The region of present-day China has historically been the most populous region in the world; however, its population development has fluctuated throughout history. In 2022, China was overtaken as the most populous country in the world, and current projections suggest its population is heading for a rapid decline in the coming decades. Transitions of power lead to mortality The source suggests that conflict, and the diseases brought with it, were the major obstacles to population growth throughout most of the Common Era, particularly during transitions of power between various dynasties and rulers. It estimates that the total population fell by approximately 30 million people during the 14th century due to the impact of Mongol invasions, which inflicted heavy losses on the northern population through conflict, enslavement, food instability, and the introduction of bubonic plague. Between 1850 and 1870, the total population fell once more, by more than 50 million people, through further conflict, famine and disease; the most notable of these was the Taiping Rebellion, although the Miao an Panthay Rebellions, and the Dungan Revolt, also had large death tolls. The third plague pandemic also originated in Yunnan in 1855, which killed approximately two million people in China. 20th and 21st centuries There were additional conflicts at the turn of the 20th century, which had significant geopolitical consequences for China, but did not result in the same high levels of mortality seen previously. It was not until the overlapping Chinese Civil War (1927-1949) and Second World War (1937-1945) where the death tolls reached approximately 10 and 20 million respectively. Additionally, as China attempted to industrialize during the Great Leap Forward (1958-1962), economic and agricultural mismanagement resulted in the deaths of tens of millions (possibly as many as 55 million) in less than four years, during the Great Chinese Famine. This mortality is not observable on the given dataset, due to the rapidity of China's demographic transition over the entire period; this saw improvements in healthcare, sanitation, and infrastructure result in sweeping changes across the population. The early 2020s marked some significant milestones in China's demographics, where it was overtaken by India as the world's most populous country, and its population also went into decline. Current projections suggest that China is heading for a "demographic disaster", as its rapidly aging population is placing significant burdens on China's economy, government, and society. In stark contrast to the restrictive "one-child policy" of the past, the government has introduced a series of pro-fertility incentives for couples to have larger families, although the impact of these policies are yet to materialize. If these current projections come true, then China's population may be around half its current size by the end of the century.

this graph was created in R:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F55a15c27e578216565ab65e502f9ecf8%2Fgraph1.png?generation=1730674251775717&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F0b481e4d397700978fe5cf15932dbc68%2Fgraph2.png?generation=1730674259213775&alt=media" alt="">

driven primarily by high birth rates in developing countries and advancements in healthcare. According to the United Nations, the global population surpassed 8 billion in 2023, marking a critical milestone in human history. This growth, however, is unevenly distributed across continents and countries, leading to varied population densities and urban pressures.

Surface area and population density play vital roles in shaping the demographic and economic landscape of each country. For instance, countries with large land masses such as Russia, Canada, and Australia have low population densities despite their significant populations, as vast portions of their land are sparsely populated or uninhabitable. Conversely, nations like Bangladesh and South Korea exhibit extremely high population densities due to smaller land areas combined with large populations.

Population density, measured as the number of people per square kilometer, affects resource availability, environmental sustainability, and quality of life. High-density areas face greater challenges in housing, infrastructure, and environmental management, often experiencing increased pollution and resource strain. In contrast, low-density areas may struggle with underdeveloped infrastructure and limited access to services due to the dispersed population.

Urbanization trends are another important aspect of these dynamics. As people migrate to cities seeking better economic opportunities, urban areas grow more densely populated, amplifying the need for efficient land use and sustainable urban planning. The UN reports that over half of the world’s population currently resides in urban areas, with this figure expected to rise to nearly 70% by 2050. This shift requires nations to balance population growth and density with sustainable development strategies to ensure a higher quality of life and environmental stewardship for future generations.

Through an understanding of population size, surface area, and density, policymakers can better address challenges related to urban development, rural depopulation, and resource allocation, supporting a balanced approach to population management and economic development.

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.

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

Population Raster American Samoa 2020 Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps (https://data.humdata.org/dataset/american-samoa-high-resolution-population-density-maps-demographic-estimates) Population allocated proportionally using 2011 census population counts at enumeration area level. Year Population Growth Rate of 0.23% has been applied to update population up to 2020

Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.

Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps https://data.humdata.org/dataset/marshall-islands-high-resolution-population-density-maps-demographic-estimates Population allocated proportionally using 2011 census population counts at enumeration area level. Year Population Growth Rate of 0.3% has been applied to update population up to 2020 The human settlement footprint with census population allocated has been converted into a 100 m resolution raster.

