ABOUT

The Global Population Growth Dataset provides a comprehensive record of population trends across various countries over multiple decades. It includes detailed information such as the country name, ISO3 country code, year-wise population data, population growth, and growth rate. This dataset is valuable for researchers, demographers, policymakers, and data analysts interested in studying population dynamics, demographic trends, and economic development.

Key features of the dataset:

✅ Covers multiple countries and regions worldwide
✅ Includes historical and recent population data
✅ Provides year-wise population growth and growth rate (%)
✅ Categorizes data by country and decade for better trend analysis

This dataset serves as a crucial resource for analyzing global population trends, understanding demographic shifts, and supporting socio-economic research and policy-making.

FILE INFORMATION

The dataset consists of structured records related to country-wise population data, compiled from official sources. Each file contains information on yearly population figures, growth trends, and country-specific data. The structured format makes it useful for researchers, economists, and data scientists studying demographic patterns and changes. The file type is CSV.

COLUMNS DESCRIPTION

• Country – The name of the country.
• ISO3 – The three-letter ISO code of the country.
• Year – The year corresponding to the population data, useful for trend analysis.
• Population – The total population of the country for the given year.
• Population Growth – The absolute increase in population compared to the previous year.
• Growth Rate (%) – The percentage change in population compared to the previous year.
• Decade – The decade classification (e.g., 1990s, 2000s) for grouping long-term trends.

Context

The world's population has undergone remarkable growth, exceeding 7.5 billion by mid-2019 and continuing to surge beyond previous estimates. Notably, China and India stand as the two most populous countries, with China's population potentially facing a decline while India's trajectory hints at surpassing it by 2030. This significant demographic shift is just one facet of a global landscape where countries like the United States, Indonesia, Brazil, Nigeria, and others, each with populations surpassing 100 million, play pivotal roles.

The steady decrease in growth rates, though, is reshaping projections. While the world's population is expected to exceed 8 billion by 2030, growth will notably decelerate compared to previous decades. Specific countries like India, Nigeria, and several African nations will notably contribute to this growth, potentially doubling their populations before rates plateau.

Content

This dataset provides comprehensive historical population data for countries and territories globally, offering insights into various parameters such as area size, continent, population growth rates, rankings, and world population percentages. Spanning from 1970 to 2023, it includes population figures for different years, enabling a detailed examination of demographic trends and changes over time.

Dataset

Structured with meticulous detail, this dataset offers a wide array of information in a format conducive to analysis and exploration. Featuring parameters like population by year, country rankings, geographical details, and growth rates, it serves as a valuable resource for researchers, policymakers, and analysts. Additionally, the inclusion of growth rates and world population percentages provides a nuanced understanding of how countries contribute to global demographic shifts.

This dataset is invaluable for those interested in understanding historical population trends, predicting future demographic patterns, and conducting in-depth analyses to inform policies across various sectors such as economics, urban planning, public health, and more.

Structure

This dataset (world_population_data.csv) covering from 1970 up to 2023 includes the following columns:

Acknowledgment

The primary dataset was retrieved from the World Population Review. I sincerely thank the team for providing the core data used in this dataset.

© Image credit: Freepik

World Population Dataset (1960-2023)

This dataset provides an extensive overview of the global population from 1960 to 2023, sourced from the World Development Indicators (WDI). It includes detailed population data for each country along with global trends. The dataset contains the following columns:

• Country Name: The name of each country.
• Country Code: A unique identifier for each country.
• Indicator Name: Population data for the respective country or region.
• Indicator Code: The code corresponding to the indicator.
• 1960 to 2023: Annual population data for 63 years.

Key Features

1. Longitudinal Data: Covers a wide temporal range, capturing population growth trends across decades.
2. Country-Level Insights: Includes population statistics for individual countries, allowing for granular analysis.
3. Global Trends: Enables analysis of global population growth patterns and comparisons between countries.
4. Comprehensive Columns: Structured with clear indicators and codes for easy interpretation and analysis.

Challenges and Objectives

This dataset opens up opportunities for tackling critical challenges and addressing complex questions about the world's future population. Suggested areas of exploration include:

1. Population Prediction:
   Develop machine learning models to predict population growth or decline for each country and globally until 2080.

2. Rate of Change Analysis:
   Determine the rate of population increase or decrease for countries and the world.

3. Visualization:
   Present the trends and predictions through graphs and visualizations, such as:

   • Line charts for population growth over time.
   • Bar graphs comparing country populations.
   • Heatmaps for global population density.

