This layer was created by Duncan Smith and based on work by the European Commission JRC and CIESIN. A description from his website follows:--------------------A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL). This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. As usual, my first thought was to make an interactive map, now online at- http://luminocity3d.org/WorldPopDen/The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time.

This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

This dataset contains estimates of the number of persons per square kilometer consistent with national censuses and population registers. There is one image for each modeled year. General Documentation The Gridded Population of World Version 4 (GPWv4), Revision 11 models the distribution of global human population for the years 2000, 2005, 2010, 2015, and 2020 on 30 arc-second (approximately 1 km) grid cells. Population is distributed to cells using proportional allocation of population from census and administrative units. Population input data are collected at the most detailed spatial resolution available from the results of the 2010 round of censuses, which occurred between 2005 and 2014. The input data are extrapolated to produce population estimates for each modeled year.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The FGGD high-resolution urban population distribution map is a global raster datalayer with a resolution of 30 arc-seconds. Each pixel classified as urban by the urban area boundaries map contains a numeric count of persons in the land area represented by the pixel. All remaining pixels contain a negative value. The method used by FAO to generate this datalayer is described in FAO, 2006, Mapping global urban and rural population distributions, by M. Salvatore, et. al.

Data publication: 2006-09-30

Supplemental Information:

This dataset is contained in Module 2 "Population" of Food Insecurity, Poverty and Environment Global GIS Database (FGGD) (FAO, 2006).

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Mirella Salvatore

Resource constraints:

copyright

Online resources:

Online link to the FAO-SD Dimension/Publications Web Site

The FGGD high resolution rural population density map is a global raster datalayer with a resolution of 30 arc-seconds. Each pixel classified as rural by the urban area boundaries map contains the number of persons per square kilometre. All remaining pixels contain no data. The method used by FAO to generate this datalayer is described in FAO, 2006, Mapping global urban and rural population distributions, by M. Salvatore, et. al.

Data publication: 2006-09-30

Supplemental Information:

This dataset is contained in Module 2 "Population" of Food Insecurity, Poverty and Environment Global GIS Database (FGGD) (FAO, 2006).

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Mirella Salvatore

Resource constraints:

copyright

Online resources:

FAO, 2006. "Mapping global urban and rural population distributions"

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 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 lives 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 few years 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.

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.

This map is part of an interactive Story Map series about global change in the US.With the global human population expected to exceed 8 billion people by 2030, our species is already irreversibly changing the future of our planet. The US itself is expected to grow by 16.5% to over 360 million people, making it the third largest country in the world, behind India and China. This population increase isn’t distributed evenly - 81% of people will live in cities, urban, and suburban areas, which will continue to shape how resources are produced, transported, and consumed. The percent of foreign-born and second-generation immigrants in the US is also expected to rise in the future, contributing to an increasingly diverse population. Across the globe, immigration will likely account for significant population changes in the near future, as climate change fuels drought, crop failures, and political instability, creating climate refugees particularly among countries who do not have the infrastructure to mitigate or adapt to global change. Numbers aren’t the only thing that matter: people of different socioeconomic backgrounds use resources differently, both within and between countries.If the rest of the world used energy as intensely as the United States does, the world population would need more than 4 entire Earths to provide us with the resources to feed this rate consumption. This unfortunately means that even regions of the US that contribute less towards the problems of global change will still feel their impacts. To ensure a high quality of life for all citizens, we must address not only population growth, but also excess consumption of and reliance on resources across different regions. Geographic, population, and economic differences among regions can provide opportunities for success in the face of global change. Renewable energy sources have created entrepreneurial economic ventures, and communities have found environmental solutions through forming sustainable local food systems. Environmental justice movements are working now to ensure that all citizens have access to nature, recreational areas, and a healthy future for all.

This layer contains WorldPop's 100m resolution annual estimates of population density from the year 2000 to 2020. Usage notes: This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population density, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. We recommend using this layer in conjunction with WorldPop's 1-km resolution Population Density layer to create web maps that allow users to pan and zoom to wider areas; this web map contains an example of this combination. The population estimates in this layer are derived WorldPop's total population data, which use a Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based dasymetric model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2), e0107042) and converts these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_100m/ImageServer, which was acquired from WorldPop in December 2021.

