering travel patterns from taxi trajectory data254 cited
  • 2017Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data225 cited
  • 2015Analyzing Cell Phone Location Data for Urban Travel180 cited
  • 2012Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation122 cited
  • 2015Inferring individual daily activities from mobile phone traces: A Boston example109 cited
  • 2017Quantifying and visualizing jobs-housing balance with big data: A case study of Shanghai94 cited
  • 2020Uncovering temporal changes in Europe’s population density patterns using a data fusion approach74 cited
  • 2017Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation68 cited
  • 2018A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data67 cited
  • 2020Mapping monthly population distribution and variation at 1-km resolution across China65 cited
  • 2017Inferring spatial interaction patterns from sequential snapshots of spatial distributions61 cited
  • 2020Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data60 cited

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Comparison of the spatiotemporal mobility patterns among typical subgroups of the actual population with mobile phone data: A case study of Beijing
58 cited
  • 2020Recognizing urban functional zones by a hierarchical fusion method considering landscape features and human activities52 cited
  • 2019Identifying Building Functions from the Spatiotemporal Population Density and the Interactions of People among Buildings48 cited
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