Why This Dataset Matters

Population data is critical for planning, resource allocation, and understanding demographic shifts. This dataset can be used to:
- Assess the impact of population growth on resources and the environment.
- Inform policies and strategies for sustainable development.
- Explore future scenarios of demographic trends.

Suggested Tools and Techniques

• Data Cleaning: Use Python libraries like Pandas for preprocessing.
• Exploratory Data Analysis: Utilize Matplotlib, Seaborn, or Plotly for data visualization.
• Machine Learning: Build regression or time-series models using libraries like Scikit-learn, XGBoost, or Prophet for prediction.
• Interactive Dashboards: by yourself Create dashboards using tools like Power BI or Tableau for insightful presentations.

About this dataset

Context

The dataset tabulates the population of Red House town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Red House town. The dataset can be utilized to understand the population distribution of Red House town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Red House town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Red House town.

Key observations

Largest age group (population): Male # 60-64 years (5) | Female # 40-44 years (4). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Red House town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Red House town is shown in the following column.
• Population (Female): The female population in the Red House town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Red House town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Red House town Population by Gender. You can refer the same here

Important Dataset Update 6/24/2020:Summit and Wasatch Counties updated.Important Dataset Update 6/12/2020:MAG area updated.Important Dataset Update 7/15/2019: This dataset now includes projections for all populated statewide traffic analysis zones (TAZs). Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.As with any dataset that presents projections into the future, it is important to have a full understanding of the data before using it. Before using this data, you are strongly encouraged to read the metadata description below and direct any questions or feedback about this data to analytics@wfrc.org. Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas. These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2019-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2015 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process. As these projections may be a valuable input to other analyses, this dataset is made available at http://data.wfrc.org/search?q=projections as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes. Wasatch Front Real Estate Market Model (REMM) ProjectionsWFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:Demographic data from the decennial census;County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature;Current employment locational patterns derived from the Utah Department of Workforce Services; Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff;Current land use and valuation GIS-based parcel data stewarded by County Assessors;Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations; andCalibration of model variables to balance the fit of current conditions and dynamics at the county and regional level.‘Traffic Analysis Zone’ ProjectionsThe annual projections are forecasted for each of the Wasatch Front’s 2,800+ Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres). ‘City Area’ ProjectionsThe TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.Summary Variables in the DatasetsAnnual projection counts are available for the following variables (please read Key Exclusions note below):DemographicsHousehold Population Count (excludes persons living in group quarters)Household Count (excludes group quarters)EmploymentTypical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)Retail Job Count (retail, food service, hotels, etc)Office Job Count (office, health care, government, education, etc)Industrial Job Count (manufacturing, wholesale, transport, etc)Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count.All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).* These variable includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.Key Exclusions from TAZ and ‘City Area’ ProjectionsAs the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10–15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

We compared population trends for rock ptarmigan (Lagopus muta) densities (2003‒2019) derived from walked transects and driven road transects in Mosfellsheiði and Slétta in southwest and northeast Iceland, respectively. The walked transects were laid out according to a random rule. Convenience-based road transects could give biased population density estimates if roads affect the distribution of ptarmigan. We used distance sampling to compare density estimates provided by the two survey types. Our results showed that road transects were more than five times faster to conduct than walked transects. Estimated ptarmigan density changed in synchrony for the two survey methods in both study areas. Mean density estimates in Mosfellsheiði were similar for the two survey methods (walked transects 1.6 males × km-2, 95% CI 1.4‒1.8; road transects 1.7 males × km-2, 95% CI 1.4‒2.0), but not in Slétta, where density estimates for road transects were significantly lower (walked transects 5.2 males × ..., Study area

Our study occurred in two distinct areas, one in southwest Iceland called Mosfellsheiði (N64.13591, W21.44585) and the other in northeast Iceland called Slétta (N66.4683, W16.476; Fig. 1). The linear distance between the two areas is 360 km. The Mosfellsheiði study area (210 km2) is 15 km from the coast and has altitudes ranging from 200 to 400 m above sea level. The Slétta study area (50 km2) is close to the coast, and altitudes range from sea level to approximately 40 m above sea level. The landscape on Slétta is best described as flat or gently undulating; on Mosfellsheiði, the ground is less flat, with low ridges and shallow depressions between them. Both study areas are treeless. The habitat types on Mosfellsheiði were more variable than those on Slétta. The dominant habitat types on Mosfellsheiði were mosslands (57%) and heathlands (23%), but other components included lava fields (7%), wetlands (7%), and fell fields, moraines, and sands (combined 4%). The dominan..., , # Distance sampling: Comparing walked transects and road transects for rock ptarmigan densities and population trends

