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.

This dataset presents information from G40 – Rent (weekly) by landlord type in Australia based on the general community profile from the 2021 Census. It contains characteristics of persons, families, and dwellings by Local Government Areas (LGA), 2021, from the Australian Statistical Geography Standard (ASGS) Edition 3.

This dataset is part of a set of web services based on the 2021 Census. It can be used as a tool for researching, planning, and analysis. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night), unless otherwise stated.

Small random adjustments have been made to all cell values to protect the confidentiality of respondents. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For further information see the 2021 Census Privacy Statement, Confidentiality, and Introduced random error/perturbation.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: G40 – Rent (weekly) by landlord type Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: About the Census, 2021 Census product release guide – Community Profiles, Understanding Census geography Source: Australian Bureau of Statistics (ABS)

This dataset presents information from G37 – Tenure and landlord type by dwelling structure in Australia based on the general community profile from the 2021 Census. It contains characteristics of persons, families, and dwellings by Statistical Areas Level 2 (SA2), 2021, from the Australian Statistical Geography Standard (ASGS) Edition 3.

This dataset is part of a set of web services based on the 2021 Census. It can be used as a tool for researching, planning, and analysis. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night), unless otherwise stated.

Small random adjustments have been made to all cell values to protect the confidentiality of respondents. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For further information see the 2021 Census Privacy Statement, Confidentiality, and Introduced random error/perturbation.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: G37 – Tenure and landlord type by dwelling structure Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: About the Census, 2021 Census product release guide – Community Profiles, Understanding Census geography Source: Australian Bureau of Statistics (ABS)

ABOUT THIS TOOL:

The Better Building’s Clean Energy for Low Income Communities Accelerator (CELICA) was launched in 2016 to help state and local partners across the nation meet their goals for increasing uptake of energy efficiency and renewable energy technologies in low and moderate income communities. As a part of the Accelerator, DOE created this Low-Income Energy Affordability Data (LEAD) Tool to assist partners with understanding their LMI community characteristics. This can be utilized for low income and moderate income energy policy and program planning, as it provides interactive state, county and city level worksheets with graphs and data including number of households at different income levels and numbers of homeowners versus renters. It provides a breakdown based on fuel type, building type, and construction year. It also provides average monthly energy expenditures and energy burden (percentage of income spent on energy).

HOW TO USE:

The LEAD tool can be used to support program design and goal setting, and they can be paired with other data to improve LMI community energy benchmarking and program evaluation. Datasets are available for all 50 states, census divisions, and tract levels. You will have to enable macros in MS Excel to interact with the data. A description of each of the files and what states are included in each U.S. Census Division can be found in the file "DESCRIPTION OF FILES".

For more information, visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities

Urban Displacement Project’s (UDP) Estimated Displacement Risk (EDR) model for California identifies varying levels of displacement risk for low-income renter households in all census tracts in the state from 2015 to 2019(1). The model uses machine learning to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP defines displacement risk as a census tract with characteristics which, according to the model, are strongly correlated with more low-income population loss than gain. In other words, the model estimates that more low-income households are leaving these neighborhoods than moving in.This map is a conservative estimate of low-income loss and should be considered a tool to help identify housing vulnerability. Displacement may occur because of either investment, disinvestment, or disaster-driven forces. Because this risk assessment does not identify the causes of displacement, UDP does not recommend that the tool be used to assess vulnerability to investment such as new housing construction or infrastructure improvements. HCD recommends combining this map with on-the-ground accounts of displacement, as well as other related data such as overcrowding, cost burden, and income diversity to achieve a full understanding of displacement risk.If you see a tract or area that does not seem right, please fill out this form to help UDP ground-truth the method and improve their model.How should I read the displacement map layers?The AFFH Data Viewer includes three separate displacement layers that were generated by the EDR model. The “50-80% AMI” layer shows the level of displacement risk for low-income (LI) households specifically. Since UDP has reason to believe that the data may not accurately capture extremely low-income (ELI) households due to the difficulty in counting this population, UDP combined ELI and very low-income (VLI) household predictions into one group—the “0-50% AMI” layer—by opting for the more “extreme” displacement scenario (e.g., if a tract was categorized as “Elevated” for VLI households but “Extreme” for ELI households, UDP assigned the tract to the “Extreme” category for the 0-50% layer). For these two layers, tracts are assigned to one of the following categories, with darker red colors representing higher displacement risk and lighter orange colors representing less risk:• Low Data Quality: the tract has less than 500 total households and/or the census margins of error were greater than 15% of the estimate (shaded gray).• Lower Displacement Risk: the model estimates that the loss of low-income households is less than the gain in low-income households. However, some of these areas may have small pockets of displacement within their boundaries. • At Risk of Displacement: the model estimates there is potential displacement or risk of displacement of the given population in these tracts.• Elevated Displacement: the model estimates there is a small amount of displacement (e.g., 10%) of the given population.• High Displacement: the model estimates there is a relatively high amount of displacement (e.g., 20%) of the given population.• Extreme Displacement: the model estimates there is an extreme level of displacement (e.g., greater than 20%) of the given population. The “Overall Displacement” layer shows the number of income groups experiencing any displacement risk. For example, in the dark red tracts (“2 income groups”), the model estimates displacement (Elevated, High, or Extreme) for both of the two income groups. In the light orange tracts categorized as “At Risk of Displacement”, one or all three income groups had to have been categorized as “At Risk of Displacement”. Light yellow tracts in the “Overall Displacement” layer are not experiencing UDP’s definition of displacement according to the model. Some of these yellow tracts may be majority low-income experiencing small to significant growth in this population while in other cases they may be high-income and exclusive (and therefore have few low-income residents to begin with). One major limitation to the model is that the migration data UDP uses likely does not capture some vulnerable populations, such as undocumented households. This means that some yellow tracts may be experiencing high rates of displacement among these types of households. MethodologyThe EDR is a first-of-its-kind model that uses machine learning and household level data to predict displacement. To create the EDR, UDP first joined household-level data from Data Axle (formerly Infogroup) with tract-level data from the 2014 and 2019 5-year American Community Survey; Affirmatively Furthering Fair Housing (AFFH) data from various sources compiled by California Housing and Community Development; Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) data; and the Environmental Protection Agency’s Smart Location Database.UDP then used a machine learning model to determine which variables are most strongly related to displacement at the household level and to predict tract-level displacement risk statewide while controlling for region. UDP modeled displacement risk as the net migration rate of three separate renter households income categories: extremely low-income (ELI), very low-income (VLI), and low-income (LI). These households have incomes between 0-30% of the Area Median Income (AMI), 30-50% AMI, and 50-80% AMI, respectively. Tracts that have a predicted net loss within these groups are considered to experience displacement in three degrees: elevated, high, and extreme. UDP also includes a “At Risk of Displacement” category in tracts that might be experiencing displacement.What are the main limitations of this map?1. Because the map uses 2019 data, it does not reflect more recent trends. The pandemic, which started in 2020, has exacerbated income inequality and increased housing costs, meaning that UDP’s map likely underestimates current displacement risk throughout the state.2. The model examines displacement risk for renters only, and does not account for the fact that many homeowners are also facing housing and gentrification pressures. As a result, the map generally only highlights areas with relatively high renter populations, and neighborhoods with higher homeownership rates that are known to be experiencing gentrification and displacement are not as prominent as one might expect.3. The model does not incorporate data on new housing construction or infrastructure projects. The map therefore does not capture the potential impacts of these developments on displacement risk; it only accounts for other characteristics such as demographics and some features of the built environment. Two of UDP’s other studies—on new housing construction and green infrastructure—explore the relationships between these factors and displacement.Variable ImportanceFigures 1, 2, and 3 show the most important variables for each of the three models—ELI, VLI, and LI. The horizontal bars show the importance of each variable in predicting displacement for the respective group. All three models share a similar order of variable importance with median rent, percent non-white, rent gap (i.e., rental market pressure calculated using the difference between nearby and local rents), percent renters, percent high-income households, and percent of low-income households driving much of the displacement estimation. Other important variables include building types as well as economic and socio-demographic characteristics. For a full list of the variables included in the final models, ranked by descending order of importance, and their definitions see all three tabs of this spreadsheet. “Importance” is defined in two ways: 1. % Inclusion: The average proportion of times this variable was included in the model’s decision tree as the most important or driving factor.2. MeanRank: The average rank of importance for each variable across the numerous model runs where higher numbers mean higher ranking. Figures 1 through 3 below show each of the model variable rankings ordered by importance. The red lines represent Jenks Breaks, which are designed to sort values into their most “natural” clusters. Variable importance for each model shows a substantial drop-off after about 10 variables, meaning a relatively small number of variables account for a large amount of the predictive power in UDP’s displacement model.Figure 1. Variable Importance for Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Figure 2. Variable Importance for Very Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet. Figure 3. Variable Importance for Extremely Low Income HouseholdsFor a description of each variable and its source, see this spreadsheet.Source: Chapple, K., & Thomas, T., and Zuk, M. (2022). Urban Displacement Project website. Berkeley, CA: Urban Displacement Project.(1) UDP used this time-frame because (a) the 2020 census had a large non-response rate and it implemented a new statistical modification that obscures and misrepresents racial and economic characteristics at the census tract level and (b) pandemic mobility trends are still in flux and UDP believes 2019 is more representative of “normal” or non-pandemic displacement trends.

