An interactive Story Map Series℠ explaining the links between the Demographic Transition Model and population pyramids (population structure) for almost all the countries in the world. It provides an excellent way to make spatial links with the demographic data. For example, each country is mapped using an interactive symbol representing its stage on the DTM. On clicking the symbol for any country, a pop-up provides a statement about its stage on the DTM and its 2018 population pyramid, provided by PopulationPyramid.net.The tabs in the Story Map Series℠ take the reader or presenter through an introduction and explanation of the DTM, followed by detail about particular places / countries currently at each stage including an example of anomalies which are less consistent with the model.The story map will be useful for a wide range of students and teachers of geography, demography and development at secondary and tertiary level.Credits and further study*Story Map template by Esri*Demographic Transition video by GeographyAllTheWay*Population structure diagrams from PopulationPyramid.net by Martin de Wulf based in Brussels, Belgium.*DTM diagram and population pyramid icons from Cool Geography *Population Education / PopEdBlog*BBC Bitesize Population growth and change*Thanks also to Ed Morgan of the ONS for very helpful feedback and further information.NB The DTM stages for each country are estimated and may be altered in due course.

As of 2023, the bulk of the Chinese population was aged between 25 and 59 years, amounting to around half of the population. A breakdown of the population by broad age groups reveals that around 61.3 percent of the total population was in working age between 16 and 59 years in 2023. Age cohorts below 25 years were considerably smaller, although there was a slight growth trend in recent years. Population development in China Population development in China over the past decades has been strongly influenced by political and economic factors. After a time of high fertility rates during the Maoist regime, China introduced birth-control measures in the 1970s, including the so-called one-child policy. The fertility rate dropped accordingly from around six children per woman in the 1960s to below two at the end of the 20th century. At the same time, life expectancy increased consistently. In the face of a rapidly aging society, the government gradually lifted the one-child policy after 2012, finally arriving at a three-child policy in 2021. However, like in most other developed countries nowadays, people in China are reluctant to have more than one or two children due to high costs of living and education, as well as changed social norms and private values. China’s top-heavy age pyramid The above-mentioned developments are clearly reflected in the Chinese age pyramid. The age cohorts between 30 and 39 years are the last two larger age cohorts. The cohorts between 15 and 24, which now enter childbearing age, are decisively smaller, which will have a negative effect on the number of births in the coming decade. When looking at a gender distribution of the population pyramid, a considerable gender gap among the younger age cohorts becomes visible, leaving even less room for growth in birth figures.

In 1800, the population of Japan was just over 30 million, a figure which would grow by just two million in the first half of the 19th century. However, with the fall of the Tokugawa shogunate and the restoration of the emperor in the Meiji Restoration of 1868, Japan would begin transforming from an isolated feudal island, to a modernized empire built on Western models. The Meiji period would see a rapid rise in the population of Japan, as industrialization and advancements in healthcare lead to a significant reduction in child mortality rates, while the creation overseas colonies would lead to a strong economic boom. However, this growth would slow beginning in 1937, as Japan entered a prolonged war with the Republic of China, which later grew into a major theater of the Second World War. The war was eventually brought to Japan's home front, with the escalation of Allied air raids on Japanese urban centers from 1944 onwards (Tokyo was the most-bombed city of the Second World War). By the war's end in 1945 and the subsequent occupation of the island by the Allied military, Japan had suffered over two and a half million military fatalities, and over one million civilian deaths.

The population figures of Japan were quick to recover, as the post-war “economic miracle” would see an unprecedented expansion of the Japanese economy, and would lead to the country becoming one of the first fully industrialized nations in East Asia. As living standards rose, the population of Japan would increase from 77 million in 1945, to over 127 million by the end of the century. However, growth would begin to slow in the late 1980s, as birth rates and migration rates fell, and Japan eventually grew to have one of the oldest populations in the world. The population would peak in 2008 at just over 128 million, but has consistently fallen each year since then, as the fertility rate of the country remains below replacement level (despite government initiatives to counter this) and the country's immigrant population remains relatively stable. The population of Japan is expected to continue its decline in the coming years, and in 2020, it is estimated that approximately 126 million people inhabit the island country.

Estimates of total number of people per grid square broken down by gender and age groupings (including 0-1 and by 5-year up to 80+) in 2018 for ArmeniaThe dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator) . The projection is Geographic Coordinate System, WGS84. The units are estimated number of male/female in each age group per grid square.The mapping approach is Pezzulo, C. et al. Sub-national mapping of population pyramids and dependency ratios in Africa and Asia. Sci. Data 4:170089 doi:10.1038/sdata.2017.89 (2017)Filenames: Example - afg_f_05_2000.tif People per pixel (PPP) for female age group 5 to 9 years (f_05) in Afghanistan for year 2000.For other datasets, m = male, 00 = age group 0 to 12months, 01 = age group 1 to 4 years, 80 = age 80 years and over

About this dataset

Context

The dataset tabulates the Cassandra population by age. The dataset can be utilized to understand the age distribution and demographics of Cassandra.

