this project graph is : ourworldindata

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For the vast majority of human existence, our global population remained a mere fraction of what it is today. However, the last few centuries have borne witness to an extraordinary transformation in human demography. In the year 1800, the global population stood at a modest one billion individuals. Fast forward to the present day, and we find ourselves amidst a staggering figure of over 8 billion people inhabiting our planet.

Yet, despite this exponential growth trajectory, demographers now project a fascinating shift on the horizon: the expectation that global population growth will plateau by the close of this century.

Within the vast repository of Our World in Data, we delve deeply into the intricacies of population dynamics, offering a comprehensive array of data, charts, and analyses elucidating the nuanced changes in population growth. From the geographical distribution of populations to temporal shifts and future projections, our platform serves as a rich tapestry of insights into this paramount aspect of human civilization.

One of the most illuminating tools at our disposal is the population cartogram—a unique visualization method that transcends traditional geographical maps to provide a more accurate depiction of global population distribution. Unlike conventional maps, which delineate territories based solely on landmass, population cartograms offer a perspective where countries are resized according to their respective populations.

In our exploration of the population cartogram for the year 2018, we uncover a myriad of revelations. Small nations characterized by high population densities manifest as enlarged entities, accentuating their significance on the global stage. Bangladesh, Taiwan, and the Netherlands emerge prominently, their amplified proportions underscoring their demographic density. Conversely, vast territories with comparatively sparse populations undergo a visual reduction in size. Countries like Canada, Mongolia, Australia, and Russia, despite their expansive landmasses, shrink in relative stature, highlighting the intriguing interplay between territory and population.

This innovative approach to mapping not only challenges conventional perceptions but also provides invaluable insights into the complex mosaic of human settlement patterns and demographic trends. By transcending the limitations of traditional cartography, population cartograms offer a nuanced lens through which to perceive the evolving dynamics of our global community.

To delve deeper into the nuances of this population cartogram and its implications, we invite you to explore our comprehensive article dedicated to this fascinating subject. Within its pages, you will find a detailed analysis, accompanied by captivating visuals and insightful commentary, elucidating the significance of population cartograms in understanding our world.

At Our World in Data, we remain committed to unraveling the complexities of global population dynamics, offering a platform that fosters informed discourse and deepens our understanding of the forces shaping our collective future. Join us on this illuminating journey as we navigate the ever-changing landscape of human demography, charting a course towards a more enlightened tomorrow.

Abstract Challenges in the field of demographic projections include, among others, the volatility of the migration component - critical for the projection of small areas; the compatibility between projections of small and large areas; and the measurement and inclusion of uncertainty in future scenarios of population growth. This article presents a new probabilistic method to conduct interregional population forecasting dealing with these three challenges. The proposed method has the following advantages: 1) it only requires information about the last place of residence and the population distributions of the last two Censuses; 2) it generates confidence intervals for the projected populations; 3) it makes the role of migration flows in the growth dynamics explicit and; 4) it facilitates the elaboration of counterfactual scenarios and sensitivity analysis using matrices of interregional population growth and distribution. We describe the patterns and trends in migration flows in the state of São Paulo applying spatial visualization tools and identifying areas in which migration is responsible for considerable shares of the demographic dynamics. About 95% of the 572 municipal projected populations of São Paulo had good precision and were within expected confidence intervals. We used data from the 1980, 1991 and 2000 Brazilian Censuses.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

A central challenge in applied ecology is understanding the effect of anthropogenic fatalities on wildlife populations and predicting which populations may be particularly vulnerable and in greatest need of management attention. We used 3 approaches to investigate potential effects of fatalities from collisions with wind turbines on 14 raptor species for both current (106 GW) and anticipated future (241 GW) levels of installed wind energy capacity in the United States. Our goals were to identify species at relatively high vs low risk of experiencing population declines from turbine collisions and to also compare results generated from these approaches. Two of the approaches used a calculated turbine-caused mortality rate to decrement population growth, where population trends were derived either from the North American Breeding Bird Survey or a matrix model parameterized from literature-derived demographic values. The third approach was potential biological removal, which estimates the number of fatalities that allow a population to reach and maintain its optimal sustainable population set by management objectives

