Global Population Data Research Articles

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Estimates of the global burden of cancer-related deaths attributable to residential exposure to petrochemical industrial complexes from 2020 to 2040

The petrochemical industry is a major industrial emitter of greenhouse gas (CO2) and environmental pollution, posing health risks to nearby communities. Although previous studies have indicated that residents living near petrochemical industrial complexes are at a higher risk of cancer, they have focused on local or regional burdens. This study aimed to estimate the global cancer burden attributable to residential exposure to petrochemical industrial complexes. The geographical coordinates of petrochemical plants and oil refineries were retrieved and verified from published sources. The ArcGIS software and global population data were used to estimate the number of people living within specific distances (exposed population). The exposure time window was framed as ranging from 1992 to 2035, extending to the latest period of the exposure time window for all cancer types to estimate the attributable deaths between 2020 and 2040. The relative risk of cancer was estimated from 15 published studies. Population attributable fraction (PAF) method was used to estimate the risk of cancer attributable to residential exposure and calculate the number of cancer-related deaths. Our findings indicate that >300 million people worldwide will be estimated to live near petrochemical industrial complexes by 2040. The overall global burden of cancer-related deaths was 19,083 in 2020, and it is estimated to increase to 27,366 deaths by 2040. The region with the highest attributable cancer deaths due to exposure is the high-income region, which had 10,584 deaths in 2020 and is expected to reach 13,414 deaths by 2040. Residential exposure to petrochemical industrial complexes could contribute to global cancer deaths, even if the proportion is relatively small, and proactive measures are required to mitigate the cancer burdens among these residents. Enforcing emissions regulations, improving monitoring, educating communities, and fostering collaboration are vital to protecting residents’ health.

Opportunities for Consistent and Holistic Metrics to Support Food Systems Transformation: A Summary of a Symposium Presented at Nutrition 2023

There is an urgent need for global food systems transformation to realize a future where planetary health reaches its full potential. Paramount to this vision is the ability of stakeholders across sectors to understand how foods and dietary patterns impact food systems inclusive of all domains of sustainability—environmental, nutrition/health, economic and social. This article is a synopsis of presentations by 3 food systems experts to share the latest science in a session entitled “How do you measure sustainability? Opportunities for consistent and holistic metrics to support food systems transformation” at the American Society for Nutrition’s 2023 annual conference. As summarized here, global population data showing widespread malnutrition underscore the important role of dietary diversity through a balance of plant- and animal-source foods to achieve nutritionally adequate diets and reduce risk of noncommunicable diseases. Yet, recent international audits of countries, companies, and organizations and their sustainability targets largely demonstrate an underrepresentation of robust nutrition/health metrics to support public nutrition and health progress. Addressing limitations in diet-sustainability modeling systems provides a viable opportunity to accurately reflect the important contributions and trade-offs of diets across all domains of sustainability to ultimately support evidence-based decision making in advancing healthy food systems.

Assessing Trends in Tree Cover, Wildfire and Population Growth in Zimbabwe since 2000

Zimbabwe’s woodland and forests have experienced substantial change over the last two decades. In this study, our objective was to assess national-scale spatio-temporal changes in tree loss, wildfire, and population growth since 2000 using global data. Our results showed rates of tree loss were highest in the urbanized Harare and Bulawayo provinces between 2000–2004, followed by Masvingo and Manicaland provinces. We found agricultural versus non-agricultural land type classes had higher tree loss, with the highest rates in small resettlement farms (‘A1’ farms, averaging 5 ha in size) between 2000–2008. The findings from our analysis of wildfire showed burning peaked in 2010, impacting 12% of the country. In the peak fire years of 2008–2012, 30% of A2 self-contained resettlement farms (‘A2’ farms, averaging 318 ha in size) burned, along with 19% of A1 resettlement farms. Analysis of global population data showed increases across all provinces, particularly in large-scale commercial farming areas, with gradual increases seen in A1 and A2 farms. Understanding the trends over two decades and the patterns in three key pressures—tree loss, population change, and fire—provides an important contribution to help guide regional assistance efforts in Zimbabwe.

