In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country's economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population's susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km.

This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Eswatini. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

The interactive map of Sierra Leone, which is broken down into 14 administrative divisions, highlights the areas and locations selected for some of the SCP components. More than 65% of the country's population lives below the minimum subsistence levels. In some regions this figure even exceeds 80%. Likewise, the level of severe malnutrition, based on the proportion of underweight children, under 5 years, is above 6%, on average for the country. (SCP) aims to provide the poor throughout the country the tools needed to increase food security and income in a sustainable way. The program is part of the National Development Plan 2010-2030 (NSADP) designed for the government of Sierra Leone to make agriculture the engine for socio-economic growth and development in the country, based on the fact that this activity is the largest employer in the country and the largest contributor to GDP. Data Sources:SCP Selected locationsSource: IFAD and GAFSP Documents Poverty (Proportion of population below the poverty line) (2004): Proportion of the population living on less than $2,111.45 Le a day.Source: Statistics Sierra Leone. “2005/2006 Edition of the Annual Statistical Digest”. Sierra Leone Integrated Household Survey (SLIHS), 2003/2004. Poverty (Proportion of population below the poverty line) (2011): Proportion of the population living on less than $1,587,746 Le a year.Source: World Bank and Statistics Sierra Leone. “A Poverty Profile for Sierra Leone 2013”. Population: (Total population) (2004): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Source: Statistics Sierra Leone. “2005/2006 Edition of the Annual Statistical Digest”. Central Statistics Office, Freetown. Malnutrition (Proportion of underweight children under 5 years) (2008): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight for age is less than minus 3 standard deviations below the median weight for age of the international reference population.Source: Measure DHS. “Sierra Leone. Demographic and Health Survey 2008”. Malnutrition (Proportion of underweight children under 5 years) (2013): Prevalence of severely underweight children is the percentage of children aged 0-59 months whose weight for age is less than minus 3 standard deviations below the median weight for age of the international reference population.Source: Statistics Sierra Leone, Ministry of Health and Sanitation and Measure DHS. "Sierra Leone Demographic and Health Survey 2013". MEASURE DHS (Demographic and Health Surveys) Project is responsible for collecting and disseminating accurate, nationally representative data on health and population in developing countries. The project is implemented by Macro International, Inc. and is funded by the United States Agency for International Development (USAID) with contributions from other donors such as UNICEF, UNFPA, WHO, UNAIDS.Total population (2004): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Source: Statistics Sierra Leone. “2005/2006 Edition of the Annual Statistical Digest”. Central Statistics Office, Freetown.Population Density (Persons per 1 square kilometer) (2004): Population divided by land area in square kilometers. Source: Statistics Sierra Leone. “2005/2006 Edition of the Annual Statistical Digest”. Central Statistics Office, Freetown.Total population (2015): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship, except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Source: Statistics Sierra Leone. “2015 Population and Housing Census”.Population Density (Persons per 1 square kilometer) (2015): Population divided by land area in square kilometers.Source: Statistics Sierra Leone. “2015 Population and Housing Census”. Rice Production (2007): Paddy production in metric tons. Total seed, other uses and losses at 5%.Source: “Sierra Leone Household Food Security Survey in Rural Areas 2008” World Food Programme WFP Market Centers: Key market centers for retail, assembly and/ or wholesale of agricultural products. FEWS NET Reference markets.Source: FEWS Net. The Famine Early Warning Systems Network (FEWS NET) is a USAID-funded activity that collaborates with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues. Livelihood Zones (2010): FEWS NET uses the Household Economy Approach (HEA) as the framework for its livelihoods work. For early warning of food insecurity, livelihoods analysis provides invaluable insight into the ability of households such as these to contend with shocks. The analysis also provides detailed information for humanitarian assistance planning and ongoing monitoring.Source: FEWS NET –USAID. “Livelihoods Zoning Plus Activity Sierra Leone 2010”. Geographic boundaries.