Content

The dataset constitues the following three datasets

• Cassandra, PA Age Group Population Dataset: A complete breakdown of Cassandra age demographics from 0 to 85 years, distributed across 18 age groups
• Cassandra, PA Age Cohorts Dataset: Children, Working Adults, and Seniors in Cassandra - Population and Percentage Analysis
• Cassandra, PA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

This statistic depicts the age distribution of the United Kingdom from 2013 to 2023. In 2023, about 17.41 percent of the population in the United Kingdom fell into the 0-14 year category, 63.35 percent into the 15-64 age group and 19.24 percent were over 65 years of age. The same year, the total UK population amounted to about 67.26 million people.

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

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 in 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 2022 Philippines National Demographic and Health Survey (2022 NDHS) 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 2022 NDHS 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 errors are 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% 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 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

This layer contains WorldPop's 100m resolution annual estimates of population density from the year 2000 to 2020. Usage notes: This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population density, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. We recommend using this layer in conjunction with WorldPop's 1-km resolution Population Density layer to create web maps that allow users to pan and zoom to wider areas; this web map contains an example of this combination. The population estimates in this layer are derived WorldPop's total population data, which use a Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based dasymetric model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2), e0107042) and converts these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_100m/ImageServer, which was acquired from WorldPop in December 2021.

Abstract

The main objectives of the 2010 Census of Population and Housing were: • To provide accurate and reliable information on the size, composition and distribution of the population of Zambia at the time of the census; • To provide information on the demographic and socioeconomic characteristics of the population of Zambia at the lowest administrative level - the ward; • To provide indicators for measuring progress towards national and international development goals in a timely and user friendly manner; • To provide information on the number and characteristics of households engaged in agriculture and other economic activities; • To provide an accurate sampling frame and sample weights for future inter-censal household and population based surveys; • To provide information identifying the number of eligible voters for the 2011 General Elections; • To provide a census that meets national and international standards and allows for comparability with other censuses; • To provide information on the housing characteristics of the population.

Universe

Census Enumerators went out visiting all buildings in Zambia whether completed, incomplete, abandoned, habitable and inhabitable for the purpose of identifying characteristics of all buildings, households and other human aspects. All persons who lived in the buildings were counted and detailed information pertaining to their characteristics obtained.

Sampling procedure

The Census mapping methodology in 2010 was Geographic Information System (GIS) driven with the use of Satellite Imagery in urban areas and Global Positioning System (GPS) in rural areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2010 Census used a single questionnaire to capture individual, household and housing characteristics from the population. The 2010 Census differs from the 2000 Census by including questions on deaths of Household Members during the 12 months period prior to the census enumeration, as well as cause of death for all reported deaths.

Included for the first time were questions on maternal deaths to women aged 12-49 years during the reference period (12 months prior to the Census). Questions were asked of female household members aged 12-49 years that were reported to have died during the reference period (12 months prior to the census), whether the death had occurred while the woman was pregnant, during childbirth or six weeks after the end of a pregnancy, regardless of the outcome of the pregnancy. Another new addition was the question on whether one was an Albino or not.

Cleaning operations

In April 2011, the Central Statistical Office started the data capture and processing of the 2010 Census questionnaires. Scanning of the 2010 Census questionnaires started in April 2011 and was successfully concluded in August 2011. The data capture used Optical Mark Reading (OMR) and Intelligent Character Recognition (ICR) technology in order to speed up the processing time. Data verification and development of edit and imputation specifications and programmes started in May and was completed in November 2011.

Data appraisal

Methods of evaluation applied were:

• Direct Method: Post Enumeration Survey (PES)- a sample of households is revisited after the census and data are again collected but on a smaller scale and later compared with that collected during the actual census. • Indirect Method: Comparison of data using both internal and external consistency checks. Internal consistency checks compare relationships of data within the same census data, whereas external consistency checks compare census data with data generated from other sources.

Coverage errors: • Omission or duplication of individuals, households, or housing units resulting in under or over enumeration. • Lack of accessibility or cooperation with respondents. • Lack of proper boundary descriptions on maps. Coverage errors can be measured by examining certain statistics such as growth rate, age composition, child woman ratio and dependency ratio.

Content errors: Content errors refer to instances where characteristics such as age, sex, marital status, economic activity, etc. of a person enumerated in a census or survey are incorrectly reported or tabulated. • Content errors are caused by either a respondent giving a wrong response or by an enumerator recording an incorrect response. • 2010 census errors were estimated by the use of the Myers' Index, Sex Ratios, Age Ratios and Population Pyramids.

