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Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

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International Journal of Digital Earth 2020年第1期13卷 78-100页

作者： Forrest R.Stevens Andrea E.Gaughan Jeremiah JNieves Adam King Alessandro Sorichetta Catherine Linard Andrew JTatem Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA WorldPop Department of Geography and EnvironmentUniversity of SouthamptonSouthamptonUK Spatial Epidemiology Lab(SpELL) UniversitéLibre de BruxellesBrusselsBelgium Department of Geography Universitéde NamurNamurBelgium flowminder foundation StockholmSweden

Mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing *** 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in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census ***,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census *** we tested two methods using remotely sensed,built-area land cover data to disaggregate population *** included simple,areal weighting and more complex statistical models with other ancillary *** were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental *** found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built *** 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 *** discuss these results and implications for future population modeling approaches.

关键词： Land cover built areas remote sensing settlement mapping population modeling

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Exploring nationally and regionally defined models for large area population mapping

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International Journal of Digital Earth 2015年第12期8卷 989-1006页

作者： A.E.Gaughan F.R.Stevens C.Linard N.N.Patel A.J.Tatem Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA Biological Control and Spatial Ecology UniversitéLibre de BruxellesBrusselsBelgium Department of Geography and Geoinformation Science George Mason UniversityFairfaxVAUSA Department of Geography and Environment University of SouthamptonSouthamptonUK Fogarty International Center National Institutes of HealthBethesdaMDUSA flowminder foundation StockholmSweden

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 *** 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 ***,the availability of spatially detailed census data still varies widely by *** this study,we develop quantitative guidelines for choosing regionally-parameterized census count disaggregation models over country-specific *** 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 *** is given to the spatial resolution of input census data using examples from East Africa and Southeast *** 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 *** study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.

关键词： human population modeling random forest regression dasymetric mapping gridded population datasets

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Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

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Big Earth Data 2019年第2期3卷 108-139页

作者： Christopher T.Lloyd Heather Chamberlain David Kerr Greg Yetman Linda Pistolesi Forrest R.Stevens Andrea E.Gaughan Jeremiah J.Nieves Graeme Hornby Kytt MacManus Parmanand Sinha Maksym Bondarenko Alessandro Sorichetta Andrew J.Tatem WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK flowminder foundation StockholmSweden Center for International Earth Science Information Network(CIESIN) Columbia UniversityPalisadesNYUSA Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA GeoData University of SouthamptonSouthamptonUK

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 *** production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multitemporal *** is in support of measurement and monitoring of UN Sustainable Development Goals and related *** response to these agendas,a method has been developed to assemble and harmonise a unique,open access,archive of geospatial *** 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 *** 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 ***,we describe these harmonised datasets and their limitations,along with the production ***,we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development *** geospatial archive is available at https://***/10.5258/SOTON/WP00650.

关键词： Human population subnational global spatial dataset multi-temporal

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Modelling changing population distributions:an example of the Kenyan Coast,1979–2009

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International Journal of Digital Earth 2017年第10期10卷 1017-1029页

作者： Catherine Linard Caroline W.Kabaria Marius Gilbert Andrew J.Tatem Andrea E.Gaughan Forrest R.Stevens Alessandro Sorichetta Abdisalan M.Noor Robert W.Snow Spatial Epidemiology Lab(SpELL) UniversitéLibre de BruxellesBrusselsBelgium Department of Geography Universitéde NamurNamurBelgium Spatial Health Metrics Group KEMRI Wellcome Trust Research ProgrammeNairobiKenya Fonds National de la Recherche Scientifique(F.R.S.-FNRS) BrusselsBelgium WorldPop Department of Geography and EnvironmentUniversity of SouthamptonSouthamptonUK Fogarty International Center National Institutes of HealthBethesdaMDUSA flowminder foundation StockholmSweden Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA Nuffield Department of Clinical Medicine Centre for Tropical Medicine and Global HealthUniversity of OxfordOxfordUK

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 *** temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover *** predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally *** 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 *** 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 *** used to fill data gaps may vary according to the local context and the objective of the *** 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.

关键词： Human population distribution modelling gridded population datasets temporal change Kenya

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