Green spaces in highly urbanized tracts tied to lower prevalence of chronic respiratory diseases: A nationwide study across levels of urbanicity

Previous studies have found conflicting results between green space and respiratory diseases, possibly due to the use of generalized green space measures and the diverse urbanicity levels in which they were conducted. Few nationwide studies examined how urbanicity modifies the relationship between green spaces and respiratory diseases while considering the geospatial relation between green space and human population, population density, and accessibility.This study evaluates the relationships between population-weighted forest and open space exposure (inside and outside park), chronic obstructive pulmonary disease (COPD), and asthma in the contiguous United States. The COPD and asthma data were obtained from the Centers for Disease Control and Prevention. The green space exposure within an 800 m buffer was computed using National Land-cover Dataset and WorldPop Global Population Data. The associations were estimated using spatial error models and adjusted for important covariates. We also performed stratified analyses by four urbanicity levels.Green spaces inside but not outside park were associated with a lower prevalence of COPD and asthma. We found that a 1 m2 increase in forest inside park was associated with a decrease of 3.57 and 2.97 cases per 100,000 population in COPD and asthma, and a 1 m2 increase in open space inside park was associated with a decrease of 4.82 and 7.66 cases per 100,000 population in COPD and asthma. A trend of an inverse relationship was observed across urbanicity: the beneficial associations occurred in highly urbanized tracts but not in the three less-urbanized tracts.Our results provide further evidence that nearby forests and open spaces inside park, particularly in urban areas, were tied to a lower prevalence of respiratory diseases in the US. The beneficial association suggests the necessity of ensuring access to nearby green spaces, especially those in parks, to promote respiratory health.

Anti-Aging Potential of Plants of the Anak Dalam Tribe, Jambi, Indonesia.

The process of skin aging is a physiological phenomenon that can not be avoided. According to global population data, the rate of aging increases by approximately 13% every year. The impact of skin aging has become a significant concern and challenge for developed countries. Consequently, there has been a search for potential new anti-aging agents. This review aims to provide an overview of the current research status of plants of the Anak Dalam Tribe (Indonesian: Suku Anak Dalam [SAD]; referred to as SAD henceforth) in Jambi Province, Indonesia, for the development of potential new anti-aging agents. One such discovery is a product derived from natural ingredients with the ability to prevent premature aging. These new anti-aging plants have been used for centuries by the Anak Dalam tribe, for treating skin diseases and maintaining skin health through traditional remedies. Recent research on herbal formulations used by the SAD community in Indonesia for skin beauty treatments, reported by Research on Medicinal Plants and Herbs or RISTOJA, indicates that 64 plant species are used for skin care. Among these plants, Toona sinensis, Curcuma heyneana, Curcuma zedoaria, Curcuma longa, and Kaempferia rotunda are the most commonly used medicinal plants with anti-aging properties. T. sinensis is a tree, while the others are herbs. T. sinensis shows the highest potential for development as an anti-aging agent, with its extracts, active fractions, and bioactive quercetin isolates known to possess strong anti-aging activities both in vitro and in vivo. Furthermore, C. heyneana, C. longa, C. zedoaria, and K. rotunda also show potential for further research, and three of them have demonstrated good potential for in vivo anti-aging activities. Only K. rotunda demonstrates relatively weaker antioxidant activity compared to T. sinensis, C. heyneana, C. longa, and C. zedoaria. Nevertheless, K. rotunda can still be developed to search for potential opportunities as agents with other activities, while T. sinensis, C. heyneana, C. longa, and C. zedoaria in the findings could be an opportunity to explore the potential of new anti-aging agents. In conclusion, of the five medicinal plants traditionally used by the SAD in Jambi, Indonesia, C. longa has received the most extensive research and shows potential for the development of anti-aging solutions. C. zedoaria, C. heyneana and K. rotunda show good potential for in vivo anti-aging activity. T. sinensis is the least-studied medicinal plant. Nevertheless, it has potential for development, as it is widely used by the SAD community for both traditional medicine and skin care.