For findings, please refer to the presentation on census data evaluation provided as external resources.

Abstract

Population and Housing Census of Bhutan 2005 collected data on demographic, eduation, health, migration, household and housing characteristics. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a specific point of time. The Census was carried out for two days, 30 and 31 May, 2005. A total of 7500 enumerators, supervisors and administrators were involved.

General Objectives: The 2005 Census seeks to create an inventory of Bhutan's population size, socio-economic information, labour and demographic characteristics.

Specific Objectives: - to obtain an up-to date count of the population size, by age and sex - to obtain geographic distribution of the population by demographic and socio-economic characteristics - to provide frames for surveys and other statistical activities - to gather information about migration and fertility

Salient features of a census: 1. The population census forms an integral part of a country’s National Statistical System. 2. The census provides valuable benchmark data on a wide range of characteristics, a frame for statistical survey and data to compile a variety of social and economic indicators. These indicators must be comparable between areas within as well as with that of other countries. 3. The census provides the demographic, housing, social and economic data not provided by population registers. 4. Most importantly a census provides data at the smallest area level like a village. Extensive and detailed cross-classification is possible. This is not possible in a sample survey. 5. The population census has a legitimate methodology, which is acceptable internationally.

Geographic coverage

National, District (Dzongkhag), Sub-district (Gewogs), Urban (or Rural) areas.

Analysis unit

Individuals, Households, Gewogs, Dzongkhags, National

Universe

The Census covered all de facto household members. It covered the entire country irrespective of human habitation or not and counted all structures, census house, households and people whether Bhutanese or non-Bhutanese residing in the country at a Census Night ( Midngiht of 30 May)

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable

Mode of data collection

Face-to-face [f2f]

Research instrument

To develop the census questionnaires, consultative meetings were conducted with all ministries. This was followed by a workshop for all sector heads to finalise the contents of the census questionnaires. Necessary changes were incorporated into the census questionnaires based on the outcome of the workshops and consultative meetings. The questionnaires were pre-tested in the three regions of the country. After making all necessary changes the forms were printed in adequate numbers.

Form PHCB - 2A - Household List Update: This section collects data on village code, structure number, census house number, use of census house, serial number of household, name of household head, sex and age with geographical codes. Form PHCB - 2B - Household Members List: This section collects information on household members, relationship, sex, age, member status, members absent and duration absent. Form PHCB -2C - Individual Member Details: This section has three parts. Part A collects information on general demographic characteristics and migration. Part B collects information on education and employment and Part C collects information on fertiliy of women age 15-49 years. Form PHCB - 2D - Household Informamtion: This section has two parts. Part A collects information on housing conditions and facilities. Part B collects information on particulars of the deceased in the past twelve months.

Cleaning operations

Data editing was done in several stages. The first editing of data was done by the field supervisors and then followed by the manual editing at the dzongkhag level immediately after the field operation. The final manual editing was done at the centre by 20 Dzongkhag Statistical Officers, 1 Registration Officer and 28 graduates who were trained and deployed on temporary basis for three months.

Response rate

100% response rate.

Note: The Royal Government of Bhutan declared 30 May - 31 May, 2005, as public holidays.

Sampling error estimates

Since PHCB, 2005 involved complete enumeration of respondents, Sampling procedures were not applicable thus sampling errors were not computed.

Data appraisal

Standard tables and graphs were generated to assess the data reliablity. This includes the computation of population pyramid, grapha of male and female population by single years of age, age and sex structure, age distribution of the household population.

A popular hypothesis in international studies states that a ‘youth bulge’ – an age pyramid dominated by large cohorts between 15 and 29 years of age – increases the risk of political violence. However, empirical evidence on this link remains inconclusive to date. In this article, we systematically assess the youth effect using new data from 183 countries between 1996 and 2015. We find that within countries, a decrease in the youth ratio is generally associated with a decrease in the number of violent deaths from terrorism or other internal conflicts, and vice versa. This is also confirmed in out-of-sample predictions. However, the association is not evident in all constellations and sensitive to modeling issues. In particular, large cohorts of young males can become a disruptive power in countries that increase enrollment in post-primary education. Although this is usually followed by fertility decline, youth bulges often remain at record levels for quite some time due to high birth rates in the past. Strong labor markets can in general suppress the detrimental consequences of youth bulges. However, the combination of growing youth cohorts and educational expansion often leads to increased political violence even in the presence of low youth unemployment.

Correlation between independent components obtained through our procedure with activity of specific cell populations for different example simulations.

Examples of indicators at different levels of the E–D Pyramid.