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.

In 2023, there were five countries, where the average woman of childbearing age can expect to have over six children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of 6.13 and 6.12 children per woman, Somalia and Chad were the countries with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

Regional GAM Species Counts

Observing Animal Species Abundance and Distribution

By [source]

About this dataset

This dataset captures detailed information about the abundance and distribution of multiple animal species in different parts of the Regional GAM network. By analyzing this data, researchers gain valuable insight into species trends over time, species population growth or decline, seasonal migration patterns, and other important ecological patterns. Moreover, this dataset helps us to understand risks associated with animal populations and ecosystems; aiding decision-making related to land use for conservation and sustainability initiatives. This data provides an easily accessible resource for monitoring changes in animals' ranges and distributions across the region – enabling powerful analysis which can inform sound management decisions to promote conservation efforts. In sum, this dataset holds great promise for scientists seeking an improved understanding of wildlife dynamics; making it a powerful tool for both monitoring biodiversity in our changing world as well as informing proactive management strategies that will ultimately help keep our planet healthy into the future

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How to use the dataset

This dataset contains information about animal species and their occurrence per site, which can be used to gain insights into species abundance and distribution in the Regional GAM network. This data can help researchers analyze species trends, population growth or decline, animal migrations, and other important ecological factors.

Users of this dataset can analyze the presence or absence of a particular species in different sites across the region, as well as their abundance by counting individual sightings. Additionally, by combining datasets such as those contained in this one with other environmental factors (e.g., water levels), users can gain further insight into animals’ behavior and ecology within any given location over time.

The following steps outline how to use this dataset to analyze animal populations: - Download all necessary files from Kaggle for your analysis - Use an online tool such as Pandas or RStudio to extract desired data from each file into one unified table - Select relevant columns for your analysis (e.g., Species Name, Location/Site Name), specify date ranges if necessary and arrange them in an easily readable manner using sorting tools within the software program you’re using
- Filter entries related to a certain period of time (e.g., last 7 days), location or unique combination of both if needed 5) Choose appropriate chart or graph types depending on what kind of data you want to present visually 6) Finally plot/display your findings on a map / basis plot / 3D-model / etc…for best clarity

This dataset provides valuable insight into environmental conditions which may affect wildlife behavior. By following these simple steps researchers should be able visualize trends associated with certain areas over periods of time allowing them better understand how animal populations are affected by land-use decisions and climate change among others!

Research Ideas

• Species Conservation: This data set can be used to assess the health of a species' population in a particular region and how this varies over time. Researchers can use data trends to identify declining populations and areas of conservation needs, allowing them to create appropriate management plans focused on species protection.
• Wildlife Monitoring: Observing the species count at different sites can provide researchers with an insight into animal behavior, migration patterns and habitat usage which in turn informs wildlife management plans.
• Climate Change: By assessing population changes over time, researchers can use this dataset to explore how climate change is impacting specific animal populations and inform conservation initiatives accordingly/

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Dataset multispecies Regional GAM.csv | Column name | Description ...

Global Population of the World (GPW) translates census population data to a latitude-longitude grid so that population data may be used in cross-disciplinary studies. There are three data files with this data set for the reference years 1990 and 1995. Over 127,000 administrative units and population counts were collected and integrated from various sources to create the gridded data. In brief, GPW was created using the following steps:

* Population data were estimated for the product reference years, 1990 and 1995, either by the data source or by interpolating or extrapolating the given estimates for other years.
* Additional population estimates were created by adjusting the source population data to match UN national population estimates for the reference years.
* Borders and coastlines of the spatial data were matched to the Digital Chart of the World where appropriate and lakes from the Digital Chart of the World were added.
* The resulting data were then transformed into grids of UN-adjusted and unadjusted population counts for the reference years.
* Grids containing the area of administrative boundary data in each cell (net of lakes) were created and used with the count grids to produce population densities.

As with any global data set based on multiple data sources, the spatial and attribute precision of GPW is variable. The level of detail and accuracy, both in time and space, vary among the countries for which data were obtained.

