According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from ~18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (~100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.

The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.

Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.

Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.

The Kazavango‐Zambezi Transfrontier Conservation Area is home to the largest remaining elephant population in Africa but is also the site of high levels of human‐elephant conflict through crop depredation. Offsetting the costs of coexisting with elephants in this area is critical to incentivizing elephant conservation within community‐based conservation (CBC) areas, and trophy hunting has long been touted as a method for generating revenue for communities from wildlife. However, the idea that sustainable elephant hunting can offset the costs of crop depredation remains largely untested. We combined household survey data, financial records, and elephant population data to compare the potential benefits of sustainable hunting with the costs of crop depredation in a CBC area in northeastern Namibia. We determined that sustainable trophy hunting only returns ~30% of the value of crops lost to the community and cannot alone offset the current costs of coexistence with elephants. As core institutions supporting the practice of conservation, CBC efforts must promote community management capacity to combine multiple wildlife‐based income streams and build partnerships at multiple scales of governance to address the challenges of elephant management.