New Insights into Urbanization Based on Global Mapping and Analysis of Human Settlements in the Rural–Urban Continuum

The clear boundary between urban and rural areas is gradually disappearing, and urban and rural areas are two poles of a gradient with many continuous human settlements in between, which is a concept known as the rural–urban continuum. Little is known about the distribution and change trajectories of the various types in the rural–urban continuum across the globe. Therefore, using global land-cover data (FROM-GLC Plus) and global population data (Worldpop) based on the decision-making tree method, this study proposed a method and classification system for global rural–urban continuum mapping and produced the mapping results on a global scale in the Google Earth Engine platform. With the expansion of built-up areas and the increase in population, the global human settlements follow the pattern that develops from wildland to villages (isolated—sparse—dense), and then to towns (sparse—dense), and finally to urban areas (edge—center). From a regional perspective, there are some obvious differences: Africa is dominated by sparse villages; Asia has the highest proportion of densely clustered towns; the proportion of dense villages in Europe is high. Rural–urban continuum mapping and analysis provide a database and new insights into urbanization and differences between urban and rural areas around the world.

C-to-U RNA deamination is the driving force accelerating SARS-CoV-2 evolution.

Understanding the molecular mechanism underlying the rampant mutation of SARS-CoV-2 would help us control the COVID-19 pandemic. The APOBEC-mediated C-to-U deamination is a major mutation type in the SARS-CoV-2 genome. However, it is unclear whether the novel mutation rate u is higher for C-to-U than for other mutation types, and what the detailed driving force is. By analyzing the time course SARS-CoV-2 global population data, we found that C-to-U has the highest novel mutation rate u among all mutation types and that this u is still increasing with time (du/dt > 0). Novel C-to-U events, rather than other mutation types, have a preference over particular genomic regions. A less local RNA structure is correlated with a high novel C-to-U mutation rate. A cascade model nicely explains the du/dt > 0 for C-to-U deamination. In SARS-CoV-2, the RNA structure serves as the molecular basis of the extremely high and continuously accelerating C-to-U deamination rate. This mechanism is the driving force of the mutation, adaptation, and evolution of SARS-CoV-2. Our findings help us understand the dynamic evolution of the virus mutation rate.

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.

Genetic diversity of 'Very Important Pharmacogenes' in two South-Asian populations.

ObjectivesReliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Telugu in the U.K. (ITU) from the 1000 Genomes Project vis-à-vis global population data was studied to understand its role in drug response.MethodsJoint genotyping approach was used to derive variants of GIH and ITU independently. SNPs of both these populations with significant allele frequency variation (minor allele frequency ≥ 0.05) with super-populations from the 1000 Genomes Project and gnomAD based on Chi-square distribution with p-value of ≤ 0.05 and Bonferroni’s multiple adjustment tests were identified. Population stratification and fixation index analysis was carried out to understand genetic differentiation. Functional annotation of variants was carried out using SnpEff, VEP and CADD score.ResultsPopulation stratification of VIP genes revealed four clusters viz., single cluster of GIH and ITU, one cluster each of East Asian, European, African populations and Admixed American was found to be admixed. A total of 13 SNPs belonging to ten pharmacogenes were identified to have significant allele frequency variation in both GIH and ITU populations as compared to one or more super-populations. These SNPs belong to VKORC1 (rs17708472, rs2359612, rs8050894) involved in Vitamin K cycle, cytochrome P450 isoforms CYP2C9 (rs1057910), CYP2B6 (rs3211371), CYP2A2 (rs4646425) and CYP2A4 (rs4646440); ATP-binding cassette (ABC) transporter ABCB1 (rs12720067), DPYD1 (rs12119882, rs56160474) involved in pyrimidine metabolism, methyltransferase COMT (rs9332377) and transcriptional factor NR1I2 (rs6785049). SNPs rs1544410 (VDR), rs2725264 (ABCG2), rs5215 and rs5219 (KCNJ11) share high fixation index (≥ 0.5) with either EAS/AFR populations. Missense variants rs1057910 (CYP2C9), rs1801028 (DRD2) and rs1138272 (GSTP1), rs116855232 (NUDT15); intronic variants rs1131341 (NQO1) and rs115349832 (DPYD) are identified to be ‘deleterious’.ConclusionsAnalysis of SNPs pertaining to pharmacogenes in GIH and ITU populations using population structure, fixation index and allele frequency variation provides a premise for understanding the role of genetic diversity in drug response in Asian Indians.