Participation rate in education, population aged 18 to 34, by age group and type of institution attended, Canada, provinces and territories. This table is included in Section E: Transitions and outcomes: Transitions to postsecondary education of the Pan Canadian Education Indicators Program (PCEIP). PCEIP draws from a wide variety of data sources to provide information on the school-age population, elementary, secondary and postsecondary education, transitions, and labour market outcomes. The program presents indicators for all of Canada, the provinces, the territories, as well as selected international comparisons and comparisons over time. PCEIP is an ongoing initiative of the Canadian Education Statistics Council, a partnership between Statistics Canada and the Council of Ministers of Education, Canada that provides a set of statistical measures on education systems in Canada.

Eurostat’s annual data collections on demographic statistics are structured as follows:

NOWCAST: Annual data collection on provisional monthly data on live births and deaths covering at least six months of the reference year (Article 4.3 of the Commission implementing regulation (EU) No 205/2014).

DEMOBAL (Demographic balance): Annual data collection on provisional data on population, total live births and total deaths at national level (Article 4.1 of the Commission implementing regulation (EU) No 205/2014).

POPSTAT (Population Statistics): The most in-depth annual national and regional demographic and migration data collection. The data relate to populations, births, deaths, immigrants, emigrants, marriages and divorces, and is broken down into several categories (Article 3 of Regulation (EU) No 1260/2013 and Article 3 of Regulation (EC) No 862/2007).

The aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate - legal acts and other agreements’.

The completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes. For more information on mandatory/voluntary data collection, see 6.1. Institutional mandate - legal acts and other agreements’.

The following statistics on deaths are collected from the National Statistical Institutes:

• Deaths by month of occurrence
• Deaths by age, year of birth, sex and by:
  • Region (NUTS 2) of residence;
  • Region (NUTS 3) of residence;
  • Country of birth;
  • Country of citizenship;
  • Legal marital status;
  • Educational attainment (ISCED 2011).
• Infant deaths by age and sex;
• Infant deaths by parents' level of educational attainment (ISCED);
• Late foetal deaths by mother's age.

Statistics on mortality: based on the different breakdowns of data on deaths received, Eurostat produces the following:

• Statistics available in the online table Population change - Demographic balance and crude rates at national level (demo_gind):
  • Natural change of population, crude death rate.
• Statistics available in the online table Infant mortality rates (demo_find):
  • Infant mortality rate;
  • Neonatal mortality rate;
  • Early neonatal mortality rate;
  • Late foetal mortality rate;
  • Perinatal mortality rate.
• Life table (demo_mlifetable);
• Life expectancy by age and sex (demo_mlexpec);
• Life expectancy by age, sex and educational attainment (ISCED 2011) (demo_mlexpecedu).

https://ec.europa.eu/eurostat/cache/metadata/en/demo_r_gind3_esms.htm" target="_self">Information about statistics on deaths by NUTS regions.

This dataset is the precipitation and population change trend and correlation analysis data of China from 2001 to 2020, including the division of arid and humid areas by precipitation data from 2001 to 2023, two dry and wet division methods, and the east and west areas of the Hu Huanyong Line. China's elevation is divided into four categories of altitude at 1000 meters, 2000 meters, and 3000 meters. Most of the data is processed by QGIS, and the other part is implemented by python code. Origin 2024 is used to carry out trend analysis and correlation analysis, and the table data is stored in excel and csv formats. The schematic diagram is in eps format.

Annual population projections, from 2024 to 2051. These datasets include population projections by age and gender organized by geography: * Projections for Ontario * Projections for each of the 6 regions * Projections for each of the 49 census divisions * Projections for each of the 34 public health units * Projections for each of the 9 Ministry of Children, Community and Social Services’ Service Delivery Division (SDD) regions For Ontario only, the projected annual components of demographic change are provided for the reference, low- and high-growth scenarios. For all other geographies, only the reference scenario is produced.

