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  1. Article ; Online: Measuring the contribution of built-settlement data to global population mapping

    Jeremiah J. Nieves / Maksym Bondarenko / David Kerr / Nikolas Ves / Greg Yetman / Parmanand Sinha / Donna J. Clarke / Alessandro Sorichetta / Forrest R. Stevens / Andrea E. Gaughan / Andrew J. Tatem

    Social Sciences and Humanities Open, Vol 3, Iss 1, Pp 100102- (2021)

    2021  

    Abstract: Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built- ...

    Abstract Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.
    Keywords Urban ; Population ; Growth model ; Built ; Settlement ; Machine learning ; History of scholarship and learning. The humanities ; AZ20-999 ; Social sciences (General) ; H1-99
    Subject code 910
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations

    Jeremiah J. Nieves / Maksym Bondarenko / Alessandro Sorichetta / Jessica E. Steele / David Kerr / Alessandra Carioli / Forrest R. Stevens / Andrea E. Gaughan / Andrew J. Tatem

    Remote Sensing, Vol 12, Iss 1545, p

    2020  Volume 1545

    Abstract: Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time- ... ...

    Abstract Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000–2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover “Urban Areas” class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011–2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of “urban” or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available “off-the-shelf” datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement ...
    Keywords Urban ; growth model ; forecast ; built ; settlement ; machine learning ; Science ; Q
    Subject code 333
    Language English
    Publishing date 2020-05-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets

    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

    Big Earth Data, Vol 0, Iss 0, Pp 1-

    2019  Volume 32

    Abstract: 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 ... ...

    Abstract 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 multi-temporal 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.
    Keywords Human population ; sub-national ; global ; spatial dataset ; multi-temporal ; Geography. Anthropology. Recreation ; G ; Geology ; QE1-996.5
    Subject code 333
    Language English
    Publishing date 2019-06-01T00:00:00Z
    Publisher Taylor & Francis Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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