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

The 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.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: 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.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

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

In the past four centuries, the population of the Thirteen Colonies and United States of America has grown from a recorded 350 people around the Jamestown colony in Virginia in 1610, to an estimated 346 million in 2025. While the fertility rate has now dropped well below replacement level, and the population is on track to go into a natural decline in the 2040s, projected high net immigration rates mean the population will continue growing well into the next century, crossing the 400 million mark in the 2070s. Indigenous population Early population figures for the Thirteen Colonies and United States come with certain caveats. Official records excluded the indigenous population, and they generally remained excluded until the late 1800s. In 1500, in the first decade of European colonization of the Americas, the native population living within the modern U.S. borders was believed to be around 1.9 million people. The spread of Old World diseases, such as smallpox, measles, and influenza, to biologically defenseless populations in the New World then wreaked havoc across the continent, often wiping out large portions of the population in areas that had not yet made contact with Europeans. By the time of Jamestown's founding in 1607, it is believed the native population within current U.S. borders had dropped by almost 60 percent. As the U.S. expanded, indigenous populations were largely still excluded from population figures as they were driven westward, however taxpaying Natives were included in the census from 1870 to 1890, before all were included thereafter. It should be noted that estimates for indigenous populations in the Americas vary significantly by source and time period. Migration and expansion fuels population growth The arrival of European settlers and African slaves was the key driver of population growth in North America in the 17th century. Settlers from Britain were the dominant group in the Thirteen Colonies, before settlers from elsewhere in Europe, particularly Germany and Ireland, made a large impact in the mid-19th century. By the end of the 19th century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. It is also estimated that almost 400,000 African slaves were transported directly across the Atlantic to mainland North America between 1500 and 1866 (although the importation of slaves was abolished in 1808). Blacks made up a much larger share of the population before slavery's abolition. Twentieth and twenty-first century The U.S. population has grown steadily since 1900, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. Since WWII, the U.S. has established itself as the world's foremost superpower, with the world's largest economy, and most powerful military. This growth in prosperity has been accompanied by increases in living standards, particularly through medical advances, infrastructure improvements, clean water accessibility. These have all contributed to higher infant and child survival rates, as well as an increase in life expectancy (doubling from roughly 40 to 80 years in the past 150 years), which have also played a large part in population growth. As fertility rates decline and increases in life expectancy slows, migration remains the largest factor in population growth. Since the 1960s, Latin America has now become the most common origin for migrants in the U.S., while immigration rates from Asia have also increased significantly. It remains to be seen how immigration restrictions of the current administration affect long-term population projections for the United States.

