The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

The total population in World was estimated at 8061.9 million people in 2023, according to the latest census figures and projections from Trading Economics. This dataset includes a chart with historical data for World Population.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is about books, has 1 rows and is filtered where the book is How life on earth began : fossils, dinosaurs, the first humans. It features 7 columns including book, author, publication date, language, and book publisher. The preview is ordered by publication date (descending).

The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution.

Purpose: To provide estimates of population density for the years 2000, 2005, 2010, 2015, and 2020, based on counts consistent with national censuses and population registers, as raster data to facilitate data integration.

Recommended Citation(s)*: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW. Accessed DAY MONTH YEAR.

About this dataset

Context

The dataset tabulates the Earth Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Earth, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Earth.

Key observations

Among the Hispanic population in Earth, regardless of the race, the largest group is of Mexican origin, with a population of 588 (94.84% of the total Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Earth
• Population: The population of the specific origin for Hispanic or Latino population in the Earth is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Earth total Hispanic or Latino population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Earth Population by Race & Ethnicity. You can refer the same here

This dataset is about book subjects. It has 2 rows and is filtered where the books is Origins : how the Earth shaped human history. It features 2 columns including publication dates.

This dataset provides a historical overview of key global indicators, including Gross Domestic Product (GDP), population growth, and CO2 emissions. It captures economic trends, demographic shifts, and environmental impacts over multiple decades, making it useful for researchers, analysts, and policymakers.

The dataset includes Real GDP (inflation-adjusted), allowing for economic trend analysis while accounting for inflation effects. Additionally, it incorporates CO2 emissions data, enabling studies on the relationship between economic growth and environmental impact.

This dataset is valuable for multiple research areas:

✅ Macroeconomic Analysis – Study global economic growth, recessions, and recovery trends.

✅ Inflation & Monetary Policy – Compare nominal vs. real GDP to assess inflationary trends.

✅ Climate Change Research – Analyze CO2 emissions alongside economic growth to identify sustainability challenges.

✅ Predictive Modeling – Train machine learning models for forecasting GDP, population, or emissions.

✅ Public Policy & Development – Evaluate the impact of economic and environmental policies over time.

This dataset is shared for educational and analytical purposes only.

This dataset is about books. It has 2 rows and is filtered where the book is Between heaven and earth : the religious worlds people make and the scholars who study them. It features 7 columns including author, publication date, language, and book publisher.

The Macquarie Island Station Area GIS Dataset is a topographic and facilities data base covering Australia's Macquarie Island Station and its immediate environs. The database includes all man made and natural features within the operational area of the station proper. Attributes are held for many facilities including, buildings, site services, communications, fuel storage, aeronautical and management zones. The spatial data have been compiled from low level aerial photography, ground surveys and engineering plans. Detail attribution of hydraulic site services includes make, size and engineering plan number.

The dataset conforms to the SCAR Feature Catalogue which includes data quality information.

The data is included in the data available for download from a Related URL below. The data conforms to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 25. Each feature has a Qinfo number which, when entered at the 'Search datasets & quality' tab, provides data quality information for the feature.

Changes have occurred at the station since this dataset was produced. For example some buildings and other structures have been removed and some added. As a result the data available for download from a Related URL below is updated with new data having different Dataset_id(s).

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

This element involves the development of software that enables easier commanding of a wide range of NASA relevant robots through the Robot Application Programming Interface Delegate (RAPID) robot messaging system and infusing the developed software into flight projects.  In June and July of 2013, RAPID was tested on ISS as the robot messaging software for the Technology Demonstration Mission (TDM) Human Exploration Telerobotics (HET) Surface Telerobotics experiment.  RAPID has also been made available to — and integrated with — the Robot Operating System (ROS), a popular software framework for developing state-of-the-art robots for ground and space. While ROS powers a number of new robots and components such as Robonaut 2’s climbing legs and R5, the addition of RAPID allows these robots to interoperate in collaborative human-robot teams, safely and effectively over time-delayed communications links. The objective this year is to take this space-tested software and extend it to providing video streaming from remote robots and delivering this new capability to the Exploration Ground Data Systems (xGDS) area within HRS.  xGDS will then deliver its software to Science Mission Directorate (SMD) funded field tests to improve the technology readiness moving leading (potentially) to being used for the Lunar Prospector Mission ground data systems.  Success will involve delivering RAPID to xGDS and then xGDS supporting SMD field test.

