Global Population Datasets Research Articles

A global dataset of 7 billion individuals with socio-economic characteristics

In global impact modeling, there is a need to address the heterogeneous characteristics of households and individuals that drive different behavioral responses to, for example, environmental risk, socio-economic policy changes and spread of diseases. In this research, we present GLOPOP-S, the first global synthetic population dataset with 1,999,227,130 households and 7,335,881,094 individuals for the year 2015, consistent with population statistics at an administrative unit 1 level. GLOPOS-S contains the following attributes: age, education, gender, income/wealth, settlement type (urban/rural), household size, household type, and for selected countries in the Global South, ownership of agricultural land and dwelling characteristics. To generate GLOPOP-S, we use microdata from the Luxembourg Income Study (LIS) and Demographic and Health Surveys (DHS) and apply synthetic reconstruction techniques to fit national survey data to regional statistics, thereby accounting for spatial differences within and across countries. Additionally, we have developed methods to generate data for countries without available microdata. The dataset can be downloaded per region or country. GLOPOP-S is open source and can be extended with other attributes.

Global population datasets overestimate flood exposure in Sweden

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.

Spectrum of rare and common mitochondrial DNA variations from 1029 whole genomes of self-declared healthy individuals from India

Mitochondrial disorders are a class of heterogeneous disorders caused by genetic variations in the mitochondrial genome (mtDNA) as well as the nuclear genome. The spectrum of mtDNA variants remains unexplored in the Indian population. In the present study, we have cataloged 2689 high confidence single nucleotide variants, small insertions and deletions in mtDNA in 1029 healthy Indian individuals. We found a major proportion (76.5 %) of the variants being rare (AF<=0.005) in the studied population. Intriguingly, we found two ‘confirmed’ pathogenic variants (m.1555 A>G and m.14484 T>C) with a frequency of ∼1 in 250 individuals in our dataset. The high carrier frequency underscores the need for screening of the mtDNA pathogenic mutations in newborns in India. Interestingly, our analysis also revealed 202 variants in our dataset which have been ‘reported’ in disease cases as per the MITOMAP database. Additionally, we found the frequency of haplogroup M (52.2 %) to be the highest among all the 18 top-level haplogroups found in our dataset. In comparison to the global population datasets, 20 unique mtDNA variants are found in the Indian population. We hope the whole genome sequencing based compendium of mtDNA variants along with their allele frequencies and heteroplasmy levels in the Indian population will drive additional genome scale studies for mtDNA. Furthermore, the identification of clinically relevant variants in our dataset will aid in better clinical interpretation of the variants in mitochondrial disorders.

Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020–2050

Assessment of current and future growth in the global rooftop area is important for understanding and planning for a robust and sustainable decentralised energy system. These estimates are also important for urban planning studies and designing sustainable cities thereby forwarding the ethos of the Sustainable Development Goals 7 (clean energy), 11 (sustainable cities), 13 (climate action) and 15 (life on land). Here, we develop a machine learning framework that trains on big data containing ~700 million open-source building footprints, global land cover, road, and population datasets to generate globally harmonised estimates of growth in rooftop area for five different future growth narratives covered by Shared Socioeconomic Pathways. The dataset provides estimates for ~3.5 million fishnet tiles of 1/8 degree spatial resolution with data on gross rooftop area for five growth narratives covering years 2020–2050 in decadal time steps. This single harmonised global dataset can be used for climate change, energy transition, biodiversity, urban planning, and disaster risk management studies covering continental to conurbation geospatial levels.

A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning

Continuously monitoring global population spatial dynamics is crucial for implementing effective policies related to sustainable development, including epidemiology, urban planning, and global inequality. However, existing global gridded population data products lack consistent population estimates, making them unsuitable for time-series analysis. To address this issue, this study designed a data fusion framework based on cluster analysis and statistical learning approaches, which led to the generation of a continuous global gridded population dataset (GlobPOP). The GlobPOP dataset was evaluated through two-tier spatial and temporal validation to demonstrate its accuracy and applicability. The spatial validation results show that the GlobPOP dataset is highly accurate. The temporal validation results also reveal that the GlobPOP dataset performs consistently well across eight representative countries and cities despite their unique population dynamics. With the availability of GlobPOP datasets in both population count and population density formats, researchers and policymakers can leverage the new dataset to conduct time-series analysis of the population and explore the spatial patterns of population development at global, national, and city levels.