https://doi.org/10.5061/dryad.zgmsbccpj

Description of the data and file structure

Spring surveys of territorial ptarmigan males have been used to derive annual densities in Iceland. These counts were started in the early 1960s using the territory mapping method on designated plots, but since 1999, walked and road (driven) transects have been included, applying the distance sampling technique to collect and analyze the transect data. While the territory mapping method assumes the detection of all individuals on the designated plot, distance sampling considers variable detection probabilities based on the distance from the transect and other covariates. Road transects for ptarmigan are less demanding than walked transects. Still, they may break one of the basic assumptions of distance sampling, namely random spacing of tran...

Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset: Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology). Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology). Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology). Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Fore more information see the related READ ME file.

About this dataset

Context

The dataset tabulates the population of Pleasanton by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Pleasanton. The dataset can be utilized to understand the population distribution of Pleasanton by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Pleasanton. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Pleasanton.

Key observations

Largest age group (population): Male # 45-49 years (3,318) | Female # 45-49 years (3,298). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Pleasanton population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pleasanton is shown in the following column.
• Population (Female): The female population in the Pleasanton is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Pleasanton for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Pleasanton Population by Gender. You can refer the same here

Abstract

The Jordan Population and Family Health Survey (JPFHS) is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 2012 Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, and fertility preferences, as well as maternal and child health and nutrition, that can be used by program managers and policymakers to evaluate and improve existing programs. The JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional, or cross-national studies.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 2012 JPFHS sample was designed to produce reliable estimates of major survey variables for the country as a whole, urban and rural areas, each of the 12 governorates, and for the two special domains: the Badia areas and people living in refugee camps. To facilitate comparisons with previous surveys, the sample was also designed to produce estimates for the three regions (North, Central, and South). The grouping of the governorates into regions is as follows: the North consists of Irbid, Jarash, Ajloun, and Mafraq governorates; the Central region consists of Amman, Madaba, Balqa, and Zarqa governorates; and the South region consists of Karak, Tafiela, Ma'an, and Aqaba governorates.

The 2012 JPFHS sample was selected from the 2004 Jordan Population and Housing Census sampling frame. The frame excludes the population living in remote areas (most of whom are nomads), as well as those living in collective housing units such as hotels, hospitals, work camps, prisons, and the like. For the 2004 census, the country was subdivided into convenient area units called census blocks. For the purposes of the household surveys, the census blocks were regrouped to form a general statistical unit of moderate size (30 households or more), called a "cluster", which is widely used in surveys as a primary sampling unit (PSU).

Stratification was achieved by first separating each governorate into urban and rural areas and then, within each urban and rural area, by Badia areas, refugee camps, and other. A two-stage sampling procedure was employed. In the first stage, 806 clusters were selected with probability proportional to the cluster size, that is, the number of residential households counted in the 2004 census. A household listing operation was then carried out in all of the selected clusters, and the resulting lists of households served as the sampling frame for the selection of households in the second stage. In the second stage of selection, a fixed number of 20 households was selected in each cluster with an equal probability systematic selection. A subsample of two-thirds of the selected households was identified for anthropometry measurements.

Refer to Appendix A in the final report (Jordan Population and Family Health Survey 2012) for details of sampling weights calculation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 JPFHS used two questionnaires, namely the Household Questionnaire and the Woman’s Questionnaire (see Appendix D). The Household Questionnaire was used to list all usual members of the sampled households, and visitors who slept in the household the night before the interview, and to obtain information on each household member’s age, sex, educational attainment, relationship to the head of the household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. Moreover, the questionnaire included questions about child discipline. The Household Questionnaire was also used to identify women who were eligible for the individual interview (ever-married women age 15-49 years). In addition, all women age 15-49 and children under age 5 living in the subsample of households were eligible for height and weight measurement and anemia testing.