The Atlas of the Biosphere is a product of the Center for Sustainability and the Global Environment (SAGE), part of the Gaylord Nelson Institute for Environmental Studies at the University of Wisconsin - Madison. The goal is to provide more information about the environment, and human interactions with the environment, than any other source.

The Atlas provides maps of an ever-growing number of environmental variables, under the following categories:

Human Impacts (Humans and the environment from a socio-economic perspective; i.e., Population, Life Expectancy, Literacy Rates);

Land Use (How humans are using the land; i.e., Croplands, Pastures, Urban Lands);

Ecosystems (The natural ecosystems of the world; i.e., Potential Vegetation, Temperature, Soil Texture); and

Water Resources (Water in the biosphere; i.e., Runoff, Precipitation, Lakes and Wetlands).

Map coverages are global and regional in spatial extent. Users can download map images (jpg) and data (a GIS grid of the data in ESRI ArcView Format), and can view metadata online.

Important Note: This item is in mature support as of June 2023 and will retire in December 2025. A new version of this item is available for your use.The layers going from 1:1 to 1:1.5M present the 2010 Census Urbanized Areas (UA) and Urban Clusters (UC). A UA consists of contiguous, densely settled census block groups (BGs) and census blocks that meet minimum population density requirements (1000 people per square mile (ppsm) / 500 ppsm), along with adjacent densely settled census blocks that together encompass a population of at least 50,000 people. A UC consists of contiguous, densely settled census BGs and census blocks that meet minimum population density requirements, along with adjacent densely settled census blocks that together encompass a population of at least 2,500 people, but fewer than 50,000 people. The dataset covers the 50 States plus the District of Columbia within United States. The layer going over 1:1.5M presents the urban areas in the United States derived from the urban areas layer of the Digital Chart of the World (DCW). It provides information about the locations, names, and populations of urbanized areas for conducting geographic analysis on national and large regional scales. To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to USA Census Urban Areas.

This map shows to which extent rainfed and irrigated agricultural systems as identified on SOLAW Map 1.3: "Major agricultural systems" suffer from land and / or water scarcity.

Land scarcity in rainfed agriculture was assessed by comparing the rural population density, (obtained from GRUMP 2000, adjusted for UN data, excluding the urban areas indicated on the GRUMP dataset) with the suitability for rainfed crops as mapped for the Global Agro-ecological Zones 2000. Since land that is very suitable for rainfed agriculture can sustain more people than land that is not suitable, it was assumed that each suitability class has its own carrying capacity regarding population. On the map, land is considered scarce if the population density is higher that the highest quintile in the density distribution for each suitability class. Land scarce areas in climates with an Aridity Index lower than 0.5 (where the Aridity Index is defined as Yearly Precipitation divided by Yearly Reference Evapotranspiration) are considered both land and water scarce.

Water scarcity in irrigated areas was assessed by combining the Map 1.2: Global distribution of physical water scarcity with the Global Map of Irrigation Areas. The areas equipped for irrigation are considered water scarce if already more than 10% of the renewable water resources in the river basin is consumed by irrigated crops.

Data publication: 2011-11-30

Contact points:

Metadata Contact: AQUASTAT

Resource Contact: Jippe Hoogeveen

Data lineage:

The spatial data used to produce the map are listed below: GRUMP Global Rural-Urban Mapping Project at 30 arc-sec (http://sedac.ciesin.columbia.edu/gpw/) Suitability for rain-fed crops at 5 arc-min (Fischer et al., 2002) - Global Map of Aridity at 10 arc-min (FAO) - Global map of irrigation areas at 5 arc-min (Siebert et al., 2007) - Global Administrative Unit Layers (GAUL 2008)

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO

Online resources:

SOLAW website

Download data from the bucket

This dataset provides agricultural lands soil property data for US Counties within the contiguous US. Variable include with this dataset include: high and low soil bulk density (g C/cm3 soil), high and low clay content (fraction), high and low soil organic carbon (g C/g soil), and soil pH.

