Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Households and Group Quarters

UNITS IDENTIFIED: - Dwellings: No - Vacant units: No - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Households: Dwelling places with fewer than five persons unrelated to a household head, excluding institutions and transient quarters. - Group quarters: Institutions, transient quarters, and dwelling places with five or more persons unrelated to a household head.

Universe

Residents of the 50 states (not the outlying areas).

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: U.S. Census Bureau

SAMPLE UNIT: Household

SAMPLE FRACTION: 1%

SAMPLE SIZE (person records): 1,799,888

Mode of data collection

Face-to-face [f2f]

Research instrument

The 1960 census used a machine-readable household form. Separate forms were used for each housing unit. Housing questions were included on the same form as the population items. Every fourth enumeration unit received a "long form," containing supplemental sample questions that were asked of all members of the unit. Sample questions are available for all individuals in every unit. Of the units receiving a long form, four-fifths received one version (the 20% questionnaire), and one-fifth received a second version with the same population questions but slightly different housing questions (the 5% questionnaire).

Response rate

UNDERCOUNT: No official estimates

Census Year 1960 Census Tracts. The dataset contains polygons representing CY 1960 census tracts, created as part of the D.C. Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Census tracts were identified from maps provided by the U.S. Census Bureau and the D.C. Office of Planning. The tract polygons were created by selecting street arcs from the WGIS planimetric street centerlines. Where necessary, polygons were also heads-up digitized from 1995/1999 orthophotographs.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Occupied dwelling

UNITS IDENTIFIED: - Dwellings: Not available in microdata sample - Households: Not available in microdata sample - Individuals: Yes - Group quarters: Not identified

UNIT DESCRIPTIONS: - Group quarters: Not defined

Universe

Mexican residents; foreign born living more than 6 months in Mexico, excluding diplomatic personnel

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: CELADE

SAMPLE DESIGN: Representative sample of individuals.

SAMPLE UNIT: Individuals

SAMPLE FRACTION: 1.5%

SAMPLE SIZE (person records): 502,800

Mode of data collection

Face-to-face [f2f]

Research instrument

Separate enumeration form for each census block

Response rate

UNDERCOUNT: No official estimates

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwellings, households and persons

UNITS IDENTIFIED: - Dwellings: Not available in microdata sample - Vacant units: no - Households: Not available in microdata sample - Individuals: yes - Group quarters: Not available in microdata sample - Special populations: no

UNIT DESCRIPTIONS: - Dwellings: A structurally separate and independent place or building that has been constructed, built, converted, or made available as a permanent or temporary place of lodging. This includes any kind of shelter, fixed or mobile, occupied as a place of lodging at the time of the census. - Households: A private census household is made up of all of the occupants of a private dwelling. It can be made up of one person who is the only occupant of the dwelling. In cases where there is more than one occupant in the dwelling, the private census household is made up of the relatives, guests, renters, and domestic employees of the person considered to be the head of the family, as well as by all other occupants. - Group quarters: A place of lodging for a group of persons who are usually not related and who generally live together for reasons of discipline, health, education, religious life, military training, work, etc. Examples include: reformatories, military bases, jails, hospitals, sanatoriums, nursing homes for the elderly, boarding schools, convents, orphanages, worker?s camps, hotels, hostels, hospices, and other similar places of lodging.

Universe

All persons who spent the night of August 6th to August 7th, 1960 in the dwelling. Usual residents who were absent the night of August 6th to August 7th, 1960 due to work, or due to accidental reasons (a party, wake, etc.) were also enumerated. Foreign diplomats and their families were not enumerated.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Centro Latinoamericano de Demografia (CELADE)

SAMPLE UNIT: Individuals

SAMPLE FRACTION: 6.6%

SAMPLE SIZE (person records): 201,556

Mode of data collection

Face-to-face [f2f]

Research instrument

Single enumeration form that requested information on dwellings, households, and individuals.

