The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

Class of worker by visible minority, selected sociodemographic characteristics and the census year: Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts (1) Frequency: Occasional Table: 98-10-0645-01 Release date: 2024-03-26 Geography: Canada, Geographical region of Canada, Province or territory, Census metropolitan area, Census metropolitan area part Universe: Persons in private households in occupied private dwellings, 2021 and 2016 censuses — 25% Sample data Variable List: Class of worker, Gender (2), Age (3) and first official language spoken (4), Immigrant and generation status (5, 6), Visible minority (7), Highest certificate, diploma or degree, Percent, Census year Abbreviation notes: List of abbreviations and acronyms found within various Census products. (https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm) Footnotes: 1 Historical comparison of geographic areas The boundaries and names of census geographies can change from one census to the next. In order to facilitate data comparisons between censuses, previous census data have been adjusted to reflect as closely as possible the 2021 boundaries of these areas. The methodology used for this adjustment involved spatially linking blocks of previous censuses (concordance to the 1996 Census used the 1996 enumeration areas to the 2021 boundaries). A previous census block was linked to the 2021 area within which its representative point fell. A limited number of interactive linkages were completed to further enhance the adjustment in certain areas. For some census geographies, it was not possible to reflect the 2021 boundaries. The 2021 boundaries may not be reflected as there was no previous census block to assign to the 2021 area. As well previous census data for some 2021 areas may not be available due to the fact that the concordance did not produce an accurate representation of the 2021 area. 2 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. The sex variable in census years prior to 2021 and the two-category gender variable in the 2021 Census are included together. Although sex and gender refer to two different concepts, the introduction of gender is not expected to have a significant impact on data analysis and historical comparability, given the small size of the transgender and non-binary populations. For additional information on changes of concepts over time, please consult the Age, Sex at Birth and Gender Reference Guide. 3 Age 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date). 4 First official language spoken First official language spoken refers to the first official language (English or French) spoken by the person. 5 'Immigrant status' refers to whether the person is a non-immigrant, an immigrant or a non-permanent resident. 'Period of immigration' refers to the period in which the immigrant first obtained landed immigrant or permanent resident status. For more information on immigration variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021. 6 Generation status Generation status refers to whether or not the person or the person's parents were born in Canada. 7 Visible minority Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. 8 Class of worker Class of worker refers to whether a person is an employee or is self-employed. The self-employed include persons with or without a business, as well as unpaid family workers. 9 'High (secondary) school diploma or equivalency certificate' includes only people who have this as their highest educational credential. It excludes persons with a postsecondary certificate, diploma or degree. 10 Includes persons aged 15 years and over who have worked at some point in time during the reference period. In 2021, this period was between January 2020 and May 2021. 11 Includes self-employed persons aged 15 years and over with or without an incorporated business and with or without paid help, as well as unpaid family workers. 13 Includes self-employed persons whose business is incorporated with or without employees. 14 Includes self-employed persons whose business is unincorporated. Also included among the self-employed are unpaid family workers. This category includes persons who work without pay in a business, farm or professional practice owned and operated by another family member living in the same dwelling.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

City boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_CITY: CDTFA incorporated city name
• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCity boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal Buffers (this dataset)Counties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCDTFA_CITY: CDTFA incorporated city nameCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections. Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor.CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information.CDTFA's source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCity boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal Buffers (this dataset)Counties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCDTFA_CITY: CDTFA incorporated city nameCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections. Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor.CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information.CDTFA's source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will

The data contains approximately 36,000 personal names derived from medieval Russian documentation. More preciously, names are collected from an edited version of the census book of Vodskaja pjatina, which was one of the five administrative areas in the late 15th century Novgorod.

Editions were compiled in parts and the first two, which cover the northernmost region, are called Переписная окладная книга по новугороду вотской пятины (1851, 1852)(POKV I‒II). The third part of the book series Новгородские пистсовые книги (1868)(NPK III) covers the southern and western parts of the study area.

The process of obtaining the personal from the inscription has been following: First, editions of the census book were obtained as scanned PDF files. These were transformed as editable copies by using OCR (=Optical Character Recognition) software Abbyy. The program read the original mid-19th century Russian text adequately with its old Russian alphabet package.

After the initial corrections, a Python script was written to harvest the personal names. This was based on exploiting the systematic formalities in how most of the names were presented in the census book. The script looked for abbreviations “дв.” and “д.” and extracted all following capitalized words until section end markers “.”, “;” or “:”. As an output, a name to pogost matrix was produced, which held the raw frequencies of each word in each pogost.

The process of cleaning the name data, in turn, has been done mostly by data wrangling program OpenRefine in following manner: For starters, all name forms shorter than four characters were removed as there were no personal names consisting of three or less letters. Furthermore, nouns that were not names were removed. This meant discarding expressions that described person’s special feature or profession, like such as being a widow (“вдова”) or working as a deacon (“діакъ”). For some reason, editors followed inconsistent conventions in capitalizing these non-name nouns.

