This is a Dataset of the World Population Consisting of Each and Every Country. I have attempted to analyze the same data to bring some insights out of it. The dataset consists of 234 rows and 17 columns. I will analyze the same data and bring the below pieces of information regarding the same.

1. Continent Population Characteristics Analysis.
2. Analysis of Countries.
   • Top 10 Most Populated and Least Populated Countries
   • Top 10 Largest and Smallest Countries as per Area
   • Population Growth From 1970 to 2020 (50 Years)
3. Countries Represent % Of World Population.
   • Countries that represent below 0.1% of the World Population.
   • Countries that represent above 2% of the world Population
   • Top 10 Over Populated Countries based on Density Per Sq KM.
   • Top 10 Least Populated Countries based on Density Per Sq KM.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

About this dataset

Context

The dataset tabulates the Blue Earth County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Blue Earth County. The dataset can be utilized to understand the population distribution of Blue Earth County by age. For example, using this dataset, we can identify the largest age group in Blue Earth County.

Key observations

The largest age group in Blue Earth County, MN was for the group of age 20 to 24 years years with a population of 10,530 (15.18%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Blue Earth County, MN was the 80 to 84 years years with a population of 1,224 (1.76%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Blue Earth County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Blue Earth County total 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 Blue Earth County Population by Age. You can refer the same here

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.

This data set contains observations of dead or alive harbor porpoises made by the public, mostly around the Swedish coast. A few observations are from Norwegian, Danish, Finish and German waters. Each observation of harbor porpoise is verified at the Swedish Museum of Natural History before it is approved and published on the web. The verification consists of controlling the accuracy of number of animals sighted, if the coordinates are correct and if pictures are attached that they really show a porpoise and not another species. If any of these three seem unlikely, the reporter is contacted and asked more detailed questions. The report is approved or denied depending on the answers given. Pictures and movies that can’t be uploaded to the database due to size problems are saved at the museum server and marked with the identification number given by the database. By the end of the year the data is submitted to HELCOM who then summarize all the member state’s data from the Baltic proper to the Kattegat basin. The porpoise is one of the smallest tooth whales in the world and the only whale species that breeds in Swedish waters. They are to be found in temperate water in the northern hemisphere where they live in small groups of 1-3 individuals. The females give birth to a calf in the summer months which then suckles for about 10 months before it is left on its own and she has a new calf. The porpoises around Sweden are divided in to three groups that don’t mix very often. The North Sea population is found on the west coast in Skagerrak down to the Falkenberg area. The Belt Sea population is to be found a bit north of Falkenberg down to Blekinge archipelago in the Baltic. The Baltic proper population is the smallest population and consists only of a few hundred animals and is considered as an endangered sub species. They are most commonly found from the Blekinge archipelago up to Åland Sea with a hot spot area south of Gotland at Hoburg’s bank and the Mid-Sea bank. The Porpoise Observation Database was started in 2005 at the request of the Swedish Environmental Protection Agency to get a better understanding of where to find porpoises with the idea to use the public to expand the “survey area”. The first year 26 sightings were reported, where 4 was from the Baltic Sea. The museum is particularly interested in sightings from the Baltic Sea due to the low numbers of animals and lack of data and knowledge about this group. In the beginning only live sightings were reported but later also found dead animals were added. Some of the animals that are reported dead are collected. Depending on where it is found and its state of decay, the animal can be subsampled in the field. A piece of blubber and some teeth are then send in by mail and stored in the Environmental Specimen Bank at the Swedish Museum of Natural History in Stockholm. If the whole animal is collected an autopsy is performed at the National Veterinary Institute in Uppsala to try and determine cause of death. Organs, teeth and parasites are sampled and saved at the Environmental Specimen Bank as well. Information about the animal i.e. location, founding date, sex, age, length, weight, blubber thickness as well as type of organ and the amount that is sampled is then added to the Specimen Bank database. If there is an interest in getting samples or data from the Specimen Bank, one have to send in an application to the Department of Environmental research and monitoring and state the purpose of the study and the amount of samples needed.

About this dataset

Context

The dataset tabulates the Black Earth town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Black Earth town. The dataset can be utilized to understand the population distribution of Black Earth town by age. For example, using this dataset, we can identify the largest age group in Black Earth town.