Response rate

COVERAGE: 92.2%

The 1960 Census Tract files were originally created by keypunching the data from the printed publications prepared by the Bureau of the Census. The work was done under the direction of Dr. Donald Bogue, whose wife, Elizabeth Mullen Bogue, completed much of the data work. Subsequently, the punchcards were converted to data files and transferred to the National Archive and Records Administration (NARA). ICPSR received copies of these files from NARA and converted the binary block-length records to ASCII format.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The census covered all households in South Africa

Kind of data

Census enumeration data

Mode of data collection

Face-to-face [f2f]

Research instrument

The dataset was obtained without a questionnaire and this is currently being sourced by DataFirst

All establishments in Japan on the date of the census falling into JSIC Division G: Wholesale and Retail Trade, except the following: 1. Establishments belonging to the national government or to public or government corporations. 2. Establishments operated by foreign governments or foreign troops stationed in Japan. 3. Outdoor stalls, hawkers, food stalls, street vendors, peddlers, etc. 4. Shops on station platforms; shops within theaters, cinemas, baseball stadiums, etc.; shops etc. operating as welfare facilities within government offices, schools, companies, factories, etc. 5. Establishments that have been closed for three months or more. 6. Establishments preparing to start business.

How many households are in the U.S.?

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

What counts as a household?

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

Household changes

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

All establishments in Japan on the date of the census, except the following: 1. Establishments that, based on the 1957 version of the Japan Standard Industrial Classification, are principally engaged in Division A: Agriculture, Division B: Forestry and Hunting, or Division C: Fisheries and Aquaculture, or that belong to Major Group 82: Domestic Services or Division M: Government (prefectural offices, municipal town halls, etc.). 2. Establishments that do not have facilities permanently fixed in one place, such as street vendors, food stalls, shoeshiners, and newspaper stands. 3. Establishments operating on the premises of another establishment and solely for that establishment, such as shops at station platforms, theaters, and cinemas, or cafeterias, barbers, and shops on the premises of companies, government offices, etc. 4. Establishments that closed down in the three months before the census date or that were expected to close down within three months of the census date. 5. Establishments without paid employees, such as a labor union without a full-time employee. 6. Establishments without permanent employees at the establishment, such as shrines that do not have a permanent employee. 7. Establishments managed and operated by foreign troops stationed in Japan or foreign governments.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Dwelling

UNITS IDENTIFIED: - Dwellings: No (dwellings in original sample are interpreted as households in IPUMS) - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Households: Structurally independent living quarters, consisting of one or more rooms with a private entrance, serving up to three families. - Group quarters: Group living together under relations of administrative subordination; group of six or more persons not related by kinship; or a dwelling with more than 3 families.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Instituto Brasileiro de Geografia e Estatística

SAMPLE UNIT: Household (called "dwelling" in original sample)

SAMPLE FRACTION: 5% (but excluding certain states; see above)

SAMPLE SIZE (person records): 3,001,439

Mode of data collection

Face-to-face [f2f]

Research instrument

Long and short enumeration forms. The short form contains general information about the characteristics of the dwelling and each of persons in the dwelling. The long form contains general and more specific information about the characteristics of the dwelling, families, and each of the people in the dwellings and was applied to a 25% sample of the population.

Response rate

COVERAGE: No official estimates, UNDERCOUNT: No official estimates

（1）農業事業体調査：日本全国の農家および農家以外の農業事業体 （2）農業集落調査：日本全国の全農業集落 （3）林業事業体調査：日本全国の林家および林家以外の林業事業体 （4）林業地域調査：日本全土の旧市町村

Abstract

For more than 150 years, the U.S. Department of Commerce, Bureau of the Census, conducted the census of agriculture. However, the 2002 Appropriations Act transferred the responsibility from the Bureau of the Census to the U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS). The 2007 Census of Agriculture for the U.S. Virgin Islands is the second census in the U.S. Virgin Islands conducted by NASS. The census of agriculture is taken to obtain agricultural statistics for each county, State (including territories and protectorates), and the Nation. The first U.S. agricultural census data were collected in 1840 as a part of the sixth decennial census. From 1840 to 1920, an agricultural census was taken as a part of each decennial census. Since 1920, a separate national agricultural census has been taken every 5 years. The 2007 census is the 14th census of agriculture of the U.S. Virgin Islands. The first, taken in 1920, was a special census authorized by the Secretary of Commerce. The next agriculture census was taken in 1930 in conjunction with the decennial census, a practice that continued every 10 years through 1960. The 1964 Census of Agriculture was the first quinquennial (5-year) census to be taken in the U.S. Virgin Islands. In 1976, Congress authorized the census of agriculture to be taken for 1978 and 1982 to adjust the data-reference year to coincide with the 1982 Economic Censuses covering manufacturing, mining, construction, retail trade, wholesale trade, service industries, and selected transportation activities. After 1982, the agriculture census reverted to a 5-year cycle. Data in this publication are for the calendar year 2007, and inventory data reflect what was on hand on December 31, 2007. This is the same reference period used in the 2002 census. Prior to the 2002 census, data was collected in the summer for the previous 12 months, with inventory items counted as what was on hand as of July 1 of the year the data collection was done.