In addition, some orthographical and morphological harmonization was done on the data. The letter ы was cut from the end of bynames, where it denotes plurality. Similarity of so called soft and hard signs, ь and ъ caused some problems. As the latter one is not used in contemporary Russian and was not used in the original documents either (Неволин 1853 : 4 (in Appendix 1)) it was removed. The soft sign ь was also removed because it was absent in the original documents and it had been used inconsistently by the editors. The letter ѣ (yat) is rarely used in personal names but nevertheless, it was changed to е (like as it is in contemporary Russian) as since it was often confused with soft and hard signs (ь and ъ). Furthermore, the letter ѳ (fita) was often erroneously recognized as о or е. As it is only found in NPK III and only in the beginning of certain names, which all are also written with “Ф” (e.g. “Ѳедко” vs. “Федко”), it was replaced with Ф.

In the second phase most of the erroneous orthographies were corrected. We do not detail herescribe all the OCR-errors here that were found, but in the following a short description is given of the most significant corrections. There were, for example, many letters whose similarity caused problems for the OCR-program (e.g. и / й and б / в). In these cases, the correct orthography was sought in the census book editions and accordingly, Openrefine was used to change erroneous forms to right correct ones.

After the corrections were made, the number of name types (= name variants) was reduced from 4942 to 2748. The Overall overall number of name tokens was dropped as well: from 36,405 to 35,726. Of the name types, more than half (1484) have only one occurrence.

The refined and harmonized data is published as pogost-by-name frequency tabulations (pogost, equivalent of English parish). The file is in tab-delimited file (.tsv) format.

References:

Неволин, К. А. 1853, О пятинах и погостах новгородских в XVI веке, с приложением карты, Санкт-Петербург (Из Записок Императорского русского географического общества, Кн. VIII).

NPK III = Новгородские писцовые книги, Т. 3 : Переписная оброчная книга Вотской пятины, 1500 года, 1868, 1868, Санкт Петербург.

POKV I, II = Переписная окладная книга по Новугороду Вотьской пятины, 1851, 1852, Имп. Моск. о-во истории и древностей рос., Москва.

Occupation (training, education, experience and responsibility category - TEER) by mobility status 5 years ago, place of residence 5 years ago and labour force status: Canada, provinces and territories, census metropolitan areas and census agglomerations with parts (1, 2) Frequency: Occasional Table: 98-10-0450-01 Release date: 2022-11-30 Geography: Canada, Province or territory, Census metropolitan area, Census agglomeration, Census metropolitan area part, Census agglomeration part Universe: Labour force aged 15 years and over in private households, 2021 Census — 25% Sample data Variable List: Mobility status 5 years ago (9), Place of residence 5 years ago (15), Labour force status (3), Age (15A), Gender (3), Statistics (3), Occupation - TEER category - National Occupational Classification (NOC) 2021 (9A) Abbreviation notes: List of abbreviations and acronyms found within various Census products. (https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm) iBall: i70 Geography name: Hamilton; Geographic area type: Census metropolitan area; Geographic area type abbreviation: CMA; Geographic level: Census metropolitan area; Province or territory abbreviation: Ont.; Dissemination Geography Unique Identifier (DGUID): 2021S0503537; Alternative geographic code: 537; Province or territory geocode: 35; Long-form total non-response rate: 3.0; Data quality flag: 00000; Data quality note: ... Footnotes: 1 Mobility Users are advised that when analyzing mobility data in connection with characteristics of the population that may change over time (i.e., marital status, educational attainment, labour force participation, etc.), these characteristics represent the status of an individual at the time of the census, not at the time of the migration. 2 Migration data for small geographic areas Estimates of internal migration may be less accurate for small geographic areas, areas with a place name that is a duplicate elsewhere, and for some census subdivisions (CSDs) where residents may have provided the name of the census metropolitan area or census agglomeration instead of the specific name of the component CSD from which they migrated. 3 Labour force status Labour force status refers to whether a person was employed, unemployed or not in the labour force during the reference period. The labour force consists of persons who contribute or are available to contribute to the production of goods and services falling within the System of National Accounts production boundary. 4 Age 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date). 5 Gender Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender. 6 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. 7 Mobility status, five years 'Mobility status, five years' refers to the status of a person with regard to the place of residence on the reference day in relation to the place of residence on the same date five years earlier. 8 Occupation (based on the National Occupational Classification [NOC] 2021 Version 1.0) Occupation refers to the kind of work performed in a job, a job being all the tasks carried out by a particular worker to complete his or her duties. An occupation is a set of jobs that are sufficiently similar in work performed. Kind of work is described in terms of tasks, duties and responsibilities, often including factors such as materials processed or used, the industrial processes used, the equipment used, and the products or services provided. Occupations are generally homogeneous with respect to skill type and skill level. Occupation applies to the contribution of labour to that part of economic activity that is within the production boundary defined for the System of National Accounts. 9 For information on the comparability of the 2021 Census labour force status data with those of the Labour Force Survey, see Appendix 2.11 of the Dictionary, Census of Population, 2021. 10 Refers to whether a person aged 15 years and over who is in the labour force was employed or unemployed during the week of Sunday, May 2 to Saturday, May 8, 2021. 11 Refers to the kind of work performed by persons aged 15 years and over as determined by their kind of work and the description of the main activities in their job. The occupation data are produced according to the variant of the National Occupational Classification (NOC) 2021 Version 1.0 for Analysis by TEER (Training, Education, Experience and Responsibility) categories. 12 Includes persons aged 15 years and over who never worked for pay or in self-employment, or last worked for pay or in self-employment prior to 2020. 13 Includes persons aged 15 years and over who have worked at some point in time between January 2020 and May 2021. 14 Management occupations refer to occupations with management responsibilities, including legislators, senior managers and middle managers. 15 Professional occupations require completion of a university degree (bachelor's, master's or doctorate); or previous experience and expertise in subject matter knowledge from a related occupation found in TEER 2 (when applicable). 16 Occupations in TEER 2 usually require completion of a post-secondary education program of two to three years at community college, institute of technology or CÉGEP; or completion of an apprenticeship training program of two to five years; or occupations with supervisory or significant safety (e.g. police officers and firefighters) responsibilities; or several years of experience in a related occupation from TEER 3 (when applicable). 17 Occupations in TEER 3 usually require completion of a post-secondary education program of less than two years at community college, institute of technology or CÉGEP; or completion of an apprenticeship training program of less than two years; or more than six months of on-the-job training, training courses or specific work experience with some secondary school education; or several years of experience in a related occupation from TEER 4 (when applicable). 18 Occupations in TEER 4 usually require completion of secondary school; or several weeks of on-the-job training with some secondary school education; or experience in a related occupation from TEER 5 (when applicable). 19 Occupations in TEER 5 usually require short work demonstration and no formal educational requirements.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This layer removes the coastal buffer polygons. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal Buffers (this dataset)Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing excludes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections. Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor.CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information.CDTFA's source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and

Abstract:
The 50-ha plot at Barro Colorado Island was initially demarcated and fully censused in 1982. Over 200,000 individual tropical trees were measured and mapped, but most demanding was the taxonomic component. Much collecting and sorting was done so that every individual could be matched to a previously described species from Croat's Flora of Barro Colorado (Stanford Univeristy, 1978). Over 300 taxa were separated and all but eight of those identified to species. After the 8th census in 2015, a total of 328 tree taxa have been documented in the plot, though the number alive in any one census has been less, always 296-303.||Taxonomy of tropical plants, however, is still in a state of flux because many of the groups have not been studied in detail for decades, and new treatments, especially those using DNA phylogenies, invariably lead to revision and reidentification. To maintain the 50-ha plot up-to-date with recent taxonomy, we have had to make numerous changes to the original list of 300 species. In a 1996 paper (Journal of Tropical Ecology, v. 12, pp. 231-256), we listed every species and added an Appendix documenting revisions done up until that date. In a 2004 paper (Journal of Tropical Ecology, v. 20, pp. 51-72), another species list was provided, but with no list of revisions. Many other papers mention some species, but none since list all species.||Here we provide a complete list after 20 years of further changes, plus a list of all obsolete names ever used with a link to the current valid name. We acknowledge, of course, that taxonomy continues and further revisions will be inevitable.||Included here are three tables of species names assigned to tree taxa from the 50-ha plot at Barro Colorado. The first table has all 471 Latin names every used for the 328 plot species, including names appearing in Croat (1978) and Condit et al. (1996, 2004). For each name, the currently accepted name is provided. This table allows users to search for any name and determine its current status. The second table gives only the 328 currently accepted Latin names, with all prior names for each included. This second table is how results on tree species from the 50-ha plot should be presented in publications. The third table gives the naming history of those 328 taxa in the three papers, at the start of the work in 1982, and the current name in 2017.||This Smithsonian Archive is permanent and will always have the same object identifier (DOI). It was frozen as of 31 December 2017, so users will always be able to consult the 2017 version. The same taxonomic tables also appear online in a searchable and sortable format designed to let users explore the data easily (http://conditdatacenter.org/taxonomy/BCI/BCIPlotFullTaxonomy.php). That web site, however, is maintained by one of the PIs and is not guaranteed to be permanent, that is, after he retires from the project it may not persist.

Description:

Table 1: Condit_FullBCITaxa.tab, is in tab-delimited ascii format and includes all 455 species names used in the BCI 50 ha plot through 2017. It has 6 columns:
...... Column 1: [Latin]: Latin name
...... Column 2: [Status] Status of the given Latin name, either Obsolete or Accepted
...... Column 3: [Current] The currently accepted Latin name for the given taxon, always diffing from Latin if Status=Obsolete but matching Latin if Status=Accepted
...... Column 4: [Mnemonic] The six-letter abbreviation used for the taxon in field notes
...... Column 5: [Family] The taxonomic family, following the Angiosperm Phylogeny Group in 2017
...... Column 6: [Authority] Taxonomic Authority
...... Column 7: [Notes] Notes about the taxon, mostly for species whose names has changed

Table 2. Condit_ValidBCITaxa.tab, is in tab-delimited ascii format and includes the 328 species names currently in use in the BCI 50-ha plot (2017). It has 6 columns:
...... Column 1: [Latin]: Latin name; all are Accepted so not status is listed
...... Column 2: [Mnemonic] The six-letter abbreviation used for the taxon in field notes
...... Column 3: [Family] The taxonomic family, following the Angiosperm Phylogeny Group in 2017
...... Column 4: [Authority] Taxonomic Authority
...... Column 5: [Notes] Notes about the taxon, mostly for species whose names has changed
...... Column 6: [aka] Previous names for the taxon
...... Column 7: [Notes] Old abbreviations for the taxon

Table 3. Taxonomic naming history of the 328 taxa in the Barro Colorado Island 50-ha plot through 8 censuses. The column Current gives the Latin name accepted as of December 2017. Subsequent columns indicate how the same species were designated in Croat's 1978 Flora of Barro Colorado Island, our original name in the 1982 census, our 1996 and 2004 papers in the Journal of Tropical Ecology, plus any additional name used.

Class of worker by visible minority, selected sociodemographic characteristics and the census year: Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts (1)Frequency: OccasionalTable: 98-10-0645-01Release date: 2024-03-26Geography: Canada, Geographical region of Canada, Province or territory, Census metropolitan area, Census metropolitan area partUniverse: Persons in private households in occupied private dwellings, 2021 and 2016 censuses — 25% Sample dataVariable List: Class of worker (5B), Gender (3a), Age and first official language spoken (10), Immigrant and generation status (9), Visible minority (15), Highest certificate, diploma or degree (6A), Percent, Census year (2)"List of abbreviations and acronyms found within various Census products.(https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm)"Footnotes:1 Historical comparison of geographic areas The boundaries and names of census geographies can change from one census to the next. In order to facilitate data comparisons between censuses, previous census data have been adjusted to reflect as closely as possible the 2021 boundaries of these areas. The methodology used for this adjustment involved spatially linking blocks of previous censuses (concordance to the 1996 Census used the 1996 enumeration areas to the 2021 boundaries). A previous census block was linked to the 2021 area within which its representative point fell. A limited number of interactive linkages were completed to further enhance the adjustment in certain areas. For some census geographies, it was not possible to reflect the 2021 boundaries. The 2021 boundaries may not be reflected as there was no previous census block to assign to the 2021 area. As well previous census data for some 2021 areas may not be available due to the fact that the concordance did not produce an accurate representation of the 2021 area.2 Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.3 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. The sex variable in census years prior to 2021 and the two-category gender variable in the 2021 Census are included together. Although sex and gender refer to two different concepts, the introduction of gender is not expected to have a significant impact on data analysis and historical comparability, given the small size of the transgender and non-binary populations. For additional information on changes of concepts over time, please consult the Age, Sex at Birth and Gender Reference Guide.4 'Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).5 First official language spoken First official language spoken refers to the first official language (English or French) spoken by the person.6 'Immigrant status' refers to whether the person is a non-immigrant, an immigrant or a non-permanent resident. 'Period of immigration' refers to the period in which the immigrant first obtained landed immigrant or permanent resident status. For more information on immigration variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021.7 Generation status refers to whether or not the person or the person's parents were born in Canada.8 "Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. "9 Highest certificate, diploma or degree is the classification used in the census to measure the broader concept of 'Educational attainment.' This variable refers to the highest level of education that a person has successfully completed and is derived from the educational qualifications questions, which asked for all certificates, diplomas and degrees to be reported. The general hierarchy used in deriving this variable (high school, trades, college, university) is loosely tied to the 'in-class' duration of the various types of education. At the detailed level, someone who has completed one type of certificate, diploma or degree will not necessarily have completed the credentials listed below it in the hierarchy. For example, a person with an apprenticeship or trades certificate or diploma may not have completed a high school certificate or diploma, nor does an individual with a 'master's degree' necessarily have a 'university certificate or diploma above bachelor level.' Although the hierarchy may not fit all programs perfectly, it gives a general measure of educational attainment. This variable is reported for persons aged 15 years and over in private households.10 Class of worker refers to whether a person is an employee or is self-employed. The self-employed include persons with or without a business, as well as unpaid family workers.11 Includes persons aged 15 years and over who have worked at some point in time during the reference period. In 2021, this period was between January 2020 and May 2021.12 Includes self-employed persons aged 15 years and over with or without an incorporated business and with or without paid help, as well as unpaid family workers.13 Includes self-employed persons whose business is incorporated with or without employees.14 Includes self-employed persons whose business is unincorporated. Also included among the self-employed are unpaid family workers. This category includes persons who work without pay in a business, farm or professional practice owned and operated by another family member living in the same dwelling.15 "Visible minority" refers to whether a person is a visible minority or not as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as "persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian Chinese Black Filipino Arab Latin American Southeast Asian West Asian Korean and Japanese. In 2021 Census analytical and communications products the term "visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere."16 For more information on visible minority and population group variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Visible Minority and Population Group Reference Guide, Census of Population, 2021.17 "In 2021 Census analytical and communications products, the term visible minority" has been replaced by the terms "racialized population" or "racialized groups" In 2021 Census analytical and communications products, the term visible minority" has been replaced by the terms "racialized population" or "racialized groups" reflecting the increased use of these terms in the public sphere."18 "The abbreviation n.i.e." means "not included elsewhere." This category includes persons who provided responses that are classified as a visible minority but that cannot be classified with a specific visible minority group. Such responses include for example "Guyanese Pacific Islander Polynesian Tibetan" and "West Indian."19 In 2021 Census analytical and communications products, this category is referred to as the rest of the population.

Youth not in education, employment or training by visible minority, selected sociodemographic characteristics and the census year: Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts (1)Frequency: OccasionalTable: 98-10-0648-01Release date: 2024-03-26Geography: Canada, Geographical region of Canada, Province or territory, Census metropolitan area, Census metropolitan area partUniverse: Persons in private households in occupied private dwellings, 2021 and 2016 censuses — 25% Sample dataVariable List: Visible minority (15), Gender (3a), Age (6), First official language spoken (5), Immigrant and generation status (7), Census year (2), Youth not in employment, education or training (1)List of abbreviations and acronyms found within various Census products.(https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm)Footnotes:1 Historical comparison of geographic areas The boundaries and names of census geographies can change from one census to the next. In order to facilitate data comparisons between censuses, previous census data have been adjusted to reflect as closely as possible the 2021 boundaries of these areas. The methodology used for this adjustment involved spatially linking blocks of previous censuses (concordance to the 1996 Census used the 1996 enumeration areas to the 2021 boundaries). A previous census block was linked to the 2021 area within which its representative point fell. A limited number of interactive linkages were completed to further enhance the adjustment in certain areas. For some census geographies, it was not possible to reflect the 2021 boundaries. The 2021 boundaries may not be reflected as there was no previous census block to assign to the 2021 area. As well previous census data for some 2021 areas may not be available due to the fact that the concordance did not produce an accurate representation of the 2021 area.2 Gender Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.3 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. The sex variable in census years prior to 2021 and the two-category gender variable in the 2021 Census are included together. Although sex and gender refer to two different concepts, the introduction of gender is not expected to have a significant impact on data analysis and historical comparability, given the small size of the transgender and non-binary populations. For additional information on changes of concepts over time, please consult the Age, Sex at Birth and Gender Reference Guide.4 Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).5 First official language spoken refers to the first official language (English or French) spoken by the person.6 Immigrant status refers to whether the person is a non-immigrant, an immigrant or a non-permanent resident. Period of immigration refers to the period in which the immigrant first obtained landed immigrant or permanent resident status. For more information on immigration variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021.7 Generation status refers to whether or not the person or the person's parents were born in Canada.8 "Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Arab, Latin American, Southeast Asian, West Asian, Korean, and Japanese.9 For more information on language variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Languages Reference Guide, Census of Population, 2021.10 Non-immigrants' includes persons who are Canadian citizens by birth.11 Immigrants' includes persons who are, or who have ever been, landed immigrants or permanent residents. Such persons have been granted the right to live in Canada permanently by immigration authorities. Immigrants who have obtained Canadian citizenship by naturalization are included in this category. In the 2021 Census of Population, 'Immigrants' includes immigrants who were admitted to Canada on or prior to May 11, 2021.12 Non-permanent residents' includes persons from another country with a usual place of residence in Canada and who have a work or study permit or who have claimed refugee status (asylum claimants). Family members living with work or study permit holders are also included, unless these family members are already Canadian citizens, landed immigrants or permanent residents.13 First generation' includes persons who were born outside Canada. For the most part, these are people who are now, or once were, immigrants to Canada.14 Second generation' includes persons who were born in Canada and had at least one parent born outside Canada. For the most part, these are the children of immigrants.15 "Refers to the proportion of youth aged 15 to 29 who were not in employment during the census reference week (in 2021, the reference week is May 2 to May 8) and who had not attended any accredited educational institution or program in the eight months preceding the census day (for example, in 2021 this period is between September 2020 and 11 May 2021). The Labor Force Survey (LFS) is the main data source for calculating national estimates of the youth not in employment, education, or training indicator, commonly known as NEET." This indicator is calculated using data from the first quarter or the average of the first three months of the calendar year which excludes summer employment. This LFS-based indicator is published on an annual basis and is used for international comparisons. The NEET indicator has regularly published by the Organization for Economic Cooperation and Development (OECD) since the late 1990s. However the census and other data sources such as social surveys like the Canadian Community Health Survey serve a different purpose. These data sources provide more specialized data that allowed deeper analysis of specific sociodemographic characteristics and conditions for a given population group which is a rich complement to understand the context and the factors behind the NEET estimates provided by the LFS. Although the Census of the Canadian population and the Labor Force Survey (LFS) measure similar concepts linked to labour market activities there are several fundamental differences between the characteristics of the two concept that measure the population of youth not in employment education or training. The most important of these differences is that in the LFS the reference period for school attendance and the reference period for employment are the same whereas in the Census they are different. Other differences between the census and the LFS include the length of the reference period the number of questions and their content the sample size the enumeration method and the coverage. For more information about the comparability of labour force status data from the Census of Population versus that of the LFS please consult the Appendix 2.11 from the Dictionary Census of Population 2021. which excludes summer employment. This LFS-based indicator is published on an annual basis and is used for international comparisons. The NEET indicator has regularly published by the Organization for Economic Cooperation and Development (OECD) since the late 1990s. However the census and other data sources such as social surveys like the Canadian Community Health Survey serve a different purpose. These data sources provide more specialized data that allowed deeper analysis of specific sociodemographic characteristics and conditions for a given population group which is a rich complement to understand the context and the factors behind the NEET estimates provided by the LFS. Although the Census of the Canadian population and the Labor Force Survey (LFS) measure similar concepts linked to labour market activities there are several fundamental differences between the characteristics of the two concept that measure the population of youth not in employment education or training. The most important of these differences is that in the LFS the reference period for school attendance and the reference period for employment are the same whereas in the Census they are different. Other differences between the census and the LFS include the length of the reference