Analysis unit

Persons Persons not organized into households; age grouped into categories

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

UNIT DESCRIPTIONS: - Dwellings: Housing units means such a residential place which has separate building structure and is separate housing unit. These could be one or more than one housing units in a building. Housing unit and house are the same by definition in population and housing census. - Households: Households consisting of more than one person living together under common cooking arrangements (i.e., they use one burner for cooking). However if a person lives alone, he shall also be considered a household. These persons are generally relatives but these could also be friends, servants of the household and other non relatives residing in them. In such a case if the members of household do not eat at the place where they live, then they will be counted at the place where they live rather than at a place where they take their meals. - Group quarters: Housing unit which has been constructed for collective residence in connection with semi-government or trading purpose. e.g. hotel , hostel , residential barracks of Armed or semi Armed forces, residential camps, jail, Sanitarium, Mental hospital, Disabled, poor , orphans, paupers and special institutions for residences of other such people.

Universe

All the people who are residing in the boundaries of Pakistan on the Census Day, which include all types of persons (i.e., infants or babies, adults or old, males or females, landlords or tenants, Pakistanis or foreigners). The staff members of diplomats and their families are exempted.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Population Census Organization

SAMPLE SIZE (person records): 8433058.

SAMPLE DESIGN: Systematic sample of every 10th person with a random start, drawn from a 38% sample containing a weight variable (Short form data) by the IPUMS. Persons were not organized into households.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two Population Census forms. The short form contains a few question on demographic characteristics including name, relationship, residential status, sex, age, marital status, religion, ability to read Quran, literacy, education level, and language used in the household. These questions were asked from about ninety percent of the population. The long form will be asked of the rest of the population, and it contains all the questions asked in the short form and additional questions on higher education, field of education, migration, economic characteristics, number of children, disability, and household members living abroad.