Future scenarios of risk of Vibrio infections in a warming planet: a global mapping study

Infections caused by non-cholera Vibrio species have undergone a global expansion over the past few decades reaching new areas of the world that were previously considered adverse for these organisms. The geographical extent of the expansion has not been uniform, and some areas have shown a rapid increase in infections. We applied a new generation of models combining climate, population, and socioeconomic projections to map future scenarios of distribution and season suitability for pathogenic Vibrio. We used the Coupled Model Intercomparison Project 6 framework. Three datasets were used: Geophysical Fluid Dynamics Laboratory's CM4.0 sea surface temperature and sea surface salinity; the coastline length dataset from the World Resources Institute; and Inter-Sectoral Impact Model Intercomparison Project 2b annual global population data. Future projections were used up to the year 2100 and historical simulations from 1850 to 2014. We also project human population at risk under different shared socioeconomic pathways worldwide. Projections showed that coastal areas suitable for Vibrio could cover 38 000 km of new coastal areas by 2100 under the most unfavourable scenario with an expansion rate of season suitability in these regions of around 1 month every 30 years. Population at risk in suitable regions almost doubled from 1980 to 2020 (from 610 million to 1100 million under the scenario of medium challenges to mitigation and adaptation, shared socioeconomic pathway 2-4.5), although the increment will be more moderate in the future and stabilises after 2050 at 1300 million. Finally, we provide the first global estimate for Vibrio infections, with values around half a million of cases worldwide in 2020. Our projections anticipated an expansion of both the temporal and spatial disease burden for Vibrio infections, in particular at high latitudes of the northern hemisphere. However, the largest extent occurred from 1980 to 2020 and a more moderate increase is expected for the future. The most positive outcome is that the projections showed that Vibrio morbidity will remain relatively stable over the coming decades. NOAA Atlantic Oceanographic and Meteorological Laboratory and NOAA OceanWatch, and by the University of Miami's Cooperative Institute for Marine and Atmospheric Studies.

Ankylosis of the Temporomandibular Joint (TMJ) Update and its treatment

In dentistry, temporomandibular joint (TMJ) ankylosis is a rare pathology where no consensus of global population data has been found and therefore has presented a challenge for professionals, since there are no treatment sequence protocols standardized. The objective is to carry out a literature review and update of the diagnosis of ankylosis, considering its clinical characteristics, classification, etiology, and epidemiology. Giving special emphasis to the types of treatments used and concluding that GAP interpositional arthroplasty and TMJ reconstruction arthroplasty would be the best options to improve maximum oral opening in a patient with ankylosis, thus allowing him to recover function and aesthetics.

Identifying coastal towns and small cities in Denmark using global population data to support climate change adaptation

ABSTRACT Coastal settlements face many hazards from climate change. Consequently, there has been extensive focus on developing and implementing adaptation. However, these efforts have prodominantly centred on larger cities. Coastal towns and small cities (urban areas between 1000 and 100,000 people) have received little attention, despite experiencing a number of barriers to adaptation. The absence of information on the global scale of the adaptation challenge within coastal towns and small cities may have contributed to these settlements being overlooked. This paper develops a method that can be used to estimate the numbers, sizes, and locations of coastal towns and small cities worldwide from global population data (Global Human Settlement data). Denmark is used as a pilot for this method with settlements over 1000 people classified with relatively high accuracy. The method developed here represents a potentially fruitful approach to supporting coastal adaptation, as coastal towns and small cities are identifiable globally, they can be classified into types. This will support an assessment of their risk to coastal hazards, and could facilitate knowledge and practice sharing between similar coastal towns and small cities.

