This dataset provides a comprehensive overview of global population trends, historical data, and future projections. It includes detailed information for various countries and regions, encompassing key demographic indicators such as population size, growth rates, and density.

The dataset covers a broad time span, from 1980 to 2050, allowing for analysis of long-term population dynamics. It incorporates data from reputable sources like the United Nations Population Division and World Population Review, ensuring data accuracy and reliability.

The data belong to a paper that empirically examines the correlation between population growth and real interest rates. Although this correlation is well founded in macroeconomic theory, the corresponding empirical results have been rather tenuous. Demographic interest rate theories are typically based on long-term relationships across generations. Accordingly, key population trends appear often only across decades, if not centuries, worth of data. To capture these trends, a distinction is made between population growth resulting from a birth surplus and net migration. Within a panel covering 12 countries and the years since 1820, the paper find robust evidence that the birth surplus is significantly correlated with the real interest rate.

ResourcesMapTeacher guide Student worksheetGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Science standardsAPES: III. B. – Population biology concepts.APES: II.B.1. – Human population dynamics - historical population sizes; distribution; fertility rates; growth rates and doubling times; demographic transition; age-structure diagrams.Learning outcomesStudents will predict total historical population trends from age-structure information.Students will relate population growth to k (carrying capacity) or r (reproductive factor) selective environmental conditions.

This dataset provides an extensive view of global population statistics and health metrics across various countries from 2014 to 2024. It combines population data with vital health-related indicators, making it a valuable resource for understanding trends in population growth and health outcomes worldwide. Researchers, data scientists, and policymakers can utilize this dataset to analyze correlations between population dynamics and health performance at a global scale.

Key Features: - Country: Name of the country. - Year: Year of the data (2014–2024). - Population: Total population for the respective year and country. - Country Code: ISO 3-letter country codes for easy identification. - Health Expenditure (health_exp): Percentage of GDP spent on healthcare. - Life Expectancy (life_expect): Average life expectancy at birth in years. - Maternal Mortality (maternal_mortality): Maternal deaths per 100,000 live births. - Infant Mortality (infant_mortality): Deaths of infants under 1 year per 1,000 live births. - Neonatal Mortality (neonatal_mortality): Deaths of newborns (0–28 days) per 1,000 live births. - Under-5 Mortality (under_5_mortality): Deaths of children under 5 years per 1,000 live births. - HIV Prevalence (prev_hiv): Percentage of the population living with HIV. - Tuberculosis Incidence (inci_tuberc): Estimated new and relapse TB cases per 100,000 people. - Undernourishment Prevalence (prev_undernourishment): Percentage of the population that is undernourished.

Use Cases: - Health Policy Analysis: Understand trends in healthcare expenditure and its relationship to health outcomes. - Global Health Research: Investigate global or regional disparities in health and nutrition. - Population Studies: Analyze population growth trends alongside health indicators. - Data Visualization: Build visual dashboards for storytelling and impactful data representation.

Summary of hosuehold types in 22 countries from IPUMS dataSee github repo for code. See pdf of poster for full factsheet.

This data collection contains de-identified clinical health service utilisation data from Bendigo Health and the General Practitioners Practices associated with the Loddon Mallee Murray Medicare Local. The collection also includes associated population health data from the ABS, AIHW and the Municipal Health Plans. Health researchers have a major interest in how clinical data can be used to monitor population health and health care in rural and regional Australia through analysing a broad range of factors shown to impact the health of different populations. The Population Health data collection provides students, managers, clinicians and researchers the opportunity to use clinical data in the study of population health, including the analysis of health risk factors, disease trends and health care utilisation and outcomes.Temporal range (data time period):2004 to 2014Spatial coverage:Bendigo Latitude -36.758711200000010000, Bendigo Longitude 144.283745899999990000