Throughout the early modern period, the largest city in Italy was Naples. The middle ages saw many metropolitan areas along the Mediterranean grow to become the largest in Europe, as they developed into meeting ports for merchants travelling between the three continents. Italy, throughout this time, was not a unified country, but rather a collection of smaller states that had many cultural similarities, and political control of these cities regularly shifted over the given period. Across this time, the population of each city generally grew between each century, but a series of plague outbreaks in the 1600s devastated the populations of Italy's metropolitan areas, which can be observed here. Naples At the beginning of the 1500s, the Kingdom of Naples was taken under the control of the Spanish crown, where its capital grew to become the largest city in the newly-expanding Spanish Empire. Prosperity then grew in the 16th and 17th centuries, before the city's international importance declined in the 18th century. There is also a noticeable dip in Naples' population size between 1600 and 1700, due to an outbreak of plague in 1656 that almost halved the population. Today, Naples is just the third largest city in Italy, behind Rome and Milan. Rome Over 2,000 years ago, Rome became the first city in the world to have a population of more than one million people, and in 2021, it was Italy's largest city with a population of 2.8 million; however it did go through a period of great decline in the middle ages. After the Fall of the Western Roman Empire in 476CE, Rome's population dropped rapidly, below 100,000 inhabitants in 500CE. 1,000 years later, Rome was an important city in Europe as it was the seat of the Catholic Church, and it had a powerful banking sector, but its population was just 55,000 people as it did not have the same appeal for merchants or migrants held by the other port cities. A series of reforms by the Papacy in the late-1500s then saw significant improvements to infrastructure, housing, and sanitation, and living standards rose greatly. Over the following centuries, the Papacy consolidated its power in the center of the Italian peninsula, which brought stability to the region, and the city of Rome became a cultural center. Across this period, Rome's population grew almost three times larger, which was the highest level of growth of these cities.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds. Methods Literature search, screening, and data extraction A primary literature search was conducted using ISI Web of Science Core Collection and any articles that referenced two popular single-sample Ne estimation software packages: LDNe (Waples & Do, 2008), and NeEstimator v2 (Do et al., 2014). The initial search included 4513 articles published up to the search date of May 26, 2020. Articles were screened for relevance in two steps, first based on title and abstract, and then based on the full text. For each step, a consistency check was performed using 100 articles to ensure they were screened consistently between reviewers (n = 6). We required a kappa score (Collaboration for Environmental Evidence, 2020) of ³ 0.6 in order to proceed with screening of the remaining articles. Articles were screened based on three criteria: (1) Is an estimate of Ne or Nb reported; (2) for a wild animal or plant population; (3) using a single-sample genetic estimation method. Further details on the literature search and article screening are found in the Supplementary Material (Fig. S1). We extracted data from all studies retained after both screening steps (title and abstract; full text). Each line of data entered in the database represents a single estimate from a population. Some populations had multiple estimates over several years, or from different estimation methods (see Table S1), and each of these was entered on a unique row in the database. Data on N̂e, N̂b, or N̂c were extracted from tables and figures using WebPlotDigitizer software version 4.3 (Rohatgi, 2020). A full list of data extracted is found in Table S2. Data Filtering After the initial data collation, correction, and organization, there was a total of 8971 Ne estimates (Fig. S1). We used regression analyses to compare Ne estimates on the same populations, using different estimation methods (LD, Sibship, and Bayesian), and found that the R2 values were very low (R2 values of <0.1; Fig. S2 and Fig. S3). Given this inconsistency, and the fact that LD is the most frequently used method in the literature (74% of our database), we proceeded with only using the LD estimates for our analyses. We further filtered the data to remove estimates where no sample size was reported or no bias correction (Waples, 2006) was applied (see Fig. S6 for more details). Ne is sometimes estimated to be infinity or negative within a population, which may reflect that a population is very large (i.e., where the drift signal-to-noise ratio is very low), and/or that there is low precision with the data due to small sample size or limited genetic marker resolution (Gilbert & Whitlock, 2015; Waples & Do, 2008; Waples & Do, 2010) We retained infinite and negative estimates only if they reported a positive lower confidence interval (LCI), and we used the LCI in place of a point estimate of Ne or Nb. We chose to use the LCI as a conservative proxy for in cases where a point estimate could not be generated, given its relevance for conservation (Fraser et al., 2007; Hare et al., 2011; Waples & Do 2008; Waples 2023). We also compared results using the LCI to a dataset where infinite or negative values were all assumed to reflect very large populations and replaced the estimate with an arbitrary large value of 9,999 (for reference in the LCI dataset only 51 estimates, or 0.9%, had an or > 9999). Using this 9999 dataset, we found that the main conclusions from the analyses remained the same as when using the LCI dataset, with the exception of the HFI analysis (see discussion in supplementary material; Table S3, Table S4 Fig. S4, S5). We also note that point estimates with an upper confidence interval of infinity (n = 1358) were larger on average (mean = 1380.82, compared to 689.44 and 571.64, for estimates with no CIs or with an upper boundary, respectively). Nevertheless, we chose to retain point estimates with an upper confidence interval of infinity because accounting for them in the analyses did not alter the main conclusions of our study and would have significantly decreased our sample size (Fig. S7, Table S5). We also retained estimates from populations that were reintroduced or translocated from a wild source (n = 309), whereas those from captive sources were excluded during article screening (see above). In exploratory analyses, the removal of these data did not influence our results, and many of these populations are relevant to real-world conservation efforts, as reintroductions and translocations are used to re-establish or support small, at-risk populations. We removed estimates based on duplication of markers (keeping estimates generated from SNPs when studies used both SNPs and microsatellites), and duplication of software (keeping estimates from NeEstimator v2 when studies used it alongside LDNe). Spatial and temporal replication were addressed with two separate datasets (see Table S6 for more information): the full dataset included spatially and temporally replicated samples, while these two types of replication were removed from the non-replicated dataset. Finally, for all populations included in our final datasets, we manually extracted their protection status according to the IUCN Red List of Threatened Species. Taxa were categorized as “Threatened” (Vulnerable, Endangered, Critically Endangered), “Nonthreatened” (Least Concern, Near Threatened), or “N/A” (Data Deficient, Not Evaluated). Mapping and Human Footprint Index (HFI) All populations were mapped in QGIS using the coordinates extracted from articles. The maps were created using a World Behrmann equal area projection. For the summary maps, estimates were grouped into grid cells with an area of 250,000 km2 (roughly 500 km x 500 km, but the dimensions of each cell vary due to distortions from the projection). Within each cell, we generated the count and median of Ne. We used the Global Human Footprint dataset (WCS & CIESIN, 2005) to generate a value of human influence (HFI) for each population at its geographic coordinates. The footprint ranges from zero (no human influence) to 100 (maximum human influence). Values were available in 1 km x 1 km grid cell size and were projected over the point estimates to assign a value of human footprint to each population. The human footprint values were extracted from the map into a spreadsheet to be used for statistical analyses. Not all geographic coordinates had a human footprint value associated with them (i.e., in the oceans and other large bodies of water), therefore marine fishes were not included in our HFI analysis. Overall, 3610 Ne estimates in our final dataset had an associated footprint value.