The maps displayed on the GAFSP web site are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

This map was designed as an overview map of the Lake Tanganika Basin. Many of the data are of coarse resolution and should be verified before used in an research or planning efforts.Sources by Layer GroupsAdmin: Populations retrieved from worldpopulationreview.com.Town and village names and locations retrieved the NGA GEOnet Names Server (GNS) http://geonames.nga.mil/gns/html/. These data may be incomplete or show incorrect spellings. Refugee camp names and locations provided by Frankfurt Zoological Society. TNC Tuungane Project Villages GPS point locations collected by TNC staff. For more information about the Tuungane Project please visit: https://www.nature.org/ourinitiatives/regions/africa/wherewework/tuungane-project.xml.Interntaional Boundaries retrieved from GADM database (www.gadm.org).Admin Level 1 & 2 subnational boundaries below the country level. This varies by country. Infrastructure:Liemba stops: Derived from https://en.wikivoyage.org/wiki/MV_Liemba\Airport names: Derived from NGA GEOnet Names Server (GNS) http://geonames.nga.mil/gns/html/Roads: The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.Credits: http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1Dams: Lehner, B., C. Reidy Liermann, C. Revenga, C. Vorosmarty, B. Fekete, P. Crouzet, P. Doll, M. Endejan, K. Frenken, J. Magome, C. Nilsson, J.C. Robertson, R. Rodel, N. Sindorf, and D. Wisser. 2011. Global Reservoir and Dam Database, Version 1 (GRanDv1): Reservoirs, Revision 01. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).http://dx.doi.org/10.7927/H4HH6H08. Accessed 28 August 2016.Credits: http://sedac.ciesin.columbia.edu/pfs/grand.htmlPower Plants: Data for power plants with total installed generating capacity > 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). Plants were georeferenced using location information from the WEPP, auxiliary GIS datasets, World Bank project documents and the internet. Locations are approximate, precision varies greatly by point, based on the source of coordinate information.The following attributes are included:PLANT: power plant name,STATUS: status (OPR, CON, PLN, OTHER, UNK),SUM_MW: total installed generating capacity,LATITUDE: approximate location, latitude,LONGITUDE: approximate location, longitude,GEN_TYPE: type of electricity generation (HYDRO, THERMAL, OTHER)Credits: http://www.infrastructureafrica.org/Transmission Lines & Railroads: Africa Infrastructure Knowledge Program http://www.infrastructureafrica.org/.Socioeconomic: FEWS Livelihood Zones, Lean Times Livelihood Hazards: These were derived form country level livelihood zones information at the Famine Early Warning System Network. : Data for individual countries with detailed descriptions of livelihood zones, inclkuding crop calendars and hazards, can be found at http://www.fews.net/.Distance to Markets:HarvestChoice, 2015. "Travel time to nearest town over 20K (mean, hours, 2000)." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at http://harvestchoice.org/data/tt_20k.Lean Times: Lean Times refer to times of the year when food shortages may occur. These were derived form country level livelihood zones information at the Famine Early Warning System Network. NOTE: None of the regions within Lake Tanganyika indicated July as a time of food shortages; therefore, July is excluded as a seperate layer.http://www.fews.net/Population Density: Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ.http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density2011 Fishiereis Frame Survey sites: Indicates at the regional or district level, the percentage of fish landing sites with described properties. Citation: LTA Secretariat, 2012.Lake Tanganyika Regional Fisheries Frame Survey 2011, Bujumbura, Burundi, 30 pFamily Planning, HIV Statistics, Women Issues, Childrens Health, Water and Sanitation,Houshold Fuel Source: Socioeconomic data from USAID-funded The Demographic and Health Surveys (DHS) Program: Produced by ICF International. Spatial Data Repository, The Demographic and Health Surveys Program. ICF International. Available from spatialdata.dhsprogram.com [Accessed 18 August 2016]. Fishiereis Frame Survey :All Datasets Indicates at the regional or district level, the percentage of fish landing sites with described properties. Citation: LTA Secretariat, 2012.Lake Tanganyika Regional Fisheries Frame Survey 2011, Bujumbura, Burundi, 30 pFishieries Frame SurveyConservation:Human Disturbance Index:Simple Human Disturbance Index to assess the relative levels of human disturbance along the lakeshore of Lake Tanganyika. Evidence from Britton et al.