Our source for the Soil organic carbon (SOC), clay content, and bulk density data was the US Environmental Protection Agency (EPA) [Imhoff JC, Carsel RF, Kittle JR, Hummel PR (1990) Data base analyzer and parameter estimator (DBAPE) interactive computer program user's manual. EPA/600/3-89/083, USEPA Environ. Res. Lab. Athens, GA 30613-7799]. They worked from a database developed by the Soil Conservation Service (now Natural Resource Conservation Service) [USDA Soil Conservation Service (1985) User manual for interactive soils databases: nation soil survey area database, soil interpretations record database, and plant name database. USDA SCS, Fort Collins, CO]. To find this and similar data now, visit the National Soils Data Access Facility web site: http://soils.usda.gov/

Our source for the pH data was the Food and Agriculture Organization of the United Nations (FAO) Digital Soil Map of the World and Derived Soil Properties, Version 3.5, Nov. 1995, original scale 1:5 000 000). See [ http://www.fao.org/ ] for general FAO information and see [ http://www.fao.org/ag/agl/agll/index.stm ] for details on the soils data. We printed a pH map for the US, overlaid a state boundaries map, and then read off values for each region of each state. The coarse resolution of the map meant that most counties (and even many states) had only a single pH value.

High and low values are reported for each county, and represent the range in a particular soil property found in the county. Many counties have only a single value and it is reported as both the high and the low value. Values are from the database surface soil layer (defined as either 'the plow layer' or 'the A, E, Ab, and EB horizons of the solum'). Values are based only on soils for agricultural lands in the county, not all soils.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

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

(Prototype - Under Development. Not to be relied upon for operational use.)This interactive web map shows real-time flood conditions across the United States and combines multiple informational layers to assist with River Forecast Center Decision Support Services for both internal and external partner use. This includes current and forecast flood conditions at service locations in the United States using live data from the National Weather Service Advanced Hydrologic Prediction System (AHPS) along with National Water Model (NWM) streamflow guidance across 2.7 Million reaches. In addition, current USGS streamflow observations along with NRCS snotel network observations can be viewed on top of a host of additional NOAA product layers including current radar and satellite imagery, past and future rainfall estimates, snowpack analysis, drought analysis, river and flash flood outlooks, weather hazard outlooks, severe weather, tropical outlook and cyclone forecasts, active hurricane tracks, and climate outlooks along with a variety of hydrologic, political and federal agency boundaries. Many of these layers have multiple temporal scales and all are viewable on top of standard basemaps, including world topographic maps.For a map that focuses on more general weather reports and current radar, see the Severe Weather Map.For a map that focuses on available National Water Model flow and anomaly layers, see the NWM Map.By using this AGOL web map, the user acknowledges that the NWM v1.0 output is prototype guidance and should not be considered an official NWS river forecast.About the data:Stream Gauges: This is Esri's Live Stream Gauges layer, symbolized to show only those gauges that are currently at or above flood stage. Click on a gauge to see the current depth, flow rate, and alert level. Five day forecasts from Advanced Hydrologic Prediction Service are shown where available.Population Density: This is Esri's World Population Estimate, which models the likely population of each 250 meter square cell, globally. It provides import context to the map, showing where flooding is likely to have a human impact.Flood Warnings (short and long term): These weather alerts are NOAA Weather Warnings, Watches, and Advisory data provided through the Common Alerting Protocol (CAP) Alert system. The long term warnings (flood warnings) are done on a county basis, while the short term warnings (flash flood and marine warnings) are more spatially precise. 72-hour Precipitation Forecast: This is the Quantitative Precipitation Forecast (QPF) from NOAA's National Digital Forecast Database. By default it shows the predicted total over the next 72 hours, but this forecast can also be viewed in six hour intervals.***** IMPORTANT disclaimer concerning NWM output *****The Office of Water Prediction (OWP) National Water Center is now producing water information from our National Water Model (NWM) version 1.0. Information about the prototype NWM output displayed on this map interface can be found in this Product Description Document. More information about the NWM is available athttp://water.noaa.gov/about/nwm. As this output is from the first version of the NWM, it does not yet contain information on the anthropogenic effects on streamflow and output should be viewed with caution. The output is undergoing extensive validation and verification to identify where updates to the science model parametrization and configurations can make the most improvements to the model output. The next version of the NWM will be released in the spring 2017 time frame. For official NWS river forecasts please see http://water.weather.gov.There is a NWM mapping interface in progress. In addition to the prototype NWM streamflow information, data layers of 2 snow products from the National Snow Analysis, Snow Depth and Snow Water Equivalent, are also available. The OWP is seeking to improve the availability and quality of its products and services based on user feedback. Comments regarding the Prototype Water Information Interface Webpage should be provided through the electronic survey via the link provided below: http://www.nws.noaa.gov/survey/nws-survey.php?code=NWMV1OUTPUTThe OWP also provides a range of NWS official water information through the following web sites.River observation and forecast information: http://water.weather.gov/ahpsSnow Information: http://www.nohrsc.noaa.govPrecipitation Frequency Estimates: http://www.nws.noaa.gov/oh/hdscContent from the sites above will be migrated to this new site over the next couple of years.Comments? Questions? Please Contact nws.nwc.ops@noaa.gov.By using this AGOL web map, the user acknowledges that the NWM v1.0 output is prototype guidance and should not be considered an official NWS river forecast.