The Woman’s Questionnaire was administered to ever-married women age 15-49 and collected information on the following topics: • Respondent’s background characteristics • Birth history • Knowledge, attitudes, and practice of family planning and exposure to family planning messages • Maternal health (antenatal, delivery, and postnatal care) • Immunization and health of children under age 5 • Breastfeeding and infant feeding practices • Marriage and husband’s background characteristics • Fertility preferences • Respondent’s employment • Knowledge of AIDS and sexually transmitted infections (STIs) • Other health issues specific to women • Early childhood development • Domestic violence

In addition, information on births, pregnancies, and contraceptive use and discontinuation during the five years prior to the survey was collected using a monthly calendar.

The Household and Woman’s Questionnaires were based on the model questionnaires developed by the MEASURE DHS program. Additions and modifications to the model questionnaires were made in order to provide detailed information specific to Jordan. The questionnaires were then translated into Arabic.

Anthropometric data were collected during the 2012 JPFHS in a subsample of two-thirds of the selected households in each cluster. All women age 15-49 and children age 0-4 in these households were measured for height using Shorr height boards and for weight using electronic Seca scales. In addition, a drop of capillary blood was taken from these women and children in the field to measure their hemoglobin level using the HemoCue system. Hemoglobin testing was used to estimate the prevalence of anemia.

Cleaning operations

Fieldwork and data processing activities overlapped. Data processing began two weeks after the start of the fieldwork. After field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman, where they were registered and stored. Special teams were formed to carry out office editing and coding of the openended questions.

Data entry and verification started after two weeks of office data processing. The process of data entry, including 100 percent reentry, editing, and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by early January 2013. A data processing specialist from ICF International made a trip to Jordan in February 2013 to follow up on data editing and cleaning and to work on the tabulation of results for the survey preliminary report, which was published in March 2013. The tabulations for this report were completed in April 2013.

Response rate

In all, 16,120 households were selected for the survey and, of these, 15,722 were found to be occupied households. Of these households, 15,190 (97 percent) were successfully interviewed.

In the households interviewed, 11,673 ever-married women age 15-49 were identified and interviews were completed with 11,352 women, or 97 percent of all eligible women.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2012 Jordan Population and Family Health Survey (JPFHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2012 JPFHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2012 JPFHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer

This dataset contains historical population data for countries around the world across multiple years. The data includes country identifiers, population series information, and population values from 1990 to 2025.

The dataset can be used for data analysis, demographic research, population growth studies, and visualization projects. It is particularly useful for data analysts, students, and researchers who want to explore global population trends, compare countries, and analyze how population has changed over time.

The dataset includes standardized country codes along with yearly population figures, making it easy to integrate with other global datasets or use in tools such as Power BI, Python, SQL, or Excel.

Possible Use Cases

Analyze global population growth trends

Compare population changes across countries

Study demographic changes over time

Build data visualization dashboards

Perform time-series analysis

Column Details 1. Unnamed: 0

An index column representing the row number of the dataset.

1. Series Name

The name of the data indicator.

In this dataset, it represents "Population, total".

1. Series Code

A standardized indicator code used to represent the population metric.

1. Country Name

The name of the country or region.

1. Country Code

A three-letter country identifier used internationally (ISO-style code).

1. 1990 [YR1990]

Total population of the country in 1990.

1. 2000 [YR2000]

Total population of the country in 2000.

1. 2016 [YR2016]

Total population of the country in 2016.

1. 2017 [YR2017]

Total population of the country in 2017.

1. 2018 [YR2018]

Total population of the country in 2018.

1. 2019 [YR2019]

Total population of the country in 2019.

1. 2020 [YR2020]

Total population of the country in 2020.

1. 2021 [YR2021]

Total population of the country in 2021.

1. 2022 [YR2022]

Total population of the country in 2022.

1. 2023 [YR2023]

Total population of the country in 2023.

1. 2024 [YR2024]

Estimated population of the country in 2024.

1. 2025 [YR2025]

Projected population value for 2025 (some entries may contain missing values).

About this dataset

Context

The dataset tabulates the population of Malta by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Malta. The dataset can be utilized to understand the population distribution of Malta by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Malta. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Malta.