Rapid environmental change presents a significant challenge to the persistence of natural populations. Rapid adaptation that increases population growth, enabling populations that declined following severe environmental change to grow and avoid extinction, is called evolutionary rescue. Numerous studies have shown that evolutionary rescue can indeed prevent extinction. Here, we extend those results by considering the demographic history of populations. To evaluate how demographic history influences evolutionary rescue, we created 80 populations of red flour beetle, Tribolium castaneum, with three classes of demographic history: diverse populations that did not experience a bottleneck, and populations that experienced either an intermediate or a strong bottleneck. We subjected these populations to a new and challenging environment for six discrete generations and tracked extinction and population size. Populations that did not experi...

Density dependence only affects increase rates in baleen whale populations at high abundance levels

Overview of files hosted on Dryad

Each file contains a time series of catch records by species/population and location, with two columns: 1. year, and 2. catch number.

Models

"StanSimulation0.stan" (Stan code for the logistic model with fixed z =2.39)

"StanSimulation.stan" (Stan code for the logistic model with estimating z value)

Overview of files hosted on Zenodo

Abundance time-series

"Abund.Bowhead.csv" (Abundance data for bowhead whales)

"Abund.GR.csv" (Abundance data for gray whales)

"Abund.Hbk.csv" (Abundance data for USWC humpback whales)

"Abund.HbkAusNoad2019.csv" (Abundance data for EAUS humpback whales)

"Abund.HbkAusSpueBb.csv" (Relative abundance data for EAUS humpback whales)

"Abund.HbkAusSpueppc.csv" (Relative abundance data for EAUS humpback whales)

"Abund.HbkAusSpuesol.csv" (Relative abundance data for EAUS humpback whales)

Descrip...

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

https://i.imgur.com/PcSDv8A.png" alt="Imgur">

Overview

The dataset provided here is a rich compilation of various data files gathered to support diverse analytical challenges and education in data science. It is especially curated to provide researchers, data enthusiasts, and students with real-world data across different domains, including biostatistics, travel, real estate, sports, media viewership, and more.

Files

Below is a brief overview of what each CSV file contains: - Addresses: Practical examples of string manipulation and address data formatting in CSV. - Air Travel: Historical dataset suitable for analyzing trends in air travel over a period of three years. - Biostats: A dataset of office workers' biometrics, ideal for introductory statistics and biology. - Cities: Geographic and administrative data for urban analysis or socio-demographic studies. - Car Crashes in Catalonia: Weekly traffic accident data from Catalonia, providing a base for public policy research. - De Niro's Film Ratings: Analyze trends in film ratings over time with this entertainment-focused dataset. - Ford Escort Sales: Pre-owned vehicle sales data, perfect for regression analysis or price prediction models. - Old Faithful Geyser: Geological data for pattern recognition and prediction in natural phenomena. - Freshman Year Weights and BMIs: Dataset depicting weight and BMI changes for health and lifestyle studies. - Grades: Education performance data which can be correlated with demographics or study patterns. - Home Sales: A dataset reflecting the housing market dynamics, useful for economic analysis or real estate appraisal. - Hooke's Law Demonstration: Physics data illustrating the classic principle of elasticity in springs. - Hurricanes and Storm Data: Climate data on hurricane and storm frequency for environmental risk assessments. - Height and Weight Measurements: Public health research dataset on anthropometric data. - Lead Shot Specs: Detailed engineering data for material sciences and manufacturing studies. - Alphabet Letter Frequency: Text analysis dataset for frequency distribution studies in large text samples. - MLB Player Statistics: Comprehensive athletic data set for analysis of performance metrics in sports. - MLB Teams' Seasonal Performance: A dataset combining financial and sports performance data from the 2012 MLB season. - TV News Viewership: Media consumption data which can be used to analyze viewing patterns and trends. - Historical Nile Flood Data: A unique environmental dataset for historical trend analysis in flood levels. - Oscar Winner Ages: A dataset to explore age trends among Oscar-winning actors and actresses. - Snakes and Ladders Statistics: Data from the game outcomes useful in studying probability and game theory. - Tallahassee Cab Fares: Price modeling data from the real-world pricing of taxi services. - Taxable Goods Data: A snapshot of economic data concerning taxation impact on prices. - Tree Measurements: Ecological and environmental science data related to tree growth and forest management. - Real Estate Prices from Zillow: Market analysis dataset for those interested in housing price determinants.