Objectives: The census of agriculture is the leading source of statistics about the U.S. Virgin Islands’s agricultural production and the only source of consistent, comparable data at the island level. Census statistics are used to measure agricultural production and to identify trends in an ever changing agricultural sector. Many local programs use census data as a benchmark for designing and evaluating surveys. Private industry uses census statistics to provide a more effective production and distribution system for the agricultural community.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was a farm, defined as "any place from which USD 500 or more of agricultural products were produced and sold, or normally would had been sold, during the calendar year 2007". According to the census definition, a farm is essentially an operating unit, not an ownership tract. All land operated or managed by one person or partnership represents one farm. In the case of tenants, the land assigned to each tenant is considered a separate farm, even though the landlord may consider the entire landholding to be one unit rather than several separate units.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Method of Enumeration As in the previous censuses of the U.S. Virgin Islands, a direct enumeration procedure was used in the 2007 Census of Agriculture. Enumeration was based on a list of farm operators compiled by the U.S. Virgin Islands Department of Agriculture. This list was compiled with the help of the USDA Farm Services Agency located in St. Croix. The statistics in this report were collected from farm operators beginning in January of 2003. Each enumerator was assigned a list of individuals or farm operations from a master enumeration list. The enumerators contacted persons or operations on their list and completed a census report form for all farm operations. If the person on the list was not operating a farm, the enumerator recorded whether the land had been sold or rented to someone else and was still being used for agriculture. If land was sold or rented out, the enumerator got the name of the new operator and contacted that person to ensure that he or she was included in the census.

(b) Frame The census frame consisted of a list of farm operators compiled by the U.S. Virgin Islands DA. This list was compiled with the help of the USDA Farm Services Agency, located in St. Croix.

(c) Complete and/or sample enumeration methods The census was a complete enumeration of all farm operators registered in the list compiled by the United States of America in the CA 2007.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire (report form) for the CA 2007 was prepared by NASS, in cooperation with the DA of the U.S. Virgin Islands. Only one questionnaire was used for data collection covering topics on:

• Land owned
• Land use
• Irrigation
• Conservation programs and crop insurance
• Field crops
• Bananas, coffee, pineapples and plantain crops
• Hay and forage crops
• Nursery, Greenhouse, Floriculture, Sod and tree seedlings
• Vegetables and melons
• Hydroponic crops
• Fruit
• Root crops
• Cattle and calves
• Poultry
• Hogs and pigs
• Aquaculture
• Other animals and livestock products
• Value of sales
• Organic agriculture
• Federal and commonwealth agricultural program payments
• Income from farm-related sources
• Production expenses
• Farm labour
• Fertilizer and chemicals applied
• Market value of land and buildings
• Machinery, equipment and buildings
• Practices
• Type of organization
• Operator characteristics

The questionnaire of the 2007 CA covered 12 of the 16 core items' recommended for the WCA 2010 round.