Key observations

Largest age group (population): Male # 35-39 years (52) | Female # 25-29 years (62). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Malta population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Malta is shown in the following column.
• Population (Female): The female population in the Malta is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Malta for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Malta Population by Gender. You can refer the same here

Life Table Data: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per year for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia Experimentalis et Applicata 116(2): 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.
Matrix Model Data: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resources in this dataset:Resource Title: Matrix model data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: MatrixModelData.xlsxResource Description: Life table data were used to provide parameters for population matrix models. Matrix models contain information about stage-specific rates for development, survival and reproduction. The model can be used to estimate overall population growth rate and can also be analyzed to determine which life stages contribute the most to changes in growth rates. Resource Title: Data Dictionary: Life table data. File Name: DataDictionary_LifeTableData.csvResource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.xlsxResource Description: Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis. Resource Title: Life table data from Naranjo, S.E. (2017) Retrospective analysis of a classical biological control program. Journal of Applied Ecology. File Name: LifeTableData.csvResource Description: CSV version of the data. Field-based, partial life table data for immature stages of Bemisia tabaci on cotton in Maricopa, Arizona, USA. Data were generated on approximately 200 individual insects per cohort with 2-5 cohorts per years for a total of 44 cohorts between 1997 and 2010. Data provide the marginal, stage-specific rates of mortality for eggs, and 1st, 2nd, 3rd, and 4th instar nymphs. Mortality is characterized as caused by inviability (eggs only), dislodgement, predation, parasitism and unknown. Detailed methods can be found in Naranjo and Ellsworth 2005 (Entomologia, Experimentalis et Applicata 116: 93-108). The method takes advantage of the sessile nature of immature stages of this insect. Briefly, an observer follows individual eggs or settled first instar nymphs from natural populations on the underside of cotton leaves in the field with a hand lens and determines causes of death for each individual over time. Approximately 200 individual eggs and nymphs are observed for each cohort. Separately, densities of eggs and nymphs are monitored with standard methods (Naranjo and Flint 1994, Environmental Entomology 23: 254-266; Naranjo and Flint 1995, Environmental Entomology 24: 261-270) on a weekly basis.

Overview

This dataset is the repository for the following paper submitted to Data in Brief:

Kempf, M. A dataset to model Levantine landcover and land-use change connected to climate change, the Arab Spring and COVID-19. Data in Brief (submitted: December 2023).

The Data in Brief article contains the supplement information and is the related data paper to:

Kempf, M. Climate change, the Arab Spring, and COVID-19 - Impacts on landcover transformations in the Levant. Journal of Arid Environments (revision submitted: December 2023).

Description/abstract

The Levant region is highly vulnerable to climate change, experiencing prolonged heat waves that have led to societal crises and population displacement. Since 2010, the area has been marked by socio-political turmoil, including the Syrian civil war and currently the escalation of the so-called Israeli-Palestinian Conflict, which strained neighbouring countries like Jordan due to the influx of Syrian refugees and increases population vulnerability to governmental decision-making. Jordan, in particular, has seen rapid population growth and significant changes in land-use and infrastructure, leading to over-exploitation of the landscape through irrigation and construction. This dataset uses climate data, satellite imagery, and land cover information to illustrate the substantial increase in construction activity and highlights the intricate relationship between climate change predictions and current socio-political developments in the Levant.

Folder structure

The main folder after download contains all data, in which the following subfolders are stored are stored as zipped files:

“code” stores the above described 9 code chunks to read, extract, process, analyse, and visualize the data.

“MODIS_merged” contains the 16-days, 250 m resolution NDVI imagery merged from three tiles (h20v05, h21v05, h21v06) and cropped to the study area, n=510, covering January 2001 to December 2022 and including January and February 2023.

“mask” contains a single shapefile, which is the merged product of administrative boundaries, including Jordan, Lebanon, Israel, Syria, and Palestine (“MERGED_LEVANT.shp”).

“yield_productivity” contains .csv files of yield information for all countries listed above.

“population” contains two files with the same name but different format. The .csv file is for processing and plotting in R. The .ods file is for enhanced visualization of population dynamics in the Levant (Socio_cultural_political_development_database_FAO2023.ods).

“GLDAS” stores the raw data of the NASA Global Land Data Assimilation System datasets that can be read, extracted (variable name), and processed using code “8_GLDAS_read_extract_trend” from the respective folder. One folder contains data from 1975-2022 and a second the additional January and February 2023 data.

“built_up” contains the landcover and built-up change data from 1975 to 2022. This folder is subdivided into two subfolder which contain the raw data and the already processed data. “raw_data” contains the unprocessed datasets and “derived_data” stores the cropped built_up datasets at 5 year intervals, e.g., “Levant_built_up_1975.tif”.