Comparing the suitability of global gridded population datasets for local landslide risk assessments

AbstractLandslide risk assessments are increasingly crucial for meeting global disaster risk reduction strategies and mobilising knowledge for local governments to protect communities and infrastructure. These assessments are data intensive, requiring large amounts of spatial demographic and natural hazard information. There are a growing number of high-resolution gridded population datasets that have global coverage with significant potential to transform disaster risk modelling, however landslide research has not yet compared the suitability of these gridded datasets for local risk assessments. Combining social vulnerability indicators with high-resolution settlement layer (HRSL) and WorldPop gridded population datasets, as well as a local household survey, we layer landslide susceptibility maps to compare three landslide risk estimates, examining the case of the Municipality of Carigara located in the central Philippines. Using statistical t tests, we compare aggregated community landslide risk for 49 communities. Findings revealed that HRSL data resulted in similar landslide risk at community scales when compared to local surveys, however WorldPop data greatly overestimated risk. Our findings point to a high level of accuracy of HRSL when used as an exposure dataset for local landslide risk studies and recommend avoiding WorldPop for such purposes. This research advances understanding of the suitability of open population datasets for use in landslide risk assessments in resource-constrained communities.

Modelling the historic total global human population

The historic total global human population dataset is available on Wikipedia and provides an opportunity for modelling with simple models such as the exponential and logistic differential equations for population. Using the per-capita population growth rate (PPGR) predicted by these two models and estimated PPGR from the data, we are able to estimate parameters for the exponential model and discover that we cannot estimate parameters for the logistic model. However, we are able to create a new differential equation that models this data using ideas from the derivation of the logistic model. This is the superexponential model that goes to infinity in finite time. While the initial exponential and superexponential models are good for approximating the population data, neither really matches the PPGRs estimated from the data. We switch to a hybrid model that better matches the estimated PPGRs. It is superexponential for the first portion of the data and logistic for the rest. This hybrid model greatly reduces the modelling error and can extrapolate from the data. We note that the model switches between superexponential and logistic around 1960, the year the FDA-approved oral contraceptives, a fascinating historical tie-in with our modelling.

Future shrinking cities on the globe: A projection map for 2020–2100 based on global gridded population dataset

Shrinking cities have become increasingly prevalent worldwide due to various factors, which pose serious challenges to affected areas in terms of population decline, economic decline, and spatial deterioration. While existing research studies have focused on identifying shrinking cities, there is a need for global projections to mitigate uncertainties in their growth trajectories. Spatially explicit population grids offer a new approach to identifying potentially shrinking cities with sufficient spatial resolution. By utilizing a global gridded population dataset from 2020 to 2100 under the SSP2 (Middle of the Road) scenario, we produce a global projection map for future shrinking cities. Among the total 19,024 natural cities, 9682 cities (50.9%) will face population decline and 1751 cities (9.2%) may lose more than half population by 2100. Cities in East Asia and East Europe may face serious population decline.

Unknown risk: assessing refugee camp flood risk in Ethiopia

The number of global refugees has been rising annually for the last decade. Many of these refugees are housed within camps, in temporary structures, vulnerable to the impacts of flooding. The flood risk of refugees is not well understood. Flood risk guidance available for camp planners and managers is vague, and existing flood risk data is often lacking in the remote areas where camps are typically located. We show how global data should, and should not, be used to assess refugee flood risk in Ethiopia; a country hosting 725 000 refugees, primarily from four neighboring countries, in 24 camps. We find that global population (GP) datasets, typically used in national flood risk assessments, do not accurately capture camp populations (CPs). Even the most accurate GP datasets are missing three fifths of camp flood exposure. We propose, and test, alternative approaches for representing exposure that combine reported estimates of CP with data on camp area, building footprints, and population density. Applying these approaches in our national flood risk assessment, we find that 95.8% of camps in Ethiopia are exposed to flooding of some degree and between 143 208 (19.8%) and 182 125 refugees (25.2%) are exposed to a 1% annual exceedance probability flood (100 year return period). South Sudanese refugees are the nationality most exposed to flooding, but Eritrean refugees are the nationality most exposed to flooding with a high risk to life. Promisingly, we find that many camps may be set up in such a way that reduces the exposure of refugees to flooding. Our study demonstrates that global data, augmented with local data, can be useful for understanding the flood risk of refugee camps. The consistent scalable approach can be used as a first-order analysis of risk, identifying risk hotspots, and help to prioritize further detailed analyses to inform within-camp adaptation.