Population genetic structure of domain I of apical membrane antigen-1\xa0in Plasmodium falciparum\xa0isolates from Hazara division of Pakistan

BackgroundThe Plasmodium falciparum apical membrane antigen-1 (PfAMA1) is considered as an ideal vaccine candidate for malaria control due to its high level of immunogenicity and essential role in parasite survival. Among the three domains of PfAMA1 protein, hyper-variable region (HVR) of domain I is the most immunogenic. The present study was conducted to evaluate the extent of genetic diversity across HVR domain I of the pfama1 gene in P. falciparum isolates from Hazara division of Pakistan.MethodsThe HVR domain I of the pfama1 was amplified and sequenced from 20 P. falciparum positive cases from Hazara division of Pakistan. The sequences were analysed in context of global population data of P. falciparum from nine malaria endemic countries. The DNA sequence reads quality assessment, reads assembling, sequences alignment/phylogenetic and population genetic analyses were performed using Staden, Lasergene v. 7.1, MEGA7 and DnaSP v.5 software packages respectively.ResultsTotal 14 mutations were found in Pakistani isolates with 12 parsimony informative sites. During comparison with global isolates, a novel non-synonymous mutation (Y240F) was found specifically in a single Pakistani sample with 5% frequency. The less number of mutations, haplotypes, recombination and low pairwise nucleotide differences revealed tightly linked uniform genetic structure with low genetic diversity at HVR domain I of pfama1 among P. falciparum isolates from Hazara region of Pakistan. This uniform genetic structure may be shaped across Pakistani P. falciparum isolates by bottleneck or natural selection events.ConclusionThe Pakistani P. falciparum isolates were found to maintain a distinct genetic pattern at HVR pfama1 with some extent of genetic relationship with geographically close Myanmar and Indian samples. However, the exact pattern of gene flow and demographic events may infer from whole genome sequence data with large sample size of P. falciparum collected from broad area of Pakistan.

Complexity of type-specific 56 kDa antigen CD4 T-cell epitopes of Orientia tsutsugamushi strains causing scrub typhus in India.

Orientia tsutsugamushi (Ots) is an obligate, intracellular, mite-transmitted human pathogen which causes scrub typhus. Understanding the diversity of Ots antigens is essential for designing specific diagnostic assays and efficient vaccines. The protective immunodominant type-specific 56 kDa antigen (TSA) of Ots varies locally and across its geographic distribution. TSA contains four hypervariable domains. We bioinformatically analyzed 345 partial sequences of TSA available from India, most of which contain only the three variable domains (VDI-III) and three spacer conserved domains (SVDI, SVDII/III, SVDIII). The total number (152) of antigenic types (amino acid variants) varied from 14–36 in the six domains of TSA that we studied. Notably, 55% (787/1435) of the predicted CD4 T-cell epitopes (TCEs) from all the six domains had high binding affinities (HBA) to at least one of the prevalent Indian human leukocyte antigen (HLA) alleles. A surprisingly high proportion (61%) of such TCEs were from spacer domains; indeed 100% of the CD4 TCEs in the SVDI were HBA. TSA sequences from India had more antigenic types (AT) than TSA from Korea. Overall, >90% of predicted CD4 TCEs from spacer domains were predicted to have HBA against one or more prevalent HLA types from Indian, Korean, Asia-Pacific region or global population data sets, while only <50% of CD4 TCEs in variable domains exhibited such HBA. The phylogenetically and immunologically important amino acids in the conserved spacer domains were identified. Our results suggest that the conserved spacer domains are predicted to be functionally more important than previously appreciated in immune responses to Ots infections. Changes occurring at the TCE level of TSA may contribute to the wide range of pathogenicity of Ots in humans and mouse models. CD4 T-cell functional experiments are needed to assess the immunological significance of these HBA spacer domains and their role in clearance of Ots from Indian patients.

Visualising world population density as an interactive multi-scale map using the global human settlement population layer

ABSTRACTRecent advances in global population data are enabling new cartographic and analytical opportunities. The Global Human Settlement Layer is the first sub-1 km cell resolution global population density product released as open data, with many applications in the fields of global population dynamics, urban studies and natural hazard risk. There are several cartographic challenges with visualising this dataset due to the range of spatial scales that are of interest, and the extensive variation in the density of settlements patterns that exist across different regions of the globe. These challenges are tackled here using interactive mapping, allowing navigation from global to city-region scales. A detailed classification and colour scheme is developed to distinguish a wide range of densities at multiple scales. Additionally, interactive statistics are presented for direct numerical comparisons at both country and city scales, further enabling global density comparisons. The interactive map produced has received 30,000 users in four months, indicating the widespread interest in understanding global population density.