Cleaning operations

DATA PROCESSING The processing of the 2007 Census of Agriculture for the U.S. Virgin Islands was done in St. Croix. Each report form was reviewed and coded prior to data keying. Report forms not meeting the census farm definition were voided. The remaining report forms were examined for clarity and completeness. Reporting errors in units of measures, illegible entries, and misplaced entries were corrected. After all the report forms had been reviewed and coded, the data were keyed and subjected to a thorough computer edit. The edit performed comprehensive checks for consistency and reasonableness, corrected erroneous or inconsistent data, supplied missing data based on similar farms, and assigned farm classification codes necessary for tabulating the data. All substantial changes to the data generated by the computer edits were reviewed and verified by analysts. Inconsistencies identified, but not corrected by the computer, were reviewed, corrected, and keyed to a correction file. The corrected data were then tabulated by the computer and reviewed by analysts. Prior to publication, tabulated totals were reviewed by analysts to identify inconsistencies and potential coverage problems. Comparisons were made with previous census data, as well as other available data. The computer system provided the capability to review up-to-date tallies of all selected data items for various sets of criteria which included, but were not limited to, geographic levels, farm types, and sales levels. Data were examined for each set of criteria and any inconsistencies or potential problems were then researched by examining individual data records contributing to the tabulated total. W hen necessary, data inconsistencies were resolved by making corrections to individual data records.

Sampling error estimates

The accuracy of these tabulated data is determined by the joint effects of the various nonsampling errors. No direct measures of these effects have been obtained; however, precautionary steps were taken in all phases of data collection, processing, and tabulation of the data in an effort to minimize the effects of nonsampling errors.

Abstract

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system.

The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Geographic coverage

National coverage

Analysis unit

Persons and households

UNITS IDENTIFIED: - Dwellings: No - Vacant units: Yes - Households: Yes - Individuals: Yes - Group quarters: Yes

UNIT DESCRIPTIONS: - Dwellings: Any place or premises structurally separate or independent, that has been built, made, or converted for use as permanent or temporary housing or lodging of persons or also any class of lodging, fixed or mobile, occupied by persons as a living quarters on the date of the census. - Households: All of the occupants of a private dwelling. The household can consist of a person who lives alone or a group of persons that includes the Head of household, the relatives of this person, boarders, tenants, domestic servants, and any other occupant. When the number of boarders and/or tenants is 6 or more, it constitutes a ?non-family group?.

Universe

All persons, whether members of the family or not, who slept in the dwelling the night of the 10th to 11th of December. All persons who live in the dwelling, even if they have not slept there, if they spent the night away from the dwelling because of work, dance, a wake, hunting, fishing, or any other incidental reason.

Kind of data

Census/enumeration data [cen]

Sampling procedure

MICRODATA SOURCE: Centro Latinoamericano de Demografia (CELADE)

SAMPLE DESIGN: 1-in-20 sample drawn by the national statistical office. Method of sampling unknown.

SAMPLE FRACTION: 5%

SAMPLE SIZE (person records): 53,553

Mode of data collection

Face-to-face [f2f]

Research instrument

Enumeration forms: (1) A single enumeration [family] form that requested information on the dwelling and individuals, (2) A group and individual form for non-family groups [group quarters], and (3) An enumeration [short] form for indigenous areas

Response rate

COVERAGE: 100%

VITAL SIGNS INDICATOR Commute Mode Choice (T2)

FULL MEASURE NAME Commute mode share by employment location

LAST UPDATED April 2020

DESCRIPTION Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of work.

DATA SOURCE U.S. Census Bureau: Decennial Census (1960-2000) - via MTC/ABAG Bay Area Census http://www.bayareacensus.ca.gov/transportation/Means19802000.htm

U.S. Census Bureau: American Community Survey Form B08601 (2018 only; place of employment) www.api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) For the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. "Other" includes bicycle, motorcycle, taxi, and other modes of transportation.

For the American Community Survey datasets, 1-year rolling average data was used for metros, region, and county geographic levels, while 5-year rolling average data was used for cities and tracts. This is due to the fact that more localized data is not included in the 1-year dataset across all Bay Area cities. Regional mode share was calculated using county modal data and calculating the weighted average based on county populations. "Auto" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. "Other" includes motorcycle, taxi, and other modes of transportation; bicycle mode share is broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents. Data for Napa County were not available due to small sample size.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary MSAs for the nine other major metropolitan areas.

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Households who are rearing livestock or kept poultry

Universe

Livestock or poultry household

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and 3 Livestock/poultry farming housing units were systematically selected and canvassed.

Sampling deviation

No Deviation

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC livestock and poultry questionnaire was divided into the following sections: - Identification/description of holdings - Funds, employment and earnings/wages - Livestock - Poultry - Fixed assets - Sales - Stock - Subsidy

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already enterd data. The completed questionnaires were collected and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

The response rate at EA level was 100 percent, while 99.3 percent was recorded at housing units level.