Aim. Halting widespread biodiversity loss will require detailed information on species’ trends and the habitat conditions correlated with population declines. However, constraints on conventional monitoring programs and commonplace approaches for trend estimation can make it difficult to obtain such information across species’ ranges. Here, we demonstrate how recent developments in machine learning and model interpretation, combined with data sources derived from participatory science, enable landscape-scale inferences on the habitat correlates of population trends across broad spatial extents. Location. Worldwide, with a case study in the western United States. Methods. We used interpretable machine learning to understand the relationships between land cover and spatially explicit bird population trends. Using a case study with three passerine birds in the western U.S. and spatially explicit trends derived from eBird data, we explore the potential impacts of simulated land cover modifi..., , , # A framework for assessing the habitat correlates of spatially explicit population trends

https://doi.org/10.5061/dryad.8pk0p2nzf

Description of the data and file structure

This file contains information and explanation for the data and code that accompany the following project:

Stillman, A.N., C.L. Davis, K.D. Dunham, V. Ruiz-Gutierrez, A.D. Rodewald, A. Johnston, T. Auer, M. Strimas-Mackey, S. Ligocki, and D. Fink. 2025. A framework for assessing the habitat correlates of spatially explicit population trends. Diversity and Distributions.

This .README file accompanies the archived data for this project which are necessary to run the case study in the manuscript. Scripts for the case study analysis are available from Zenodo along with supplemental results files.Â

Data (Dryad)

All data files necessary to run the analysis in this repository. Files include land cover descriptions for 2007, land cover descriptions for 2021, eBird T...,

The data and codes that support the findings of the study of "A new method for building-level population estimation by integrating LiDAR, nighttime light, and POI data".

1. Many migratory species are in decline across their geographical ranges. Single-population studies can provide important insights into drivers at a local scale, but effective conservation requires multi-population perspectives. This is challenging because relevant data are often hard to consolidate, and state-of-the-art analytical tools are typically tailored to specific datasets.
2. We capitalized on a recent data harmonization initiative (SPI-Birds) and linked it to a generalized modeling framework to identify the demographic and environmental drivers of large-scale population decline in migratory pied flycatchers (Ficedula hypoleuca) breeding across Britain.
3. We implemented a generalized integrated population model (IPM) to estimate age-specific vital rates, including their dependency on environmental conditions, and total and breeding population size of pied flycatchers using long-term (34–64 years) monitoring data from seven locations representative of the British breeding range. We then quantified the relative contributions of different vital rates and population structures to changes in short- and long-term population growth rates using transient life table response experiments (LTREs).
4. Substantial covariation in population sizes across breeding locations suggested that change was the result of large-scale drivers. This was supported by LTRE analyses, which attributed past changes in short-term population growth rates and long-term population trends primarily to variation in annual survival and dispersal dynamics, which largely act during migration and/or non-breeding season. Contributions of variation in local reproductive parameters were small in comparison, despite sensitivity to local temperature and rainfall within the breeding period.
5. We show that both short- and longer-term population changes of British-breeding pied flycatchers are likely linked to factors acting during migration and in non-breeding areas, where future research should be prioritized. We illustrate the potential of multi-population analyses for informing management at (inter)national scales and highlight the importance of data standardization, generalized and accessible analytical tools, and reproducible workflows to achieve them. Methods Data collection protocols are described in the paper, and further references provided therein. Raw data were harmonised and converted to a standard format by SPI-Birds (https://spibirds.org/) and then collated into the input data provided here using code deposited on https://github.com/SPI-Birds/SPI-IPM. Details on this step of data processing will be added to https://spi-birds.github.io/SPI-IPM/. The MCMC sample data files are the outputs of the integrated population models fitted in the study. Please refer to the published article and material deposited on the associated GitHub repository for more details.

Database Name: population_cities

Description: The population_cities dataset provides information on the population of various cities worldwide. It includes key details such as the city's name, the country it is located in, the total population, and the continent it belongs to. This dataset is ideal for researchers, data analysts, and enthusiasts looking to explore global population trends, conduct regional comparisons, or analyze urban demographics across continents.

