The Vatican City, often called the Holy See, has the smallest population worldwide, with only *** inhabitants. It is also the smallest country in the world by size. The islands Niue, Tuvalu, and Nauru followed in the next three positions. On the other hand, India is the most populous country in the world, with over *** billion inhabitants.

The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

As of February 2025, 5.56 billion individuals worldwide were internet users, which amounted to 67.9 percent of the global population. Of this total, 5.24 billion, or 63.9 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 20254. In The Netherlands, Norway and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide – over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a ten percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most significant usage penetration, 98 percent. In comparison, the worldwide average for the age group 15–24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

Abstract

The objectives of the 2001 Population Census were:

a. to develop a set of benchmark data for different purposes, b. to provide data for small administrative areas of the country on population, housing and household facilities, c. to provide reliable frames for different types of sample surveys, d. to provide sex disaggregated data of the population and other variables related to households, demographic, social and economic conditions of the country, and e. to provide detailed information on women, children, the aged and the disabled.

Geographic coverage

National coverage

Analysis unit

Individual and household

Universe

The survey covered all household members (usual residents) in the household.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The 2001 census collected data based on short form for the complete enumeration of the benchmark information and the long form for the sample enumeration of other socio-economic and demographic information. The long form was administered for population dwellings in about 20 percent of the total housing units. Based on these, estimates were generated at the district level with reliable degree of precision.

The sampling scheme of the 2001 Population Census for the long form is summarized below.

1. The sampling covered the private households only. For the institutional population, Schedule-1 only was administered.

2. For the sampling, 75 administrative districts formed the main strata and VDC's and municipalities within the district formed the domains.

3. There were around 36,000 wards in the country at the time of the census. For the purpose of the census enumeration some of the large wards were further divided into sub-wards. These wards and sub-wards formed the EA's for sampling. The total number of EAs thus formed were around 40,000.

4. Sampling was carried out in each EA; housing unit being the sampling unit.

5. The list of housing units and households served as the sampling frame for the EA. The housing units were selected by systematic sampling method. The sampling interval taken was 8.

6. The list of selected housing units was made available to the enumerator for the enumeration. All households and persons found in the selected units were enumerated.

7. The ratio method was used in making estimates for the sample.

8. Tabulation groups were created separately for tabulation of persons and those for households. The main control variables for the majority of tabulations for persons were two variables: age sex. Tabulation groups for household tabulations were formed in a different manner: taking households as a tabulation group in the domain.

9. To implement the ratio estimation, first weights were calculated. The weights for sample data were computed by dividing the 100 percent counts for the same tabulation groups in the domain by sample counts for the same tabulation groups in the domain. To avoid inconsistency due to rounding, the figures were converted to whole numbers.

This detailed sampling procedure is provided in the document 'Sample Design for the 2001 Census of Housing and Population, Nepal'.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of schedules were prepared. Form 1 for complete enumeration and Form 2 for sample enumeration. Both schedules contained questions on household as well as individuals.

Content of the Census Questionnaire:

Short Form : Schedule 1 / FORM 1: (COMPLETE Enumeration)

Household Information (Question relating to Household) 1. Type of housing unit occupied by the household
2. Tenure of housing unit
3. Whether any land operated for agriculture
4. Area of agricultural land operated
5. Whether any livestock/ poultry raised
6. Number of livestock/ poultry on the holding
7. Whether any female member owned any house/land - Area of land owned
8. Whether any female member owned any livestock
- Number of livestock (big and small head)

Individual Information (Question relating to Individuals) 1. Serial number of household member
2. Full name of the household member
3. Male/Female
4. Age
5. Caste/Ethnicity
6. Relationship of the household head 7. Religion 8. Language spoken
- Mother tongue
- Second language
9. Citizenship 10. Disability

Long Form : Schedule 2 / FORM 2: (SAMPLE Enumeration)

Household Information (Question relating to Household) 1. Main source of drinking water
2. Main fuel used for cooking
3. Main source of light 4. Toilet facility
5. Household conveniences
6. Whether any death in the household
7. Information on the deceased person(s)
- Sex, age, date, and cause

Individual Information (Question relating to Individuals) 1. Serial number of household member
2. Full name and sex of the household member
3. Age
4. Place of birth
5. Duration of stay at the present place
6. Reason for staying in this district
7. Residence five years ago 8. Whether able to read and write
9. Level of education
10. Whether currently attending any school
11. Marital status
12. Age at first marriage
13. No. of children ever born
14. Any live births during last 12 months
15. Work usually done during the last 12 months 16. No. of months worked during the last 12 months
17. Occupation (type of usual work) 18. Industry (place of usual work)
19. Employment Status
20. Reasons for usually not working
21. Living arrangements of children below 16 years