(2017) indicates that human activity in the nearshore environment will significantly influence fish populations along the lakeshore. For detailed methods see https://tnc.box.com/s/k65bdhh72gjjv7f3v0gwvn2856onh9h9.Credits: Dr. Tracy Baker, The Nature Conservancy Africa Program: tracy.baker@tnc.orgHydroBASINS Level 08 Average HDI:Average level of human disturbance at the HydroBASINS Level 8. This level correxpond to the unit of analysis for IUCN Red List data. Credits: Dr. Tracy Baker, The Nature Conservancy Africa Program: tracy.baker@tnc.orgProtected Areas:IUCN and UNEP-WCMC (year), The World Database on Protected Areas (WDPA) [On-line], [January, 2017], Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net.Priority Aquatic Sites: Aquaruim trade watch fish: Estimated ranges of cichlids considered to be endangered or critically endagered, Credit Ad KoningsProposed Lake Key Biodiversity Area & Key Biodiversity Area Trigger Species Ranges: The Nature Conservancy staff worked with IUCN and other experts to compile and analyze available spatial data for Lake Tanganyika, to identify candidate areas within the lake that have exceptional potential to meet the revised KBAcriteria and thresholds based on the new standard, as well as having practical potential for application of local and regional management and conservation strategies. This layer represents a draft version of this work. The work still must undergo a national level stakeholder consultation. Credits: Dr. Kristen Blann, The Nature Conservancy - Freshwater Ecologist, Minnesota Priority Fisheries Conservation Sites - TAFIRI: TAFIRI Conservation Priorities derived from 2013 presentation by Dr. Ismael Kimirei, TAFIRI Director, Kigoma. Priorities were ranked by a quatitative assessment at each site. Priority Fisheries Conservation Sites - Zambia Fisheries: Zambia Fisheries priority sites acquired via personal communication with Mr. Taylor Banda, Senior Fisheries Officer at Mpulungu. The sites represent the current planning scenario alon the Zambia side of the lake. Lake & Freshwater Species & Basin Freshwater Species: Known and accessible information on freshwater species within Lake Tanganyika. Data may not include all known species for a taxon. Spatial unit used to calcuate total freshwater species richness is the HydroBASINS Level 11 dataset boundaries.Species level data were derived from the IUCN Red List of Threatened Species (http://www.iucnredlist.org), the Lake Tanganyika Biodiversity Program (http://www.ltbp.org/), and Ad Konings. Zambia Terrestrial Species Distributions: Mean probability of species presence, conditioned on environmental variables.See: https://tnc.box.com/s/hvqdyawz26i75lm5lnlj7dh0uut65rk7Credits: Dr. Anne Trainor, The Nature Conservancy Africa Program - Smart Growth Director anne.trainor@tnc.orgMammals & Amphibians : Modeled number of mammal species across the Lake Tangnayika Basin. This is a surface layer with no individual species level information given. International Union for Conservation of Nature - IUCN, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2015. Gridded Species Distribution: Global Mammal Richness Grids, 2015 Release. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4N014G5.Credits: http://sedac.ciesin.columbia.edu/data/set/species-global-mammal-richness-2015Terrestrial Ecoregions & Greater Mahale Ecosystem: Olson, D. M. and E. Dinerstein. 2002. The Global 200: Priority ecoregions for global conservation. (PDF file) Annals of the Missouri Botanical Garden 89:125-126. -The Nature Conservancy, USDA Forest Service and U.S. Geological Survey, based on Bailey, Robert G. 1995. Description of the ecoregions of the United States (2nd ed.). Misc. Pub. No. 1391, Map scale