The team is also developing algorithms for sensors capable of reconstructing remote worlds and efficiently shipping that remote environment back to earth using the RAPID robot messaging system.  This type of system could eventually lead to scientists on earth gain new insights as they are able to step into the remote world.  This sensor also has the ability to engage the public, bringing remote worlds back to earth.  During FY13, this task used science operations personnel from current SMD projects to objectively measure improvement in remote science target selection and decision-making based. The team continues to work with SMD projects to ensure that the technologies being developed are directly responsive to SMD project personnel needs. The objective of this work in FY14 is to expand the range of science operations tasks addressed by the technology, and to perform laboratory demonstrations for JPL/SMD stakeholders of the immersive visualization of data from a sensor using an SMD representative environment.

During 2014, the “Controlling Robots Over Time Delay” project element will develop two technologies:

• Develop RAPID robot messaging for unified cross-center operations platform for TDM, xGDS, and CCSDS
• Sensor Systems for the Construction of Immersive Virtual Environments

The High Resolution Settlement Layer (HRSL) provides estimates of human population distribution at a resolution of 1 arc-second (approximately 30m) for the year 2015. The population estimates are based on recent census data and high-resolution (0.5m) satellite imagery from DigitalGlobe. The population grids provide detailed delineation of settlements in both urban and rural areas, which is useful for many research areas—from disaster response and humanitarian planning to the development of communications infrastructure. The settlement extent data were developed by the Connectivity Lab at Facebook using computer vision techniques to classify blocks of optical satellite data as settled (containing buildings) or not. Center for International Earth Science Information Networks (CIESIN) at Earth Institute Columbia University used proportional allocation to distribute population data from subnational census data to the settlement extents. Citation: Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe.

Human settlement maps are useful in understanding growth patterns, population distribution, resource management, change detection, and a variety of other applications where information related to earth surface is required. Human settlements classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputRaster, mosaic dataset, or image service. (Preferred cell size is 30 meters.)Note: This model is trained to work on Landsat 8 Imagery datasets which are in WGS 1984 Web Mercator (auxiliary sphere) coordinate system (WKID 3857).OutputClassified layer containing two classes: settlement and otherApplicable geographiesThis model is expected to work well in the United States.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 91.6 percent.Training dataThis model has been trained on an Esri proprietary human settlements classification dataset.Sample resultsHere are a few results from the model.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

Update Frequency: Biannual

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

https://cloud.google.com/bigquery/public-data/world-bank-hnp

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Citation: The World Bank: Health Nutrition and Population Statistics

Banner Photo by @till_indeman from Unplash.

Inspiration

What’s the average age of first marriages for females around the world?

Dataset

This dataset was created by Justin

Released under Data files © Original Authors

Contents

Context

This data set is as part of Data Science for Good : Kiva Crowdfunding

Content

The data set consists of HDI, GDI, population, Education, Health related data. New data set consists of countries boundary in Geojson format helpful for Geo spatial analysis.

Acknowledgements

The data set is downloaded from United nation development program site. Source
Geojson Sourse

Inspiration

Some interesting analysis which could be done using the dataset are

• Visual representation of different indicators at different countries of the world
• How Kiva loans are distributed based on the economic / social / other indicators.?

This data contains a set of images of human skulls, horse skulls, and lions that can be used to make classification using deep learning.

human skull The skull is made up of cranial bones (bones that surround and protect the brain) and facial bones (bones that form the eye sockets, nose, cheeks, jaw, and other parts of the face). An opening at the base of the skull is where the spinal cord connects to the brain. Also

horse skull ‪description of horse skull‬‏ The large and slender horse skull has long, broad, tapering nasals. At the front of the mouth are large incisors, designed for cropping grass. At the back of the mouth are six large, squared molars on either side of the maxilla and the mandible. The area

lion skull What is the description of a lion? description of lion skull‬‏ Lions have strong, compact bodies and powerful forelegs, teeth and jaws for pulling down and killing prey. Their coats are yellow-gold, and adult males have shaggy manes that range in color from blond to reddish-brown to black.