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files

Response rate

At the time of census there were 3,914 VDC's and 58 municipalities. VDC's contained a total of 35,226 wards while urban areas contained 806 wards. Thus total numbers of wards in the country were 36,032. Out of these wards, 957 wards (including 2 urban wards) were affected due to the political disturbances in the country. Works in 83 VDC's of 12 districts were completely affected. 747 wards were completely affected. 2 wards of 2 municipalities and some wards of 37 VDC's were partially affected. In Salyan and Kalikot even listing was disturbed in some areas. In these districts population was estimated on the basis of listing sheet and following other estimation procedures.

For form 2, there is no available data for response rate.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors.

The sampling error is not available.

Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the population census 2001 to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

This method is discussed in detail in the document 'Sample Design for the 2001 Census of Housing and Population, Nepal'.

Data appraisal

The post enumeration survey was conducted to assess the completeness in the census enumeration and also the quality of the answers given to the questions asked in the population census. An independent verification of the census enumeration through a PES on a sample basis can provide an estimate of the extent of under enumeration or over enumeration that occurred at the census.

The PES 2001 was planned as an independent intensive re-interviews of all households in the sampled enumeration areas. The sample was restricted to a manageable size as mentioned elsewhere. A single stage stratified sampling design was adopted for the household enumeration sampling 7900 households and a two stage stratified design was used for the individual questionnaire. The Dual System Estimation metod was adopted for the survey design.

The detailed information can be found in PES Report under Census Report.

Abstract

Censuses are principal means of collecting basic population and housing statistics required for social and economic development, policy interventions, their implementation and evaluation.The census plays an essential role in public administration. The results are used to ensure: • equity in distribution of government services • distributing and allocating government funds among various regions and districts for education and health services • delineating electoral districts at national and local levels, and • measuring the impact of industrial development, to name a few The census also provides the benchmark for all surveys conducted by the national statistical office. Without the sampling frame derived from the census, the national statistical system would face difficulties in providing reliable official statistics for use by government and the public. Census also provides information on small areas and population groups with minimum sampling errors. This is important, for example, in planning the location of a school or clinic. Census information is also invaluable for use in the private sector for activities such as business planning and market analyses. The information is used as a benchmark in research and analysis.

Census 2011 was the third democratic census to be conducted in South Africa. Census 2011 specific objectives included: - To provide statistics on population, demographic, social, economic and housing characteristics; - To provide a base for the selection of a new sampling frame; - To provide data at lowest geographical level; and - To provide a primary base for the mid-year projections.

Geographic coverage

National

Analysis unit

Households, Individuals

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

About the Questionnaire : Much emphasis has been placed on the need for a population census to help government direct its development programmes, but less has been written about how the census questionnaire is compiled. The main focus of a population and housing census is to take stock and produce a total count of the population without omission or duplication. Another major focus is to be able to provide accurate demographic and socio-economic characteristics pertaining to each individual enumerated. Apart from individuals, the focus is on collecting accurate data on housing characteristics and services.A population and housing census provides data needed to facilitate informed decision-making as far as policy formulation and implementation are concerned, as well as to monitor and evaluate their programmes at the smallest area level possible. It is therefore important that Statistics South Africa collects statistical data that comply with the United Nations recommendations and other relevant stakeholder needs.

The United Nations underscores the following factors in determining the selection of topics to be investigated in population censuses: a) The needs of a broad range of data users in the country; b) Achievement of the maximum degree of international comparability, both within regions and on a worldwide basis; c) The probable willingness and ability of the public to give adequate information on the topics; and d) The total national resources available for conducting a census.

In addition, the UN stipulates that census-takers should avoid collecting information that is no longer required simply because it was traditionally collected in the past, but rather focus on key demographic, social and socio-economic variables.It becomes necessary, therefore, in consultation with a broad range of users of census data, to review periodically the topics traditionally investigated and to re-evaluate the need for the series to which they contribute, particularly in the light of new data needs and alternative data sources that may have become available for investigating topics formerly covered in the population census. It was against this background that Statistics South Africa conducted user consultations in 2008 after the release of some of the Community Survey products. However, some groundwork in relation to core questions recommended by all countries in Africa has been done. In line with users' meetings, the crucial demands of the Millennium Development Goals (MDGs) should also be met. It is also imperative that Stats SA meet the demands of the users that require small area data.

