Abstract

The main aim and objectives of the census is to provide benchmark statistics and a comprehensive profile of the population and households of Niue at a given time. This information obtained from the census is very crucial and useful in providing evidence to decision making and policy formulation for the Government, Business Community, Local Communities or Village Councils, Non Government Organisations of Niue and The International Communities who have an interest in Niue and its people.

Geographic coverage

National

Analysis unit

• Household
• Individual/Person
• Members Oversea

Universe

All households in Niue and all persons in the household including those temporarily overseas and those absent for not more than 12 months.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionaire was published in English, a translated questionnaire was on hand when on demand by the respondent.

The questionnaire design differed slightly from the design of previous census questionnaires. As usual, government departments were asked to submit a list of questions on any specific topic they would like to add. Responses were not forthcoming in this census, although a few new questions were included.

There were two types of questionaires used in the census: the household questionaire and the individual questionnaire. An enumerator manual was prepared to assist the enumerators in their duties.

The questionnaire was pre-tested by the enumerators before they were to go out for field enumeration.

Cleaning operations

Census processing began as soon as questionaires were checked and coded. Forms were checked, edited and coded before being entered into the computer database.

Data processing was assisted by the Secretariat of the Pacific Community (SPC) using the computer software program CSPro for data entry and for generating tables. Tables were then exported to Excel for analysis.

Occupation and Industry were coded using the United Nations International Standard Classification of Occupation and International Standard Industrial Classification.

It is standard practice that as each area was completed the forms were first checked by the field supervisors for missing information and obvious inconsistencies. Omissions and errors identified at this stage were corrected by the enumerators.

The next stage was for the field supervisors to go through the completed forms again in the office to check in more detail for omissions and logical inconsistencies. Where they were found, the supervisors were responsible to take the necessary action.

Once the questionnaires had been thoroughly checked and edited, they were then coded in preparation for data processing.

Checking, editing and coding of the questionnaires in office were done after normal working hours as to ensure that the confidentiality of the survey is well observed.

This feature set contains jobs projections from Projections 2040 for the San Francisco Bay Region. This forecast represents job projections resulting from Plan Bay Area 2040. Numbers are provided by Census Tract. Jobs numbers are included for 2010 (two versions), 2015, 2020, 2025, 2030, 2035, and 2040. For 2010, two data points are provided:A tabulation (base year A) from the 2010 model simulation (base year A); and(Preferred) A tabulation (base year B) from the 2010 pre-run microdata, designed to approximate (but may still differ from) Census 2010 counts.Projection data is included for:Total jobsAgriculture and natural resources jobsFinancial and professional service jobsHealth, educational, and recreational service jobsRetail jobsManufacturing, wholesale, and transportation jobsInformation, government, and construction jobsThis feature set was assembled using unclipped Census Tract features. For those who prefer Projections 2040 data using jurisdiction features with ocean and bay waters clipped out, the data in this feature service can be joined to San Francisco Bay Region 2010 Census Tracts (clipped). Clipping the Census Tract features does result in the removal of some water tracts, which are usually empty, so there is a difference in the number of features between the two services.Other Projections 2040 feature sets:Households and population per countyHouseholds and population per jurisdiction (incorporated place and unincorporated county)Households and population per Census TractJobs and employment per countyJobs and employment per jurisdiction (incorporated place and unincorporated county)Female population, by age range, per countyFemale population, by age range, per jurisdiction (incorporated place and unincorporated county)Male population, by age range, per countyMale population, by age range, per jurisdiction (incorporated place and unincorporated county)Total population, by age range, per countyTotal population, by age range, per jurisdiction (incorporated place and unincorporated county)

Abstract

The Census is the official count of population and dwellings in Tonga, providing a ‘snapshot’ of the society and its most precious resource, its people, at a point in time. The official reference period of the census was midnight, the 30th of November, 2006.

The census provides a unique source of detailed demographic, social and economic data relating the entire population at a single point in time. Census information is used for policy setting and implementation, research, planning and other decision-making. The census is often the primary source of information used for the allocation of public funding, especially in areas such as health, education and social policy. The main users of this information are the government, local authorities, education facilities (such as schools and tertiary organizations), businesses, community organizations and the public in general.