During a January 2024 global survey among marketers, nearly 60 percent reported plans to increase their organic use of YouTube for marketing purposes in the following 12 months. LinkedIn and Instagram followed, respectively mentioned by 57 and 56 percent of the respondents intending to use them more. According to the same survey, Facebook was the most important social media platform for marketers worldwide.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for low oil and gas development, high population size, and no climate component. The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

PART I: Distribution table: Interval Frequency Cumulative Frequency Percentage distribution Cumulative percentage distribution 10-12 2 2 13.33 13.33 12.1-14 5 7 33.33 46.66 14.1-16 8 15 53.33 99.99 16.1-18 0 15 0 99.99

18.1 0 15 0 99.99

Majority of the countries, eight, fall in the 14.1-16 category. Five countries fall in the 12.1-14 category and two countries in the 10-12 bin. The remaining categories have zero entries. This means the data does not follow a normal distribution since most of the countries are concentrated at the highest peak. This data could be better visualized in a histogram.

Frequency distribution with revised interval: Interval Frequency Cumulative Frequency Percentage Frequency Cumulative percentage <12 2 2 13.33 13.33 12-12.9 1 3 6.67 20 13-13.9 4 7 26.67 46.67 14-14.9 4 11 26.67 73.34 15-15.9 3 14 20 93.34 16-16.9 1 15 6.67 100.01 17-17.9 0 15 0 100.01

18 0 15 0 100.01 Eight countries have between 14% and 18% of their population above age 65. The number of countries with 14% - 18% of their population above 65 years remain the same even after revising the interval. The percentage of countries that have between 14-18 percent of their population above age 65 is 53.33%.

PART II Q1. Time series chart for divorce rate in Netherlands

Q2. Describe divorce rate in Netherlands before and after 1970. There is a decline in divorce rate between 1950 and 1960. There is a moderate rise in divorce rate between 1960 and 1970, the rate steadily rises between 1970 and 1980 and thereafter exhibits a slight decline between 1980 and 1990. The rate shifts to a declining trend after the year 2000. The decline does not indicate negative number of divorces, this could be attributed to increased population size and fewer number of divorce cases filed. Q3. A bar graph would best display the divorce rate for each year, hence easy comparison. Q4. Bar graph The highest number of divorce cases were recorded in the year 2000, while the least number was observed in 1960.

Set 2: Show how different elements contributed to population change in 2018

Immigration contributed 34 percent of the change in population; births, Emigration, and deaths contributed almost equal change in population.

Q2. Elements of population growth

Immigration contributed the largest change in population growth compared to birth.

Q3. A time series to show changes in male and female population

Both populations show an increasing trend over the 4 years. We could also conclude there are more females than males in the country’s population.

According to the age distribution of China's population in 2024, approximately 68.6 percent of the population were in their working age between 15 and 64 years of age. Retirees aged 65 years and above made up about 15.6 percent of the total population. Age distribution in China As can be seen from this statistic, the age pyramid in China has been gradually shifting towards older demographics during the past decade. Mainly due to low birth rates in China, the age group of 0 to 14 year-olds has remained at around 16 to 17 percent since 2010, whereas the age groups 65 years and over have seen growth of nearly seven percentage points. Thus, the median age of the Chinese population has been constantly rising since 1970 and is forecast to reach 52 years by 2050. Accompanied by a slightly growing mortality rate of more than 7 per thousand, China is showing strong signs of an aging population. China's aging society The impact of this severe change in demographics is the subject of an ongoing scientific discussion. Rising standards of living in China contain the demand for better health care and pension insurance for retirees, which will be hard to meet with the social insurance system in China still being in its infancy. Per capita expenditure on medical care and services of urban households has grown more than ninefold since 2000 with a clear and distinctive upward trend for the near future. As for social security spending, public pension expenditure is forecast to take up approximately nine percent of China's GDP by 2050.