Accuracy of data depends on a well-designed questionnaire that is short and to the point. The interview to complete the questionnaire should not take longer than 18 minutes per household. Accuracy also depends on the diligence of the enumerator and honesty of the respondent.On the other hand, disadvantaged populations, owing to their small numbers, are best covered in the census and not in household sample surveys.Variables such as employment/unemployment, religion, income, and language are more accurately covered in household surveys than in censuses.Users'/stakeholders' input in terms of providing information in the planning phase of the census is crucial in making it a success. However, the information provided should be within the scope of the census.

1. The Household Questionnaire is divided into the following sections:
2. Household identification particulars

3. Individual particulars Section A: Demographics Section B: Migration Section C: General Health and Functioning Section D: Parental Survival and Income Section E: Education Section F: Employment Section G: Fertility (Women 12-50 Years Listed) Section H: Housing, Household Goods and Services and Agricultural Activities Section I: Mortality in the Last 12 Months The Household Questionnaire is available in Afrikaans; English; isiZulu; IsiNdebele; Sepedi; SeSotho; SiSwati;Tshivenda;Xitsonga

4. The Transient and Tourist Hotel Questionnaire (English) is divided into the following sections:

5. Name, Age, Gender, Date of Birth, Marital Status, Population Group, Country of birth, Citizenship, Province.

6. The Questionnaire for Institutions (English) is divided into the following sections:

7. Particulars of the institution

8. Availability of piped water for the institution

9. Main source of water for domestic use

10. Main type of toilet facility

11. Type of energy/fuel used for cooking, heating and lighting at the institution

12. Disposal of refuse or rubbish

13. Asset ownership (TV, Radio, Landline telephone, Refrigerator, Internet facilities)

14. List of persons in the institution on census night (name, date of birth, sex, population group, marital status, barcode number)

15. The Post Enumeration Survey Questionnaire (English)

These questionnaires are provided as external resources.

Cleaning operations

Data editing and validation system The execution of each phase of Census operations introduces some form of errors in Census data. Despite quality assurance methodologies embedded in all the phases; data collection, data capturing (both manual and automated), coding, and editing, a number of errors creep in and distort the collected information. To promote consistency and improve on data quality, editing is a paramount phase in identifying and minimising errors such as invalid values, inconsistent entries or unknown/missing values. The editing process for Census 2011 was based on defined rules (specifications).

The editing of Census 2011 data involved a number of sequential processes: selection of members of the editing team, review of Census 2001 and 2007 Community Survey editing specifications, development of editing specifications for the Census 2011 pre-tests (2009 pilot and 2010 Dress Rehearsal), development of firewall editing specifications and finalisation of specifications for the main Census.

Editing team The Census 2011 editing team was drawn from various divisions of the organisation based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors. Census 2011 editing team was drawn from various divisions of the organization based on skills and experience in data editing. The team thus composed of subject matter specialists (demographers and programmers), managers as well as data processors.

The Census 2011 questionnaire was very complex, characterised by many sections, interlinked questions and skipping instructions. Editing of such complex, interlinked data items required application of a combination of editing techniques. Errors relating to structure were resolved using structural query language (SQL) in Oracle dataset. CSPro software was used to resolve content related errors. The strategy used for Census 2011 data editing was implementation of automated error detection and correction with minimal changes. Combinations of logical and dynamic imputation/editing were used. Logical imputations were preferred, and in many cases substantial effort was undertaken to deduce a consistent value based on the rest of the household’s information. To profile the extent of changes in the dataset and assess the effects of imputation, a set of imputation flags are included in the edited dataset. Imputation flags values include the following: 0 no imputation was performed; raw data were preserved 1 Logical editing was performed, raw data were blank 2 logical editing was performed, raw data were not blank 3 hot-deck imputation was performed, raw data were blank 4 hot-deck imputation was performed, raw data were not blank

Data appraisal

Independent monitoring and evaluation of Census field activities Independent monitoring of the Census 2011 field activities was carried out by a team of 31 professionals and 381 Monitoring

