Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource ...Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.展开更多
Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area...Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.展开更多
Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections...Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes.Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit.Here we make use of recently released multi-temporal high-resolution global settlement layers,historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast.We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach.Strategies used to fill data gaps may vary according to the local context and the objective of the study.This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.展开更多
Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplin...Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.展开更多
基金This work was supported by the Bill and Melinda Gates Foundation[OPP1134076,OPP1106427,OPP1032350,OPP1094793]National Institute of Allergy and Infectious Diseases[U19AI089674]Wellcome Trust[106866/Z/15/Z].
文摘Multi-temporal,globally consistent,high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health,wealth,and resource access,and monitoring change in these over time.The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal scales.This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas.In response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial datasets.Datasets are provided as global,annual time series,where pertinent at the timescale of population analyses and where data is available,for use in the construction of population distribution layers.The archive includes sub-national census-based population estimates,matched to a geospatial layer denoting administrative unit boundaries,and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density.Here,we describe these harmonised datasets and their limitations,along with the production workflow.Further,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics.The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.
基金FRS,AEG,JNN,AK,and AS are funded by the Bill&Melinda Gates Foundation(OPP1134076)AJT is supported by funding from U.S.National Institutes of Health/National Institute of Allergy and Infectious Diseases(U19AI089674)+1 种基金the Bill&Melinda Gates Foundation(OPP1106427,OPP1032350,OPP1134076)the Clinton Health Access Initiative,National Institutes of Health,and a Wellcome Trust Sustaining Health Grant(106866/Z/15/Z).
文摘Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing methods.These advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census data.However,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census data.Here we tested two methods using remotely sensed,built-area land cover data to disaggregate population data.These included simple,areal weighting and more complex statistical models with other ancillary information.Outcomes were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental characteristics.We found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built areas.For these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive capacity.We discuss these results and implications for future population modeling approaches.
基金supported by the Belgian Science Policy(BELSPO)under the Research programme for Earth Obser-vation“STEREO III”[grant number SR/00/304]AJT is supported by a Wellcome Trust Sustaining Health Grant(106866/Z/15/Z)+4 种基金AJT,AS,AEG and FRS are supported by funding from the Bill and Melinda Gates Foundation[grant number OPP1106427],[grant number 1032350][grant number OPP1134076]supported by the Well-come Trust,UK as an intermediate fellow[grant number 095127]RWS is supported by the Wellcome Trust as Prin-cipal Research Fellow[grant number 103602]that also supported CWK.CWK is also grateful to the KEMRI Wellcome Trust Overseas Programme Strategic Award[grant number 084538]for additional support.
文摘Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates.Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes.Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit.Here we make use of recently released multi-temporal high-resolution global settlement layers,historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast.We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach.Strategies used to fill data gaps may vary according to the local context and the objective of the study.This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.
基金This work was supported by the RAPIDD program of the Science and Technology Directorate,Department of Homeland Security,and the Fogarty International Center,National Institutes of HealthNIH/NIAID[grant number U19AI089674]and the Bill and Melinda Gates Foundation[grant number OPP1106427],[grant number 1032350].CL is supported by the Fonds National de la Recherche Scientifique(F.R.S./FNRS),Brussels,Belgium.This work forms part of the outputs of the WorldPop Project(www.worldpop.org.uk)and Flowminder Foundation(www.flowminder.org).
文摘Interactions between humans,diseases,and the environment take place across a range of temporal and spatial scales,making accurate,contemporary data on human population distributions critical for a variety of disciplines.Methods for disaggregating census data to finer-scale,gridded population density estimates continue to be refined as computational power increases and more detailed census,input,and validation datasets become available.However,the availability of spatially detailed census data still varies widely by country.In this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific models.We examine underlying methodological considerations for improving gridded population datasets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts.Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia.Results suggest that for many countries more accurate population maps can be produced by using regionally-parameterized models where more spatially refined data exists than that which is available for the focal country.This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.