This interactive map of Ethiopia identifies the woredas (districts) where the AGP is active. GAFSP contributes about 23% of the total AGP financing, which is also supported by other development partners, including the Canadian International Development Agency (CIDA), the Spanish Agency for International Development Cooperation (AECID), the Kingdom of the Netherlands, the United Nations Development Program (UNDP), and the United States Agency for International Development (USAID). GAFSP funds are being channeled into a pooled AGP fund to increase donor coordination and to decrease project administrative costs. The map is broken down into 11 regions, 81 zones, and 550 woredas (districts). The 83 AGP project areas (at the woreda level) are spread across the four regions of Amhara, Oromiya, Tigray, and Southern Nations, Nationalities, and Peoples Region (SNNPR). AGP activities are primarily in the highlands temperate mixed zones, where the climatic conditions are relatively temperate and that, with AGP support, have considerable potential for agricultural growth. In these areas, small-scale farmers crop an average area of less than 1 hectare (ranging between 0.25 and 2.3 hectares). The interactive map shows sub-national poverty and population density data, as well as information on the predominant farming systems in the various regions. Data Sources: AGP Project LocationsSource: Project Appraisal Document (PAD). Africa Juice Project LocationSource: IFC - GAFSP Documents. Poverty (Proportion of population below the poverty line) (2005): Proportion of the population living on less than US$1.25 a day, measured at 2005 international prices, adjusted for purchasing power parity (PPP).Source: Harvest Choice / Multiple national household surveys; PovcalNet; The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2011. Sub-national poverty headcount ratios derived from 23 nationally representative household surveys and population census information conducted in various years. Rates are for the $1.25/day (extreme poverty) expressed in 2005 international equivalent purchasing power parity (PPP) dollars. Rates are in percentages of total population. (Aggregation type: WGHTD). Poverty (Proportion of population below the poverty line) (2011): Proportion of the population living on less than 3,781 Birr per adult per year.Source: Ministry of Finance and Economic Development. “Ethiopia’s Progress Towards Eradicating Poverty: An Interim Report on Poverty Analysis Study (2010/11).” Malnutrition (Proportion of underweight children under 5 years) (2011): 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: “Demographic and Health Survey 2011.” Measure DHS.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. Malnutrition (Proportion of underweight children under 5 years) (2016): 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: Central Statistical Agency CAS. “Demographic and Health Survey 2016.” Measure DHS. Population Density (Persons per 1 square kilometer) (2007): Population divided by land area in square kilometers.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: Central Statistical Agency CAS. Projections based on the results of the May 2007 National Population and Housing Census of Ethiopia. Population Density (2015): Population divided by land area in square kilometers.Source: Central Statistical Agency CAS. Projections based on the results of the May 2007 National Population and Housing Census of Ethiopia. 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. Farming Systems (2001): Farming systems according to FAO methodology: Agro-pastoral land, millet-sorghum, highland perennial, maize mixed, irrigated land, pastoral land and sparse arid Land.Source: Harvest Choice / Dixon, J. and A. Gulliver with David Gibbon, Principal Editor: Malcolm Hall. Improving Farmers' Livelihoods in a Changing World. FAO/World Bank. 2001. (Aggregation type: NONE) Land cover (2009): Land cover defined as the physical material at the surface or earth, vegetation planted or man-made constructions (water, ice, bare rock, sand, grass, asphalt, trees, etc.). Land cover can be determined by analyzing satellite and aerial imagery.Source: 3R Initiative (RAIN, Acacia Water, MetaMeta, Aqua for all, BGR and IGRAC). “Global Land Cover.” www.hoefsloot.com/horn/ Sorghum Area (2015-16): Area in hectares of agriculture land used for sorghum.Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.) Report on Area and Production of Major Crops.” Sorghum Production (2015-16): Sorghum harvested expressed in tons.Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.). Report on Area and Production of Major Crops.” Maize Area (2015-16): Area in hectares of agriculture land used for Maize.Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.). Report on Area and Production of Major Crops.” Maize Production (2015-16): Maize harvested expressed in tons.