Code structure

1_MODIS_NDVI_hdf_file_extraction.R

This is the first code chunk that refers to the extraction of MODIS data from .hdf file format. The following packages must be installed and the raw data must be downloaded using a simple mass downloader, e.g., from google chrome. Packages: terra. Download MODIS data from after registration from: https://lpdaac.usgs.gov/products/mod13q1v061/ or https://search.earthdata.nasa.gov/search (MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061, last accessed, 09th of October 2023). The code reads a list of files, extracts the NDVI, and saves each file to a single .tif-file with the indication “NDVI”. Because the study area is quite large, we have to load three different (spatially) time series and merge them later. Note that the time series are temporally consistent.

2_MERGE_MODIS_tiles.R

In this code, we load and merge the three different stacks to produce large and consistent time series of NDVI imagery across the study area. We further use the package gtools to load the files in (1, 2, 3, 4, 5, 6, etc.). Here, we have three stacks from which we merge the first two (stack 1, stack 2) and store them. We then merge this stack with stack 3. We produce single files named NDVI_final_*consecutivenumber*.tif. Before saving the final output of single merged files, create a folder called “merged” and set the working directory to this folder, e.g., setwd("your directory_MODIS/merged").

3_CROP_MODIS_merged_tiles.R

Now we want to crop the derived MODIS tiles to our study area. We are using a mask, which is provided as .shp file in the repository, named "MERGED_LEVANT.shp". We load the merged .tif files and crop the stack with the vector. Saving to individual files, we name them “NDVI_merged_clip_*consecutivenumber*.tif. We now produced single cropped NDVI time series data from MODIS. The repository provides the already clipped and merged NDVI datasets.

4_TREND_analysis_NDVI.R

Now, we want to perform trend analysis from the derived data. The data we load is tricky as it contains 16-days return period across a year for the period of 22 years. Growing season sums contain MAM (March-May), JJA (June-August), and SON (September-November). December is represented as a single file, which means that the period DJF (December-February) is represented by 5 images instead of 6. For the last DJF period (December 2022), the data from January and February 2023 can be added. The code selects the respective images from the stack, depending on which period is under consideration. From these stacks, individual annually resolved growing season sums are generated and the slope is calculated. We can then extract the p-values of the trend and characterize all values with high confidence level (0.05). Using the ggplot2 package and the melt function from reshape2 package, we can create a plot of the reclassified NDVI trends together with a local smoother (LOESS) of value 0.3.To increase comparability and understand the amplitude of the trends, z-scores were calculated and plotted, which show the deviation of the values from the mean. This has been done for the NDVI values as well as the GLDAS climate variables as a normalization technique.

5_BUILT_UP_change_raster.R

Let us look at the landcover changes now. We are working with the terra package and get raster data from here: https://ghsl.jrc.ec.europa.eu/download.php?ds=bu (last accessed 03. March 2023, 100 m resolution, global coverage). Here, one can download the temporal coverage that is aimed for and reclassify it using the code after cropping to the individual study area. Here, I summed up different raster to characterize the built-up change in continuous values between 1975 and 2022.

6_POPULATION_numbers_plot.R

For this plot, one needs to load the .csv-file “Socio_cultural_political_development_database_FAO2023.csv” from the repository. The ggplot script provided produces the desired plot with all countries under consideration.

7_YIELD_plot.R

In this section, we are using the country productivity from the supplement in the repository “yield_productivity” (e.g., "Jordan_yield.csv". Each of the single country yield datasets is plotted in a ggplot and combined using the patchwork package in R.

8_GLDAS_read_extract_trend

The last code provides the basis for the trend analysis of the climate variables used in the paper. The raw data can be accessed https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS%20Noah%20Land%20Surface%20Model%20L4%20monthly&page=1 (last accessed 9th of October 2023). The raw data comes in .nc file format and various variables can be extracted using the [“^a variable name”] command from the spatraster collection. Each time you run the code, this variable name must be adjusted to meet the requirements for the variables (see this link for abbreviations: https://disc.gsfc.nasa.gov/datasets/GLDAS_CLSM025_D_2.0/summary, last accessed 09th of October 2023; or the respective code chunk when reading a .nc file with the ncdf4 package in R) or run print(nc) from the code or use names(the spatraster collection). Choosing one variable, the code uses the MERGED_LEVANT.shp mask from the repository to crop and mask the data to the outline of the study area.From the processed data, trend analysis are conducted and z-scores were calculated following the code described above. However, annual trends require the frequency of the time series analysis to be set to value = 12. Regarding, e.g., rainfall, which is measured as annual sums and not means, the chunk r.sum=r.sum/12 has to be removed or set to r.sum=r.sum/1 to avoid calculating annual mean values (see other variables). Seasonal subset can be calculated as described in the code. Here, 3-month subsets were chosen for growing seasons, e.g. March-May (MAM), June-July (JJA), September-November (SON), and DJF (December-February, including Jan/Feb of the consecutive year).From the data, mean values of 48 consecutive years are calculated and trend analysis are performed as describe above. In the same way, p-values are extracted and 95 % confidence level values are marked with dots on the raster plot. This analysis can be performed with a much longer time series, other variables, ad different spatial extent across the globe due to the availability of the GLDAS variables.

