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  1. Article ; Online: A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India

    Carla Pezzulo / Natalia Tejedor-Garavito / Ho Man Theophilus Chan / Ilda Dreoni / David Kerr / Samik Ghosh / Amy Bonnie / Maksym Bondarenko / Mihretab Salasibew / Andrew J. Tatem

    Scientific Data, Vol 10, Iss 1, Pp 1-

    2023  Volume 12

    Abstract: Abstract Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health ... ...

    Abstract Abstract Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015–16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.
    Keywords Science ; Q
    Subject code 360
    Language English
    Publishing date 2023-02-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: High-resolution gridded population datasets for Latin America and the Caribbean using official statistics

    Tom McKeen / Maksym Bondarenko / David Kerr / Thomas Esch / Mattia Marconcini / Daniela Palacios-Lopez / Julian Zeidler / R. Catalina Valle / Sabrina Juran / Andrew J. Tatem / Alessandro Sorichetta

    Scientific Data, Vol 10, Iss 1, Pp 1-

    2023  Volume 17

    Abstract: Abstract “Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by ... ...

    Abstract Abstract “Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.
    Keywords Science ; Q
    Subject code 333
    Language English
    Publishing date 2023-07-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. 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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  4. Article ; Online: Association between environmental and climatic risk factors and the spatial distribution of cystic and alveolar echinococcosis in Kyrgyzstan.

    Giulia Paternoster / Gianluca Boo / Roman Flury / Kursanbek M Raimkulov / Gulnara Minbaeva / Jumagul Usubalieva / Maksym Bondarenko / Beat Müllhaupt / Peter Deplazes / Reinhard Furrer / Paul R Torgerson

    PLoS Neglected Tropical Diseases, Vol 15, Iss 6, p e

    2021  Volume 0009498

    Abstract: Background Cystic and alveolar echinococcosis (CE and AE) are neglected tropical diseases caused by Echinococcus granulosus sensu lato and E. multilocularis, and are emerging zoonoses in Kyrgyzstan. In this country, the spatial distribution of CE and AE ... ...

    Abstract Background Cystic and alveolar echinococcosis (CE and AE) are neglected tropical diseases caused by Echinococcus granulosus sensu lato and E. multilocularis, and are emerging zoonoses in Kyrgyzstan. In this country, the spatial distribution of CE and AE surgical incidence in 2014-2016 showed marked heterogeneity across communities, suggesting the presence of ecological determinants underlying CE and AE distributions. Methodology/principal findings For this reason, in this study we assessed potential associations between community-level confirmed primary CE (no.=2359) or AE (no.=546) cases in 2014-2016 in Kyrgyzstan and environmental and climatic variables derived from satellite-remote sensing datasets using conditional autoregressive models. We also mapped CE and AE relative risk. The number of AE cases was negatively associated with 10-year lag mean annual temperature. Although this time lag should not be considered as an exact measurement but with associated uncertainty, it is consistent with the estimated 10-15-year latency following AE infection. No associations were detected for CE. We also identified several communities at risk for CE or AE where no disease cases were reported in the study period. Conclusions/significance Our findings support the hypothesis that CE is linked to an anthropogenic cycle and is less affected by environmental risk factors compared to AE, which is believed to result from spillover from a wild life cycle. As CE was not affected by factors we investigated, hence control should not have a geographical focus. In contrast, AE risk areas identified in this study without reported AE cases should be targeted for active disease surveillance in humans. This active surveillance would confirm or exclude AE transmission which might not be reported with the present passive surveillance system. These areas should also be targeted for ecological investigations in the animal hosts.
    Keywords Arctic medicine. Tropical medicine ; RC955-962 ; Public aspects of medicine ; RA1-1270
    Subject code 910
    Language English
    Publishing date 2021-06-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. 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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  6. 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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