Columns:
1. City: Name of the city.
2. Country: Name of the country where the city is located.
3. Population: Total population of the city.
4. Continent: The continent where the city is situated (e.g., Asia, Europe, Africa, etc.).

Potential Uses: - Comparative analysis of city populations across continents.
- Visualization of population density in specific regions.
- Studies on urbanization trends and growth patterns.
- Development of machine learning models for population prediction or clustering analysis.

Feel free to explore and share insights from this dataset!

A case study on human population growth that includes Human-Environment Interactions (HEI) from the Ecological Society of America's 4 dimensional ecology teaching framework. Meant to be used in lieu of a lecture on population growth.

Percentage Population growth has been calculated from the change between the 2001 and the 2006 Population and Housing Census data. The 2001 data was concorded to 2006 boundaries by ABS, and the calculations were completed by BRS. The change between 2001-2006 has been presented as a percentage population growth and attributed to each Statistical Local Area and then rasterised. Capital cities have been masked out of this analysis.

This dataset consists of spatiotemporal data on counts of the soil mite Rostrozetes ovulum (Oribatida: Haplozetidae) in central Amazonia, along with data on climate and litterfall variables used to model the mite's population dynamics.We sampled the mite in 20 transects a 800-ha forest remnant in Manaus, northern Brazil (03°04’34”S; 59°57’30”W). Each transect was 20-m long. Transects were distributed all over the forest landscape and sampled from June 2014 to June 2015. Ten transects were in valleys, while the remaining transects were located on plateaus, at least 150 m away from any drainage catchment. At each transect, one soil sample was taken each meter using an aluminum soil corer (3.5 × 3.5 × 5 cm), covering a total of 245 cm2. This material was taken to the laboratory, where the soil fauna was extracted using a Berlese-Tullgren apparatus (Franklin & Morais 2006). Each soil core was put in a sieve with mesh size 1.5 mm, which was placed in a plastic funnel. Then, the funnel was put into a wooden box, where it was fitted through a perforated polystyrene board, with a glass vial filled with 95 percent alcohol below it. Next, the box was gradually heated from ambient temperature (ca. 27ºC) to 35 – 40 ºC using light bulbs (25 W). Vials were checked daily for fallen animals. Heating lasted until the core was completely dry and animals stopped falling into the vial (7 to 10 days). The collected material was surveyed under a stereomicroscope for R. ovulum. Adult individuals were counted and preserved in 95 percent alcohol. Transects were sampled on nine months (June to September and November 2014; and January, March, April and June 2015). Therefore, the spatiotemporal coverage of our study was 20 transects × 13 months = 240 spatiotemporal units, of which 20 transects × 9 surveys = 180 counts were recorded from a total of 3600 soil cores.Environmental seasonality data were obtained from research sites nearby the study area, or estimated from such sites. Temperature and rainfall data were gathered online from the nearest station of the Brazilian Institute for Meteorology (INMET), which is 1 km from the study area. We extracted daily readings to compute cumulative rainfall (mm) and maximum daily air temperature (°C) for each transect and month covered by our sampling.Litterfall was estimated using time series of monthly litter production per habitat (plateau and valley) from the Cuieiras Biological Reserve (22,735-ha), 60 km from the study area. Litterfall was sampled with 30 PVC collectors (50 × 50 cm) randomly placed 50 cm above ground in each habitat, between May 2004 and December 2005, January 2009 and December 2010, and November 2014 and August 2015. In parallel, we obtained meteorological data from the INMET station corresponding to the litterfall measurements to model the latter as a function of (1) monthly sunlight hours, monthly cumulative rainfall and their interaction, (2) habitat (valley or plateau), and (3) time (months, coded as integers spanning the temporal coverage of the data) in order to account for any long-term trend. The model was the used to predict the expected litterfall for each spatiotemporal unit in which the mite was sampled, given the corresponding environmental conditions.