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data. There were two levels of supervision involving the supervisors at the first level, NBS State Officers and Zonal Controllers at second level and finally the NBS Headquarters staff constituting the second level supervision.

The Idaho Statistics Update project is made possible by a 1997/98 Seed Grant from the University of Idaho Research Office. The grant was used to hire three student assistants to input the data and to convert the data to a usable format for the Web. The underta king of this project is possible to accomplish only with the assistance of several librarians at the University of Idaho. Some of the original chapters included here were published as volume one of the Idaho Statistical Abstract, 4th edition, by University of Idaho, Center for Business Development and Research. Efforts were made to use the sources listed in the original chapters to update the data when available. The chapters intended for volume 2 of Idaho Statistical Abstract, 4th edition, are new data collected from various sources by Lily Wai, the Compiler-in-Chief. The Idaho Department of Commerce also contributed some funds for this project. This is an on-going project with periodic updates planned when funding becomes available. In the interest of improving the quality and coverage of future updates, users of this site are encouraged to address suggestions to Lily Wai, Head of Government Documents, University of Idaho Library, Moscow, Idaho 83844-2353.

Census of historic parks and gardens. When a garden is included in the census, it is recorded on a card and documented in a simplified way. Sheets are a planning tool. However, they do not have the force of law. The limit for enrollment in the census was set at 1960. The main evaluation criteria are as follows: material substance, historical importance, general context. In summary, it is not a question of raising the most beautiful gardens of a municipality, but the most characteristic gardens of an era. With the Florence Charter or Charter of Historic Gardens, ICOMOS (International Council on Monuments and Sites) initiated in 1981 the European Campaign for the Census of Historic Gardens. It is in this context that the working group "Conservation of historical gardens" of ICOMOS Switzerland has set itself the goal of drawing up, in the form of a census, a very simple list of the historic parks and gardens of our country. This census is mainly supported by the Federal Office of Culture, Swiss Heritage and the Swiss Federation of Landscape Architects.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

VITAL SIGNS INDICATOR
Commute Mode Choice (T1)

FULL MEASURE NAME
Commute mode share by residential location

LAST UPDATED
January 2023

DESCRIPTION
Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter usually uses to travel to work, such as driving alone, biking, carpooling or taking transit. The dataset includes metropolitan area, regional, county, city and census tract tables by place of residence.

DATA SOURCE
U.S. Census Bureau: Decennial Census (1960, 1970) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation/Means19602000.htm

U.S. Census Bureau: Decennial Census (1980-2000) - via MTC/ABAG Bay Area Census - http://www.bayareacensus.ca.gov/transportation/Means19802000.htm

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2006-2021
Form B08301 (1-year and 5-year)

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Commute mode choice, also known as commute mode share, refers to the mode of transportation that a commuter usually uses to travel to work, such as driving alone, biking, carpooling or taking transit. For the decennial Census datasets, the breakdown of auto commuters between drive alone and carpool is not available before 1980. American Community Survey 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. This will result in discrepancies in cases like San Francisco where it is both a city and a county. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020. Additionally, for the County by place of residence breakdown, Napa was missing ACS 1-Year commute mode choice data for all modes for 2007, 2008, 2011 and 2021. 5-Year estimates were used to fill the missing data for 2011 and 2021, but not 2007 or 2008 since the 5-Year estimates start in 2009.

Regional mode shares are population-weighted averages of the nine counties' modal shares. "Auto" includes drive alone and carpool for the simple data tables and is broken out in the detailed data tables accordingly, as it was not available before 1980. "Transit" includes public operators (Muni, BART, etc.) and employer-provided shuttles (e.g., Google shuttle buses). "Other" includes motorcycle, taxi, and other modes of transportation; bicycle mode share was broken out separately for the first time in the 2006 data and is shown in the detailed data tables. Census tract data is not available for tracts with insufficient numbers of residents or workers.

The metropolitan area comparison was performed for the nine-county San Francisco Bay Area in addition to the primary metropolitan statistical areas (MSAs) for other major metropolitan areas.