description: The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).; abstract: The Global 15x15 Minute Grids of the Downscaled GDP Based on the Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990 and 2025, are geospatial distributions of Gross Domestic Product (GDP) per unit area (GDP densities). These global grids were generated using the Country-level GDP and Downscaled Projections Based on the SRES B2 Scenario, 1990-2100 dataset, and CIESIN's Gridded Population of World, Version 2 (GPWv2) dataset as the base map. First, the GDP per capita was developed at a country-level for 1990 and 2025. Then the gridded GDP was developed within each country by applying the GDP per capita to each grid cell of the GPW, under the assumption that the GDP per capita was uniform within a country. This dataset is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Data Update Notice 数据更新通知

We are pleased to announce that the GlobPOP dataset for the years 2021-2022 has undergone a comprehensive quality check and has now been updated accordingly. Following the established methodology that ensures the high precision and reliability, these latest updates allow for even more comprehensive time-series analysis. The updated GlobPOP dataset remains available in GeoTIFF format for easy integration into your existing workflows.

2021-2022 年的 GlobPOP 数据集经过全面的质量检查,现已进行相应更新。 遵循确保高精度和可靠性的原有方法,本次更新允许进行更全面的时间序列分析。 更新后的 GlobPOP 数据集仍以 GeoTIFF 格式提供,以便轻松集成到您现有的工作流中。

To reflect these updates, our interactive web application has also been refreshed. Users can now explore the updated national population time-series curves from 1990 to 2022. This can be accessed via the same link: https://globpop.shinyapps.io/GlobPOP/. Thank you for your continued support of the GlobPOP, and we hope that the updated data will further enhance your research and policy analysis endeavors.

交互式网页反映了人口最新动态,用户现在可以探索感兴趣的国家1990 年至 2022 年人口时间序列曲线,并将其与人口普查数据进行比较。感谢您对 GlobPOP 的支持,我们希望更新的数据将进一步加强您的研究和政策分析工作。

If you encounter any issues, please contact us via email at lulingliu@mail.bnu.edu.cn.

如果您遇到任何问题,请通过电子邮件联系我们。

Introduction

Continuously monitoring global population spatial dynamics is essential for implementing effective policies related to sustainable development, such as epidemiology, urban planning, and global inequality.

Here, we present GlobPOP, a new continuous global gridded population product with a high-precision spatial resolution of 30 arcseconds from 1990 to 2020. Our data-fusion framework is based on cluster analysis and statistical learning approaches, which intends to fuse the existing five products(Global Human Settlements Layer Population (GHS-POP), Global Rural Urban Mapping Project (GRUMP), Gridded Population of the World Version 4 (GPWv4), LandScan Population datasets and WorldPop datasets to a new continuous global gridded population (GlobPOP). The spatial validation results demonstrate that the GlobPOP dataset is highly accurate. To validate the temporal accuracy of GlobPOP at the country level, we have developed an interactive web application, accessible at https://globpop.shinyapps.io/GlobPOP/, where data users can explore the country-level population time-series curves of interest and compare them with census data.

With the availability of GlobPOP dataset in both population count and population density formats, researchers and policymakers can leverage our dataset to conduct time-series analysis of population and explore the spatial patterns of population development at various scales, ranging from national to city level.

Data description

The product is produced in 30 arc-seconds resolution(approximately 1km in equator) and is made available in GeoTIFF format. There are two population formats, one is the 'Count'(Population count per grid) and another is the 'Density'(Population count per square kilometer each grid)

Each GeoTIFF filename has 5 fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below with the example filename:

GlobPOP_Count_30arc_1990_I32

Field 1: GlobPOP(Global gridded population)
Field 2: Pixel unit is population "Count" or population "Density"
Field 3: Spatial resolution is 30 arc seconds
Field 4: Year "1990"
Field 5: Data type is I32(Int 32) or F32(Float32)

More information

Please refer to the paper for detailed information:

Liu, L., Cao, X., Li, S. et al. A 31-year (1990–2020) global gridded population dataset generated by cluster analysis and statistical learning. Sci Data 11, 124 (2024). https://doi.org/10.1038/s41597-024-02913-0.