The United States senior living market, valued at $112.93 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 5.86% from 2025 to 2033. This expansion is fueled by several key drivers. The aging population, particularly the baby boomer generation, is a significant factor, creating an increasing demand for assisted living, independent living, memory care, and nursing care facilities. Furthermore, rising disposable incomes and increasing awareness of the benefits of senior living communities contribute to market growth. Technological advancements in senior care, such as telehealth and remote monitoring, are also enhancing the quality of life for residents and boosting market appeal. However, the market faces some restraints, including the rising costs of healthcare and senior care services, potentially limiting accessibility for some segments of the population. Furthermore, staffing shortages within the industry represent a significant challenge. The market is segmented by property type, with assisted living, independent living, and memory care facilities representing the largest segments. Key states driving market growth include New York, Illinois, California, North Carolina, and Washington, reflecting higher concentrations of the senior population and higher disposable incomes. Major players in the market such as Ensign Group Inc, Sunrise Senior Living, Brookdale Senior Living Inc, and Atria Senior Living Inc, compete fiercely, driving innovation and service improvements. The forecast period (2025-2033) anticipates continued growth, driven by the ongoing demographic shifts and increased demand for high-quality senior care options. Strategic partnerships, acquisitions, and investments in technology are likely to shape the competitive landscape in the coming years. The industry will continue to adapt to meet the evolving needs of the aging population, focusing on personalized care, innovative technologies, and cost-effective solutions. This comprehensive report provides an in-depth analysis of the booming United States senior living market, covering the period from 2019 to 2033. With a base year of 2025 and a forecast period spanning 2025-2033, this report is an invaluable resource for investors, industry professionals, and anyone seeking to understand the dynamics of this rapidly evolving sector. The report leverages extensive data analysis to provide insightful projections and uncover key trends shaping the future of senior care in the US. Expect detailed breakdowns of key segments, including assisted living, independent living, memory care, and nursing care, across major states like California, New York, Illinois, North Carolina, and Washington. Recent developments include: July 2023: Spring Cypress senior living site expansion is set to open at the end of 2024 and will consist of three phases. The first phase of the expansion will include 19 independent-living, two-bedroom cottages. The second phase will include 24 townhomes. The third phase will feature 95 apartments. The final phase will feature a resort with several luxury amenities., Apr 2023: For seniors looking for innovative, high-quality care, Avista Senior Living is transitioning away from its SafelyYou partnership to empower safer, more personalized dementia care with real-time, AI video and remote clinical experts 24/7.. Key drivers for this market are: 4., Increase in Aging Population Driving the Market4.; Healthcare and Long-term Care Needs Driving the Market. Potential restraints include: 4., High Affordability and Cost of Care Affecting the Market4.; Staffing and Workforce Challenges Affecting the Market. Notable trends are: Senior Housing Witnessing Increased Demand.

Population Raster New Caledonia 2020 Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps https://data.humdata.org/dataset/new-caledonia-high-resolution-population-density-maps-demographic-estimates Population allocated proportionally using 2011 census population counts at enumeration area (districts de recensement) level. Year Population Growth Rate of 0.2% (0.001957) has been applied to update population up to 2020 The human settlement footprint with census population allocated has been converted into a 100 m resolution raster.

• The variable names are used in Random Forest model output and throughout the text as reference to the specific data they were derived from. The first three letters are derived from the data type (e.g. “lan” indicates land cover) and the last three letters, if present, indicates what type of data each variable represents (e.g. “_cls” is a binary classification and “_dst” is a calculated Euclidean distance-to variable.† The default data for populated places is merged from several VMAP0 data sources. There are three VMAP0 data sets used: The point data pop/builtupp and pop/mispopp are buffered to 100 m and merged with the pop/builtupa polygons creating avector-based built layer. This layer is then converted to binary class and distance-to rasters for use in modeling.Country-specific data sources and variable names used for population density estimation used for dasymetric weights.

