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.

No abstract

In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022 forcibly displaced millions of people from their homes including nearly 6m refugees flowing across the Nowcasting population displacement in Ukraine using social media border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged near real-time social media marketing data to estimate subnational population sizes every day disaggregated by age and sex. Our metric of internal displacement estimated that 5.3m people had been internally displaced away from their baseline administrative region by March 14. Results revealed four distinct displacement patterns: large scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While this innovative approach provided one of the only quantitative estimates of internal displacement in virtual real-time, we conclude by acknowledging risks and challenges for the future.

Tracking spatiotemporal changes in GHG emissions is key to successful implementation of the United Nations Framework Convention on Climate Change (UNFCCC). And while emission inventories often provide a robust tool to track emission trends at the country level, subnational emission estimates are often not reported or reports vary in robustness as the estimates are often dependent on the spatial modeling approach and ancillary data used to disaggregate the emission inventories. Assessing the errors and uncertainties of the subnational emission estimates is fundamentally challenging due to the lack of physical measurements at the subnational level. To begin addressing the current performance of modeled gridded CO 2 emissions, this study compares two common proxies used to disaggregate CO 2 emission estimates. We use a known gridded CO 2 model based on satelliteobserved nighttime light (NTL) data (Open Source Data Inventory for Anthropogenic CO 2 , ODIAC) and a gridded population dataset driven by a set of ancillary geospatial data. We examine the association at multiple spatial scales of these two datasets for three countries in Southeast Asia: Vietnam, Cambodia and Laos and characterize the spatiotemporal similarities and differences for 2000, 2005, and 2010. We specifically highlight areas of potential uncertainty in the ODIAC model, which relies on the single use of NTL data for disaggregation of the non-point emissions estimates. Results show, over time, how a NTL-based emissions disaggregation tends to concentrate CO 2 estimates in different ways than population-based estimates at the subnational level. We discuss important considerations in the disconnect between the two modeled datasets and argue that the spatial differences between data products can be useful to identify areas affected by the errors and uncertainties associated with the NTL-based downscaling in a region with uneven urbanization rates.

This paper presents progresses made on aircraft installation effects using numerical methods under WP 3.2 of SYMPHONY, a project supported by Technology Strategy Board, UK. Large-eddy simulations (LES) were performed for turbulent flow around a wing under the influence from engine jet flow by solving the compressible Navier-Stokes equations using an in-horse high-order finite difference code. Simulations were performed for jet under both a clean wing and the wing at high-lift configuration. Installation effects on both the jet and the wing are analysed by comparing with LES results performed for three baseline cases: jet along, clean wing along and the wing in high-lift configuration. It is found that the flow is two-dimensional near the leading edge of the wing. Further downstream three-dimensional flow features are developed. Interaction with vortical jet stream accelerates developments of the flow underneath the wing. Stronger turbulent structures are seen within the jet shear layer near the wing and their interaction with the wing causes surface pressure fluctuations, which results in increased radiated noise. Interaction with the jet causes a reduction in lift for the clean wing, however the contribution from the flap is increased when the wing is in high-lift configuration. For the current geometry the jet stream does not hit the clean wing, and it is shifted towards the wing by a small angle (one degree) due to low pressure region under the wing. When the flap is deployed, jet stream hits the flap and is deflected away from the wing.