The fully reproducible codes are publicly available at GitHub: https://github.com/lulingliu/GlobPOP.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Zimbabwe. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

This is an assessment of pedestrian accessibility in the world's main urban centers, aggregated at country level. Indicators include the average walking time to different categories of destinations, as well as the proportion of inhabitants that can access each category of services within a 15 minutes walk. The data is produced and maintained by the UN's Sustainable Development Solutions Network (SDSN) as part of the SDG Transformation Center.Pedestrian accessibility is the extent to which the built environment supports walking access to destinations of interest. This measure is particularly useful for assessing spatial justice in cities, usually represented by underpriviledged communities which are pushed to live in deteriorated urban areas receiving a minor share of public investments and thus low levels of accessibility. Monitoring spatial indicators of pedestrian accessibility helps planners and policymakers evaluate the impacts of urban design and transport interventions and guides targeted interventions towards creating healthy, sustainable cities, and achieving the United Nations (UN) Sustainable Development Goals (SDGs).Data SourcesTwo main sources of data are behind this study. OpenStreetMap is used to collect data on pedestrian infrastructure and geographically allocated places of interest (POI): hospitals, schools, supermarkets, restaurants, schools, etc. Pedestrian infrastructure networks are returned by the OpenStreetMap API as networks of nodes and edges, where each node represents a street intersection and each edge represents a segment of road with walkable features. Data on population density for every city is retrieved from the European Commission's 2020 Global Human Settlement Layer (GHSL) . This data is retrieved in the form of a grid of 100m by 100m squares and their associated population density values covering the entire world.Geographical extentThe geographical extent of a particular city or region often varies according to different authorities and interpretations. Novel projects, such as the Global Human Settlements (GHS) Urban Centres Database (UCDB), seek to establish a consistent, shared geographic definition of “urban centres” globally. This study does not consider municipal boundaries for defining city borders. Rather, it considers "Functional Urban Areas" as defined by the OECD and the European Commission . The boundaries of Functional Urban Areas consider urbanization factors such as commuting flows and population density, and are less arbitrary than municipal boundaries. For this reason, cities presented here may have a different (and often bigger) shape expected.Accessibility analysisTo measure accessibility to services for each city, we perform a network analysis on the pedestrian street networks and POIs data to quantify and map accessibility to urban infrastructure at the street intersection level. For each 100m cell from the population grid data, the resulting "walking time" reflects the time that a person residing inside that cell would have to walk for, using the existing pedestrian infrastructure, to reach the first amenity from a given category of services. The analysis was performed using geopandas and pandana python packages. These calculations were performed for all cities where at least one POI could be identified for each square kilometer. This threshold is applied in order to enforce representativity and accuracy. These scores were then be generalized for each country, by taking the population weighted average of the accessibility score for each point in the population grid. Countries where less than 40% of the urban population is represented after applying the aforementioned thresholds were excluded from the final dataset.Code for generating these results is publicly available at: https://github.com/sdsna/sdg-accessibilityThis methodology was expanded from Nicoletti, L., Verma, T., Sirenko, M. (2022). Disadvantaged Communities Have Lower Access to Urban Infrastructure. Environment and Planning B: Urban Analytics and City Science, 0(0) and the CityAccessMap project.