(9_workflow_diagramme) this simple code can be used to plot a workflow diagram and is detached from the actual analysis.

Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, and Funding acquisition: Michael

About this dataset

Context

The dataset tabulates the population of Porter by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Porter. The dataset can be utilized to understand the population distribution of Porter by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Porter. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Porter.

Key observations

Largest age group (population): Male # 5-9 years (289) | Female # 25-29 years (272). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Porter population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Porter is shown in the following column.
• Population (Female): The female population in the Porter is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Porter for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Porter Population by Gender. You can refer the same here

Population Over Time (US Cities)

Population size over time for the top US cities

By Bob Burggraaf [source]

About this dataset

This dataset reveals the faces of America's urbanization by providing the total population of USA cities in 2015. Through this dataset, you can explore and analyze the populations of cities across the United States. This dataset has undergone a series of data cleaning activities to help make sure that it is easy-to-use with visualization tools, such as cleaning up names of city and joining all cities into one formatted table. Therefore, allowing you to quickly visualize various aspects - like population trends or city demographics - in order to get an informative understanding about how our country is growing. With this knowledge, engaging in discussions related to city planning recommendations is easier than ever!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to Use this Dataset

This dataset contains information about the population of the major cities in the United States. The columns in this dataset include city, summary level, place Fips code, state, state Fips code and total population.

Using this dataset you can explore a variety of topics related to urbanization including population growth over time and comparative analysis between cities. You can also use it to study specific social or demographic trends such as age distribution or race/ethnicity among other key metrics. With the right analysis you could even predict which areas may experience significant growth or decline in their populations over time. Lastly if you want to compare American cities with other global metropolises then you could easily create aggregate tables that include those data points too!

Research Ideas

• Use the data to calculate and demonstrate population growth for cities in the USA over time, providing a strong visual of population changes such as migration, birth/death rates and even shows how urbanization is playing a role in US's population change.
• Analyze correlations between population size and economic indicators (such as GDP) across various cities to examine job opportunities or comparative housing prices.
• Compare different city populations by state to compare disparate areas of the country and determine how much citizens from one state may be attracted to another based on economic advantages or cultural ties

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Total_Population_By_City_Acs_2015_5_E_AgeSex.csv | Column name | Description | |:---------------------|:----------------------------------------------------------------------| | City | Name of the city. (String) | | Summary_Level | Level of detail of the data. (Integer) | | Place_Fips | Federal Information Processing Standard code for the city. (Integer) | | State | Name of the state. (String) | | State_Fips | Federal Information Processing Standard code for the state. (Integer) | | Total_Population | Total population of the city. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Bob Burggraaf.

About this dataset

Context

The dataset tabulates the population of Outlook by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Outlook. The dataset can be utilized to understand the population distribution of Outlook by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Outlook. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Outlook.

Key observations

Largest age group (population): Male # 10-14 years (11) | Female # 5-9 years (10). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Outlook population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Outlook is shown in the following column.
• Population (Female): The female population in the Outlook is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Outlook for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Outlook Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Queen Creek by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Queen Creek. The dataset can be utilized to understand the population distribution of Queen Creek by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Queen Creek. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Queen Creek.

Key observations

Largest age group (population): Male # 10-14 years (3,151) | Female # 10-14 years (2,918). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Queen Creek population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Queen Creek is shown in the following column.
• Population (Female): The female population in the Queen Creek is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Queen Creek for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Queen Creek Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Hillburn by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Hillburn. The dataset can be utilized to understand the population distribution of Hillburn by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Hillburn. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Hillburn.

Key observations

Largest age group (population): Male # 50-54 years (52) | Female # 0-4 years (95). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Hillburn population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Hillburn is shown in the following column.
• Population (Female): The female population in the Hillburn is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Hillburn for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hillburn Population by Gender. You can refer the same here