TSUNAMI HAZARD AND CASUALTY ESTIMATION IN A COASTAL AREA THAT NEIGHBORS THE INDIAN OCEAN AND SOUTH CHINA SEA

In the Indian Ocean and the South China Sea, many hundreds of thousands of lives have been lost due to tsunami events, and almost half of the lives lost occurred following the 2004 Indian Ocean event. Potential tsunami case scenarios have been simulated in these regions by a number of researchers to calculate the hazard level. The hazard level is based on a variety of conditions, such as the tsunami height, the inundation area, and the arrival time. However, the current assessments of the hazard levels do not focus on the tsunami risk to a coastal population. This study proposes a new method to quantify the risk to the coastal population in the region that includes the Indian Ocean and the South China Sea. The method is simple and combines the use of readily available tsunami data, far-field tsunami simulation models to determine the regional risk and global population data. An earthquake-generated tsunami was simulated, following an earthquake that had a magnitude larger than 8.5 Mwand occurred along a potential subduction zone. The 2004 Indian Ocean event seemed to be a "worst case scenario"; however, it has been estimated that a potential tsunami, occurring in a coastal region with a high population density, could cause significantly greater casualties.

PROBABILISTIC TSUNAMI HAZARD ANALYSIS AND RISK TO COASTAL POPULATIONS IN THAILAND

The study of tsunami hazards in Thailand has been an ongoing topic of research. However, the hazards from tsunami sources based on probabilistic study and population risk are still unclear. In this study, potential tsunami sources along rupture zones were selected. A series of far-field tsunami simulations were performed with scaled fault parameters based on fault lengths from 100–600 km. The results show that within a few centuries, the maximum tsunami height could be 2–5 m at the west coast and less than 2 m at the east coast. The potential tsunami exposure (PTE) of populations in an estimated inundation zone was calculated using global population data in relation to tsunami height. The results show that much attention should be paid to fault ruptures longer than 300 km (≈ 8.5 moment magnitude, Mw) that originate from 4°–6° N and 14°–17° N for the Sumatra subduction zone and the Manila trench, respectively. A quarter of a million people are at risk of exposure to a maximum 9 m tsunami height after 100 min of the arrival of the first wave at the Andaman coast. One million people near the Gulf of Thailand are at risk of a tsunami height less than 3 m after 9 hr.

Simulating the spatial distribution of population and emissions to 2100

Urbanization and economic development have important implications for many environmental processes including global climate change. Although there is evidence that urbanization depends endogenously on economic variables, long-term forecasts of the spatial distribution of population are often made exogenously and independent of economic conditions. It is common for research concerning long-run projections of global environmental change to use population density as the primary means to spatially distribute emissions projections. However, researchers typically utilize year 1990 cross-sectional population data to distribute their emissions projections for both the short- and long-term, without projecting any changes in population density. Thus, a beta distribution for individual countries/regions is estimated to describe the geographical distribution of population using a one-degree-by-one-degree latitude–longitude global population data set. Cross-sectional country/regional data are then used to estimate an empirical relationship between parameters of the beta distribution and macroeconomic variables as they vary among countries/regions. This conditional beta distribution allows the simulation of a changing distribution of population, including the growth of urban areas, driven by economic forecasts until the year 2100.

Global landslide and avalanche hotspots

Allocating resources for natural hazard risk management has high priority in development banks and international agencies working in developing countries. Global hazard and risk maps for landslides and avalanches were developed to identify the most exposed countries. Based on the global datasets of climate, lithology, earthquake activity, and topography, areas with the highest hazard, or “hotspots”, were identified. The applied model was based on classed values of all input data. The model output is a landslide and avalanche hazard index, which is globally scaled into nine levels. The model results were calibrated and validated in selected areas where good data on slide events exist. The results from the landslide and avalanche hazard model together with global population data were then used as input for the risk assessment. Regions with the highest risk can be found in Colombia, Tajikistan, India, and Nepal where the estimated number of people killed per year per 100 km2 was found to be greater than one. The model made a reasonable prediction of the landslide hazard in 240 of 249 countries. More and better input data could improve the model further. Future work will focus on selected areas to study the applicability of the model on national and regional scales.