The 2006 Census was taken under the authority of Section 8 of Statistical Act Chap. 53 of 1978 which empowers the Minister of Finance to make regulations necessary to conduct the population Census. This regulation was approved by the Cabinet and cited as Census Regulation 2006. The Census regulations also indicate that the Government Statistician would be responsible for the administration and completion of the Census. In addition, the regulations enabled the Statistics Department to carry out the necessary activities required to plan, manage and implement all the necessary Census activities.

Census planning and management

From a planning and management perspective, the Census had two main objectives. Firstly, it was to ensure that the process of collecting, compiling, evaluating, analyzing and disseminating of demographic, economic and social data was conducted in a timely and accurate manner. The development of procedures and processes for the 2006 Census of Population and Housing made use of the lessons learned in previous censuses, and built upon recommendations for improvements.

Secondly, it was a valuable opportunity for building the capacities of employees of the Statistics Department (SD), thus resulting in enhancing the image, credibility and reputation of the Department and at the same time, strengthening its infrastructure. Emphasis was placed on having a senior staff with a wide perspective and leadership qualities. Through the use of vision, planning, coordination, delegation of responsibility and a strong team spirit, the census work was conducted in an effective and efficient manner. Staffs at all levels were encouraged to have an innovative mindset in addressing issues. Incentives for other parties to participate, both within Statistics Department Tonga Tonga 2006 Census of Population and Housing viii and outside the government, were encouraged. As a result, the wider community including donors such as AusAID, the Secretariat of the Pacific Community (SPC) in Noumea, that provided the technical assistance and the general public, were able to support the census project.

Extensive and detailed planning is needed to conduct a successful census. Areas that required planning include: enumeration procedures and fieldwork, public communication, data processing and output systems, mapping and the design of census block boundaries, dissemination procedures, content determination and questionnaire development and training. These aspects, and how they interacted with each other, played a crucial role in determining the quality of all of the census outputs. Each phase therefore required careful, methodical planning and testing. The details of such activities, and their implementation and responsibilities were assigned to 5 subcommittees composed of staff members of the SD.

Organizational structure of the Census

A census organizational structure is designed to implement a number of interrelated activities. Each of these activities was assigned to a specific sub-committee. The census manuals provided guidelines on processes, organizational structures, controls for quality assurance and problem solving. The challenge for managers was developing a work environment that enabled census personnel to perform all these tasks with a common goal in mind. Each sub-committee was responsible for its own outputs, and specific decisions for specific situations were delegated to the lowest level possible. Problem situations beyond the scope of the sub-committee were escalated to the next higher level.

The organizational structure of the census was as follows: a) The Steering Committee (consisting of the Head of both Government and nongovernment organizations), chaired by Secretary for Finance with the Government Statistician (GS) as secretary. b) The Census Committee (consisted of all sub-committee leaders plus the GS, and chaired by the Assistant Government Statistician (AGS) who was the officer in charge of all management and planning of the Census 2006 operations. c) There were five Sub-committees (each sub-committee consisted of about 5 members and were chaired by their Sub-committee leader). These committees included: Mapping, Publicity, Fieldwork, Training and Data Processing. In this way, every staff member of the SD was involved with the census operation through their participation on these committees.

The census steering committee was a high level committee that approved and endorsed the plans and activities of the census. Policy issues that needed to be addressed were submitted to the steering committee for approval prior to the census team and sub-committees designation of the activities necessary to address the tasks.

Part of the initial planning of the 2006 Census involved the establishment of a work-plan with specific time frames. This charted all activities that were to be undertaken and, their impact and dependencies on other activities. These time frames were an essential part of the overall exercise, as they provided specific guides to the progress of each area, and alerted subcommittees’ team leaders (TL) to areas where problems existed and needed to be addressed. These also provided the SD staff with a clear indication of where and how their roles impacted the overall Census process.

Monitoring of the timeframe was an essential part of the management of the Census program. Initially, weekly meetings were held which involved the GS, AGS and team leaders (TL) of the Census committee. As the Census projects progressed, the AGS and TL’s met regularly with their sub-committees to report on the progress of each area. Decisions were made on necessary actions in order to meet the designated dates. Potential risks that could negatively affect the deadlines and actions were also considered at these meetings.

For the 5 sub-committees, one of their first tasks was to verify and amend their terms of reference using the “Strengths, Weaknesses, Opportunities and Threats” (SWOT) analysis methodology, as it applied to past censuses. Each committee then prepared a work-plan and listed all activities for which that particular sub-committee was responsible. This listing included the assignment of a responsible person, together with the timeline indicating the start and end dates required to complete that particular activity. These work-plans, set up by all the 5 sub-committees, were then used by the AGS to develop a detailed operational plan for all phases of the census, the activities required to complete these phases, start and end dates, the person responsible and the dependencies, - all in a Ghant chart format. These combined work-plans were further discussed and amended in the Census team and reported to the Steering committee on regular basis as required.

Geographic coverage

National coverage, which includes the 5 Divisions and both Urban and Rural Areas of Tonga.

Analysis unit

Individual and Households.

Universe

All individuals in private and institutional households.

Kind of data

Census/enumeration data [cen]

Sampling procedure

The National Population Census was a complete enumeration census, hence no sampling procedure was employed. A Mapping Sub-committee was formed to ensure complete coverage of the country.

The Mapping Sub-committee

Led by Mr. Winston Fainga'anuku, this committee's mandate was to ensure that good quality maps were produced. The objective was to ensure that the maps provided complete coverage of the country, were designed to accommodate a reasonable workload of one census enumerator and, that geographic identifiers could be used for dissemination purposes by the PopGIS system. Collaborations with the Ministry of Land, Survey and Natural Resources (MLSNR) began in 2004 to ensure that digitized maps for Tonga could be used for 2006 Census. Mr. Fainga'anuku was attached to the MLSNR in April 2005 to assist 'Atelea Kautoke, Samuela Mailau, Lilika and others to complete the task of digitizing the maps for Tonga. In addition, frequent visits by Mr. Scott Pontifex from the Secretariat of the Pacific Community (SPC) in Noumea, assisted to ensure that quality digitized maps were prepared. SPC also assisted by lending its digitizer which was used in this mapping project. The staff of the Statistics Department (SD) visited household sites throughout Tongatapu and the main outer islands. This exercise was to redesign the Census Block boundaries by amalgamating or splitting existing census blocks to achieve an average of 50 households per census block. Various updates within the census block maps were made. These included the names of the head of household; roads and other landmarks to ensure that current and accurate information was provided to the enumerators. Reliable maps, both for enumerators and supervisors are necessary ingredients to assist in avoiding any under or over - counting during

Analysis unit

Persons and households

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: A census building is a free standing structure which is fixed on earth or on water permanently or temporarily (regardless the material used in building it) and it is used for residence or doing any activity in it (work, sport, pious work….etc.). - Households: Consist of one person or a group of persons (related or non related to each other) sharing their housing unit and food together. A household includes: a) servants and the like who are living with the household; b) visitors who spent the census night with the household (except military persons); c) household members who spent the census night apart from their household, like members of armed forces and persons who always or temporarily work at night shifts or otherwise would not be counted by the census elsewhere; d) workers on Egyptian or foreign means of transporation who were present within or out of the territorial boundaries but have no residing place outside the country. - Group quarters: Not applicable

Universe

All individuals (Egyptians and foreigners) who were present within the political boundaries of Egypt at census night.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Central Agency for Public Mobilisation and Statistics

SAMPLE SIZE (person records): 5902243.

SAMPLE DESIGN: Sample of private households drawn by Egyptian statistical office. Sample method unknown.

Mode of data collection

Face-to-face [f2f]

Research instrument

Special Households Questionnaires; Public Living Quarters Questionnaire; Household and Housing Condition Questionnaire

Abstract

The 2000 Republic of Palau Census of Population and Housing was the second census collected and processed entirely by the republic itself. This monograph provides analyses of data from the most recent census of Palau for decision makers in the United States and Palau to understand current socioeconomic conditions. The 2005 Census of Population and Housing collected a wide range of information on the characteristics of the population including demographics, educational attainments, employment status, fertility, housing characteristics, housing characteristics and many others.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Universe

The 1990, 1995 and 2000 censuses were all modified de jure censuses, counting people and recording selected characteristics of each individual according to his or her usual place of residence as of census day. Data were collected for each enumeration district - the households and population in each enumerator assignment - and these enumeration districts were then collected into hamlets in Koror, and the 16 States of Palau.

Kind of data

Census/enumeration data [cen]

Sampling procedure

No sampling - whole universe covered

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 censuses of Palau employed a modified list-enumerate procedure, also known as door-to-door enumeration. Beginning in mid-April 2000, enumerators began visiting each housing unit and conducted personal interviews, recording the information collected on the single questionnaire that contained all census questions. Follow-up enumerators visited all addresses for which questionnaires were missing to obtain the information required for the census.

Cleaning operations

The completed questionnaires were checked for completeness and consistency of responses, and then brought to OPS for processing. After checking in the questionnaires, OPS staff coded write-in responses (e.g., ethnicity or race, relationship, language). Then data entry clerks keyed all the questionnaire responses. The OPS brought the keyed data to the U.S. Census Bureau headquarters near Washington, DC, where OPS and Bureau staff edited the data using the Consistency and Correction (CONCOR) software package prior to generating tabulations using the Census Tabulation System (CENTS) package. Both packages were developed at the Census Bureau's International Programs Center (IPC) as part of the Integrated Microcomputer Processing System (IMPS).

The goal of census data processing is to produce a set of data that described the population as clearly and accurately as possible. To meet this objective, crew leaders reviewed and edited questionnaires during field data collection to ensure consistency, completeness, and acceptability. Census clerks also reviewed questionnaires for omissions, certain inconsistencies, and population coverage. Census personnel conducted a telephone or personal visit follow-up to obtain missing information. The follow-ups considered potential coverage errors as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Following field operations, census staff assigned remaining incomplete information and corrected inconsistent information on the questionnaires using imputation procedures during the final automated edit of the data. The use of allocations, or computer assignments of acceptable data, occurred most often when an entry for a given item was lacking or when the information reported for a person or housing unit on an item was inconsistent with other information for that same person or housing unit. In all of Palau’s censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in place of blanks or unacceptable entries enhanced the usefulness of the data.

Sampling error estimates

Human and machine-related errors occur in any large-scale statistical operation. Researchers generally refer to these problems as non-sampling errors. These errors include the failure to enumerate every household or every person in a population, failure to obtain all required information from residents, collection of incorrect or inconsistent information, and incorrect recording of information. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires. To reduce various types of non-sampling errors, Census office personnel used several techniques during planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

Census staff implemented several coverage improvement programs during the development of census enumeration and processing strategies to minimize under-coverage of the population and housing units. A quality assurance program improved coverage in each census. Telephone and personal visit follow-ups also helped improve coverage. Computer and clerical edits emphasized improving the quality and consistency of the data. Local officials participated in post-census local reviews. Census enumerators conducted additional re-canvassing where appropriate.

The manual contains a list of mandatory requirements for conducting a census, as well as a number of guidelines and recommendations. The first sections of the manual describe the authority for conducting a municipal census, the role of the municipal council, and how to apply the Freedom of Information and Protection of Privacy Act (FOIP) to a municipal census. The subsequent sections describe the roles of census coordinator and the census enumerator. The final section provides a set of additional census questions that municipalities may choose to use in their census. The appendices contain various sample census materials. The methodologies, terms, and techniques for census-taking described in this manual are accepted by Alberta Municipal Affairs for determining the population of municipalities as described in the Determination of Population Regulation. The statistical concepts and principles reflected in this manual are based on those recognized by Statistics Canada and other statistical agencies.

The Accessibility Observatory at the University of Minnesota produces census block level access to jobs data by four modes (auto, bike, walk and transit). https://www.cts.umn.edu/programs/ao

Accessibility is the ease and feasibility of reaching valued destinations. It can be measured for a wide array of transportation modes, to different types of destinations, and at different times of day. There are a variety of ways to define accessibility, but the number of destinations reachable within a given travel time is the most comprehensible and transparent as well as the most directly comparable across different geographies.

Reports published by mode have detailed information on how these data are produced.
Auto: https://hdl.handle.net/11299/266465 Bike: https://hdl.handle.net/11299/266466 Walk: https://hdl.handle.net/11299/266468 Transit: https://hdl.handle.net/11299/266467

These data are provided in two formats. The first format is an ESRI Map Package that includes feature classes for each mode. The second format is a zipped shapefile of census blocks and four CSV files, one for each mode. For every census block, for each mode, there is a reported value of the number of jobs that can be reached within a specified time threshold. The bicycle mode uses Level of Traffic Stress (LTS) 3 (medium stress) to define usable routes.

This study arose out of the Kingston Local History Project. The purpose of this project is to construct a database detailing major aspects of Kingston's economic and social evolution during the second half of the nineteenth century. The study contains complete census enumerator' books for the census years 1851, 1861, 1871, 1891.

Abstract

The Census of Agriculture investigates information on agricultural establishments and agricultural activities developed inside them, including characteristics of the producers and establishments, economy and employment in the rural area, livestock, cropping and agribusiness. Its data collection unit is every production unit dedicated, either entirely or partially, to agricultural, forest or aquaculture activities, subordinated to a single administration – producer or administrator –, regardless of its size, legal nature or location, aiming at producing either for living or sales.

The first Census of Agriculture dates back to 1920, and it was conducted as part of the General Census. It did not take place in the 1930s due to reasons of political and institutional nature. From 1940 onward, the survey was decennial up to 1970 and quinquennial later on, taking place in the beginning of the years ending in 1 and 6 and relating to the years ending in 0 and 5. In the 1995-1996 Census of Agriculture, the information was related to the crop year (August 1995 to July 1996). In the 2006 Census of Agriculture, the reference for the data returned to be the calendar year. The 2006 edition was characterized both by the technological innovation introduced in the field operation, in which the paper questionnaire was replaced by the electronic questionnaire developed in Personal Digital Assistants - PDAs and by the methodological refinement, particularly concerning the redesign of its contents and incorporation of new concepts. That edition also implemented the National Address List for Statistical Purposes - Cnefe, which gathers the detailed description of the addresses of housing units and agricultural establishments, geographic coordinates of every housing unit and establishment (agricultural, religious, education, health and other) in the rural area, bringing subsidies for the planning of future IBGE surveys. The 2017 Census of Agriculture returned to reference the crop year – October 2016 to September 2017 –, though in a different period than that adopted in the 1995-1996 Census of Agriculture. New technologies were introduced in the 2017 survey to control the data collection, like: previous address list, use of satellite images in the PDAs to better locate the enumerator in relation to the terrain, and use of coordinates of the address and location where the questionnaire is open, which allowed a better coverage and assessment of the work.

The survey provides information on the total agricultural establishments; total area of those establishments; characteristics of the producers; characteristics of the establishments (use of electricity, agricultural practices, use of fertilization, use of agrotoxins, use of organic farming, land use, existence of water resources, existence of warehouses and silos, existence of tractors, machinery and agricultural implements, and vehicles, among other aspects); employed personnel; financial transactions; livestock (inventories and animal production); aquaculture and forestry (silviculture, forestry, floriculture, horticulture, permanent crops, temporary crops and rural agribusiness).

The periodicity of the survey is quinquennial, though the surveys in 1990, 1995, 2000 and 2005, 2010 and 2015 were not carried out due to budget restrictions from the government; the 1990 Census of Agriculture did not take place; the 1995 survey was carried out in 1996 together with the Population Counting; the 2000 survey did not take place; that of 2005 was carried out in 2007, together with the Population Counting once again; that of 2010 did not take place and that of 2015 was carried out in 2017. Its geographic coverage is national, with results disclosed for Brazil, Major Regions, Federation Units, Mesoregions, Microregions and Municipalities. The results of the 2006 Census of Agriculture, which has the calendar year as the reference period, are not strictly comparable with those from the 1995-1996 Census of Agriculture and 2017 Census of Agriculture, whose reference period is the crop year in both cases.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the agricultural holding, defined as any production unit dedicated wholly or partially to agricultural, forestry and aquaculture activities, subject to a single management, with the objective of producing for sale or subsistence, regardless of size, legal form (own, partnership, lease, etc.) or location (rural or urban). The agricultural holdings were classified according to the legal status of the producer as: individual holder, condominium, consortium or partnership; cooperative; incorporated or limited liability company; public utility institutions (church, NGO, hospital), or government.

Kind of data

Census/enumeration data [cen]

Sampling procedure

(a) Frame The 2000 Population and Housing Census and the cartographic documentation constituted the source of the AC 2006 frame. No list frames were available in digital media with georeferenced addresses of the holdings. Census coverage was ensured on the basis of the canvassing of the EAs by enumerators.

(b) Complete and/or sample enumeration methods The AC 2006 was a complete enumeration operation of all agricultural holdings in the country.

Mode of data collection

Face-to-face [f2f]

Research instrument

An electronic questionnaire was used for data collection on:

Total agricultural establishments Total area of agricultural establishments Total area of crops Area of pastures Area of woodlands Total tractors Implements Machinery and vehicles Characteristics of the establishment and of the producer Total staff employed Total cattle, buffallo, goats, Sheep, pigs, poultry (chickens, fowls, chickens and chicks) Other birds (ducks, geese, teals, turkeys, quails, ostriches, partridges, pheasants and others) Plant production

The AC 2006 covered all 16 items recommended by FAO under the WCA 2010.

Cleaning operations

(a) DATA PROCESSING AND ARCHIVING The entire data collection and supervision software was developed in house by IBGE, using the Visual Studio platform in the Microsoft Operations Manager 2005 environment and Microsoft SQL Server 2000, with the assistance of Microsoft Brazil consulting. In addition, the GEOPAD application was installed to view, navigate and view maps and use GPS guidance. Updated versions of the software were installed automatically as soon as census enumerators connected the PDAs to the central server to transmit the data collected. Once internally validated by the device, the data were immediately transmitted to the database at the IBGE state unit. The previous AC (1996) served as the basis for defining the parameter values for the electronic editing process.

(b) CENSUS DATA QUALITY Automatic validation was incorporated into PDAs. Previously programmed skip patterns and real-time edits, performed during enumeration, ensured faster and more reliable interviews. In addition, the Bluetooth® technology incorporated into the PDAs allowed for direct data transmission to IBGE's central mainframe by each of enumerators on a weekly basis.

Data appraisal

The preliminary census results were published in 2007. The final results were released in 2009 through a printed volume and CD-ROMs. The census results were disseminated at the national and subnational scope (country, state and municipality) and are available online at IBGE's website.

This spatial data set contains Statistics Canada 2021 Census information for Class of Worker Including Job Permanency by census tract. For more information please visit the Statistics Canada Census Dictionary: https://www12.statcan.gc.ca/census-recensement/2021/ref/dict/index-eng.cfm

It is recommended to use the Field Dictionary in conjunction with this data: Click Here

Graph and download economic data for Output per Worker for Private Nonfarm in the Midwest Census Region (IPUZNW001983000) from 2008 to 2024 about Midwest Census Region, output, nonfarm, private, employment, and USA.

On 14th June 2016, the Central Statistics Office (CSO) released the Preliminary Report for Census 2016. The preliminary results are the initial count of the census. They are based on the summary counts for each enumeration area which were compiled by the 4,663 census enumerators and which have been returned to the CSO in advance of the census forms themselves. Further detailed results will be released in different phases as they become available during 2017, commencing with the Principal Demographic Results

Analysis unit

Persons and households

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: Independent and separate space with areas of exclusive use, inhabited or designed to be inhabited by one or more persons. - Households: A person or group of people, related or not, who occupy all or part of a dwelling; attend to basic needs charged to a common budget, and they generally share food. - Group quarters: An institution in which a group of people generally not related lives and sleeps; done for reasons of study, work, religion, military discipline, administrative labors, rehabilitation, and others.

Universe

The entire population of the country, including all households and dwellings. In addition, an inventory was made of economic establishments and agricultural units associated with the rural dwelling. The microdata sample consists of the 20% survey. It excludes population in group quarters.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Departmento Administrativo Nacional de Estadística (DANE)

SAMPLE SIZE (person records): 4117607.

SAMPLE DESIGN: Systematic sample of every other private dwelling. Drawn by the IPUMS from 20% microdata of private dwellings. The census office described the method for the original sample as a Bernoulli stratified design for elements of households, Poisson for dwellings, and of conglomerates for persons.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three enumeration questionnaires were used: 1. Questionnaire for the Urban environment. This is carried out by the field supervisor, through observation at the block level. It seeks to provide and overview of the predominant urban setting, based on criteria of habitat which are fundamentally physical. 2. Questionnaire of Census Units. This is carried out by the enumerator starting with the information reported directly by the units. It is divided into sections for: dwellings, households, persons, economic units and agricultural units. 3. Questionnaire for Special Housing Units (LEA). In the case of military barracks and penitentiary centers, this is carried out by resident personnel, trained for this purpose by the municipal coordinator. In the remaining cases, it is the responsibility of the enumerator.

The aim of this project was to investigate the working population of Downham, an agricultural fen parish in north Cambridgeshire, using information derived from the census enumerators' books for 1851 and 1891. This project originated as part of the course of study for Open University Course DA301 (Studying family and community history: nineteenth and twentieth centuries).

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.

For a deep dive into the data model including every specific metric, see the ACS 2016-2020 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e20

Estimate from 2016-20 ACS

_m20

Margin of Error from 2016-20 ACS

_e10

2006-10 ACS, re-estimated to 2020 geography

_m10

Margin of Error from 2006-10 ACS, re-estimated to 2020 geography

_e10_20

Change, 2010-20 (holding constant at 2020 geography)

Geographies

AAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)

ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)

Census Tracts (statewide)

CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)

City (statewide)

City of Atlanta Council Districts (City of Atlanta)

City of Atlanta Neighborhood Planning Unit (City of Atlanta)

City of Atlanta Neighborhood Planning Unit STV (subarea of City of Atlanta)

City of Atlanta Neighborhood Statistical Areas (City of Atlanta)

County (statewide)

Georgia House (statewide)

Georgia Senate (statewide)

MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)

Regional Commissions (statewide)

State of Georgia (statewide)

Superdistrict (ARC region)

US Congress (statewide)

UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)

WFF = Westside Future Fund (subarea of City of Atlanta)

ZIP Code Tabulation Areas (statewide)

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2016-2020). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Source: U.S. Census Bureau, Atlanta Regional Commission Date: 2016-2020 Data License: Creative Commons Attribution 4.0 International (CC by 4.0)

Link to the manifest: https://opendata.atlantaregional.com/documents/GARC::acs-2020-data-manifest/about

This project aimed to extend knowledge of late nineteenth century Scottish, and hence British, demography, by producing four parallel longitudinal data sets by linking individuals in the decennial censuses of 1861-1901 with the births, deaths and marriages from civil registers for the lowland town of Kilmarnock, the Hebridean Island of Skye, and the rural parishes of Torthorwald and Rothiemay, places with contrasting economic and social structures and physical environments. The resulting data source is rich in information relating to the social, occupational, household, and demographic characteristics of individuals, who can also be situated within their physical environment. The relatively large scale of the data-sets allows greater accuracy and detail in the multivariate analysis of mortality, fertility, nuptiality and migration. Special permission was granted by the General Register Office, Edinburgh, for access to the civil registers of births, marriages and deaths, and these have been linked to the census entries for the same individuals, allowing the creation of full or partial life histories. Linkage has been carried out using a sets of related individuals approach in a relational data-base system using computer algorithms and hand-finishing (see Reid, Davies and Garrett (2006) 'Nineteenth century Scottish demography from linked censuses and civil registers: a sets of related individuals approach', History and Computing, 14(1+2), 2002 (publ. 2006), pp. 61-86).

The terms of our agreement with the General Register Office for Scotland do not allow us to deposit the births, marriages and deaths for archiving, which prevents deposit of the linked material. However we can deposit the census material annotated to include the years of birth, death and marriage of an individual (but no other details of these events). Two separate census deposits are being made. This is a version of the raw data, with no standardisation or enhancement apart from corrections or changes to ensure consistency, notes made by data entry personnel, and household and individual identifiers.

The data consist of transcriptions of the census enumerators' books for Skye, Kilmarnock, Rothiemay and Torthorwald, for the years 1861, 1871, 1881, 1891 and 1901. The following information on all individuals within all households on census night is provided: address (three fields); forename(s), surname(s); relationship to head of household; marital status; age; sex; occupation; other occupational information such as employment status, where available; county or birth, parish of birth; language; and number of rooms in the household. The data collection also contains details of where each individual appears in the census returns by giving the name of the registration district, the number of the enumeration district, the schedule number as indicated on the census enumerators' books, and household and person identification codes created by the project. Some variables are not available for all years or places: see project documentation for more details.

VITAL SIGNS INDICATOR Jobs (LU2)

FULL MEASURE NAME Employment estimates by place of work

LAST UPDATED March 2020

DESCRIPTION Jobs refers to the number of employees in a given area by place of work. These estimates do not include self-employed and private household employees.

