2024
Unraveling near real-time spatial dynamics of population using geographical ensemble learning
Song Y, Wu S, Chen B, Bell M. Unraveling near real-time spatial dynamics of population using geographical ensemble learning. International Journal Of Applied Earth Observation And Geoinformation 2024, 130: 103882. PMID: 38938876, PMCID: PMC11210339, DOI: 10.1016/j.jag.2024.103882.Peer-Reviewed Original ResearchGeospatial dataHourly time seriesOpen-source geospatial dataGlobal change studiesGeospatial big dataSpatio-temporal heterogeneityUrban planningSpatial non-stationarityChange studiesPopulation spatializationSpatial disaggregationDisaster reductionSeamless mapTime seriesSpatial scalesTemporal resolutionPopulation mapsSpatial resolutionNon-stationarityRoot-mean-square deviationPopulation distributionPopulation prediction modelSpatial dynamicsAdvanced machine learning modelsSpatial statistics
2019
A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China
Jin L, Berman JD, Warren JL, Levy JI, Thurston G, Zhang Y, Xu X, Wang S, Zhang Y, Bell ML. A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China. Environmental Research 2019, 177: 108597. PMID: 31401375, DOI: 10.1016/j.envres.2019.108597.Peer-Reviewed Original ResearchConceptsHigh-rise buildingsVertical variationVertical concentration gradientsLUR modelsVertical profile measurementsHigh spatial resolutionGround levelGround-level NOProfile measurementsOgawa badgesLanzhou urban areaConcentration heterogeneitySpatial resolutionRoad networkConcentration gradientBuildingsAverage seasonal concentrationsParticulate matterHigh densityGovernment monitorsLand coverLand use regression modelsSpatial variationSubstantial spatial variationVariation function