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Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is fundamental and use of freely available, high-resolution, gridded datasets on global population as a source of such information is increasing. However, there is little research to guide users in dataset choice. This study evaluates five of the most commonly used global gridded population datasets against a high-resolution Swedish population dataset on a pixel level. We show that datasets which employ more complex modeling techniques exhibit lower errors overall but no one dataset performs best under all situations. Furthermore, differences exist in how unpopulated areas are identified and changes in algorithms over time affect accuracy. Our results provide guidance in navigating the differences between the most commonly used gridded population datasets and will help researchers and policy makers identify the most suitable datasets under varying conditions.

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.

The simultaneous occurrence of both extreme droughts and heatwaves has become more frequent with global warming, resulting in increases in the frequency and potential impact of compound drought and heatwave (CDHW) globally. It is critical to evaluate the impacts of CDHW and assess global socio-economic risks to formulate appropriate risk mitigation strategies. Most studies have focused on projecting the likely variation in the multidimensional hazard of CDHW. However, the discrepancies among global population projection datasets based on shared socioeconomic pathways (SSPs) and their potential impacts on disaster risk assessments remain underexplored. In this study, multiple global high-resolution population projection datasets are used in combination with projected CDHW hazards via the multimodel ensemble from Coupled Model Intercomparison Project Phase 6 (CMIP6) to investigate how different sources of population data could affect the assessment of CDHW-exposed populations under SSPs. The results show that at the global scale, the spatial pattern and temporal evolution of the CDHW-exposed population under climate change can be depicted consistently on the basis of different population data. However, at the subcontinental scale, substantial spatial heterogeneity exists in the projected exposure. For regions such as the Mediterranean, South Asia, and western Central Asia, the projections from different datasets are consistent with low uncertainty. In contrast, for regions including the northern hemisphere above 40°N, Oceania, eastern Central Asia, East Asia, the South American monsoon region, western Africa, Central Africa, etc., the uncertainty in the estimated exposed population is higher and is expected to increase from the 2020s to the end of the 21st century. Additional locational socioeconomic data should be collected in these areas to reduce uncertainty in future socioeconomic projections. The findings highlight the critical need to consider different elements-at-risk and choose fit-for-purpose datasets, providing essential guidance for disaster risk assessments that support climate adaptation strategies and sustainable development goals.

The field of human population mapping is constantly evolving, leveraging the increasing availability of high-resolution satellite imagery and the advancements in the field of machine learning. In recent years, the emergence of global built-area datasets that accurately describe the extent, location, and characteristics of human settlements has facilitated the production of new population grids, with improved quality, accuracy, and spatial resolution. In this research, we explore the capabilities of the novel World Settlement Footprint 2019 Imperviousness layer (WSF2019-Imp), as a single proxy in the production of a new high-resolution population distribution dataset for all of Africa—the WSF2019-Population dataset (WSF2019-Pop). Results of a comprehensive qualitative and quantitative assessment indicate that the WSF2019-Imp layer has the potential to overcome the complexities and limitations of top-down binary and multi-layer approaches of large-scale population mapping, by delivering a weighting framework which is spatially consistent and free of applicability restrictions. The increased thematic detail and spatial resolution (~10 m at the Equator) of the WSF2019-Imp layer improve the spatial distribution of populations at local scales, where fully built-up settlement pixels are clearly differentiated from settlement pixels that share a proportion of their area with green spaces, such as parks or gardens. Overall, eighty percent of the African countries reported estimation accuracies with percentage mean absolute errors between ~15% and ~32%, and 50% of the validation units in more than half of the countries reported relative errors below 20%. Here, the remaining lack of information on the vertical dimension and the functional characterisation of the built-up environment are still remaining limitations affecting the quality and accuracy of the final population datasets.

