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Evaluating spatial statistical and machine learning models in urban dynamic population mapping
12 references·0 citing·47 second hop
Evaluating spatial statistical and machine learning models in urban dynamic population mapping (2022)Evaluating spatial statistical and machin…Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China (2021)Mapping hourly population dyn…Characterizing the complex influence of the urban built environment on the dynamic population distribution of Shenzhen, China, using geographically and temporally weighted regression (2021)Characterizing the complex in…Mapping monthly population distribution and variation at 1-km resolution across China (2020)Mapping monthly population di…Impacts of changing urban land-use structure on sustainable city growth in China: A population-density dynamics perspective (2020)Impacts of changing urban lan…A Bayesian spatio-temporal model to analyzing the stability of patterns of population distribution in an urban space using mobile phone data (2020)A Bayesian spatio-temporal mo…Spatial modeling with R‐INLA: A review (2018)Spatial modeling with R‐INLA:…Fine-grained prediction of urban population using mobile phone location data (2018)Fine-grained prediction of ur…Space–time multiple regression model for grid-based population estimation in urban areas (2016)Space–time multiple regressio…Geographical and Temporal Weighted Regression (GTWR) (2015)Geographical and Temporal Wei…Dynamic population mapping using mobile phone data (2014)Dynamic population mapping us…Spatial and spatio-temporal models with R-INLA (2013)Spatial and spatio-temporal m…Population Health Metrics (2009)Population Health MetricsA Computer Movie Simulating Urban Growth in the Detroit Region (1970)STATISTICS FOR SPATIAL DATA (1992)Statistics for Spatial Data (1993)Geographically weighted regression and multicollinearity: dispelling the myth (2016)Geographically weighted regression with parameter-specific distance metrics (2016)Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices (2018)Understanding individual human mobility patterns (2008)Computational Social Science (2009)Limits of Predictability in Human Mobility (2010)A survey of results on mobile phone datasets analysis (2015)Human development report 2015: work for human development (2015)Predicting poverty and wealth from mobile phone metadata (2015)Statistics for Spatial Data. 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this work references cited by second hop

References

  • 2014Dynamic population mapping using mobile phone data908 cited
  • 2015Geographical and Temporal Weighted Regression (GTWR)621 cited
  • 2013Spatial and spatio-temporal models with R-INLA460 cited
  • 2018Spatial modeling with R‐INLA: A review411 cited
  • 2020Impacts of changing urban land-use structure on sustainable city growth in China: A population-density dynamics perspective135 cited
  • 2009Population Health Metrics116 cited
  • 2018Fine-grained prediction of urban population using mobile phone location data74 cited
  • 2020Mapping monthly population distribution and variation at 1-km resolution across China65 cited
  • 2021Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China40 cited
  • 2016Space–time multiple regression model for grid-based population estimation in urban areas36 cited
  • 2020A Bayesian spatio-temporal model to analyzing the stability of patterns of population distribution in an urban space using mobile phone data25 cited
  • 2021Characterizing the complex influence of the urban built environment on the dynamic population distribution of Shenzhen, China, using geographically and temporally weighted regression22 cited

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