Monitoring population dynamics is of fundamental importance in conservation but assessing trends in abundance can be costly, especially in large and rough areas. Obtaining trend estimations from counts performed in only a portion of the total area (sample counts) can be a cost-effective method to improve the monitoring and conservation of species difficult to count. We tested the effectiveness of sample counts in monitoring population trends of wild animals, using as a model population the Alpine ibex (Capra ibex) in the Gran Paradiso National Park (Italy), both with computer simulations and using historical count data collected over the last 65 years. Despite sample counts failed to correctly estimate the true population abundance, sampling half of the target area could reliably monitor the trend of the target population. In case of strong changes in abundance, an even lower proportion of the total area could be sufficient to identify the direction of the population trend. However, when there is a high yearly trend variability, the required number of samples increases and even counting in the entire area can be ineffective to detect population trends. The effect of other parameters, such as which portion of the area is sampled and detectability, was lower, but these should be tested case by case. Sample counts could therefore constitute a viable alternative to assess population trends, allowing for important, cost-effective improvements in the monitoring of wild animals of conservation interest. Methods We here provide the R script to run all the simulations in the paper. See Methods and Supplementary materials S1 and S2 for more info

Context

This dataset is in context of the real world data science work and how the data analyst and data scientist work.

Content

The dataset consists of four columns Year, Level_1(Ethnic group/gender), Level_2(Age group), and population

Acknowledgements

I would sincerely thank GeoIQ for sharing this dataset with me along with tasks. Just having a basic knowledge of Pandas and Numpy and other python data science libraries is not enough. How can you execute tasks and how can you preprocess the data before making any prediction is very important. Most of the datasets in Kaggle are clean and well arranged but this dataset thought me how real world data science and analysis works. Every data science beginner must work on this dataset and try to execute the tasks. It would only give them a good exposer to the real data science world.

Inspiration

1. Identify the largest Ethnic group in Singapore. Their average population growth over the years and what proportion of the total population do they constitute.
2. Identify the largest age group in Singapore. Their average population growth over the years and what proportion of the total population do they constitute.
3. Identify the group (by age, ethnicity and gender) that: a. Has shown the highest growth rate b. Has shown the lowest growth rate c. Has remained the same
4. Plot a graph for population trends

Population data was obtained at the county level from the Texas Department of State Health Services (TDSHS, http://www.dshs.state.tx.us/chs/popdat/default.shtm). TDSHS estimates were used over US Census values so that population data could be compared with other datasets (which were collected in years the US Census did not occur - 1997, 2002, and 2007) in the Texas Land Trends study. TDSHS and US Census data may vary slightly.

According to latest figures, the Chinese population decreased by 1.39 million to around 1.408 billion people in 2024. After decades of rapid growth, China arrived at the turning point of its demographic development in 2022, which was earlier than expected. The annual population decrease is estimated to remain at moderate levels until around 2030 but to accelerate thereafter. Population development in China China had for a long time been the country with the largest population worldwide, but according to UN estimates, it has been overtaken by India in 2023. As the population in India is still growing, the country is very likely to remain being home of the largest population on earth in the near future. Due to several mechanisms put into place by the Chinese government as well as changing circumstances in the working and social environment of the Chinese people, population growth has subsided over the past decades, displaying an annual population growth rate of -0.1 percent in 2024. Nevertheless, compared to the world population in total, China held a share of about 17 percent of the overall global population in 2024. China's aging population In terms of demographic developments, the birth control efforts of the Chinese government had considerable effects on the demographic pyramid in China. Upon closer examination of the age distribution, a clear trend of an aging population becomes visible. In order to curb the negative effects of an aging population, the Chinese government abolished the one-child policy in 2015, which had been in effect since 1979, and introduced a three-child policy in May 2021. However, many Chinese parents nowadays are reluctant to have a second or third child, as is the case in most of the developed countries in the world. The number of births in China varied in the years following the abolishment of the one-child policy, but did not increase considerably. Among the reasons most prominent for parents not having more children are the rising living costs and costs for child care, growing work pressure, a growing trend towards self-realization and individualism, and changing social behaviors.