Youth not in education, employment or training by visible minority, selected sociodemographic characteristics and the census year: Canada, geographical regions of Canada, provinces and territories and census metropolitan areas with parts (1)Frequency: OccasionalTable: 98-10-0648-01Release date: 2024-03-26Geography: Canada, Geographical region of Canada, Province or territory, Census metropolitan area, Census metropolitan area partUniverse: Persons in private households in occupied private dwellings, 2021 and 2016 censuses — 25% Sample dataVariable List: Visible minority (15), Gender (3a), Age (6), First official language spoken (5), Immigrant and generation status (7), Census year (2), Youth not in employment, education or training (1)List of abbreviations and acronyms found within various Census products.(https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm)Footnotes:1 Historical comparison of geographic areas The boundaries and names of census geographies can change from one census to the next. In order to facilitate data comparisons between censuses, previous census data have been adjusted to reflect as closely as possible the 2021 boundaries of these areas. The methodology used for this adjustment involved spatially linking blocks of previous censuses (concordance to the 1996 Census used the 1996 enumeration areas to the 2021 boundaries). A previous census block was linked to the 2021 area within which its representative point fell. A limited number of interactive linkages were completed to further enhance the adjustment in certain areas. For some census geographies, it was not possible to reflect the 2021 boundaries. The 2021 boundaries may not be reflected as there was no previous census block to assign to the 2021 area. As well previous census data for some 2021 areas may not be available due to the fact that the concordance did not produce an accurate representation of the 2021 area.2 Gender Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman). Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender.3 Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category “non-binary persons” are distributed into the other two gender categories and are denoted by the “+” symbol. The sex variable in census years prior to 2021 and the two-category gender variable in the 2021 Census are included together. Although sex and gender refer to two different concepts, the introduction of gender is not expected to have a significant impact on data analysis and historical comparability, given the small size of the transgender and non-binary populations. For additional information on changes of concepts over time, please consult the Age, Sex at Birth and Gender Reference Guide.4 Age' refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well-defined reference date).5 First official language spoken refers to the first official language (English or French) spoken by the person.6 Immigrant status refers to whether the person is a non-immigrant, an immigrant or a non-permanent resident. Period of immigration refers to the period in which the immigrant first obtained landed immigrant or permanent resident status. For more information on immigration variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2021.7 Generation status refers to whether or not the person or the person's parents were born in Canada.8 "Visible minority refers to whether a person is a visible minority or not, as defined by the Employment Equity Act. The Employment Equity Act defines visible minorities as persons other than Aboriginal peoples who are non-Caucasian in race or non-white in colour." The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Arab, Latin American, Southeast Asian, West Asian, Korean, and Japanese.9 For more information on language variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Languages Reference Guide, Census of Population, 2021.10 Non-immigrants' includes persons who are Canadian citizens by birth.11 Immigrants' includes persons who are, or who have ever been, landed immigrants or permanent residents. Such persons have been granted the right to live in Canada permanently by immigration authorities. Immigrants who have obtained Canadian citizenship by naturalization are included in this category. In the 2021 Census of Population, 'Immigrants' includes immigrants who were admitted to Canada on or prior to May 11, 2021.12 Non-permanent residents' includes persons from another country with a usual place of residence in Canada and who have a work or study permit or who have claimed refugee status (asylum claimants). Family members living with work or study permit holders are also included, unless these family members are already Canadian citizens, landed immigrants or permanent residents.13 First generation' includes persons who were born outside Canada. For the most part, these are people who are now, or once were, immigrants to Canada.14 Second generation' includes persons who were born in Canada and had at least one parent born outside Canada. For the most part, these are the children of immigrants.15 "Refers to the proportion of youth aged 15 to 29 who were not in employment during the census reference week (in 2021, the reference week is May 2 to May 8) and who had not attended any accredited educational institution or program in the eight months preceding the census day (for example, in 2021 this period is between September 2020 and 11 May 2021). The Labor Force Survey (LFS) is the main data source for calculating national estimates of the youth not in employment, education, or training indicator, commonly known as NEET." This indicator is calculated using data from the first quarter or the average of the first three months of the calendar year which excludes summer employment. This LFS-based indicator is published on an annual basis and is used for international comparisons. The NEET indicator has regularly published by the Organization for Economic Cooperation and Development (OECD) since the late 1990s. However the census and other data sources such as social surveys like the Canadian Community Health Survey serve a different purpose. These data sources provide more specialized data that allowed deeper analysis of specific sociodemographic characteristics and conditions for a given population group which is a rich complement to understand the context and the factors behind the NEET estimates provided by the LFS. Although the Census of the Canadian population and the Labor Force Survey (LFS) measure similar concepts linked to labour market activities there are several fundamental differences between the characteristics of the two concept that measure the population of youth not in employment education or training. The most important of these differences is that in the LFS the reference period for school attendance and the reference period for employment are the same whereas in the Census they are different. Other differences between the census and the LFS include the length of the reference period the number of questions and their content the sample size the enumeration method and the coverage. For more information about the comparability of labour force status data from the Census of Population versus that of the LFS please consult the Appendix 2.11 from the Dictionary Census of Population 2021. which excludes summer employment. This LFS-based indicator is published on an annual basis and is used for international comparisons. The NEET indicator has regularly published by the Organization for Economic Cooperation and Development (OECD) since the late 1990s. However the census and other data sources such as social surveys like the Canadian Community Health Survey serve a different purpose. These data sources provide more specialized data that allowed deeper analysis of specific sociodemographic characteristics and conditions for a given population group which is a rich complement to understand the context and the factors behind the NEET estimates provided by the LFS. Although the Census of the Canadian population and the Labor Force Survey (LFS) measure similar concepts linked to labour market activities there are several fundamental differences between the characteristics of the two concept that measure the population of youth not in employment education or training. The most important of these differences is that in the LFS the reference period for school attendance and the reference period for employment are the same whereas in the Census they are different. Other differences between the census and the LFS include the length of the reference

Frequency: OccasionalTable: 98-10-0003-01Release date: 2022-02-09Geography: Census subdivision, Census metropolitan area, Census agglomerationUniverse: All persons, 2021 and 2016 censuses – 100% dataVariable List: Population and dwelling counts (13)List of abbreviations and acronyms found within various Census products.(https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm)Footnotes appear in brackets in text.1) Content considerationsThe 2021 Census population counts for a particular geographic area represent the number of Canadians whose usual place of residence is in that area, regardless of where they happened to be on Census Day. Also included are any Canadians who were staying in that area on Census Day and who had no usual place of residence elsewhere in Canada, as well as those considered to be non-permanent residents. For most areas, there is little difference between the number of usual residents and the number of people staying in the area on Census Day. For certain places, however, such as tourist or vacation areas, or those including large work camps, the number of people staying in that area at any particular time could significantly exceed the number of usual residents shown here. The population counts include Canadians living in other countries, but do not include foreign residents living in Canada. Given these differences, users are advised not to interpret population counts as being the number of people living in the reported dwellings.The dwelling counts refer to total private dwellings and private dwellings occupied by usual residents in Canada. The census dwelling counts do not include collective dwellings, which are dwellings of a commercial, institutional or communal nature. The usual residents in collective dwellings are, however, included in the population counts.Changes occur to the names, boundaries and other characteristics of geographic areas (e.g., census subdivisions may amalgamate, or there may be an annexation or a change of name or status). Since the geographic framework is used for census data collection, the geographic reference date must be set several months before the date of the census in order to have these changes made in time. For the 2021 Census, the geographic reference date was January 1, 2021.Land area is the area in square kilometres of the land-based portions of standard geographic areas. The data are unofficial, and are provided for the sole purpose of calculating population density. Land area data for the standard geographic areas reflect the boundaries in effect on January 1, 2021 (the geographic reference date for the 2021 Census of Canada).DefinitionsThe Census Dictionary is a reference document which contains detailed definitions of Census of Population concepts, universes, variables, and geographic terms, as well as historical informationIncompletely enumerated reserves and settlementsIn 2021, a total of 63 census subdivisions defined as reserves and settlements were incompletely enumerated. For these reserves and settlements, dwelling enumeration was either not permitted or could not be completed because of the various reasons below.This represents an increase compared with the 14 census subdivisions defined as reserves and settlements that were incompletely enumerated in the 2016 Census. Health and safety restrictions put in place to slow the spread of COVID-19 and natural events (including evacuations because of forest fires) contributed to the incomplete enumeration of many reserves and settlements.The 2021 Census population and dwelling counts are not available for the 63 incompletely enumerated reserves and settlements, and are not included in 2021 Census tabulations. Data for geographic areas containing one or more of these reserves and settlements are noted accordingly. Because of the missing data, users are cautioned that—for the affected geographic areas—comparisons (e.g., percentage change) between 2016 and 2021 may not be precise. The impact of the missing data for higher-level geographic areas (Canada, provinces and territories, census metropolitan areas and census agglomerations) is usually very/ small. However, the impact can be significant for lower-level geographic areas (e.g., census divisions), where incompletely enumerated reserves and settlements account for a higher proportion of the population. This is especially true for lower-level geographic areas where a particular reserve or settlement was incompletely enumerated for the 2021 Census but enumerated for the 2016 Census and, vice versa.Adjustment of population countsStatistics Canada is committed to protect the privacy of all Canadians and the confidentiality of the data they provide to us. As part of this commitment, some population counts of geographic areas are adjusted in order to ensure confidentiality.Counts of the total population are rounded to a base of 5 for any dissemination block having a population of less than 15. Population counts for all standard geographic areas above the dissemination block level are derived by summing the adjusted dissemination block counts. The adjustment of dissemination block counts is controlled to ensure that the population counts for dissemination areas will always be within 5 of the actual values. The adjustment has no impact on the population counts of census divisions and large census subdivisions. Dwelling counts are not adjusted.Difference between census counts and population estimates.The Census of Population is designed to conduct a complete count of the population. Inevitably, however, some individuals will not be enumerated (undercoverage), while others, usually less numerous, will be enumerated more than once (overcoverage).To determine the number of people who were missed or counted more than once, Statistics Canada conducts postcensal studies of the coverage of the census population, using representative samples of the population. Results of these studies are usually available two years after Census Day. They are used, in combination with census figures and other sources, to develop the population estimates produced by Statistics Canada on a regular basis. Population estimates are used for equalization payments, to follow trends in the Canadian population on a quarterly basis and to understand the underlying components of population change (for example, births, deaths, immigrants, emigrants and non-permanent residents). Population estimates differ from census counts and are usually higher, because census counts are not adjusted for under-coverage or over-coverage.Cite: Statistics Canada. Table 98-10-0003-01 Population and dwelling counts: Census metropolitan areas, census agglomerations and census subdivisions (municipalities)https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=9810000301

"Total income groups by place of work status: Census metropolitan areas, tracted census agglomerations and census tracts Frequency: Occasional Table: 98-10-0474-01 Release date: 2022-11-30 Geography: Census metropolitan area, Census agglomeration, Census tract Universe: Employed labour force aged 15 years and over in private households, 2021 Census — 25% Sample data Variable List: Place of work status (7), Total income groups (18) Symbol legend: x suppressed to meet the confidentiality requirements of the Statistics Act Abbreviation notes: List of abbreviations and acronyms found within various Census products. (https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm) Footnotes: 1 'Place of work status' refers to whether a person worked at home, worked outside Canada, had no fixed workplace address, or worked at a specific address (usual place of work). This variable applies to persons aged 15 years and over, in private households, with a job or absent from their job or business during the week of Sunday, May 2 to Saturday, May 8, 2021. 2 Total income Total income refers to the sum of certain incomes (in cash and, in some circumstances, in kind) of the statistical unit during a specified reference period. The components used to calculate total income vary between: – Statistical units of social statistical programs such as persons, private households, census families and economic families; – Statistical units of business statistical programs such as enterprises, companies, establishments and locations; and – Statistical units of farm statistical programs such as farm operator and farm family. In the context of persons, total income refers to receipts from certain sources, before income taxes and deductions, during a specified reference period. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of economic families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. In the context of households, total income refers to receipts from certain sources of all household members, before income taxes and deductions, during a specified reference period. The monetary receipts included are those that tend to be of a regular and recurring nature. Receipts that are included as income are: employment income from wages, salaries, tips, commissions and net income from self-employment (for both unincorporated farm and non-farm activities); income from investment sources, such as dividends and interest on bonds, accounts, guaranteed investment certificates (GICs) and mutual funds; income from employer and personal pension sources, such as private pensions and payments from annuities and registered retirement income funds (RRIFs); other regular cash income, such as child support payments received, spousal support payments (alimony) received and scholarships; income from government sources, such as social assistance, child benefits, Employment Insurance benefits, Old Age Security benefits, COVID-19 benefits and Canada Pension Plan and Québec Pension Plan benefits and disability income. Receipts excluded from this income definition are: one-time receipts, such as lottery winnings, gambling winnings, cash inheritances, lump-sum insurance settlements and tax-free savings account (TFSA) or registered retirement savings plan (RRSP) withdrawals; capital gains because they are not by their nature regular and recurring. It is further assumed that they are more relevant to the concept of wealth than the concept of income; employers' contributions to registered pension plans, Canada Pension Plan, Québec Pension Plan and Employment Insurance; voluntary inter-household transfers, imputed rent, goods and services produced for barter and goods produced for own consumption. For the 2021 Census, the reference period for income data is the calendar year 2020, unless otherwise specified. 3 Classification of respondents according to whether they worked at home, worked outside Canada, had no fixed workplace address or worked at a specific address (usual place of work). "