Key observations

The largest age group in Black Earth Town, Wisconsin was for the group of age 50 to 54 years years with a population of 63 (15.22%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Black Earth Town, Wisconsin was the 85 years and over years with a population of 6 (1.45%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Black Earth town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Black Earth town total 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 Black Earth town Population by Age. You can refer the same here

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Population covered by at least one social protection benefit (%)





  Dataset Description

This dataset provides country-level data for the indicator "1.3.1 Population covered by at least one social protection benefit (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/population-covered-by-at-least-one-social-protection-benefit-for-african-countries.

Series Name: Countries that have conducted at least one population and housing census in the last 10 years (1 = YES; 0 = NO)Series Code: SG_REG_CENSUSNRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 17.19.2: Proportion of countries that (a) have conducted at least one population and housing census in the last 10 years; and (b) have achieved 100 per cent birth registration and 80 per cent death registrationTarget 17.19: By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countriesGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

About this dataset

Context

The dataset tabulates the Earth population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Earth. The dataset can be utilized to understand the population distribution of Earth by age. For example, using this dataset, we can identify the largest age group in Earth.

Key observations

The largest age group in Earth, TX was for the group of age 10 to 14 years years with a population of 102 (10.89%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Earth, TX was the 85 years and over years with a population of 4 (0.43%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Earth is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Earth total 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 Age. You can refer the same here

All results of the primary interrupted time-series results evaluating targeted and total border closures that met the following criteria: 1) at least seven days of data is available before and after the intervention point, 2) for multiple intervention time series, at least seven days has passed since the last intervention point, and 3) for multiple sequential targeted border closures, the second (or third) intervention is observed to indicate an increase of at least 20% of the world’s population being targeted by the new border closures.

This raster dataset contains areas of 30 second cells. The data table (VAT) contains the cell areas in the field: [Area]. The units are square kilometers. The Value Field simply represents a row number for a specific Latitude. All cells on the same row have the same area. Cell areas are largest at the equator and smallest at the poles. This dataset is part of the LandScan global 2013.

Global Volcano Total Economic Loss Risk Deciles is a 2.5 minute grid of global volcano total economic loss risks. First, subnational distributions of Gross Domestic Product (GDP) are computed using a two-fold process. Where applicable, the proportional contribution of subnational Units are determined following the methodology of Sachs et al. (2003) and these proportions are used against World Bank Development Indicators to determine a GDP value for the subnational Unit. Once a national GDP has been spatially stratified into the smallest administrative Units available, it is further distributed based upon Gridded Population of the World, Version 3 (GPWv3) population distributions. A per capita contribution value is determined for each Unit, and this value is multiplied by the population per grid cell. Once the GDP has been determined on a per grid cell basis, then the spatially variable loss rate as derived from EM-DAT historical records is used to determine the total economic loss posed to a grid cell by volcano hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

United States US: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Total: % Cumulative data was reported at 32.501 % in 2015. This records an increase from the previous number of 31.956 % for 2014. United States US: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Total: % Cumulative data is updated yearly, averaging 31.956 % from Dec 2013 to 2015, with 3 observations. The data reached an all-time high of 32.501 % in 2015 and a record low of 31.661 % in 2013. United States US: Educational Attainment: At Least Bachelor's or Equivalent: Population 25+ Years: Total: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.; ; UNESCO Institute for Statistics; ;

MD: Educational Attainment: At Least Completed Lower Secondary: Population 25+ Years: Female: % Cumulative data was reported at 95.509 % in 2015. This records an increase from the previous number of 95.207 % for 2014. MD: Educational Attainment: At Least Completed Lower Secondary: Population 25+ Years: Female: % Cumulative data is updated yearly, averaging 92.360 % from Dec 1989 (Median) to 2015, with 10 observations. The data reached an all-time high of 95.509 % in 2015 and a record low of 65.600 % in 1989. MD: Educational Attainment: At Least Completed Lower Secondary: Population 25+ Years: Female: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Education Statistics. The percentage of population ages 25 and over that attained or completed lower secondary education.; ; UNESCO Institute for Statistics; ;

