Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

The geographic distribution of human population is key to understanding the effects of humans on the natural world and how natural events such as storms, earthquakes, and other natural phenomenon affect humans. Dataset SummaryThis layer was created with a model that combines imagery, road intersection density, populated places, and urban foot prints to create a likelihood surface. The likelihood surface is then used to create a raster of population with a cell size of 0.00221 degrees (approximately 250 meters).The population raster is created usingDasymetriccartographic methods to allocate the population values in over 1.6 million census polygons covering the world.The population of each polygon was normalized to the 2013 United Nations population estimates by country.Each cell in this layer has an integer value depicting the number of people that are likely to reside in that cell. Tabulations based on these values should result in population totals that more accurately reflect the population of areas of several square kilometers.This layer has global coverage and was published by Esri in 2014.More information about this layer is available:Building the Most Detailed Population Map in the World

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.

The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.

This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

Explore the patterns of world population in terms of total population, arithmetic density, total fertility rate, natural increase rate, life expectancy, and infant mortality rate. The GeoInquiry activity is available here.Educational standards addressed:APHG: II.A. Analyze the distribution patterns of human populations.APHG: II.B. Understand that populations grow and decline over time and space.This map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

Estimated density of people per grid-cell, approximately 1km (0.008333 degrees) resolution. The units are number of people per Km² per pixel, expressed as unit: "ppl/Km²". The mapping approach is Random Forest-based dasymetric redistribution. The WorldPop project was initiated in October 2013 to combine the AfriPop, AsiaPop and AmeriPop population mapping projects. It aims to provide an open access archive of spatial demographic datasets for Central and South America, Africa and Asia to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and peer-reviewed methods to produce easily updatable maps with accompanying metadata and measures of uncertainty. Acknowledgements information at https://www.worldpop.org/acknowledgements

Human population data from UN

Before the activity, students are divided into 3 groups that are assigned 3 different reading assignments (land use, atmosphere, or water quality). On the day of the activity, students work collaboratively with students from the same reading assignment group for 20 – 40 minutes to answer questions and address concepts from their particular assigned reading. Next, students are shuffled (jigsaw-style) into small teams of 3 students (one student from each reading group). Students educate each other with concepts from their respective reading groups and then work collaboratively on a shared project to select, define, and potentially solve an environmental challenge.

This data release includes gridded estimates of population sizes at approximately 100 m resolution with national coverage across Ghana. This includes estimates of total population sizes, populations in 36 different age-sex groups, people per household, people per building, households per building, and statistical measures of uncertainty. These results were produced using census microdata from IPUMS and building footprints from Maxar/Ecopia.

Estimates suggest that by 2023, the number of voice assistants in existence will be roughly equal to the global population, reaching around eight billion. As of 2019, this number stands at around 2.45 billion, implying that the voice assistant industry is set for continued, rapid growth over the coming years.

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.

GWPv4_Historic_PopulationData: https://cloud.environmentalcrossroads.net/s/Cm7ryazNoCHim4NUnit:people

This dataset is a compilation and synthesis of secondary data in South Florida (Martin, Palm Beach, Broward, Miami-Dade, and Monroe Counties) corresponding to the following topics: Human population changes near coral reefs, Economic impact of coral reef fishing to jurisdiction, Economic impact of dive/snorkel tourism to jurisdiction, Community well-being, Physical infrastructure, and Governance. Data are collected from a variety of publicly available sources to supplement primary data collected through resident surveys. These secondary data are collected to address topics outside the scope of NCRMP resident surveys, and are collected on an annual basis throughout the US coral reef jurisdictions. The primary data that were collected as part of this study in Florida are available in NCEI Accession 0161541.

The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.

This data package includes nine population proximity index layers for 2005, 2030 and 2050, for rural, urban and total populations. The layers are distributed as 1km GeoTIFFs and GeoJPGss at 1km. The aim of these layers is to describe the population which may be likely to visit a specific locality where access is determined by Euclidean distance. By using the layers alongside other geographic datasets relating to disease risk it may help identify where people may come into contact with a disease. Human population layers are often used in models to identify risk areas where humans and viruses interact, however most pathogens are not restricted to areas of human habitation: many are found in lesser populated areas such as forests. This dataset will help identify less populated areas that may well still receive high visitor numbers. The layers have been projected to 2030 and 2050 to enable projections of human/disease interfaces in the medium-term which are required to inform policy makers at country and continental level. Urban and rural populations have been separated into individual layers as in some cases it is useful to distinguish between the behaviour and associated risks attributed to the different population segments. There may be a different risk of contacting diseases in rural habitats for rural workers than for than urban visitors.

Data from Nationmaster.

Provides data visualizations for demographic change over the last 25 years in 43 Alaska villages

This repository includes census-disaggregated population gridded estimates for Burkina Faso, using a top-down approach based on Random Forest modelling. A breakdown by age and sex groups is joined to the gridded population count. A technical report explains the methodology, the validation procedures, the input data used and the limitations of the modelling. The data used for modelling are also attached.

Context

This US Total Population data was retrieved using the World Bank API and then saved as a .txt file and will be used in my US Shark Attack Analysis.

Content

Each record contains the year and total population of the United States.

Acknowledgements

World Bank API https://datahelpdesk.worldbank.org/knowledgebase/topics/125589-developer-information

Inspiration

This data will be useful in analyzing whether or not the number of shark attacks in the United States is rising with the total human population.