Abstract (en): Summary File 2 contains 100-percent United States decennial Census data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, and group quarters occupancy. Housing items include occupancy status, vacancy status, and tenure (owner-occupied or renter- occupied). The 100-percent data are presented in 36 population tables ("PCT") and 11 housing tables ("HCT") down to the census tract level. Each table is iterated for 250 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), and 39 Hispanic or Latino groups. The presentation of tables for any of the 250 population groups is subject to a population threshold of 100 or more people, that is, if there were fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. All persons in housing units in South Dakota in 2000. 2013-05-24 Multiple Census data file segments were repackaged for distribution into a single zip archive per dataset. No changes were made to the data or documentation.2006-01-12 All files were removed from dataset 256 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 255 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 254 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 253 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 252 and flagged as study-level files, so that they will accompany all downloads. Because of the number of files per state in Summary File 2, ICPSR has given each state its own ICPSR study number in the range ICPSR 13233-13284. Data for each state are being released as they become available.The data are provided in four segments (files) per iteration. These segments are PCT1-PCT4, PCT5-PCT19, PCT20-PCT36, and HCT1-HCT11. The iterations are Parts 1-250, the Geographic Header file is Part 251. The Geographic Header file is in fixed-format ASCII and the Table files are in comma-delimited ASCII format. The Geographic Header file has 85 variables, Segment 01 has 224 variables, Segment 02 has 240 variables, Segment 03 has 179 variables, and Segment 04 has 141 variables. When all the segments are merged there are 849 variables.

This dataset presents information from G37 – Tenure and landlord type by dwelling structure in Australia based on the general community profile from the 2021 Census. It contains characteristics of persons, families, and dwellings by Local Government Areas (LGA), 2021, from the Australian Statistical Geography Standard (ASGS) Edition 3.

This dataset is part of a set of web services based on the 2021 Census. It can be used as a tool for researching, planning, and analysis. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night), unless otherwise stated.

Small random adjustments have been made to all cell values to protect the confidentiality of respondents. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For further information see the 2021 Census Privacy Statement, Confidentiality, and Introduced random error/perturbation.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: G37 – Tenure and landlord type by dwelling structure Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: About the Census, 2021 Census product release guide – Community Profiles, Understanding Census geography Source: Australian Bureau of Statistics (ABS)

This dataset provides Census 2021 estimates that classify households in England and Wales by tenure, by number of people per room in household, and by accommodation type. The estimates are as at Census Day, 21 March 2021.

There is evidence of people incorrectly identifying their type of landlord as ”Council or local authority” or “Housing association”. You should add these two categories together when analysing data that uses this variable. Read more about this quality notice.

It is inappropriate to measure change in number of persons per room from 2011 to 2021, as Census 2021 used Valuation Office Agency data for the number of rooms variable. Instead use Census 2021 estimates for number of persons per bedroom for comparisons over time. Read more about this quality notice.

We have made changes to housing definitions since the 2011 Census. Take care if you compare Census 2021 results for this topic with those from the 2011 Census. Read more about this quality notice.

Area type

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

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

Lower tier local authorities

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

Coverage

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

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

Tenure of household

Whether a household owns or rents the accommodation that it occupies.

Owner-occupied accommodation can be:

• owned outright, which is where the household owns all of the accommodation
• with a mortgage or loan
• part-owned on a shared ownership scheme

Rented accommodation can be:

• private rented, for example, rented through a private landlord or letting agent
• social rented through a local council or housing association

This information is not available for household spaces with no usual residents.

Number of people per room in household

The number of household members is divided by the number of rooms in the household.

Accommodation type

The type of building or structure used or available by an individual or household.

This could be:

• the whole house or bungalow
• a flat, maisonette or apartment
• a temporary or mobile structure, such as a caravan

More information about accommodation types

Whole house or bungalow:

This property type is not divided into flats or other living accommodation. There are three types of whole houses or bungalows.

Detached:

None of the living accommodation is attached to another property but can be attached to a garage.

Semi-detached:

The living accommodation is joined to another house or bungalow by a common wall that they share.

Terraced:

A mid-terraced house is located between two other houses and shares two common walls. An end-of-terrace house is part of a terraced development but only shares one common wall.

Flats (Apartments) and maisonettes:

An apartment is another word for a flat. A maisonette is a 2-storey flat.

