Provides data visualizations for demographic change over the last 25 years in 43 Alaska villages

A collection of population life tables covering a multitude of countries and many years. Most of the HLD life tables are life tables for national populations, which have been officially published by national statistical offices. Some of the HLD life tables refer to certain regional or ethnic sub-populations within countries. Parts of the HLD life tables are non-official life tables produced by researchers. Life tables describe the extent to which a generation of people (i.e. life table cohort) dies off with age. Life tables are the most ancient and important tool in demography. They are widely used for descriptive and analytical purposes in demography, public health, epidemiology, population geography, biology and many other branches of science. HLD includes the following types of data: * complete life tables in text format; * abridged life tables in text format; * references to statistical publications and other data sources; * scanned copies of the original life tables as they were published. Three scientific institutions are jointly developing the HLD: the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, the Department of Demography at the University of California at Berkeley, USA and the Institut national d''��tudes d��mographiques (INED) in Paris, France. The MPIDR is responsible for maintaining the database.

This supporting document should allow one to recreate the analysis performed as part of “The optimal species richness environments for human populations, ” By Freeman et al. 2018 Submitted to PNAS July 2018. The annotated scripts in this directory (RichnessSIScripts.pdf) contain code to replicate the analysis, as well as code for additional analyses not included in the main paper or supplemental information. To replicate the analysis, one can either analyze the data files provided or build their own data set. As discussed in the main body of the text, we built three data sets following the procedures outlined by Tallavaara et al. (2017) for linking species richness values, net primary productivity and pathogen stress to each ethnographic case. We do not replicate the scripts provided by Tallavaara et al.(2017) as these are available, clear and should be cited when used.To replicate our analysis, one needs to set their working directory in R to the file location that contains the data files. There are 11 files that follow the naming convention “name.csv.” The 11 files are “MainFinal.csv”. “AGPOP3Eco.csv”, “HGFEM4R.csv”, “AGPOPClass.csv”, “CountryMeansEco2.csv”, “AGPOP3EcoH.csv”, “AGPOP3EcoL.csv”, “HiHG.csv”, “LowHG,csv”, “CountryMeansEco2H.csv”, and “CountryMeansEco2L.csv”. The first five files are the main files, the second six files are divided into high and low species richness environments by economy type for convenience. In each file, the variables are defined as follows:

1. Group/Country–name of the ethnographic society of country

2. Latitude–the latitude at the geographic center of a group’s territory or a country’s territory.

3. Longitude–the longitude at the geographic center of a group’s territory or a country’s territory.

4. Class–an ordinal ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details)

5. Class2–an binary ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details).

6. ECI–The average economic complexity index since 1973 as measured among modern countries.

7. DENSITY–Population density in people per square kilometer. This is a point in time estimatefor hunter-gatherer and agricultural groups and an average density since 1973 among nation states.

8. LnDENSITY–The natural log of population density

9. npp–net primary productivity estimated at the center of each group’s territory

10. npp2-Net primary productivity squared

11. biodiv–Standardized estimate of species richness at the center of each group’s range.

12. biodiv2–Species richness *100 ad squared.

13. pathos–Index of pathogen stress at the center of a group’s territory.

14. DivDiff–The absolute value of species richness-the species richness value of peak population density (values identified in Fig. 2 of the main manuscript).1

5. ID–A nominal variable that denotes economy type. HG=hunter-gatherer, AG=subsistence agriculturalist, IND=modern nation state

Tallavaara, M., J. T. Eronen, and M. Luoto2017. Supporting data and script for ”productivity, biodiversity, and pathogens influence the global hunter-gatherer population density” (Tallavaara et al. pnas 2018).https://doi.org/10.5281/zenodo.1167852

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 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 live 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 decade 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 dataset summarizes land protection, conservation prioritization layer scores, and human demographics within New England communities, defined as census tracts. This dataset was created to identify disparities in land protection according to metrics of social marginalization and assess how incorporating environmental justice criteria into land conservation prioritization systems might change conservation priorities.

This map is for human geography classrooms and tied to the AP benchmarks. Learn more about GeoInquiries at www.esri.com/geoinquiries

The Gridded Population of the World, Version 4 (GPWv4): Basic Demographic Characteristics, Revision 11 consists of estimates of human population by age and sex as counts (number of persons per pixel) and densities (number of persons per square kilometer), consistent with national censuses and population registers, for the year 2010. To estimate the male and female populations by age in 2010, the proportions of males and females in each 5-year age group from ages 0-4 to ages 85+ for the given census year were calculated. These proportions were then applied to the 2010 estimates of the total population to obtain 2010 estimates of male and female populations by age. In some cases, the spatial resolution of the age and sex proportions was coarser than the resolution of the total population estimates to which they were applied. The population density rasters were created by dividing the population count rasters by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions.

Digital data of Administrative Boundaries of Kathmandu Valley:

• Districts and Village Development Committee from 1997 map.