It is estimated that the cities of Cordova (modern-day Córdoba) and Palermo were the largest cities in Europe in 1050, and had between fifteen and twenty times the population of most other entries in this graph, Despite this the cities of Cordova (the capital city of the Umayyad caliphate, who controlled much of the Iberian peninsula from the seventh to eleventh centuries), and Palermo (another Arab-controlled capital in Southern Europe) were still the only cities in Western Europe with a population over one hundred thousand people, closely followed by Seville. It is also noteworthy to point out that the five largest cities on this list were importing trading cities, in modern day Spain or Italy, although the largest cities become more northern and western European in later lists (1200, 1330, 1500, 1650 and 1800). In 1050, todays largest Western European cities, London and Paris, had just twenty-five and twenty thousand inhabitants respectively.

The period of European history (and much of world history) between 500 and 1500 is today known as the 'Dark Ages'. Although the term 'Dark Ages' was originally applied to the lack of literature and arts, it has since been applied to the lack or scarcity of recorded information from this time. Because of these limitations, much information about this time is still being debated today.

In 1800, the population of the region of present-day India was approximately 169 million. The population would grow gradually throughout the 19th century, rising to over 240 million by 1900. Population growth would begin to increase in the 1920s, as a result of falling mortality rates, due to improvements in health, sanitation and infrastructure. However, the population of India would see it’s largest rate of growth in the years following the country’s independence from the British Empire in 1948, where the population would rise from 358 million to over one billion by the turn of the century, making India the second country to pass the billion person milestone. While the rate of growth has slowed somewhat as India begins a demographics shift, the country’s population has continued to grow dramatically throughout the 21st century, and in 2020, India is estimated to have a population of just under 1.4 billion, well over a billion more people than one century previously. Today, approximately 18% of the Earth’s population lives in India, and it is estimated that India will overtake China to become the most populous country in the world within the next five years.

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.

Proportion of ancestry assigned to each Old World population (columns) in the Old World painting that have a more recent common ancestor within the same subpopulation in the Global Painting.

Understanding how bottom-up and top-down forces affect resource selection can inform restoration efforts. With a global population size of <500 individuals, the hirola Beatragus hunteri is the world's most endangered antelope, with a declining population since the 1970s. While the underlying mechanisms are unclear, some combination of habitat loss and predation are thought to be responsible for low abundances of contemporary populations. Efforts to conserve hirola are hindered by a lack of understanding as to why population density remains low, despite eradication of the viral disease, rinderpest. To elucidate factors underlying chronically low numbers, we examined resource selection and landscape change within the hirola's native range. Because hirola are grazers, we hypothesized that the availability of open areas would be linked both to forage and safety from predators. We quantified: (1) changes in tree cover across the hirola's historical range in eastern Kenya over the past 27 ...

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden.

The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones.

The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

National excluding: а) persons, doing their military service at the conscription or by contract; b) persons under imprisonment before trial and convicted; c) persons living in old people’s home, psycho-neurological hospitals and other closed institutions; d) persons living in remote or difficult for access regions of Far North and Far East; e) persons living in Chechnya and Ingushetia; f) persons residing in rural settlements with less than 50 inhabitants; g) homeless peoples

Analysis unit

Household Individual

Universe

National Population, Both sexes,18 and more years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 2500

At the first stage of selection of primary sampling units (PSU’s) urban and rural settlements will be selected. All the PSU’s are distributed among eight Federal districts (Northwestern, Central, Volga, Southern, North Caucasus, Ural, Siberian and Far Eastern), and in every Federal district, independently of each other - by strata according to the number of their population: cities with 1 million and more population; cities with from 500 thousands up to 1 million population; cities with from 100 thousands up to 500 thousands population, urban settlements with up to 100 thousands population, rural settlements.

