High Resolution Global Gridded Data for Use in Population Studies

Lloyd, Christopher T.

doi:10.5194/isprs-archives-xlii-4-w2-117-2017

Cited by 19 publications

(19 citation statements)

References 12 publications

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“…, Lloyd et al. ), with potential implications for human mortality, health risks from smoke and particulate emission, and the financial burden of wildfire management (Reid et al. , Radeloff et al.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima

Joseph

Rossi

Mietkiewicz

et al. 2019

Ecological Applications

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Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30‐yr wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero‐inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump‐shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima

Joseph

Rossi

Mietkiewicz

et al. 2019

Ecological Applications

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“…(a) Spatial distribution of sampling sites of tap water and population density in China. The population density in 2015 is derived from Lloyd et al (). The tap water sampling locations of Zhao et al () are also shown.…”

Section: Methodsmentioning

confidence: 99%

Water Source Signatures in the Spatial and Seasonal Isotope Variation of Chinese Tap Waters

Wang

Zhang

Bowen

et al. 2018

Water Resources Research

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Different water sources exploited for public use have different exposure to risks associated with climatic and environmental change. Isotope ratios of tap water have previously been studied as a potential tool to link public supply waters with water source characteristics at local to continental scales, providing information on the footprint of and potential risks associated with the water sources used. Work that combines intensive spatial and temporal sampling with independent water management data has been limited, however. In this study, an extensive observation network was established during 2014–2016 to provide monthly tap water sampling across China. We show that the spatial distribution of annual mean tap water isotope ratios is generally consistent with that of local precipitation across China. We identify seasonal correlation between tap water and precipitation isotope ratios in south China, where use of surface water is prevalent. In contrast, relatively invariant tap water isotope ratios elsewhere in China, which are not correlated with seasonal variation of precipitation isotope ratios, can be attributed to use of groundwater or water from river basins with longer storage times. The tap water isotope signatures identified here could be widely applied to characterize water supplies and associated sustainability challenges in different regions worldwide.

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“…annual time series) produced are: DMSP-OLS version four night-time lights (2000-2011) composites (US NOAA, 2015;Zhang, Pandey, & Seto, 2016); VIIRS version 1 night-time lights (2012-2016) composites (US NOAA, 2017); ESA CCI annual global land cover for 2000-2015 (ESA CCI, 2017a); UNEP/ IUCN World Database of Protected Areas for 2000-2017 (UNEP-WCMC and IUCN, 2017); and built settlement grids for 2000, 2012, and 2014, which combine the JRC Global Human Settlement Layer (Pesaresi et al, 2015) with the ESA CCI built settlement landcover class and the DLR Global Urban Footprint (DLR EOC, 2016) dataset and which were extrapolated and interpolated into an annual time series as described in Nieves et al (2018). The workflow for standardising and harmonising geospatialgeospatial layers is a significant development and expansion of methods discussed in Lloyd et al (2017), and Lloyd (2017). Workflow is visualised diagrammatically in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2019

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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.

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High Resolution Global Gridded Data for Use in Population Studies

Cited by 19 publications

References 12 publications

Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima

Spatiotemporal prediction of wildfire size extremes with Bayesian finite sample maxima

Water Source Signatures in the Spatial and Seasonal Isotope Variation of Chinese Tap Waters

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

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