• Demographic data from 1991 census

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This table summarizes the observed patterns of sex-specific differences in demographic parameters reported in a number of recent studies. The first column lists the location of the sampled populations, or indicates whether the study is conducted at a global scale. The second column gives the markers used, and the third column indicates the statistical methods employed. The fourth column provides indications on social organization, available a priori for the populations under study. In the fifth and sixth columns, the authors' interpretations of sex-specific differences in demographic parameters are given, with respect to skewed gene flow and/or effective numbers.aIndications on social organization, marriage rules, etc., as provided by the authors.bThe differences in demographic parameters between males and females, as inferred by the authors, are given in terms of sex-biased gene flow, and skewed effective numbers; the authors' interpretation to the observed pattern is given in parentheses, when available.cSingle nucleotide polymorphisms.dAnalysis of molecular variance [69].eNot available (no detailed information given by the authors concerning social organization, marriage rules, etc.).fShort tandem repeats.gTime to the most recent common ancestor.hmtDNA and NRY were not sampled in the same individuals or populations.iThe authors discussed a possible difference in demographic parameters between males and females, but considered it as negligible.jThe authors did not consider this pattern.kFood-producer populations.lHunter-gatherer populations.mMonte Carlo Markov chain method to estimate population sizes and migration rates [70].nVariance in Reproductive Success.opopulation-mutation parameter.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

This transformed view of Employee Demographics - Public dataset counts the number of and percentage of city employees by race as self-reported by employee based on EEOC classification. This information is used by "City Employee vs. Community Demographics dataset" at https://citydata.mesaaz.gov/Economic-Development/Chart-Data-for-City-Employee-vs-Community-Demograp/bt2n-zimw

Full country-level estimates of kin availability. We include two datasets in csv format. The file "table_data_agg.csv" shows estimates by type of kin, country, year, and age of Focal (by five-year groups). The file "table_data_desagg.csv" shows estimates by type of kin, country, year, age of Focal, and age of kin (by five-year groups). Column names are self-explanatory, except for the column 'Variant.' This takes a value of 'Estimate' for all years before 2020 (i.e., years for which the estimates rely on historical demographic rate data). For projected years (i.e., after 2020), the column takes the value 'median_living' for the median of the 1000 projection trajectories; 'ci_low_living' and 'ci_upp_living' represent the lower and upper 80% projection intervals, respectively.

In more than a decade of research on fertility in the Russian Empire and the Soviet Union, the Office of Population Research at Princeton University has assembled a large inventory of quantitative information. Some of the data from early years are handwritten; others exist only in computer printouts. Much of the material was not included in published results for one reason or another. For the convenience of other researchers interested in the population of Russia, selected primary data have been put in machine-readable form.

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.

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Estimated density of people per grid-cell, approximately 1km (0.008333 degrees) resolution. The units are number of people per Km² per pixel, expressed as unit: "ppl/Km²". The mapping approach is Random Forest-based dasymetric redistribution. The WorldPop project was initiated in October 2013 to combine the AfriPop, AsiaPop and AmeriPop population mapping projects. It aims to provide an open access archive of spatial demographic datasets for Central and South America, Africa and Asia to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and peer-reviewed methods to produce easily updatable maps with accompanying metadata and measures of uncertainty. Acknowledgements information at https://www.worldpop.org/acknowledgements

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

One of the central puzzles in the study of sociocultural evolution is how and why transitions from small-scale human groups to large-scale, hierarchically more complex ones occurred. Here we develop a spatially explicit agent-based model as a first step towards understanding the ecological dynamics of small and large-scale human groups. By analogy with the interactions between single-celled and multicellular organisms, we build a theory of group lifecycles as an emergent property of single cell demographic and expansion behaviours. We find that once the transition from small-scale to large-scale groups occurs, a few large-scale groups continue expanding while small-scale groups gradually become scarcer, and large-scale groups become larger in size and fewer in number over time. Demographic and expansion behaviours of groups are largely influenced by the distribution and availability of resources. Our results conform to a pattern of human political change in which religions and nation st...

The Global Demographic Data collection holds global gridded data products describing demographic information and water demand in relation to population data. Currently, water demand data are being distributed; population data will be added in the near future.

Country-level urban, rural and total population estimate data from World Resources Institute (WRI) for the years 1985, 1995, and 2025 were gridded by the University of New Hampshire's Water Systems Analysis Groupusing methods outlined in Vorosmarty et al. (2000) for use in estimating global water resources based on climate and population changes.

Currently available are five relative water demand (RWD) fraction data sets/ maps, produced by Vorosmarty et al. in their analysis of future water resources. The relative water demand is defined to be the total volume of water used either domestically, industrially or agriculturally (DIA) divided by the water discharge (Q). "Values of .2 to .4 indicate medium to high stress." (see Vorosmarty et al., 2000) This analysis deals only with sustainable water sources, and does not look at nonsustainable water sources, such a ground water mining. The RWD is computed on a .5 by .5 degree grid for two sentinel years: 1985 and 2025, which are two of the data sets. The ratio of the RWD for these two years provides a measure of change under scenarios of climate change only, population change only and the combination of climate change and population to produce the other three datasets. The ratio RWD values is relative to the RWD in the base year, 1985.