Mode of data collection

Face-to-face [f2f]

Research instrument

For each wave, suggestions for questions are solicited by social scientists from all over the world and a final master questionnaire is developed in English. Since the start in 1981 each successive wave has covered a broader range of societies than the previous one. Analysis of the data from each wave has indicated that certain questions tapped interesting and important concepts while others were of little value. This has led to the more useful questions or themes being replicated in future waves while the less useful ones have been dropped making room for new questions.

The questionnaire is translated into the various national languages and in many cases independently translated back to English to check the accuracy of the translation. In most countries, the translated questionnaire is pre-tested to help identify questions for which the translation is problematic. In some cases certain problematic questions are omitted from the national questionnaire.

WVS requires implementation of the common questionnaire fully and faithfully, in all countries included into one wave. Any alteration to the original questionnaire has to be approved by the EC. Omission of no more than a maximum of 12 questions in any given country can be allowed.

Response rate

76%

Sampling error estimates

Estimated error: 2.0

According to Cognitive Market Research, the global Cleaning Equipment And Supplies Market size will be USD 132648.1 million in 2025. It will expand at a compound annual growth rate (CAGR) of 7.40% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 49079.80 million in 2025 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue with a market size of USD 38467.95 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 31835.54 million in 2025 and will grow at a compound annual growth rate (CAGR) of 9.4% from 2025 to 2033.
South America has a market share of more than 4% of the global revenue with a market size of USD 5040.63 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.4% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 5305.92 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2025 to 2033.
Africa had a market share of around 2.2% of the global revenue and was estimated at a market size of USD 2918.26 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.1% from 2025 to 2033.
Equipment category is the fastest growing segment of the Cleaning Equipment And Supplies Market

Market Dynamics of Cleaning Equipment And Supplies Market

Key Drivers for Cleaning Equipment And Supplies Market

Rapid Urbanization and Growing Population

Rapid urbanization combined with an increasing global population is a key driver for the Cleaning Equipment and Supplies Market. Urban centers witness higher concentrations of people, which elevates the demand for cleaning products to manage hygiene in residential, commercial, and public spaces. Expanding infrastructure, such as new residential complexes, offices, malls, and transportation hubs, requires efficient cleaning equipment to maintain sanitary conditions. Moreover, growing population density in cities intensifies waste generation and contamination risks, prompting the need for reliable cleaning solutions. This trend is particularly strong in emerging economies where urban migration is accelerating. Thus, the demand for both professional-grade and consumer cleaning supplies surges as cities strive to ensure healthy living environments amid rising population pressures. The United Nations reports that by 2030, the global urban population is expected to increase by approximately 500 million people, reaching about 5 billion urban residents. This rapid urbanization, coupled with population growth, intensifies the need for effective cleaning solutions in residential, commercial, and public spaces, thereby driving the demand for cleaning equipment and supplies.

https://www.un.org/uk/desa/68-world-population-projected-live-urban-areas-2050-says-unRising Awareness of Hygiene and Sanitation

Increasing global awareness of hygiene and sanitation, especially following the COVID-19 pandemic, has significantly driven the demand for cleaning equipment and supplies. Both consumers and businesses have recognized the critical role of cleanliness in preventing infections and maintaining health. This heightened consciousness has led to stricter sanitation protocols across industries such as healthcare, hospitality, education, and commercial spaces. As a result, organizations are investing in advanced cleaning technologies, disinfectants, and eco-friendly supplies to ensure safer environments. Additionally, public health campaigns and government regulations mandating hygiene standards have further accelerated market growth. The growing emphasis on maintaining clean, germ-free surfaces continues to boost demand for innovative and effective cleaning solutions worldwide.

Restraint Factor for the Cleaning Equipment and Supplies Market

High Initial Cost of Advanced Cleaning Equipment

The high upfront cost of advanced cleaning equipment acts as a significant restraint for the Cleaning Equipment and Supplies Market. Modern machines like robotic vacuum cleaners, industrial floor scrubbers, and automated disinfection units come with high price tags due to their sophisticated technology and energy-efficient features. For small businesses, residential users, or institutions with tight budgets, this initi...

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The Survey covers Ukraine.

Analysis unit

• Household
• Individual

Universe

The WVS covers national population aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design: multi-staged, stratified, using the random route selection method at the final stage.