Prior to 1829, the area of modern day Greece was largely under the control of the Ottoman Empire. In 1821, the Greeks declared their independence from the Ottomans, and achieved it within 8 years through the Greek War of Independence. The Independent Kingdom of Greece was established in 1829 and made up the southern half of present-day, mainland Greece, along with some Mediterranean islands. Over the next century, Greece's borders would expand and readjust drastically, through a number of conflicts and diplomatic agreements; therefore the population of Greece within those political borders** was much lower than the population in what would be today's borders. As there were large communities of ethnic Greeks living in neighboring countries during this time, particularly in Turkey, and the data presented here does not show the full extent of the First World War, Spanish Flu Pandemic and Greko-Turkish War on these Greek populations. While it is difficult to separate the fatalities from each of these events, it is estimated that between 500,000 and 900,000 ethnic Greeks died at the hands of the Ottomans between the years 1914 and 1923, and approximately 150,000 died due to the 1918 flu pandemic. These years also saw the exchange of up to one million Orthodox Christians from Turkey to Greece, and several hundred thousand Muslims from Greece to Turkey; this exchange is one reason why Greece's total population did not change drastically, despite the genocide, displacement and demographic upheaval of the 1910s and 1920s. Greece in WWII A new Hellenic Republic was established in 1924, which saw a decade of peace and modernization in Greece, however this was short lived. The Greek monarchy was reintroduced in 1935, and the prime minister, Ioannis Metaxas, headed a totalitarian government that remained in place until the Second World War. Metaxas tried to maintain Greek neutrality as the war began, however Italy's invasion of the Balkans made this impossible, and the Italian army tried invading Greece via Albania in 1940. The outnumbered and lesser-equipped Greek forces were able to hold off the Italian invasion and then push them backwards into Albania, marking the first Allied victory in the war. Following a series of Italian failures, Greece was eventually overrun when Hitler launched a German and Bulgarian invasion in April 1941, taking Athens within three weeks. Germany's involvement in Greece meant that Hitler's planned invasion of the Soviet Union was delayed, and Hitler cited this as the reason for it's failure (although most historians disagree with this). Over the course of the war approximately eight to eleven percent of the Greek population died due to fighting, extermination, starvation and disease; including over eighty percent of Greece's Jewish population in the Holocaust. Following the liberation of Greece in 1944, the country was then plunged into a civil war (the first major conflict of the Cold War), which lasted until 1949, and saw the British and American-supported government fight with Greek communists for control of the country. The government eventually defeated the Soviet-supported communist forces, and established American influence in the Aegean and Balkans throughout the Cold War. Post-war Greece From the 1950s until the 1970s, the Marshall Plan, industrialization and an emerging Tourism sector helped the Greek economy to boom, with one of the strongest growth rates in the world. Apart from the military coup, which ruled from 1967 to 1974, Greece remained relatively peaceful, prosperous and stable throughout the second half of the twentieth century. The population reached 11.2 million in the early 2000s, before going into decline for the past fifteen years. This decline came about due to a negative net migration rate and slowing birth rate, ultimately facilitated by the global financial crisis of 2007 and 2008; many Greeks left the country in search of work elsewhere, and the economic troubles have impacted the financial incentives that were previously available for families with many children. While the financial crisis was a global event, Greece was arguably the hardest-hit nation during the crisis, and suffered the longest recession of any advanced economy. The financial crisis has had a consequential impact on the Greek population, which has dropped by 800,000 in 15 years, and the average age has increased significantly, as thousands of young people migrate in search of employment.

How many households are in the U.S.?

In 2023, there were 131.43 million households in the United States. This is a significant increase from 1960, when there were 52.8 million households in the U.S.

What counts as a household?

According to the U.S. Census Bureau, a household is considered to be all persons living within one housing unit. This includes apartments, houses, or single rooms, and consists of both related and unrelated people living together. For example, two roommates who share a living space but are not related would be considered a household in the eyes of the Census. It should be noted that group living quarters, such as college dorms, are not counted as households in the Census.

Household changes

While the population of the United States has been increasing, the average size of households in the U.S. has decreased since 1960. In 1960, there was an average of 3.33 people per household, but in 2023, this figure had decreased to 2.51 people per household. Additionally, two person households make up the majority of American households, followed closely by single-person households.