Format

The enclosed data respect the comma-separated values (CSV) file format standards, ensuring compatibility with most data processing libraries in Python, R, and other languages. The datasets are ready for import into Jupyter notebooks, RStudio, or any other integrated development environment (IDE) used for data science.

Quality Assurance

The data is pre-checked for common issues such as missing values, duplicate records, and inconsistent entries, offering a clean and reliable dataset for various analytical exercises. With initial header lines in some CSV files, users can easily identify dataset fields and start their analysis without additional data cleaning for headers.

Acknowledgements

The dataset adheres to the GNU LGPL license, making it freely available for modification and distribution, provided that the original source is cited. This opens up possibilities for educators to integrate real-world data into curricula, researchers to validate models against diverse datasets, and practitioners to refine their analytical skills with hands-on data.

This dataset has been compiled from https://people.sc.fsu.edu/~jburkardt/data/csv/csv.html, with gratitude to the authors and maintainers for their dedication to providing open data resources for educational and research purposes. https://i.imgur.com/HOtyghv.png" alt="Imgur">

Southern Whale Sanctuary Declaration - Covers the recent global approval of a southern whale sanctuary—anchored in Mexico—and the momentum among Pacific nations to support whale conservation. Tuvalu’s Population Growth Strategy - Focuses on Tuvalu’s rising population and the potential environmental and public-health challenges it poses. The country’s response includes drafting a comprehensive national population-growth management strategy. Solomon Islands Logging Tax - Reports on the Solomon Islands government's introduction of a new tax targeting log exports that exceed sustainable yield. The measure is designed to give the government legal leverage to curb overharvesting and protect forests.Call Number: [EL]Physical Description: 23:16

The Earth′s climate is warming, especially in the mid- and high latitudes of the Northern Hemisphere. The northern elephant seal (Mirounga angustirostris) breeds and haul-outs on islands and the mainland of Baja California, Mexico, and California, U.S.A. At the beginning of the 21st century, numbers of elephant seals in California are increasing, but the status of Baja California populations is unknown, and some data suggest they may be decreasing. We hypothesize that the elephant seal population of Baja California is experiencing a decline because the animals are not migrating as far south due to warming sea and air temperatures. Here we assessed population trends of the Baja California population, and climate change in the region. The numbers of northern elephant seals in Baja California colonies have been decreasing since the 1990s, and both the surface waters off Baja California and the local air temperatures have warmed during the last three decades. We propose that declining population sizes may be attributable to decreased migration towards the southern portions of the range in response to the observed temperature increases. Further research is needed to confirm our hypothesis; however, if true, it would imply that elephant seal colonies of Baja California and California are not demographically isolated which would pose challenges to environmental and management policies between Mexico and the United States.