Gridded population projections constitute an essential input for climate change impacts, adaptation, and vulnerability (IAV) assessments as they allow for exploring how future changes in the spatial distribution of population drive climate change impacts. We develop such spatial population projections, using a gravity-based modeling approach that accounts for rural-urban and inland-coastal migration as well as for spatial development patterns (i.e. urban sprawl). We calibrate the model (called CONCLUDE ) to the socioeconomically diverse Mediterranean region, additionally considering differences in socioeconomic development in two geographical regions: the northern Mediterranean and the southern and eastern Mediterranean. We produce high-resolution population projections (approximately 1 km) for 2020–2100 that are consistent with the Shared Socioeconomic Pathways (SSPs), both in terms of qualitative narrative assumptions as well as national-level projections. We find that future spatial population patterns differ considerably under all SSPs, with four to eight times higher urban population densities and three to 16 times higher coastal populations in southern and eastern Mediterranean countries compared to northern Mediterranean countries in 2100. In the South and East, the highest urban density (8000 people km −2 ) and coastal population (107 million) are projected under SSP3, while in the North, the highest urban density (1500 people km −2 ) is projected under SSP1 and the highest coastal population (15.2 million) under SSP5. As these projections account for internal migration processes and spatial development patterns, they can provide new insights in a wide range of IAV assessments. Furthermore, CONCLUDE can be extended to other continental or global scales due to its modest data requirements based on freely available global datasets.

The release of global gridded population datasets, including the Gridded Population of the World (GPW), Global Human Settlement Population Grid (GHS-POP), WorldPop, and LandScan, have greatly facilitated cross-comparison for ongoing research related to anthropogenic impacts. However, little attention is paid to the consistency and discrepancy of these gridded products in the regions with rapid changes in local population, e.g., Mainland Southeast Asia (MSEA), where the countries have experienced fast population growth since the 1950s. This awkward situation is unsurprisingly aggravated because of national scarce demographics and incomplete census counts, which further limits their appropriate usage. Thus, comparative analyses of them become the priority of their better application. Here, the consistency and discrepancy of the four common global gridded population datasets were cross-compared by combing the 2015 provincial population statistics (census and yearbooks) via error-comparison based statistical methods. The results showed that: (1) the LandScan performs the best both in spatial accuracy and estimated errors, then followed by the WorldPop, GHS-POP, and GPW in MSEA. (2) Provincial differences in estimated errors indicated that the LandScan better reveals the spatial pattern of population density in Thailand and Vietnam, while the WorldPop performs slightly better in Myanmar and Laos, and both fit well in Cambodia. (3) Substantial errors among the four gridded datasets normally occur in the provincial units with larger population density (over 610 persons/km2) and a rapid population growth rate (greater than 1.54%), respectively. The new findings in MSEA indicated that future usage of these datasets should pay attention to the estimated population in the areas characterized by high population density and rapid population growth.

Gridded population projections consistent with the shared socioeconomic pathways (SSPs) are critical for the studies of climate change impacts and their mitigation. Existing gridded population projections under the SSPs have relatively coarse resolution and issue of overestimation in populated areas, which further bias the analysis of climate change impacts. In this study, we proposed a scheme by integrating high‐resolution historical population maps and machine learning models to predict future built‐up land and population distributions, which were rendered consistent with the SSPs. Using this proposed method in China, we generated a set of 100‐m SSPs population maps for China from 2015 to 2050 at 5‐year intervals. Our projections revealed different spatial structures for the population distribution at the grid level and three modes of provincial population change across the five SSPs from 2015 to 2050. By applying the 100‐m SSPs population grids, we showed that, from 2015 to 2050, exposure to extreme heat in China will increase by 121–136% and 164–191% under the representative concentration pathways 4.5 and 8.5, respectively. We also found a severe spatial bias in the existing 1/8 ° SSPs population grids, i.e., 30–43% of the estimated population is wrongly allocated in cropland, forest, and pastureland. This bias results in substantial underestimation of extreme heat exposure in high‐density metropolitan areas and overestimation in medium and low‐density areas. Plain Language Summary In this study, we proposed a scheme by integrating high‐resolution historical population maps and machine learning models to predict future built‐up land and population distributions, which were rendered consistent with the SSPs. Using this proposed method, we generated a set of 100‐m SSPs population maps for China from 2015 to 2050 at 5‐year intervals. Our projections revealed different spatial structures for the population distribution at the grid level and three modes of provincial population change across the five different SSPs from 2015 to 2050. By applying the 100‐m SSPs population grids, we showed that, from 2015 to 2050, exposure to extreme heat in China will increase by 121–136% and 164–191% under the representative concentration pathways 4.5 and 8.5, respectively. We further compared our projections with the existing 1/8 ° SSPs population grids and we found a severe spatial bias in the 1/8 ° SSPs population grids: 30–43% of the estimated population is wrongly allocated in cropland, forest, and pastureland. This bias results in substantial underestimation of extreme heat exposure in high‐density metropolitan areas and overestimation in medium and low‐density areas. Key Points We developed 100‐m gridded SSPs population projections for China The 100‐m population grids reveal different structures and modes across five SSPs The 100‐m population grids yield higher estimates of exposure to heat extreme