Understanding the urban structure of the Algerian cities using emerging data: Case study of Algiers

Despite the identification of urban structure and urban centers being crucial for effective policy making (transport policies, urban risk management policies etc) there is a serious lack of studies covering developing countries' urban structure changes, especially north African cities. This is mainly due to the unavailability of the necessary data for urban structure identification. In this contribution we investigate the polycentric development in the Algerian capital, Algiers, using innovative data resources such as LandScanTM High Resolution Global Population Dataset and the POI approach, and spatial statistics. The findings suggest that the structure of Algiers is indeed developing into a polycentric city. The emerging data helped us overcome a serious methodological problem and objectively identify the city structure for better planning.

Genetic landscape of human neutrophil antigen variants in India from population-scale genomes.

Antibodies against human neutrophil antigens (HNAs) play a significant role in various clinical conditions such as neonatal alloimmune neutropenia, transfusion-related acute lung injury, and other nonhemolytic transfusion reactions. This study aims to identify the genotype and allele frequencies of HNAs in the healthy Indian population. Ten genetic variants in four human genes encoding alleles of HNAs class I-V approved by the International Society of Blood Transfusion-Granulocyte Immunobiology Working Party were used in the analysis. Genetic variants from whole genome sequences of 1029 healthy Indian individuals corresponding to HNA alleles were analyzed. The frequencies of the variants were compared with global population datasets using an in-house computational pipeline. In HNA class I, allele frequencies of FCGR3B*01, FCGR3B*02, and FCGR3B*03 encoding HNA-1a, HNA-1b, and HNA-1c were 0.07, 0.8, and 0.13, respectively. HNA class 3 alleles namely SLC44A2*01 (encoding HNA-3a) and SLC44A2*02 (encoding HNA-3b) were found at allele frequencies of 0.78 and 0.22, respectively. The frequencies of ITGAM*01 encoding HNA-4a and ITGAM*02 encoding HNA-4a were 0.95 and 0.05, respectively. Furthermore, allele frequencies of HNA class 5 alleles were 0.32 for ITGAL*01 (encoding HNA-5a) and 0.68 for ITGAL*02 (encoding HNA-5b). Interestingly, it was also found that rs2230433 variant deciding the HNA class 5 alleles, was highly prevalent (78.2%) in the Indian population compared with other global populations. This study presents the first comprehensive report of HNA allele and genotype frequencies in the Indian population using population genome datasets of 1029 individuals. Significant difference was observed in the prevalence of HNA5a and HNA5b in India in comparison to other global populations.

City spatial structure and smart city innovation: the case of China

PurposeThis study aims to analyze the relevance of the city spatial structure for smart city innovation from the perspective of agglomeration externalities, and discusses whether there is heterogeneity in innovation across different geographical areas and population scales of cities.Design/methodology/approachThe authors construct the centralization and concentration indexes to conceptualize the city spatial structure of 286 cities (prefecture-level) in China based on the LandScan Global Population Dataset from 2001 to 2016. A fixed-effects panel data model is employed to analyze the relationship between the spatial structure and the innovation ability of smart cities; the results were validated through robustness tests and heterogeneity analyses.FindingsThe study found that the more concentrated and more evenly the distribution of urban population, namely the more city spatial structure tends to be weak-monocentricity, the higher the level of innovation in smart cities. The relevance of the weak-monocentricity structure and smart city innovation varies significantly depending on their geographical location and the size of the city. This result is more applicable to cities in the eastern and central regions, as well as to cities with smaller populations.Originality/valueThe adjustment and optimization of the city spatial structure is important for enhancing smart city construction. Unlike previous studies, which mostly use a single dimension of “the proportion of population in sub-centres to the population of all central areas” to measure city spatial structure, the authors employed the spatial centralization and spatial concentration. It is hoped that this study can guide smart city construction from the perspective of the development model of city spatial structure.