Sample forming:

1. First stage:

2. Division into regions - in accordance with similarities and differences of social, economic, historical and geographic activities all territory of Ukraine was divided into 4 regions SOUTHERN, WESTERN, EASTERN, CENTRAL - Definition of a settlement size - according to the size and type of living population all population units are divided into following 5 sizes: 1) village; 2) up to 50 thousand; 3) 50-99 thousand.; 4) 100-499 thousand.; 5) 500+ thousand

3. Second stage:

4. Settlement selection (cities selection from National Representative survey Product Brand, representative sample 2800; loading per each city proportionally for each strata (region and settlement size) according to universe, no less then 10 interviews per city)

5. Third stage: - Selection of sample units inside the settlement. The distance between the sample units in one settlement presses toward maximum and is determined based on: the size of a settlement; number of sampling units in each given settlement; building type (single-storied, many-storied); compactness of population in different city regions. In one sample unit research is conducted by one interviewer. That is why the number sample units is determined from the optimal loading on one interviewer. Under optimal loading the quantity of interviews that are conducted by one interviewer is understood, under which short terms and quality of conduction are obtained. Optimal loading per one interviewer in a given research: 8-15 interviews

Remarks about sampling: 1. If calculated flat/house are not existed or uninhabited (for example office) ? we turned to another 2. If potential respondent refused to take part in interview or out for all period of investigation then interviewer moving to another flat

The sample size for Ukraine is N=1000 and includes national population aged 18 years and over, for both sexes.

Mode of data collection

Face-to-face [f2f]

Response rate

Addresses established as empty, demolished or containing no private dwellings 73 Selected respondent away during survey period 99 No contact at selected address 387 No contact with selected person 380 Refusal at selected address 471 Personal refusal by selected respondent 397 Other type of unproductive (please write in full details in the box below 371 Full productive interview 1000 Partial productive interview 9

Sampling error estimates

+/- 3,2%

A range of indicators for a selection of cities from the New York City Global City database.

Dataset includes the following:

Geography

City Area (km2)

Metro Area (km2)

People

City Population (millions)

Metro Population (millions)

Foreign Born

Annual Population Growth

Economy

GDP Per Capita (thousands $, PPP rates, per resident)

Primary Industry

Secondary Industry

Share of Global 500 Companies (%)

Unemployment Rate

Poverty Rate

Transportation

Public Transportation

Mass Transit Commuters

Major Airports

Major Ports

Education

Students Enrolled in Higher Education

Percent of Population with Higher Education (%)

Higher Education Institutions

Tourism

Total Tourists Annually (millions)

Foreign Tourists Annually (millions)

Domestic Tourists Annually (millions)

Annual Tourism Revenue ($US billions)

Hotel Rooms (thousands)

Health

Infant Mortality (Deaths per 1,000 Births)

Life Expectancy in Years (Male)

Life Expectancy in Years (Female)

Physicians per 100,000 People

Number of Hospitals

Anti-Smoking Legislation

Culture

Number of Museums

Number of Cultural and Arts Organizations

Environment

Green Spaces (km2)

Air Quality

Laws or Regulations to Improve Energy Efficiency

Retrofitted City Vehicle Fleet

Bike Share Program

Abstract

High Frequency Phone Survey for Displaced Population in Somalia helps to fill the important data and knowledge gaps on displaced populations and host communities to support timely and evidence-informed decisions that can improve the lives of one of the most vulnerable groups in Somalia. Displaced population including IDPs, refugees and returnees are recognized as among the most vulnerable groups in the Somalia National Development Plan, but the paucity of data makes it difficult to adequately prescribe policy recommendations that will improve their lives. Humanitarian partners, including UNHCR and the International Organization for Migration, benefit from the information generated to better target their responses in times of crisis. It will also be used by the World Bank to support country dialogue, inform operations, and expand the knowledge base on displacement in Somalia. The time-series nature of the survey will enable the tracking of the impact of shocks on specific socio-economic indicators to allow for better timing of interventions.

Two survey rounds conducted from November 2021 to August 2022 yield samples for five population groups: host communities for IDPs, IDPs in and out of settlements, refugees and asylum seekers and refugee returnees. Implemented by the World Bank in collaboration with the United Nations High Commissioner for Refugees (UNHCR) and the National Bureau of Statistics (NBS) in Somalia, this cost-effective phone-based survey aimed to follow the same respondents over a period of time.

Geographic coverage

National

Analysis unit

• Household
• Individual

Universe

Households with access to phones.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample consists of five strata: (i) host communities; (ii) IDPs living in settlements; (iii) IDPs living outside settlements; (iv) refugees; and (v) refugee returnees. Each stratum consisted of about 500 households, making up the total sample of around 2,500 respondents.