The 1960 Growth of American Families(GAF) Study was conceived as a follow-up and extension of the first GAF Study."The 1960 GAF Study provided, for the first time, an opportunity to evaluate the validity of women's fertility expectations; to determine whether the total number of children expected by such women changed significantly between 1955 and 1960; and to begin the analysis of time trends in the proportions using contraception, the level of family size desired, and the patterns o f group differences in fertility. One important difference in the 1960 Study was the inclusion of nonwhite women in the sample, which provides the opportunity to estimate parameters for the total population. Factual information included: age; education; income; occupation; employment history; residence history; marriage history; religious preference and attendance; parent's occupation, religion, and nationality."

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.

Population dynamics, its types. Population migration (external, internal), factors determining it, main trends. Impact of migration on population health.

Under the guidance of Moldoev M.I. Sir By Riya Patil and Rutuja Sonar

Abstract

Population dynamics influence development and vice versa, at various scale levels: global, continental/world-regional, national, regional, and local. Debates on how population growth affects development and how development affects population growth have already been subject of intensive debate and controversy since the late 18th century, and this debate is still ongoing. While these two debates initially focused mainly on natural population growth, the impact of migration on both population dynamics and development is also increasingly recognized. While world population will continue growing throughout the 21st century, there are substantial and growing contrasts between and within world-regions in the pace and nature of that growth, including some countries where population is stagnating or even shrinking. Because of these growing contrasts, population dynamics and their interrelationships with development have quite different governance implications in different parts of the world.

1. Population Dynamics

Population dynamics refers to the changes in population size, structure, and distribution over time. These changes are influenced by four main processes:

Birth rate (natality)

Death rate (mortality)

Immigration (inflow of people)

Emigration (outflow of people)

Types of Population Dynamics

Natural population change: Based on birth and death rates.

Migration-based change: Caused by people moving in or out of a region.

Demographic transition: A model that explains changes in population growth as societies industrialize.

Population distribution: Changes in where people live (urban vs rural).

2. Population Migration

Migration refers to the movement of people from one location to another, often across political or geographical boundaries.

Types of Migration

External migration (international):

Movement between countries.

Examples: Refugee relocation, labor migration, education.

Internal migration:

Movement within the same country or region.

Examples: Rural-to-urban migration, inter-state migration.

3. Factors Determining Migration

Migration is influenced by push and pull factors:

Push factors (reasons to leave a place):

Unemployment

Conflict or war

Natural disasters

Poverty

Lack of services or opportunities

Pull factors (reasons to move to a place):

Better job prospects

Safety and security

Higher standard of living

Education and healthcare access

Family reunification

4. Main Trends in Migration

Urbanization: Mass movement to cities for work and better services.

Global labor migration: Movement from developing to developed countries.

Refugee and asylum seeker flows: Due to conflict or persecution.

Circular migration: Repeated movement between two or more locations.

Brain drain/gain: Movement of skilled labor away from (or toward) a country.

5. Impact of Migration on Population Health

Positive Impacts:

Access to better healthcare (for migrants moving to better systems).

Skills and knowledge exchange among health professionals.

Remittances improving healthcare affordability in home countries.

Negative Impacts:

Migrants’ health risks: Increased exposure to stress, poor living conditions, and occupational hazards.

Spread of infectious diseases: Especially when health screening is lacking.

Strain on health services: In receiving areas, especially with sudden or large influxes.

Mental health challenges: Due to cultural dislocation, discrimination, or trauma.

Population dynamics is one of the fundamental areas of ecology, forming both the basis for the study of more complex communities and of many applied questions. Understanding population dynamics is the key to understanding the relative importance of competition for resources and predation in structuring ecological communities, which is a central question in ecology.