Untangling the spatial and temporal processes that influence population dynamics of migratory species is challenging, because changes in abundance are shaped by variation in vital rates across heterogeneous habitats and throughout the annual cycle. We developed a full-annual-cycle, integrated population model and used demographic data collected between 2011 and 2014 in southern Indiana and Belize to estimate stage-specific vital rates of a declining migratory songbird, the Wood Thrush (Hylocichla mustelina). Our primary objective was to understand how spatial and temporal variation in demography contributes to local and regional population growth. Our full-annual-cycle model allowed us to estimate: 1) age-specific, seasonal survival probabilities, including latent survival during both spring and autumn migration, and 2) how the relative contribution of vital rates to population growth differed among habitats. Wood Thrushes in our study populations experienced the lowest apparent survival rates during migration and apparent survival was lower during spring migration than during fall migration. Both mortality and high dispersal likely contributed to low apparent survival during spring migration. Population growth in high-quality habitat was most sensitive to variation in fecundity and apparent survival of juveniles during spring migration, whereas population growth in low-quality sites was most sensitive to adult apparent breeding-season survival. These results elucidate how full-annual-cycle vital rates, particularly apparent survival during migration, interact with spatial variation in habitat quality to influence population dynamics in migratory species.

This dataset presents a fine-grained population map of Tanzania with a resolution of 100 meters for 2020, generated using the POMELO super-resolution technique that is based on deep learning. Please refer to our Nature Scientific Reports publication for more details.

Background: Traditionally, many countries, including those in sub-Saharan Africa, rely on aggregated census data over expansive spatial units, which are not always timely or accurate. The need for detailed population maps is paramount in several sectors, including urban development, environmental supervision, public health, and humanitarian initiatives. Addressing this gap, the POMELO methodology leverages coarse census data in conjunction with open geodata to produce high precision population maps.

Key Features: Resolution: The map offers a granular view with a 100m ground sampling distance, providing intricate details about population distributions in Tanzania. Data Sources: Utilizing a combination of projected admisistrative census data (UN), and supplementing it with open geodata. Reliability: In comparative experiments conducted in sub-Saharan Africa, POMELO's ability to disaggregate coarse census counts achieved R2 values of 85-89%. Furthermore, its potential to predict population numbers without any census data reached accuracy levels of 48-69%.

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. The data-sets contain the population surfaces, metadata, and data quality layers. The population data surfaces are stored as GeoTIFF files for use in remote sensing or geographic information system (GIS) software. 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.

Population Raster Fiji 2020

Data Input: Settlement footprint generated by combining 2007 household listing locations and Facebook's High-Resolution Population Density Maps (https://data.humdata.org/dataset/fiji-high-resolution-population-density-maps-demographic-estimates).

Population allocated proportionally using 2017 census population counts at enumeration area level.

Year Population Growth Rate of 0.38% has been applied to update population up to 2020

The human settlement footprint with population allocated has been converted into a 100 m resolution raster.

2020 5 years old derived from simple average annual growth rate applied for the three year period at Tikina level, with simplifying assumption that national rates apply evenly (without other input data, it is seen as appropriate for this exercise)

Two different error assessment methods are presented: root mean square error (RMSE), also expressed as a percentage of the mean population size of the administrative level (% RMSE); and the mean absolute error (MAE).Accuracy assessment results for the RF, Afri/AsiaPop, GRUMP and GPW modeling methods for Cambodia, Vietnam and Kenya.

The China assisted living market is experiencing robust growth, driven by an aging population, increasing disposable incomes, and a rising awareness of senior care needs. With a CAGR exceeding 12% since 2019 and a projected market size of (estimated) $XX million in 2025, this sector presents significant investment opportunities. Key drivers include government initiatives promoting senior care infrastructure development, urbanization leading to increased demand for professional care services, and a growing preference for non-family-based living arrangements among older adults. The market is segmented geographically, with Shanghai, Beijing, Chongqing, Wuhan, and Chengdu representing significant hubs of activity. Leading players like China Vanke, Sino-Ocean Group, and Taikang Life are actively shaping the market landscape through strategic investments and service innovations. However, challenges remain, including the high cost of high-quality assisted living, limited availability of skilled professionals, and regional disparities in service provision. Future growth will depend on overcoming these restraints through strategic partnerships, technological advancements (such as telehealth integration), and continuous improvement in service standards to meet the evolving needs of an increasingly aging populace. The forecast period (2025-2033) anticipates continued market expansion, fueled by sustained demographic shifts and government policies that aim to improve the overall quality of senior care. While challenges related to staffing and affordability will persist, innovative service models and technological advancements are expected to mitigate some of these pressures. The expansion into secondary and tertiary cities presents a significant avenue for growth, as demand for assisted living solutions expands beyond the major metropolitan areas. Successful players will be those who can adapt quickly to changing regulatory landscapes, deliver cost-effective and high-quality services, and effectively address the diverse needs of their residents. Recent developments include: In September 2021, the Grand Opening of Lendlease's landmark senior living project in Qingpu, Shanghai, was announced. Ardo Gardens provides a welcoming and well-being-focused environment for seniors to live vibrant and active lives, supported by luxury facilities and the best services., In May 2021, New China Life Insurance Co. Ltd opened a new elderly care community in Beijing's Yanqing district, totaling 280,000 sq. m and 2,000 apartments. The community will provide about 200 long-term apartments tailored for the elderly and 100 short-term guest rooms in the project's first phase, along with entertainment, catering, sports, medical care, social exchange, and wealth management services.. Notable trends are: Increase in Senior Population and Life Expectancy.