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

The Community Resilience Estimates (CRE) program provides an easily understood metric for how socially vulnerable every neighborhood in the United States is to the impacts of disasters.This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census, CRE, and ACS when using this data.Overview:Community resilience is the capacity of individuals and households within a community to prepare, absorb, respond, and recover from a disaster. Local planners, policy makers, public health officials, emergency managers, and community stakeholders need a variety of estimates to help assess the potential resiliency and vulnerabilities of communities and their constituent populations to help prepare and plan mitigation, recovery, and response strategies. Community Resilience Estimates (CRE) focuses on developing a tool to identify socio-economic vulnerabilities within populations. The 2022 Community Resilience Estimates (CRE) are produced using information on individuals and households from the 2022 American Community Survey (ACS) and the Census Bureau’s Population Estimates Program (PEP). The CRE uses small area modeling techniques that can be used for a broad range of disaster related events (hurricanes, tornadoes, floods, economic shocks, etc.) to identify population concentrations likely to be relatively more impacted by and have greater difficulties overcoming disasters. The end result is a data product which measures vulnerability more accurately and timely. Data:The ACS is a nationally representative survey with data on the characteristics of the U.S. population. The sample is selected from all counties and county-equivalents and has a sample size of about 3.5 million housing units each year. It is the premier source for timely and detailed population and housing information about our nation and its communities. We also use auxiliary data from the PEP, the Census Bureau’s program that produces and publishes estimates of the population living at a given time within a geographic entity in the U.S. and Puerto Rico. We use population data from the PEP by age group, race and ethnicity, and sex. Since the PEP does not go down to the census tract level, the CRE uses the Public Law 94-171 summary files (PL94) and Demographic Housing Characteristics File (DHC) tables from the 2020 Decennial Census to help produce the population base estimates. Once the weighted estimates are tabulated, small area modeling techniques are used to create the estimates for the CRE. Components of Social Vulnerability (SV): Resilience to a disaster is partly determined by the components of social vulnerability exhibited within a community’s population. To measure these components and construct the community resilience estimates, we designed population estimates based on individual- and household-level components of social vulnerability. These components are binary indicators or variables that add up to a maximum of 10 possible components using data from the ACS. The specific ACS-defined measures we use are as follows: Components of Social Vulnerability (SV) for Households (HH) and Individuals (I):SV 1: Income-to-Poverty Ratio (IPR) < 130 percent (HH). SV 2: Single or zero caregiver household - only one or no individuals living in the household who are 18-64 (HH). SV 3: Unit-level crowding with >= 0.75 persons per room (HH). SV 4: Communication barrier defined as either: Limited English-speaking households1 (HH) orNo one in the household over the age of 16 with a high school diploma (HH). SV 5: No one in the household is employed full-time, year-round. The flag is not applied if all residents of the household are aged 65 years or older (HH). SV 6: Disability posing constraint to significant life activity. Persons who report having any one of the six disability types (I): hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. SV 7: No health insurance coverage (I). SV 8: Being aged 65 years or older (I). SV 9: No vehicle access (HH). SV 10: Households without broadband internet access (HH). Each individual is assigned a 0 or 1 for each of the components based upon their individual or household attributes listed above. It is important to note that SV 4 is not double flagged. An individual will be assigned a 1, if either of the characteristics is true for their household. For example, if a household is linguistically isolated and no one over the age of 16 has attained a high school diploma or more education, the household members are only flagged once. The result is an index that produces aggregate-level (tract, county, and state) small area estimates: the CRE. The CRE provide an estimate for the number of people with a specific number of social vulnerabilities. In its current data file layout form, the estimates are categorized into three groups: zero , one-two, or three plus social vulnerability components. Differences with CRE 2021:The number of census tracts have increased from 84,414 in CRE 2021 to 84,415 in CRE 2022. This is due to the boundary changes in Connecticut implemented in 2022 census data products. To accommodate the boundary change, Connecticut also now has nine planning regions instead of eight counties in CRE 2022.To avoid confusion, the modeled rates are now set to equal zero in CRE 2022 for geographic areas with zero population in universe. To improve the population base estimates, CRE 2022 uses more detailed decennial estimates from the 2020 DHC in addition to PL94, whereas CRE 2021 just used PL94 due to availability at the time. See “2022 Community Resilience Estimates: Detailed Technical Documentation” for more information. Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). This dataset does not contain values for Puerto Rico or Island Areas at any level of geography.Further Information:Community Resilience Estimates Program Website https://www.census.gov/programs-surveys/community-resilience-estimates.htmlCommunity Resilience Estimates Technical Documentation https://census.gov/programs-surveys/community-resilience-estimates/technical-documentation.htmlFor Data Questionssehsd.cre@census.gov