Background To date, few HIV-related population-based data are available for refugee populations. Household surveys typically require reliable population counts and well-defined geographic areas, which are often not available for refugee settlements. We describe the gridded population sampling approach as an option for conducting such a survey in Uganda and describe its application for a household survey in Uganda and assess its utility among refugee populations. Methods The Uganda Refugee Population-based HIV Impact Assessment (RUPHIA) 2021 was a cross-sectional, population-based HIV survey among refugee households in Ugandan settlements, excluding Kampala. We collected shapefiles and population counts for the refugee settlements. These shapefiles from the various geographic areas of interest represented the aggregated refugee settlement zones (including all settlements with available zone shapefiles) and served as the base for creating the sample frame. The sample frame was constructed by disaggregating United Nations High Commission for Refugees population counts from large refugee settlement zones into 100 × 100 m grid cells using WorldPop’s peanutButter-Disaggregate app that uses building footprint information to distribute the population into the grid cells. We then utilized a gridded population sampling approach which redistributed the population into manageable-sized areas of contiguous grid cells based on their estimated population size, forming enumeration area-like sampling units using the publicly available GridEZ algorithm. Results The resulting gridded population dataset had 43,193 100 m x 100 m cells with an estimated mean of 31 people per cell and a range from 2 to 1028. The final gridded population sample frame had 2636 GridEZ units with an average population of 500 ranging from 178 to 1531. The sample frame performed well for survey activities, with few issues encountered in the field, although the size measures for number of households had some inaccuracies, due to issues such as compounds having multiple structures. Conclusions Gridded population sampling was successfully utilized for this refugee study, saving time and money that would have been needed if enumeration of all the refugee settlements had been required. Gridded population sampling is a useful tool when census data are outdated or unavailable or when the population is dynamic, such as with refugees or other mobile or at-risk populations for surveillance or as part of a humanitarian response.

Population estimates for high-resolution spatial grid cells data can reflect detailed spatial distribution of population, which are valuable for epidemiological studies, disaster risk assessments, and public resource allocation. However, choice of source data and methods for producing gridded population data sets can introduce spatial bias, especially in regions with complex geography. We assess the performance of four gridded population data sets from 2015 for the Dian-Gui-Qian region of Southwest China: Gridded Population of the World version 4 (GPW4), Global Human Settlement (GHS), LandScan, and WorldPop. At the town-scale, we found that GHS and WorldPop most closely resembled the 2015 population data used for validation. At the intra-town scale, for which spatially disaggregated population validation data do not exist, we compared each data set against Google Earth high-resolution images and found that WorldPop most closely resembled the population distribution that could be inferred from the imagery. We conclude that in general, WorldPop performs better than GPW, GHS, and LandScan.

The measurement and characterization of urbanization crucially depends upon defining what counts as urban. The government of India estimates that only 31% of the population is urban. We show that this is an artifact of the definition of urbanity and an underestimate of the level of urbanization in India. We use a random forest-based model to create a high-resolution (~ 100 m) population grid from district-level data available from the Indian Census for 2001 and 2011, a novel application of such methods to create temporally consistent population grids. We then apply a community-detection clustering algorithm to construct urban agglomerations for the entire country. Compared with the 2011 official statistics, we estimate 12% more of urban population, but find fewer mid-size cities. We also identify urban agglomerations that span jurisdictional boundaries across large portions of Kerala and the Gangetic Plain.