MD: Educational Attainment, At Least Completed Upper Secondary: Population 25+ Years: Total: % Cumulative data was reported at 74.726 % in 2015. This records an increase from the previous number of 74.721 % for 2014. MD: Educational Attainment, At Least Completed Upper Secondary: Population 25+ Years: Total: % Cumulative data is updated yearly, averaging 74.437 % from Dec 2007 (Median) to 2015, with 9 observations. The data reached an all-time high of 75.191 % in 2009 and a record low of 72.918 % in 2007. MD: Educational Attainment, At Least Completed Upper Secondary: Population 25+ Years: Total: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Moldova – Table MD.World Bank.WDI: Education Statistics. The percentage of population ages 25 and over that attained or completed upper secondary education.; ; UNESCO Institute for Statistics; ;

Aruba AW: Educational Attainment, At Least Completed Upper Secondary: Population 25+ Years: Total: % Cumulative data was reported at 32.080 % in 2010. This records an increase from the previous number of 16.400 % for 2000. Aruba AW: Educational Attainment, At Least Completed Upper Secondary: Population 25+ Years: Total: % Cumulative data is updated yearly, averaging 16.400 % from Dec 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 32.080 % in 2010 and a record low of 7.190 % in 1991. Aruba AW: Educational Attainment, At Least Completed Upper Secondary: Population 25+ Years: Total: % Cumulative data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Aruba – Table AW.World Bank.WDI: Social: Education Statistics. The percentage of population ages 25 and over that attained or completed upper secondary education.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;;

The Global Earthquake Total Economic Loss Risk Deciles is a 2.5 minute grid of global earthquake total economic loss risks. A process of spatially allocating Gross Domestic Product (GDP) based upon the Sachs et al. (2003) methodology is utilized. First the proportional contributions of subnational Units to their respective national GDP are determined using sources of various origin. The contribution rates are then applied to published World Bank Development Indicators to determine a GDP value for the subnational Unit. Once the national GDP has been spatially stratified into the smallest administrative Units available, GDP values for grid cells are derived using Gridded Population of the World, Version 3 (GPWv3) data population distributions. A per capita contribution value is determined within each subnational Unit, and then this value is multiplied by the population per grid cell. Once a GDP value has been determined on a per grid cell basis, then the regionally variable loss rate as derived from the historical records of EM-DAT is used to determine the total economic loss risks posed to a grid cell by earthquake hazards. The final surface does not present absolute values of total economic loss, but rather a relative decile (1-10 with increasing risk) ranking of grid cells based upon the calculated economic loss risks. This data set is the result of collaboration among the Columbia University Center for Hazards and Risk Research (CHRR), International Bank for Reconstruction and Development/The World Bank, and Columbia University Center for International Earth Science Information Network (CIESIN).

Series Name: Proportion of population achieving at least a fixed level of proficiency in functional skills by sex age and type of skill (percent)Series Code: SE_ADT_FUNSRelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 4.6.1: Proportion of population in a given age group achieving at least a fixed level of proficiency in functional (a) literacy and (b) numeracy skills, by sexTarget 4.6: By 2030, ensure that all youth and a substantial proportion of adults, both men and women, achieve literacy and numeracyGoal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

A dataset listing Florida cities by population for 2024.

The "Tree Proximate People" (TPP) dataset provides an estimate of the number of people living in or within 1 kilometer of trees outside forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level. Trees outside forests are defined as areas classified as croplands with at least 10% tree cover.

For more detail, such as the theory behind, the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Maintainer: Sarah E., Castle

Data lineage:

The TPP data are generated using Google Earth Engine. Trees outside forests (TOFs) are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) fractional cover data layer using a minimum of 10% tree cover on croplands lands. Any area classified as land with TOFs sized ≥ 1 ha in 2019 was included in this definition. Lands classified as forests in CGLC were excluded from the analysis. Croplands were defined using the FAO-LCCS2 land use classification layer from MODIS Land Cover (MCD12Q1.006). Croplands were defined as the total of three classifications: 1) “Herbaceous Croplands”: dominated by herbaceous annuals (<2m) with at least 60% cover and a cultivated fraction >60%, 2) “Natural Herbaceous/Croplands Mosaics”: mosaics of small-scale cultivation 40-60% with natural shrub or herbaceous vegetation, and 3) “Forest/Cropland Mosaics”: mosaics of small-scale cultivation 40-60% with >10% natural tree cover. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 1 kilometer of TOFs on croplands in 2019 were classified as tree proximate people. Euclidean distance was used as the measure to create a 1-kilometer buffer zone around each TOF pixel. The scripts for generating the tree-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

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 (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Tree proximate people – Croplands, 1km cutoff distance"