Source: Central Statistical Agency CAS. “Agricultural Sample Survey (AgSS) 2015/2016 (2008 E.C.). Report on Area and Production of Major Crops.”The maps displayed on this 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.

The interactive map of Lao PDR highlights the 12 districts in Oudomxai, Phongsaly, Xieng Khouang and Houaphan provinces, targeted by the Strategic Support for Food Security Project (SSFSNP) and located in the mountainous regions in the North of the country. The project is expecting to reduce extreme poverty and malnutrition in 400 food insecure villages and 34,000 poor smallholder households, with a predominantly non-Tai ethnic population. The map shows that according to the most recent reports the selected districts are located in provinces with more than 40% of the population living below the country poverty line.

Data Sources:

SSFSNP Locations:

Source: GAFSP Documents.

Poverty Incidence (Proportion of population below the poverty line) (2007): Proportion of the population living on less than Kip 92,959 (US$8.79) per person per month.

Source: Lao Statistics Bureau - World Bank. “Lao PDR Poverty Trends 1992/93-2002/03 (2004).”

Malnutrition (Proportion of underweight children under 5 years) (2011-12): Prevalence of severely underweight children is the percentage of children under age 5 whose weight-for-age is more than 3 standard deviations below the median for the international reference population ages 0-59 months.

Source: Measure DHS - Ministry of Health (MoH) and Lao Statistics Bureau (LSB). “Lao PDR Lao Social Indicator Survey (LSIS) 2011-12 (MULTIPLE INDICATOR CLUSTER SURVEY / DEMOGRAPHIC AND HEALTH SURVEY (2012).”

Total Population (2012): 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: LAO Statistics Bureau (LSB). “Statistical Yearbook 2012 –Population Estimation and Density 2012.”

Population Density (2010): Population divided by land area in square kilometers.

Source: LAO Statistics Bureau (LSB). “Statistical Yearbook 2012 –Population Estimation and Density 2012.”

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: LAO Statistics Bureau (LSB). "The 4th Population and Housing Census 2015 (PHC) 2015."

Population Density (2015): Population divided by land area in square kilometers.

"The 4th Population and Housing Census 2015 (PHC) 2015."

Rice Harvested Area and Production: Harvested area in hectares by rice type and total production in tons by rice type 2012.

Source: Lao PDR Statistics Bureau (LBR) - Ministry of Agriculture and Forestry. “Statistical Yearbook 2012.”

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.

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 Namibia. 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.