Populations of Afro-Palearctic migrant birds have shown severe declines in recent decades. To identify the causes of these declines, accurate measures of both demographic rates (seasonal productivity, apparent survival, immigration) and environmental parameters will allow conservation and research actions to be targeted effectively. We used detailed observations of marked breeding birds from a ‘stronghold’ population of whinchats Saxicola rubetra in England (stable against the declining European trend) to reveal both on-site and external mechanisms that contribute to population change. From field data, a population model was developed based on demographic rates from 2011 to 2014. Observed population trends were compared to the predicted population trends to assess model-accuracy and the influence of outside factors, such as immigration. The sensitivity of the projected population growth rate to relative change in each demographic rate was also explored. Against expectations of high productivity, we identified low seasonal breeding success due to nocturnal predation and low apparent first-year survival, which led to a projected population growth rate (?) of 0.818, indicating a declining trend. However, this trend was not reflected in the census counts, suggesting that high immigration was probably responsible for buffering against this decline. Elasticity analysis indicated ? was most sensitive to changes in adult survival but with covariance between demographic rates accounted for, ? was most sensitive to changes in productivity. Our study demonstrates that high quality breeding habitat can buffer against population decline but high immigration and low productivity will expose even such stronghold populations to potential decline or abandonment if either factor is unsustainable. First-year survival also appeared low, however this result is potentially confounded by high natal dispersal. First-year survival and/or dispersal remains a significant knowledge gap that potentially undermines local solutions aimed at counteracting low productivity.

Land use is likely to be a key driver of population dynamics of species inhabiting anthropogenic landscapes, such as farmlands. Understanding the relationships between land use and variation in population growth rates is therefore critical for the management of many farmland species. Using 24 years of data of a declining farmland bird in an integrated population model, we examined how spatiotemporal variation in land use (defined as habitats with "Short" and "Tall" ground vegetation during the breeding season) and habitat‐specific demographic parameters relates to variation in population growth taking into account individual movements between habitats. We also evaluated contributions to population growth using transient life table response experiments which gives information on contribution of past variation of parameters and real‐time elasticities which suggests future scenarios to change growth rates. LTRE analyses revealed a clear contribution of Short habitats to the annual variation in population growth rate that was mostly due to fledgling recruitment, whereas there was no evidence for a contribution of Tall habitats. Only 18% of the variation in population growth was explained by the modeled local demography, the remaining variation being explained by apparent immigration (i.e., the residual variation). We discuss potential biological and methodological reasons for high contributions of apparent immigration in open populations. In line with LTRE analysis, real‐time elasticity analysis revealed that demographic parameters linked to Short habitats had a stronger potential to influence population growth rate than those of Tall habitats. Most particularly, an increase of the proportion of Short sites occupied by Old breeders could have a distinct positive impact on population growth. High‐quality Short habitats such as grazed pastures have been declining in southern Sweden. Converting low‐quality to high‐quality habitats could therefore change the present negative population trend of this, and other species with similar habitat requirements.

This statistic depicts the predicted change in general physicians in the United States and the adult patient population from 2010 to 2025. In 2015, total generalists are expected to increase by ** percent in the country while the volume of adult patients is expected to increase for ** percent. The urgent care market is projected to grow as it is often a lower cost option, and provides high quality and cost-effective medical care. However, rising costs, aging population, increased population with insurance will all create challenges in this market.

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.