Samples for the host communities and IDPs living outside settlements were selected from the previous national phone survey (Somalia high frequency phone survey - SHFPS) conducted by the World Bank in Somalia from June 2020 until October 2021. The sample for host communities was selected on the basis of frequency of interaction with IDP populations, with households that reported that they had had interacted with the IDPs at least once a month collected for the sample. For IDPs living in the settlements, phone numbers were collected by UNHCR from the settlements in Bay and Banadir, while those for refugees and refugee returnees were provided from the UNHCR database.

Except for IDPs in settlements, the majority of the displacement-affected households surveyed live in urban areas. The majority of the refugees in Somalia are either from Ethiopia (54 percent) and Yemen (41 percent). Therefore, this survey focused on these two refugee groups. The refugee households mostly live in Somaliland (53 percent) with a considerable number in Puntland (28 percent) and Banadir (15 percent). In the case of refugee returnees, about 11,606 households were registered in the UNHCR database at the time of sample selection, mostly coming from Kenya (97 percent) and Yemen (2 percent). Both these groups were included in the sample proportionally to their population share. The majority of the sampled refugee returnees live in Jubaland (78 percent). As for settlement-based IDPs, two main regions—Banadir and Bay—which host almost 50 percent of the settlement-based IDPs in Somalia were focused.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Only households that consented to being interviewed were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset.

AbstractUnderstanding how bottom-up and top-down forces affect resource selection can inform restoration efforts. With a global population size of <500 individuals, the hirola Beatragus hunteri is the world's most endangered antelope, with a declining population since the 1970s. While the underlying mechanisms are unclear, some combination of habitat loss and predation are thought to be responsible for low abundances of contemporary populations. Efforts to conserve hirola are hindered by a lack of understanding as to why population density remains low, despite eradication of the viral disease, rinderpest. To elucidate factors underlying chronically low numbers, we examined resource selection and landscape change within the hirola's native range. Because hirola are grazers, we hypothesized that the availability of open areas would be linked both to forage and safety from predators. We quantified: (1) changes in tree cover across the hirola's historical range in eastern Kenya over the past 27 years; (2) how tree cover has influenced resource selection by hirola; and (3) interactions between tree cover and predation. Between 1985 and 2012, tree cover increased by 251% across the historical range of hirola. Tree encroachment was associated with a 98% decline of hirola and elephant Loxodonta africana populations, a 74% decline in cattle Bos indicus, an increase in browsing livestock by 327%, and a reduction in rainfall. Although hirola avoided tree cover, we found no evidence that predation on hirola increased with increasing tree cover. Synthesis and applications. Hirola may qualify as a refugee species, in which contemporary populations are restricted to suboptimal habitat and exhibit low survival, reproduction, or both. The extinction of hirola would be the first of a mammalian genus on the African continent in modern history. We conclude that contemporary low numbers of hirola are due at least partly to habitat loss via tree encroachment, triggered by some combination of elephant extirpation, overgrazing, drought, and perhaps fire suppression. We recommend a combination of rangeland restoration efforts (including conservation of elephants, manual clearing of trees, and grass seeding), increased enforcement of an existing protected area (Arawale National Reserve), and reintroductions to enhance recovery for this endangered species. These efforts will rely on enhanced support from the international conservation community and the cooperation of pastoralist communities with which the hirola coexist. Usage notesLivestock, Cattle, elephants abundance in hirola's range_77_2011This is aerial survey data from the Department of Resource Survey and Remote Sensing Kenya from 1977 to 2011 to give historical estimates wildlife and livestock abundances. We estimated the trends of livestock, hirola, elephants abundances from this data.Masalani_daily rainfallThe rainfall data file contains daily rainfall observations for Masalani station in the centre of hirola's geographic range for the period from 1970-2009.Hirolacollardatamay2nd 2015The hirola collar data contains the telemetry data of collared hirola individuals within the hirola's geographic range. Note: due to the sensitive nature of the data, GPS location information for the critically endangered Beatragus hunteri have been removed from the dataset.Imagery analysisP166R61zip contains details of the imagery analysis (1985 and 2012) for the hirola's geographic range.P166R61_zip.zipHirolakillsites_2015Hirola kill sites data contains observed hirola kill sites collected by network of scouts within the hirola's geographic range. Note: due to the sensitive nature of the data, GPS location information for the critically endangered Beatragus hunteri have been removed from the dataset.