Abstract (en): Summary File 2 contains 100-percent United States decennial Census data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, and group quarters occupancy. Housing items include occupancy status, vacancy status, and tenure (owner-occupied or renter- occupied). The 100-percent data are presented in 36 population tables ("PCT") and 11 housing tables ("HCT") down to the census tract level. Each table is iterated for 250 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), and 39 Hispanic or Latino groups. The presentation of tables for any of the 250 population groups is subject to a population threshold of 100 or more people -- that is, if there were fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. All persons in housing units in United States in 2000. 2013-05-24 Multiple Census data file segments were repackaged for distribution into a single zip archive per dataset. No changes were made to the data or documentation.2006-01-12 All files were removed from dataset 256 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 255 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 254 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 253 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 252 and flagged as study-level files, so that they will accompany all downloads. The data are provided in four segments (files) per iteration. These segments are PCT1-PCT4, PCT5-PCT19, PCT20-PCT36, and HCT1-HCT11. The iterations are Parts 1-250, the Geographic Header file is Part 251. The Geographic Header file is in fixed-format ASCII and the Table files are in comma-delimited ASCII format. The Geographic Header file has 85 variables, Segment 01 has 224 variables, Segment 02 has 240 variables, Segment 03 has 179 variables, and Segment 04 has 141 variables. When all the segments are merged there are 849 variables.

Abstract (en): Summary File 2 contains 100-percent United States decennial Census data, which is the information compiled from the questions asked of all people and about every housing unit. Population items include sex, age, race, Hispanic or Latino origin, household relationship, and group quarters occupancy. Housing items include occupancy status, vacancy status, and tenure (owner-occupied or renter- occupied). The 100-percent data are presented in 36 population tables ("PCT") and 11 housing tables ("HCT") down to the census tract level. Each table is iterated for 250 population groups: the total population, 132 race groups, 78 American Indian and Alaska Native tribe categories (reflecting 39 individual tribes), and 39 Hispanic or Latino groups. The presentation of tables for any of the 250 population groups is subject to a population threshold of 100 or more people, that is, if there were fewer than 100 people in a specific population group in a specific geographic area, their population and housing characteristics data are not available for that geographic area. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.. All persons in housing units in Colorado in 2000. 2013-05-24 Multiple Census data file segments were repackaged for distribution into a single zip archive per dataset. No changes were made to the data or documentation.2006-01-12 All files were removed from dataset 256 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 255 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 254 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 253 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 252 and flagged as study-level files, so that they will accompany all downloads. Because of the number of files per state in Summary File 2, ICPSR has given each state its own ICPSR study number in the range ICPSR 13233-13284. Data for each state are being released as they become available.The data are provided in four segments (files) per iteration. These segments are PCT1-PCT4, PCT5-PCT19, PCT20-PCT36, and HCT1-HCT11. The iterations are Parts 1-250, the Geographic Header file is Part 251. The Geographic Header file is in fixed-format ASCII and the Table files are in comma-delimited ASCII format. The Geographic Header file has 85 variables, Segment 01 has 224 variables, Segment 02 has 240 variables, Segment 03 has 179 variables, and Segment 04 has 141 variables. When all the segments are merged there are 849 variables.

This dataset presents information from G40 – Rent (weekly) by landlord type in Australia based on the general community profile from the 2021 Census. It contains characteristics of persons, families, and dwellings by Statistical Areas Level 2 (SA2), 2021, from the Australian Statistical Geography Standard (ASGS) Edition 3.

This dataset is part of a set of web services based on the 2021 Census. It can be used as a tool for researching, planning, and analysis. The data is based on place of usual residence (that is, where people usually live, rather than where they were counted on Census night), unless otherwise stated.

Small random adjustments have been made to all cell values to protect the confidentiality of respondents. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals. For further information see the 2021 Census Privacy Statement, Confidentiality, and Introduced random error/perturbation.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It will bring together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas.

Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: G40 – Rent (weekly) by landlord type Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: About the Census, 2021 Census product release guide – Community Profiles, Understanding Census geography Source: Australian Bureau of Statistics (ABS)

This dataset provides Census 2022 estimates for Household tenure by age of Household Reference Person in Scotland.

Age

A person’s age on Census day (20 March 2022) calculated from their date of birth. Infants less than one year old are classed as being 0 years of age.

Household reference person

The concept of a Household Reference Person (HRP) was introduced in the 2001 Census (in common with other government surveys in 2001/2) to replace the traditional concept of the 'head of the household'. HRPs provide an individual person within a household to act as a reference point for producing further derived statistics and for characterising a whole household according to characteristics of the chosen reference person.

For a person living alone, it follows that this person is the HRP.

If a household contains only one family (with or without ungrouped individuals) then the HRP is the same as the Family Reference Person (FRP).

The Family Reference Person (FRP) is identified by criteria based on the family make up:

In a lone parent family it is taken to be the lone parent.

In a couple family, the FRP is chosen from the two people in the couple on the basis of their economic activity (in the priority order: full-time job, part-time job, unemployed, retired, other). If both people have the same economic activity, the FRP is identified as the elder of the two or, if they are the same age, the first member of the couple on the form.

If there is more than one family in a household the HRP is chosen from among the FRPs using the same criteria used to choose the FRP. This means the HRP will be selected from the FRPs on the basis of their economic activity, in the priority order:

• Economically active, employed, full-time, non-student
• Economically active, employed, full-time, student
• Economically active, employed, part-time, non-student
• Economically active, employed, part-time, student
• Economically active, unemployed, non-student
• Economically active, unemployed, student
• Economically inactive, retired
• Economically inactive, other

If some or all FRPs have the same economic activity, the HRP is the eldest of the FRPs. If some or all are the same age, the HRP is the first of the FRPs from the order in which they were listed on the questionnaire.

For families in which there is generational divide between family members that cannot be determined (Other related family), there is no FRP. Members of these families are treated the same as ungrouped individuals.

If a household is made up entirely of any combination of ungrouped individuals and other related families, the HRP is chosen from among all people in the household, using the same criteria used to choose between FRPs. Students at their non term-time address cannot be the HRP.

Details of classification can be found here

Tenure of household

A classification of whether a household rents or owns the accommodation that it occupies. For rented households, this variable also includes information about the type of landlord who owns or manages the accommodation.

This variable is derived from two questions on the household form on household tenure and landlord.

Household question 12: Does your household own or rent this accommodation?

• Owns with a mortgage or loan
• Owns outright
• Owns with shared equity (for example, LIFT, Help-to-Buy)
• Rents (with or without housing benefit)
• Part owns and part rents (shared ownership)
• Lives rent free

Household question 13: Who is your landlord? (Only asked of households who are renting)

• Council (Local Authority) or Housing Association / Registered Social Landlord
• Private landlord or letting agency
• Other

Details of classification can be found here

The quality assurance report can be found here

This dataset provides Census 2022 estimates for Tenure in the unit of occupied households in Scotland.

Tenure of household

A classification of whether a household rents or owns the accommodation that it occupies. For rented households, this variable also includes information about the type of landlord who owns or manages the accommodation.

This variable is derived from two questions on the household form on household tenure and landlord.

Household question 12: Does your household own or rent this accommodation?

• Owns with a mortgage or loan
• Owns outright
• Owns with shared equity (for example, LIFT, Help-to-Buy)
• Rents (with or without housing benefit)
• Part owns and part rents (shared ownership)
• Lives rent free

Household question 13: Who is your landlord? (Only asked of households who are renting)

• Council (Local Authority) or Housing Association / Registered Social Landlord
• Private landlord or letting agency
• Other

Details of classification can be found here

The quality assurance report can be found here

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood