Comprehensive profile of Northern Ireland Health and Social Care workforce by staff group and organisation, including breakdowns by age, gender and part time working. Also includes information on movers, leavers and joiners, plus summary vacancy information. Source agency: Health, Social Service and Public Safety (Northern Ireland) Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Workforce Census

This is an overview document covering the annual NHS workforce census (2004 to 2014). All NHS Staff refers to those directly employed by the NHS in Hospital and Community Health Services (HCHS) and by GP practices contracted to the NHS. It excludes high street dentists and ophthalmic practitioners. This publication is made up of three main staff group areas: Non-Medical Staff 2004-2014 Medical & Dental Staff 2004-2014 General Practice Staff 2004-2014 The links to each of these publications is available below. Each report provides a detailed view of NHS staff training as at 30 September 2004 to 2014. NHS staff data (excluding GPs and their staff) is also published each month, the December 2014 data is published on the same day as these reports (see links below).

Population representations, participation rate, unemployment rate and earnings for the designated groups, 2006 Census and 2011 National Household Survey

Abstract

National Labor Force Survey (SAKERNAS) is a survey that is designed to observe the general situation of workforce and also to understand whether there is a change of workforce structure between the enumeration period. Since the survey was initiated in 1976, it has undergone a series of changes affecting its coverage, the frequency of enumeration, the number of households sampled and the type of information collected. It is the largest and most representative source of employment data in Indonesia. For each selected household, the general information about the circumstances of each household member that includes the name, relationship to head of household, sex, and age were collected. Household members aged 10 years and over will be prompted to give the information about their marital status, education and employment.

SAKERNAS is aimed to gather informations that meet three objectives: 1. Employment by education, working hours, industrial classification and employment status, 2. Unemployment by different characteristics and efforts on looking for work, 3. Working age population not in the labor force (e.g. attending schools, doing housekeeping and others).

The data was gathered in February 2010 covered all provinces in Indonesia with 69.824 total number of households in 4.364 of census blocks in all provinces of Indonesia including urban and rural area. The main household data is taken from core questionnaires SAK09-AK.

Geographic coverage

National coverage*, including urban and rural area, representative until provincial level.

*) Although covering all of Indonesia, there are some circumstances when not all provincial were covered. For example, in 2000, the Province of Maluku excluded in SAKERNAS because horizontal conflicts occurred there. Also, the separation of East Timor from Indonesia in 1999 also changed the scope of SAKERNAS for the years to come. After that, due to the expansion of regional autonomy as a consequence, the proportion of samples per Province is also changed, as in 2006 when the number of provinces are already 33. However, the difference is only on the number of influential scope/level but not to the pattern. On the other hand, changes in the methodology (including sample size) over time is likely to affect the outcome, for example in years 2000 and 2001, when sample size is only 32.384 and 34.176 households, the level of data presentation is only representative to island level, (insufficient sample size even to make it representative to provincial level).

Analysis unit

Individual

Universe

The survey covered all de jure household members (usual residents), aged 10+ years resident in the household. However, Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

Kind of data

Sample survey data

Sampling procedure

Sakernas February 2010 is implemented in the whole territory of the Republic of Indonesia with a total sample of about 69.824 households, scattered on 4.364 census blocks from all provinces, both in rural and urban areas. Diplomatic Corps households, households that are in the specific enumeration area and specific households in the regular enumeration area are not chosen as a sample.

The sampling method* for SAKERNAS 2010 is probability sampling with two-stage cluster sampling technique where census blocks as the primary sampling unit (PSU) and households as the ultimate sampling unit. These census blocks (PSUs) were selected with probability proportional to size. A number of households were taken randomly from selected census blocks. However, there is documentation explained about how the sample size was determined at the domain level, or stratification measures that were implemented and also the sample size allocation across strata. The sampling frame used for the 2011 and later Sakernas surveys is sample frame of Population Census 2010 (SP 2010). Sampling frame** used in Sakernas August 2010 is the list of chosen census blocks from Sakernas 2007, using the "list head of household names" result of August 2007's listing process. This sampling frame is used for sampling period 2008 to 2010 (February and August).

*) Sampling method used is varied in different years. For example, in SAKERNAS period of 1986-1989 sampling method used is the method of rotation, where most of the households selected at one period was re-elected in the following period. This often happens on quarterly SAKERNAS on that period. At other periods often use multi-stages sampling method (two or three stages depend on whether sub block census included or not), or a combination of multi stages sampling also with rotation method (e.g. SAKERNAS 2006).

**) Commonly annual SAKERNAS sample frame comes from the last population census result undertaken before SAKERNAS. For example, for annual SAKERNAS 2003 used sample frame derived from "listing process" of household results of Population Census 2000. Also can refer to sampling frame of some periodic household based cencus like Economic Census, e.g. in forming block census sample frame of SAKERNAS 2007 using Economic Census 2006 result. In the other hand sample frame used for quarterly SAKERNAS is from the list of households obtained from National Socio-Economic Survey (SUSENAS) Core activities held before Sakernas. For example, for quarterly SAKERNAS 2002/2003 activities, which used sample frame derived from the household of the selected districts of SUSENAS 2002.

Mode of data collection

Face-to-face

Research instrument

In SAKERNAS, the questionnaire has been designed in a simple and concise way. It is expected that respondents will understand the aim of question of survey and avoid the memory lapse and uninterested respondents during data collection. Furthermore, the design of SAKERNAS's questionnaire remains stable in order to maintain data comparison.

A household questionnaire was administered in each selected household, which collected general information of household members that includes name, relationship with head of the household, sex and age. Household members aged 10 years and over were then asked about their marital status, education and occupation.

Cleaning operations

Stages of data processing in Sakernas are through process of: - Batching - Editing - Coding - Data Entry - Validation - Tabulate

Sampling error estimates

Sampling error results are presented at the end of the publication of The State of Labor Force in Indonesia and in publication of The State of Workers in Indonesia.

A survey of undergraduate students in England, covering their background, career intentions, sources of careers information, whether they have considered teaching and their views on teaching. The study has important implications for workforce planning in the civil service, and for human capital theory about the social determinants of people's choice of career. It is of interest to non-academic users: teachers' unions, the NCTL and teacher training agency. The findings will identify potential challenges and suggest areas that merit further investigation. The findings can also be seen as working towards a randomised controlled trial in a future project.

Understanding the complex determinants of teacher supply is important for effective workforce planning. The current teacher supply 'crisis' is expected to get worse. Despite the body of work in this area the issue has never been investigated in an integrated way, as this project will. We need to know why: demand for teachers has increased, teacher supply is not sufficient to meet demand and the Teacher Supply Model has failed to predict accurately the number of teachers needed, so that targeted and appropriate initiatives can be used. Teacher shortages are at least partly created by government policies as much as by the mere increase in school intake population. Policy measures, such as raising the education and training leaving age to 18, introduction of the English Baccalaureate, changes in admissions criteria to initial teacher training, caps on intake targets for the different routes into teacher training, the level and method of funding to schools, and the increase in number and diversity of schools, can all influence teacher demand and supply. Modelling cannot anticipate such changes years ahead and these factors are rarely considered in accounts of teacher recruitment and retention. Reanalysis of secondary data suggests that the recent historical patterns of teacher numbers are not closely related to the economic and employment cycles. Therefore, current financial incentives to increase teacher supply are not likely to be effective by themselves. We need to look at alternative approaches to understand why some people are attracted to teaching, and more importantly why some people are not. Much of the evidence so far has focused on the motivations of people who are already in teaching, ignoring those who are not in teaching or who have left. Understanding the reasons for non-participation is important for policy, and this requires a consideration of the motivations and the subjective opportunistic structure of those who do not consider, or even rejected, teaching as a career. This new study will:

1. Clarify the complex determinants of teacher demand and supply.
2. Identify people's intention to go into teaching, those who might consider teaching and those who are on other paths.
3. Characterise these intending, potential and non- teachers.
4. Understand all kinds of barriers to entering teaching.
5. Review the impact of policy initiatives to increase teacher supply.
6. Identify approaches that have the most promise of success.

This new study will use a combination of approaches to look at the issue holistically. We will reanalyze teacher data using various official and other sources, such as the School Workforce Census, DfE, HESA, National College for Teaching and Leadership (NCTL), School Teachers' Review Body, Graduate Teacher Training Registry as well as government reports from 1990 to 2018. We will look at the patterns of teacher demand and supply over time to establish the determinants of teacher supply and demand, and to see how education policies may have an effect on teacher demand and supply. We will review international studies to evaluate the impact of recruitment and retention policies to identify promising ones, giving greater weight to studies with a causal or quasi-experimental design. We will conduct a survey of undergraduates to gather information about their career decisions, plans and motivations. The results will supplement conclusions drawn from the secondary data reanalysis, as well as provide further insights into the impact of policy initiatives.

This study will have important implications for workforce planning in the civil service, and for human capital theory about the social determinants of people's choice of career. It will be of interest to non-academic users: teachers' unions, the NCTL and teacher training agency. Eight users,including the DfE and the Chartered College of Teaching, have confirmed support and expressed an urgency for an independent evaluation of the issue.

Workforce population showing representation by Employment Equity Occupational Groups and National Occupational Classification unit groups for women, Aboriginal peoples and visible minorities, 2021 Census

Canadian citizen workforce population showing representation by Employment Equity Occupational Groups and National Occupational Classification unit groups for women, Aboriginal peoples and visible minorities, 2016 Census

Figures on coronavirus (COVID-19) cases and vaccination uptake among the school workforce in state-funded primary, secondary, and special schools in England broken down by demographic and geographic characteristics. Using a linked School Workforce Census, NHS Test and Trace and National Immunisation Management system dataset (experimental statistics).

Females awarded bachelor’s degrees in STEM fields.

The data covers different aspects of the school workforce in Wales, using the data collected from the School Workforce Annual Census (SWAC).

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by industry and by age. The estimates are as at Census Day, 21 March 2021.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Industry (current)

Classifies people aged 16 years and over who were in employment between 15 March and 21 March 2021 by the Standard Industrial Classification (SIC) code that represents their current industry or business.

The SIC code is assigned based on the information provided about a firm or organisation’s main activity.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.

Note: the Excel file 'nhs-staf-2003-2013-non-med-tab' (tab 2a) was updated at 12:00 on 19th September 2014 to include an updated version of table 2a to correct the total combined nursing headcount figure for 2010 from 631,626 to 631,345. All other figures in this table and elsewhere in the detailed results / census bulletin are correct and have not changed. All users are reminded to refer to the latest version of time series data when attempting comparisons as the 2010 figure in previous bulletins has not been updated. The HSCIC apologises for the inconvenience caused. Note: the Excel file 'NHS Workforce Statistics in England, Non-medical staff - 2003-2013' was updated at 14:40 on 3rd April 2014 to include corrected versions of tables 1.3c and 1.3d to include staff working within Neurophysiology. The total figures in each of these tables and elsewhere in the detailed results / census bulletin are correct and have not changed. The HSCIC apologises for the inconvenience caused. This report is one of three that make up the NHS Staff 2003 - 2013 publication, along with: Medical and Dental staff 2003 - 2013 General Practice Staff 2003 - 2013 NHS Staff 2003 - 2013 Overview report A detailed view of the NHS non-medical workforce including nurses, scientists and support staff. Excluding medical or dental doctors within the Hospital and Community Health Services (HCHS) and GPs and their staff. The detailed results contain further data tables for September 2013 for England by age, gender, staff group and various other selected data by Health Education England area and individual organisation.

*If content does not automatically open in a new tab, click the Open button to the right.OnTheMap for Emergency Management is a public data tool from the U.S. Census Bureau that provides an intuitive web-based interface for accessing U.S. population and workforce statistics, in real time, for areas being affected by natural disasters. The tool allows users to retrieve reports containing detailed workforce, population, and housing characteristics for hurricanes, floods, wildfires, winter storms, and federal disaster declaration areas.To provide users this information for rapidly changing hazard event areas, OnTheMap for Emergency Management automatically incorporates real time data updates from the National Weather Service’s (NWS) National Hurricane Center, Department of Interior (DOI), Department of Agriculture (DOA), and the Federal Emergency Management Agency (FEMA).Highlights: Detailed social, economic, and housing data from the American Community Survey (ACS) Generate reports for specific communities for regional, local, and comparative analysesBar charts and an intuitive dashboard interfaceEvent search tool for easy access to current and historical emergency eventsLinkable maps and reports for easy sharing of maps and reports For more information and documentation, please see this page on the Census Bureau website.

Census 2021 data on the highest qualification levels of the economically active population, by region and local authority. These tables also include a composite index score used to compare the qualification levels of the workforces in different areas, and the most similar local authorities in terms of qualification level profile.

Figures are individually rounded to the nearest 5. Figures may not add exactly due to this rounding. The composite index score is based on unrounded counts, so when calculated based on rounded counts will not be exactly the same.

Figures are for economically active usual residents aged 16 and above. The current workforce are economically active people who are employed and unemployed (including those who are looking for work and could start in the next two weeks, and those who are waiting to start a job that has been offered and accepted).

Figures are based on geography boundaries as of 1 April 2022.

Quality notes can be found here

Economically Active (referred to as ‘total workforce’ in data tables)

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

Highest Level of Qualification

The highest level of qualification is derived from the question asking people to indicate all qualifications held, or their nearest equivalent This may include foreign qualifications where they were matched to the closest UK equivalent.

See a more detailed description here

Highest Level of Qualification Index Score

For Census 2021, we use a qualification rank index score to compare how highly qualified population groups are. It converts a person's highest qualification into a single metric and creates an average rank score for the population.

See more details here

Usual Resident

A usual resident is anyone who on Census Day, 21 March 2021 was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more, or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months

Dataset population: Persons aged 16 to 74 in employment the week before the census

Economic activity

Economic activity relates to whether or not a person who was aged 16 and over was working or looking for work in the week before census. Rather than a simple indicator of whether or not someone was currently in employment, it provides a measure of whether or not a person was an active participant in the labour market.

A person's economic activity is derived from their 'activity last week'. This is an indicator of their status or availability for employment - whether employed, unemployed, or their status if not employed and not seeking employment. Additional information included in the economic activity classification is also derived from information about the number of hours a person works and their type of employment - whether employed or self-employed.

The census concept of economic activity is compatible with the standard for economic status defined by the International Labour Organisation (ILO). It is one of a number of definitions used internationally to produce accurate and comparable statistics on employment, unemployment and economic status.

Occupation (detailed)

A person's occupation relates to their main job and is derived from either their job title or details of the activities involved in their job. This is used to assign responses to an occupation code based on the Standard Occupational Classification 2010 (SOC2010).

We report results from the first comprehensive total quality evaluation of five major indicators in the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) Program Quarterly Workforce Indicators (QWI): total employment, beginning-of-quarter employment, full-quarter employment, total payroll, and average monthly earnings of full-quarter employees. These are the data that were produced for those five indicators.

Note

Disclaimer

Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed (DRB request dated 2013-11-22, submitted 2013-12-02, approved 2013-12-17, ARTS445/ CMS538).

Acknowledgements

Abowd and Vilhuber acknowledge direct support from the U.S. Census Bureau (prior to Abowd’s appointment) and NSF Grants SES-0922005, BCS 0941226, TC-1012593, and SES-1131848. This research uses data from the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics Program, which was partially supported by the following National Science Foundation Grants: SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging Grant AG018854; and grants from the Alfred P. Sloan Foundation.

This data compilation on the basis of official statistics of labor force gives a summarized overview over participation in work force in Germany.Those are the key themes of the compilation: - Overviews on population development (population by age groups, employable resident population by age groups and sex);- Resident population by participation in work force; - School leavers, trainees by se and by training area; - Labor force, working population, employment rates by age groups;- Working population by occupational status; - Working population by economic sectors;- Participation in labor force in the federal states;- Working time.The compilation contains data tables with (synthetic) annual averages as well as chosen results of the micro census. These data were complemented with data on employment from the national accounts after the revised version if ESA 95. Data tables in Histat:A. Overviews on population developmentA1 Population and areas (annual averages), former West Germany, newly formed German states, Germany (1946-2000)A2 Population by age group (at the end of each year), former West Germany, former GDR, Germany (1950-2000)A3 Employable resident population by age groups and sex (annual averages), former West Germany (1950-2000)A4a Employable resident population by age groups and sex (at the end of each year), Germany (1989-2000)A4b Employable resident population by age groups and sex (at the end of each year),Newly formed German states (1989-2000) B. Resident population by participation in work force B1 Tables with annual averages B1.1 Population, working population (nationals, residents) and employers (annual averages, national accounts), former West Germany, Germany (1950-1997)B1.2 Resident population, working population, employment rate, unemployed (annual averages is 1000), former West Germany, Germany (1950-1997)B1.3 Population by sex, foreigners (annual averages), former West Germany, Germany (1950-2000)B1.4 Population, employment and unemployment (annual averages), former West Germany, Germany (1950-1997)B1.5 Employees subject to mandatory social insurance contribution (end of June), former West Germany, Germany (1974-2000)B1.6 Employees (inland) in full-time and part time employment, short-time workers, unemployed (annual averages), former West Germany (1960-2000)B1.7 Foreign employees, unemployed foreigners (annual averages), former West Germany (1954-2000)B1.8 School leavers and trainees, former West Germany, Germany (1950-2000)B1.9 Trainees by sex and training areas (at the end of each year), former West Germany, Germany (1960-2000) B2 Tables with extrapolated results from the micro censusB2.1 Employable population, working population, unemployed, labor force altogether (micro census) former West Germany, Germany (1959-2000)B2.2 Employable population, working population, unemployed, labor force by sex (micro census), former West Germany, Germany (1959-2000)B2.3 Population by participation in labor force and sex (micro census), former West Germany, Newly formed German states (1957-2000)B2.4 Employees by volume of employment and sex (micro census), Former West Germany, newly formed German states, Germany (1985-2000)B2.5 Resident population by main income source and sex (micro census), former West Germany, newly formed German states, Germany (1975-2000)B2.6 Working population by nationality, occupational status and sex (micro census) former West Germany, Germany (1976-2000) B3 Revised results after ESA 95B3.1 Population, working population and employees (ESA 95), unemployed (ILO), former West Germany, Germany (1950-2000)B3.2 National working population: comparison of the revisions of the employment statistics, Germany (1991-2000) C. Working population, employees, employment rates by age groups C1 Tables with annual averages C2 Tables with extrapolated results from the micro censusC2.1a Employable resident population by age groups and sex in 1000 (micro census), Germany (1991-2000)C2.1b Employable resident population by age groups and sex in 1000 (micro census), former West Germany (1962-2000)C2.1c Employable resident population by age groups and sex in 1000 (micro census), newly formed German states (1991-2000)C2.2 Working population in 1000 by age groups (micro census), former West Germany, newly formed German states, Germany (1950-2000)C2.3 Labor force, employment rates by sex (micro census), former West Germany, Germany (1950-2000)C2.4 Labor force, employment rates and national working population by sex (annual averages) foreign employers, former West Germany, Germany (1950-1995)C2.5a Employment rates by age groups and sex (micro census), Germany (1991-2000)C2.5b Employment rates by age groups and sex (micro census), former West Germany (1959-2000)C2.5c Employment rates by age groups and sex (micro census), newly formed German states (1991-2000)C2.5d Employment rates by age groups and sex (micro census), former West Germany, Germany (1958-2000)C2.6a Labor force by age groups and sex...

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission to represent the United States Census Bureau's 2000 Decennial Census data at the block geography.Attributes:FIPSSTCO = The Federal Information Processing Series (FIPS) state and county codes. FIPS codes were formerly known as Federal Information Processing Standards codes, until the National Institute of Standards and Technology (NIST) announced its decision in 2005 to remove geographic entity codes from its oversight. The Census Bureau continues to maintain and issue codes for geographic entities covered under FIPS oversight, albeit with a revised meaning for the FIPS acronym. Geographic entities covered under FIPS include states, counties, congressional districts, core based statistical areas, places, county subdivisions, subminor civil divisions, consolidated cities, and all types of American Indian, Alaska Native, and Native Hawaiian areas. FIPS codes are assigned alphabetically according to the name of the geographic entity and may change to maintain alphabetic sort when new entities are created or names change. FIPS codes for specific geographic entity types are usually unique within the next highest level of geographic entity with which a nesting relationship exists. For example, FIPS state, congressional district, and core based statistical area codes are unique within nation; FIPS county, place, county subdivision, and subminor civil division codes are unique within state. The codes for American Indian, Alaska Native, and Native Hawaiian areas also are unique within state; those areas in multiple states will have different codes for each state.TRACT2000 = Census Tract Codes and Numbers. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.BLOCK2000= Census Block Numbers are numbered uniquely with a four-digit census block number from 0000 to 9999 within census tract, which nest within state and county. The first digit of the census block number identifies the block group. Block numbers beginning with a zero (in Block Group 0) are only associated with water-only areas.STFID = A concatenation of FIPSSTCO, TRACT2000, and BLOCK2000, which creates the entire FIPS code for this geography.WFD = Workforce Development Area (WFD) is a seven-county area created by agreement of county chief-elected officials, administered by the Atlanta Regional Commission and funded for training and employment activities under the federal Workforce Investment Act (WIA). For more information on ARC’s Workforce Development programs and services please consult www.atlantaregional.com/workforce/workforce.html.RDC_AAA = ARC Area Agency on Aging is a 10-county area funded by the Department of Human Resources and designated by the Older Americans Act to plan for the needs of the rapidly expanding group of older citizens in the Atlanta region. It is part of a statewide network of 12 AAAs and a national network of more than 670 AAAs. For more information on aging services please consult www.agewiseconnection.com.MNGWPD = The Metro North Georgia Water Planning District provides water resource plans, policies and coordination for metropolitan Atlanta. The District has developed regional plans for stormwater management, wastewater treatment and water supply and water conservation. The 15-county Water Planning District includes the ten counties in the ARC plus five additional counties (Bartow, Coweta, Forsyth, Hall, & Paulding). For more information please consult www.northgeorgiawater.org. MPO = The Metropolitan Planning Organization (MPO) is a 19-county area federally-designated for regional transportation planning to meet air quality standards and for programming projects to implement the adopted Regional Transportation Plan (RTP). The MPO planning area boundary includes the 10-county state-designated Regional Commission and nine additional counties (all of Coweta, Forsyth, & Paulding and parts of Barrow, Dawson, Newton, Pike, Spalding and Walton). This boundary takes into consideration both the current urbanized area as well as areas forecast to become urbanized in the next 20 years.MSA = the 29-County “Atlanta-Sandy Springs-Roswell, GA” Metropolitan Statistical Area (MSA) and the 39-county “Atlanta--Athens-Clarke County--Sandy Springs, GA” Combined Statistical Area (CSA), which includes the 29 counties of the Atlanta MSA along with the Athens-Clarke County and Gainesville MSAs and the micropolitan statistical areas of Calhoun, Cedartown, Jefferson, LaGrange and Thomaston, GA. The U.S. Office of Management and Budget (OMB) defines CSAs, MSAs and the smaller micropolitan statistical areas nationwide according to published standards applied to U.S. Census Bureau data. These various statistical areas describe substantial core areas of population together with adjacent communities having a high degree of economic and social integration, often illustrated in high rates of commuting from the adjacent areas to job locations in the core. For more information, please consult http://www.census.gov/population/metro/data/metrodef.htmlF1HR_NA = The Federal 1-Hour Air Quality Non-Attainment Area is a fine particulate matter standard (PM2.5). The non-attainment area under this standard includes the 15-county eight-hour ozone nonattainment area plus Barrow, Carroll, Hall, Spalding, Walton, and small parts of Heard and Putnam counties.F8HR_NA: The Federal 8-Hour Air Quality Non-Attainment Area for the 2008 eight-hour ozone standard is 15 counties.ACRES = The number of acres contained within the Block.SQ_MILES = The number of square miles contained within the Block.Source: United States Census Bureau, Atlanta Regional CommissionDate: 2000For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com