This interactive map of Kenya highlights the following counties: Kitui, Makueni, Machakos, Tana River, Bomet, Meru, Tharaka Nithi, Nyandarua, Murang'a, Kajiado and Nyeri, which were selected for the implementation of the Small Scale Irrigation and Value Addition Project (SIVAP). These eleven counties were selected based on high levels of poverty, high food insecurity, potential for agriculture and low or moderate rainfall. The project builds on the success of the Small-Scale Horticulture Development Project (SHDP-1) and it will focus on improving high-value crop production through construction and rehabilitation of twelve (12) irrigation schemes (3,205 ha) in eleven counties. Additionally, the project aims to improve access to markets, enhance agro-processing, storage and post-harvest handling technologies and strengthen community-based institutions (Farmer Associations, Irrigation Water Users Associations and Women Groups). The project is expected to improve the livelihoods of more than 100,000 households.

Data Sources:

SIVAP Selected Counties

Source: African Development Bank and GAFSP Documents.

Poverty Incidence (Proportion of population below the poverty line) (2009): Proportion of the population below the national poverty line.

Source: Kenya National Bureau of Statistics KNBS. "Economic Survey 2014."

Malnutrition (Proportion of underweight children under 5 years) (2014): Prevalence of severely underweight children is the percentage of children under age 5 whose weight-for-age is more than 3 three standard deviations below the median for the international reference population ages 0-59 months.

Source: Kenya National Bureau of Statistics, Kenya Ministry of Health, Kenya National AIDS Control Council, Kenya Medical Research Institute, Kenya National Council for Population and Development. Measure DHS. “Kenya Demographic and Health Survey 2014.”

Total Population (2009): Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin.

Source: Kenya National Burea of Statistics KNBS. "Population and Housing Census 2009 - County Statistics."

Population Density (2009): Population divided by land area in square kilometers.

Source: Kenya National Burea of Statistics KNBS. "Population and Housing Census 2009 - County Statistics."

Livelihood Zones (2011): FEWS NET uses the Household Economy Approach (HEA) as the framework for its livelihoods work. For early warning of food insecurity, livelihoods analysis provides invaluable insight into the ability of households such as these to contend with shocks. The analysis also provides detailed information for humanitarian assistance planning and ongoing monitoring.

Source: FEWS NET - USAID. “Livelihood zoning plus activity in Kenya 2010.”

The maps displayed on the GAFSP website are for reference only. The boundaries, colors, denominations and any other information shown on these maps do not imply, on the part of GAFSP (and the World Bank Group), any judgment on the legal status of any territory, or any endorsement or acceptance of such boundaries.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

Which county has the most Facebook users? There are more than 383 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country, then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 196.9 million, 122.3 million, and 111.65 million Facebook users respectively. Facebook – the most used social media Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3.5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising. Facebook usage by device As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

In a global survey conducted in 2023, ***** percent of respondents from 30 countries identified themselves as transgender, non-binary/non-conforming/gender-fluid, or in another way. In Switzerland, around *** percent of the respondents stated to identify themselves with one of the listed genders.

The world's Jewish population has had a complex and tumultuous history over the past millennia, regularly dealing with persecution, pogroms, and even genocide. The legacy of expulsion and persecution of Jews, including bans on land ownership, meant that Jewish communities disproportionately lived in urban areas, working as artisans or traders, and often lived in their own settlements separate to the rest of the urban population. This separation contributed to the impression that events such as pandemics, famines, or economic shocks did not affect Jews as much as other populations, and such factors came to form the basis of the mistrust and stereotypes of wealth (characterized as greed) that have made up anti-Semitic rhetoric for centuries. Development since the Middle Ages The concentration of Jewish populations across the world has shifted across different centuries. In the Middle Ages, the largest Jewish populations were found in Palestine and the wider Levant region, with other sizeable populations in present-day France, Italy, and Spain. Later, however, the Jewish disapora became increasingly concentrated in Eastern Europe after waves of pogroms in the west saw Jewish communities move eastward. Poland in particular was often considered a refuge for Jews from the late-Middle Ages until the 18th century, when it was then partitioned between Austria, Prussia, and Russia, and persecution increased. Push factors such as major pogroms in the Russian Empire in the 19th century and growing oppression in the west during the interwar period then saw many Jews migrate to the United States in search of opportunity.