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

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 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.

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

“…Global or large scale gridded population datasets considered state-of-the-art in terms of open access archives of population distribution data include: the Rural-Urban Mapping Project (GRUMP) [5], the Gridded Population of the World, Version 4 (GPWv4) [6,7], the LandScan Global Population database [8,9], the Global Human Settlement Layer-Population grid (GHS-POP) [10,11], the WorldPop datasets [12][13][14][15][16] and the recently developed High Resolution Settlement Layer (HRSL) population grids [17]. Current and previous versions of these products have proved to be an important source of information and essential input for a wide range of cross-disciplinary applications including: poverty mapping [18][19][20], epidemiological modelling and disease burden estimation [21][22][23], interconnectivity and accessibility analyses [24][25][26], deriving past and future population estimates [15,27,28], disaster management [29][30][31] and human settlement characterisation [32] among others.…”

“…The most commonly employed geospatial covariates include: land cover and land use types, intensity of nightlights, climatic factors, human settlements, urban/rural extents, water features, road networks and topographic elevation and slope. In this regard, LandScan and WorldPop population grids use multiple best-available local or global covariates that are statistically assessed to produce a weighted layer that is used as input in the dasymetric modelling method [8,12,16]. Here, the resulting population grids show an asymmetrical distribution of population counts per administrative unit, in which each grid cell is assigned a portion of the population depending on the individual calculated weights [34].…”

Annually Modelling Built-settlements between Remotely-sensed Observations Using Relative Changes in Subnational Populations and Lights at Night

“…In the case presented here, annually available covariates, or single time point covariates reasonably assumed to be time invariant, were used either in the direct calculation of transition probabilities or in the remainder of the disaggregative process (Table 2). As detailed in Lloyd et al (2019), all covariates were preprocessed, appropriately resampled, and matched to a common spatial grid having a resolution of 3 arc seconds; with the latter chosen as a compromise between the higher resolutions of some of the covariates (Table 2) and the ESA datasets. All data used to restrict the area of modelling and inform the redistribution of transitions are also detailed in Table 2.…”

Annual Projections of Future Built-Settlement Expansion Using Relative Changes in Projected Small Area Population and Short Time-Series of Built-Extents

“…Annual subnational unit area (hereafter simply "unit,") population estimates, for 2000 through 2020, were based upon the Gridded Population of the World version 4 (GPWv4) input data [38] were produced by the Center for International Earth Science Information Network (CIESIN) and spatially harmonized as described in Lloyd et al [33]. Each unit possesses a unique ID referencing a globally consistent grid (3 arc seconds) defining the unit areas with globally harmonized coastlines and international borders.…”

“…VIIRS [33,40] Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: [43] a Some classes were collapsed: 10-30 → 11; 40-120 → 40; 150-153 → 150; 160-180 → 160 (Sorichetta et al>, 2015) b Covariates involved in Demand Quantification were used to determine the demand for non-BS to BS transitions at the subnational unit level for every given year. Covariates involved in Spatial Allocation were either used as predictive covariates in the random forest calculated probabilities of transition (see c) or as a post-random forest year specific weight on those probabilities and the spatial allocation of transitions within each given unit area.…”