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020–2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways’ (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

1029 genomes of self-declared healthy individuals from India reveal prevalent and clinically relevant cardiac ion channelopathy variants

BackgroundThe prevalence and genetic spectrum of cardiac channelopathies exhibit population-specific differences. We aimed to understand the spectrum of cardiac channelopathy-associated variations in India, which is characterised by a genetically diverse population and is largely understudied in the context of these disorders.ResultsWe utilised the IndiGenomes dataset comprising 1029 whole genomes from self-declared healthy individuals as a template to filter variants in 36 genes known to cause cardiac channelopathies. Our analysis revealed 186,782 variants, of which we filtered 470 variants that were identified as possibly pathogenic (440 nonsynonymous, 30 high-confidence predicted loss of function ). About 26% (124 out of 470) of these variants were unique to the Indian population as they were not reported in the global population datasets and published literature. Classification of 470 variants by ACMG/AMP guidelines unveiled 13 pathogenic/likely pathogenic (P/LP) variants mapping to 19 out of the 1029 individuals. Further query of 53 probands in an independent cohort of cardiac channelopathy, using exome sequencing, revealed the presence of 3 out of the 13 P/LP variants. The identification of p.G179Sfs*62, p.R823W and c.420 + 2 T > C variants in KCNQ1, KCNH2 and CASQ2 genes, respectively, validate the significance of the P/LP variants in the context of clinical applicability as well as for large-scale population analysis.ConclusionA compendium of ACMG/AMP classified cardiac channelopathy variants in 1029 self-declared healthy Indian population was created. A conservative genotypic prevalence was estimated to be 0.9–1.8% which poses a huge public health burden for a country with large population size like India. In the majority of cases, these disorders are manageable and the risk of sudden cardiac death can be alleviated by appropriate lifestyle modifications as well as treatment regimens/clinical interventions. Clinical utility of the obtained variants was demonstrated using a cardiac channelopathy patient cohort. Our study emphasises the need for large-scale population screening to identify at-risk individuals and take preventive measures. However, we suggest cautious clinical interpretation to be exercised by taking other cardiac channelopathy risk factors into account.

The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets

Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess its capacity to capture the distribution of population counts in several urban areas, spread across the major world regions. This analysis was performed to assess its suitability for global population modelling. We acquired the most detailed local population data available for several cities and compared this with the GHS-POP layer. Results showed diverse error rates and degrees depending on the geographic context. In general, cities in High-Income (HIC) and Upper-Middle-Income Countries (UMIC) had fewer model errors as compared to cities in Low- and Middle-Income Countries (LMIC). On a global average, 75% of all urban spaces were wrongly estimated. Generally, in central mixed or non-residential areas, the population was overestimated, while in high-density residential areas (e.g., informal areas and high-rise areas), the population was underestimated. Moreover, high model uncertainties were found in low-density or sparsely populated outskirts of cities. These geographic patterns of errors should be well understood when using population models as an input for urban growth models, as they introduce geographic biases.

Proposal of a health gut microbiome index based on a meta-analysis of Korean and global population datasets.

The disruption of the human gut microbiota has been linked to host health conditions, including various diseases. However, no reliable index for measuring and predicting a healthy microbiome is currently available. Here, the sequencing data of 1,663 Koreans were obtained from three independent studies. Furthermore, we pooled 3,490 samples from public databases and analyzed a total of 5,153 fecal samples. First, we analyzed Korean gut microbiome covariates to determine the influence of lifestyle on variation in the gut microbiota. Next, patterns of microbiota variations across geographical locations and disease statuses were confirmed using a global cohort and di-sease data. Based on comprehensive comparative analysis, we were able to define three enterotypes among Korean cohorts, namely, Prevotella type, Bacteroides type, and outlier type. By a thorough categorization of dysbiosis and the evaluation of microbial characteristics using multiple datasets, we identified a wide spectrum of accuracy levels in classifying health and disease states. Using the observed microbiome patterns, we devised an index named the gut microbiome index (GMI) that could consistently predict health conditions from human gut microbiome data. Compared to ecological metrics, the microbial marker index, and machine learning approaches, GMI distinguished between healthy and non-healthy individuals with a higher accuracy across various datasets. Thus, this study proposes a potential index to measure health status of gut microbiome that is verified from multiethnic data of various diseases, and we expect this model to facilitate further clinical application of gut microbiota data in future.

Assessing OSM building completeness using population data

OpenStreetMap (OSM) is currently an important source for building data, despite the existence of potential quality issues. Previous studies have assessed OSM data quality by comparing it with reference building data, which may not otherwise be readily available. This study assessed OSM building completeness using population data, and investigated the effectiveness of using population data for building reference data. We proposed various approaches, including type-based and regression-based approaches and their subtypes, and designed measures and methods to evaluate these approaches. Our evaluation examined four study areas in two countries, using global population data sets at three spatial resolutions (1-km, 100-m, and 30-m). Results showed that the type-based approach correctly classified approximately 80–99% of the assessed grid cells. The regression-based approach resulted in a high linear correlation (0.7 or greater) between the population counts and the referenced building count/building area size, with the strongest correlation present for the 1-km population dataset. We conclude that the use of population data as referenced building data is an effective method for the assessment of OSM building completeness. The paper concludes with the advantages and limitations of using both the type-based and the regression-based approaches.

Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

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.

Increasing population exposure to global warm-season concurrent dry and hot extremes under different warming levels

Projecting future changes in concurrent dry and hot extremes (CDHEs) and the subsequent socio-economic risks (e.g. population exposure) is critical for climate adaptation and water management under different warming targets. However, to date, this aspect remains poorly understood on both global and regional scales. In this study, the changes in future CDHEs and their population exposures under 1.5 °C, 2 °C, and 3 °C warming were quantified using a Standardized Dry and Hot Index calculated based on the newly released Coupled Model Intercomparison Project Phase 6 climate model outputs and global population datasets. It was found that relative to the baseline period (1986–2005), the severity of CDHEs would increase on the global scale and in most regions, such as in Southern Europe, the Mediterranean, Sahara, West Africa, Central America, Mexico, the Amazon, and the west coast of South America under 1.5 °C, 2 °C, and 3 °C of warming. Stabilizing the warming at 1.5 °C would constrain the adverse influence of CDHEs on the population suffering from severe CDHEs in most regions (especially in Central Europe, Southern Europe, the Mediterranean, Eastern North America, West Asia, East Asia, and Southeast Asia). Globally, the population impacted by severe CDHEs (with a constant 2000 population) would increase by 108 and 266 million (149 and 367 million when constant 2080 population is applied) for 2 °C and 3 °C increase compared to a 1.5 °C increase. These findings provide scientific evidence of the benefit of limiting anthropogenic warming to 1.5 °C in terms of the socio-economic risks related to CDHEs.

Human Consequences of Multiple Nuclear Detonations in New Delhi (India): Interdisciplinary Requirements in Triage Management.

The human casualties from simulated nuclear detonation scenarios in New Delhi, India are analyzed, with a focus on the distribution of casualties in urban environments and the theoretical application of a nuclear-specific triage system with significant innovation in interdisciplinary disaster management applicable generally to urban nuclear detonation medical response. Model estimates of nuclear war casualties employed ESRI’s ArcGIS 9.3, blast and prompt radiation were calculated using the Defense Nuclear Agency’s WE program, and fallout radiation was calculated using the Defense Threat Reduction Agency’s (DTRA’s) Hazard Prediction and Assessment Capability (HPAC) V404SP4, as well as custom GIS and database software applications. ESRI ArcGISTM programs were used to calculate affected populations from the Oak Ridge National Laboratory’s LandScanTM 2007 Global Population Dataset for areas affected by thermal, blast and radiation data. Trauma, thermal burn, and radiation casualties were thus estimated on a geographic basis for New Delhi, India for single and multiple (six) 25 kt detonations and a single 1 mt (1000 kt) detonation. Major issues related to the emergency management of a nuclear incident are discussed with specific recommendations for improvement. The consequences for health management of thermal burn and radiation patients is the worst, as burn patients require enormous resources to treat, and there will be little to no familiarity with the treatment of radiation victims. Of particular importance is the interdisciplinary cooperation necessary for such a large-scale emergency response event, which would be exemplified by efforts such as the application of a Nuclear Global Health Workforce.