Timor-Leste experienced a fundamental social and economic upheaval after its people voted for independence from Indonesia in a referendum in August 1999. Population was displaced, and public and private infrastructure was destroyed or rendered inoperable. Soon after the violence ceased, the country began rebuilding itself with the support from UN agencies, the international donor community and NGOs. The government laid out a National Development Plan (NDP) with two central goals: to promote rapid, equitable and sustainable economic growth and to reduce poverty. Formulating a national plan and poverty reduction strategy required data on poverty and living standards, and given the profound changes experienced, new data collection had to be undertaken to accurately assess the living conditions in the country. The Planning Commission of the Timor-Leste Transitional Authority undertook a Poverty Assessment Project along with the World Bank, the Asian Development Bank, the United Nations Development Programme and the Japanese International Cooperation Agency (JICA). This project comprised three data collection activities on different aspects of living standards, which taken together, provide a comprehensive picture of well-being in Timor-Leste. The first component was the Suco Survey, which is a census of all 498 sucos (villages) in the country. It provides an inventory of existing social and physical infrastructure and of the economic characteristics of each suco, in addition to aldeia (hamlet) level population figures. It was carried out between February and April 2001. A second element was the Timor-Leste Living Standards Measurement Survey (TLSS). This is a household survey with a nationally representative sample of 1,800 families from 100 sucos. It was designed to diagnose the extent, nature and causes of poverty, and to analyze policy options facing the country. It assembles comprehensive information on household demographics, housing and assets, household expenditures and some components of income, agriculture, labor market data, basic health and education, subjective perceptions of poverty and social capital. Data collection was undertaken between end August and November 2001. The final component was the Participatory Potential Assessment (PPA), which is a qualitative community survey in 48 aldeias in the 13 districts of the country to take stock of their assets, skills and strengths, identify the main challenges and priorities, and formulate strategies for tackling these within their communities. It was completed between November 2001 and January 2002.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Historical chart and dataset showing total population for India by year from 1950 to 2025.

The power of genome-wide association studies (GWAS) relies heavily on the sample size. A strategy to increase sample size is to combine datasets from different populations. However, this approach introduces challenges due to heterogeneity between populations. With this data, we set up a statistically sound model to account for such heterogeneity. Using this model, we combined up to four distinct barley populations in GWAS to detect genomic regions associated with heading date and stem lodging. Each population represented an applied breeding program with unique combinations of growth habit (winter versus spring) and row type (2-rowed versus 6-rowed). By comparing single-population GWAS with multi-population GWAS, we identified both quantitative trait loci (QTLs) that were shared across populations and population-specific QTLs. We found that multi-population GWAS provided greater statistical power than single-population analyses, revealed QTLs that were undetectable in small populations, and explained an overall larger proportion of the phenotypic variance. Our findings offer a promising approach to accelerate genomics-based breeding in new breeding populations with limited data. This methodology is applicable to a wide range of datasets where sample sizes are limited for various reasons.

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

This brief outlines the socioeconomic implications of the aging population of the People's Republic of China. Hazards of population aging, and China’s position regarding aging are discussed. The challenges ahead are then outlined: sustaining inclusive economic growth, improving mobility and quality of the labour force, and strengthening safety nets. The brief concludes with policy directions for the PRC.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

Large-scale disturbances, such as megafires, motivate restoration at equally large extents. Measuring the survival and growth of individual plants plays a key role in current efforts to monitor restoration success. However, the scale of modern restoration (e.g., >10,000 ha) challenges measurements of demographic rates with field data. In this study, we demonstrate how unoccupied aerial system (UAS) flights can provide an efficient solution to the tradeoff of precision and spatial extent in detecting demographic rates from the air. We flew two, sequential UAS flights at two sagebrush (Artemisia tridentata) common gardens to measure the survival and growth of individual plants. The accuracy of Bayesian-optimized segmentation of individual shrub canopies was high (73–95%, depending on the year and site), and remotely sensed survival estimates were within 10% of ground-truthed survival censuses. Stand age structure affected remotely sensed estimates of growth; growth was overestimated relative to field-based estimates by 57% in the first garden with older stands, but agreement was high in the second garden with younger stands. Further, younger stands (similar to those just after disturbance) with shorter, smaller plants were sometimes confused with other shrub species and bunchgrasses, demonstrating a need for integrating spectral classification approaches that are increasingly available on affordable UAS platforms. The older stand had several merged canopies, which led to an underestimation of abundance but did not bias remotely sensed survival estimates. Advances in segmentation and UAS structure from motion photogrammetry will enable demographic rate measurements at management-relevant extents.