DATA SOURCE California Employment Development Department: Current Employment Statistics 1990-2018 http://www.labormarketinfo.edd.ca.gov/

U.S. Census Bureau: LODES Data Longitudinal Employer-Household Dynamics Program (2005-2010) http://lehd.ces.census.gov/

U.S. Census Bureau: American Community Survey 5-Year Estimates, Tables S0804 (2010) and B08604 (2010-2017) https://factfinder.census.gov/

Bureau of Labor Statistics: Current Employment Statistics Table D-3: Employees on nonfarm payrolls (1990-2018) http://www.bls.gov/data/

METHODOLOGY NOTES (across all datasets for this indicator) The California Employment Development Department (EDD) provides estimates of employment, by place of employment, for California counties. The Bureau of Labor Statistics (BLS) provides estimates of employment for metropolitan areas outside of the Bay Area. Annual employment data are derived from monthly estimates and thus reflect “annual average employment.” Employment estimates outside of the Bay Area do not include farm employment. For the metropolitan area comparison, farm employment was removed from Bay Area employment totals. Both EDD and BLS data report only wage and salary jobs, not the self-employed.

For measuring jobs below the county level, Vital Signs assigns collections of incorporated cities and towns to sub-county areas. For example, the cities of East Palo Alto, Menlo Park, Portola Valley, Redwood City and Woodside are considered South San Mateo County. Because Bay Area counties differ in footprint, the number of sub-county city groupings varies from one (San Francisco and San Jose counties) to four (Santa Clara County). Estimates for sub-county areas are the sums of city-level estimates from the U.S. Census Bureau: American Community Survey (ACS) 2010-2017.

The following incorporated cities and towns are included in each sub-county area: North Alameda County – Alameda, Albany, Berkeley, Emeryville, Oakland, Piedmont East Alameda County - Dublin, Livermore, Pleasanton South Alameda County - Fremont, Hayward, Newark, San Leandro, Union City Central Contra Costa County - Clayton, Concord, Danville, Lafayette, Martinez, Moraga, Orinda, Pleasant Hill, San Ramon, Walnut Creek East Contra Costa County - Antioch, Brentwood, Oakley, Pittsburg West Contra Costa County - El Cerrito, Hercules, Pinole, Richmond, San Pablo Marin – all incorporated cities and towns Napa – all incorporated cities and towns San Francisco – San Francisco North San Mateo - Brisbane, Colma, Daly City, Millbrae, Pacifica, San Bruno, South San Francisco Central San Mateo - Belmont, Burlingame, Foster City, Half Moon Bay, Hillsborough, San Carlos, San Mateo South San Mateo - East Palo Alto, Menlo Park, Portola Valley, Redwood City, Woodside North Santa Clara - Los Altos, Los Altos Hills, Milpitas, Mountain View, Palo Alto, Santa Clara, Sunnyvale San Jose – San Jose Southwest Santa Clara - Campbell, Cupertino, Los Gatos, Monte Sereno, Saratoga South Santa Clara - Gilroy, Morgan Hill East Solano - Dixon, Fairfield, Rio Vista, Suisun City, Vacaville South Solano - Benicia, Vallejo North Sonoma - Cloverdale, Healdsburg, Windsor South Sonoma - Cotati, Petaluma, Rohnert Park, Santa Rosa, Sebastopol, Sonoma

Displaced Workers: Provides data on workers who lost a job in the last 3 years due to plant closing, shift elimination, or other work-related reason. Job Tenure: Provides data that will measure an individual's tenure with his/her current employer and in his/her current occupation.

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the U.S. Census Bureau.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2014-2018). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

s

Significance flag for change: 1 = statistically significant with a 90% Confidence Interval, 0 = not statistically significant, blank = cannot be computed

Suffixes:

_e18

Estimate from 2014-18 ACS

_m18

Margin of Error from 2014-18 ACS

_00_v18

Decennial 2000 in 2018 geography boundary

_00_18

Change, 2000-18

_e10_v18

Estimate from 2006-10 ACS in 2018 geography boundary

_m10_v18

Margin of Error from 2006-10 ACS in 2018 geography boundary

_e10_18

Change, 2010-18