Population dynamics plays a central role in many approaches to preserving biodiversity, which until now have been primarily focused on a single species approach. The calculation of the intrinsic growth rate of a species from a life table is often the central piece of conservation plans. Similarly, management of natural resources, such as fisheries, depends on population dynamics as a way to determine appropriate management actions.

Population dynamics can be characterized by a nonlinear system of difference or differential equations between the birth sizes of consecutive periods. In such a nonlinear system, when the feedback elasticity of previous events on current birth size is larger, the more likely the dynamics will be volatile. Depending on the classification criteria of the population, the revealed cyclical behavior has various interpretations. Under different contextual scenarios, Malthusian cycles, Easterlin cycles, predator–prey cycles, dynastic cycles, and capitalist–laborer cycles have been introduced and analyzed

Generally, population dynamics is a nonlinear stochastic process. Nonlinearities tend to be complicated to deal with, both when we want to do analytic stochastic modelling and when analysing data. The way around the problem is to approximate the nonlinear model with a linear one, for which the mathematical and statistical theories are more developed and tractable. Let us assume that the population process is described as:

(1)Nt=f(Nt−1,εt)

where Nt is population density at time t and εt is a series of random variables with identical distributions (mean and variance). Function f specifies how the population density one time step back, plus the stochastic environment εt, is mapped into the current time step. Let us assume that the (deterministic) stationary (equilibrium) value of the population is N* and that ε has mean ε*. The linear approximation of Eq. (1) close to N* is then:

(2)xt=axt−1+bϕt

where xt=Nt−N*, a=f

f(N*,ε*)/f

N, b=ff(N*,ε*)/fε, and ϕt=εt−ε*

The term population refers to the members of a single species that can interact with each other. Thus, the fish in a lake, or the moose on an island, are clear examples of a population. In other cases, such as trees in a forest, it may not be nearly so clear what a population is, but the concept of population is still very useful.

Population dynamics is essentially the study of the changes in the numbers through time of a single species. This is clearly a case where a quantitative description is essential, since the numbers of individuals in the population will be counted. One could begin by looking at a series of measurements of the numbers of particular species through time. However, it would still be necessary to decide which changes in numbers through time are significant, and how to determine what causes the changes in numbers. Thus, it is more sensible to begin with models that relate changes in population numbers through time to underlying assumptions. The models will provide indications of what features of changes in numbers are important and what measurements are critical to make, and they will help determine what the cause of changes in population levels might be.

To understand the dynamics of biological populations, the study starts with the simplest possibility and determines what the dynamics of the population would be in that case. Then, deviations in observed populations from the predictions of that simplest case would provide information about the kinds of forces shaping the dynamics of populations. Therefore, in describing the dynamics in this simplest case it is essential to be explicit and clear about the assumptions made. It would not be argued that the idealized population described here would ever be found, but that focusing on the idealized population would provide insight into real populations, just as the study of Newtonian mechanics provides understanding of more realistic situations in physics.

Population migration

The vast majority of people continue to live in the countries where they were born —only one in 30 are migrants.

In most discussions on migration, the starting point is usually numbers. Understanding changes in scale, emerging trends, and shifting demographics related to global social and economic transformations, such as migration, help us make sense of the changing world we live in and plan for the future. The current global estimate is that there were around 281 million international migrants in the world in 2020, which equates to 3.6 percent of the global population.

Overall, the estimated number of international migrants has increased over the past five decades. The total estimated 281 million people living in a country other than their countries of birth in 2020 was 128 million more than in 1990 and over three times the estimated number in 1970.

There is currently a larger number of male than female international migrants worldwide and the growing gender gap has increased over the past 20 years. In 2000, the male to female split was 50.6 to 49.4 per cent (or 88 million male migrants and 86 million female migrants). In 2020 the split was 51.9 to 48.1 per cent, with 146 million male migrants and 135 million female migrants. The share of