This indicator is defined as the percentage of the population living in an overcrowded household. A person is considered as living in an overcrowded household if the household does not have at its disposal a minimum of rooms equal to: - one room for the household; - one room by couple in the household; - one room for each single person aged 18 and more; - one room by pair of single people of the same sex between 12 and 17 years of age; - one room for each single person between 12 and 17 years of age and not included in the previous category; - one room by pair of children under 12 years of age. The indicator is presented by degree of urbanisation.

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

This indicator is defined as the percentage of the population living in an overcrowded household. A person is considered as living in an overcrowded household if the household does not have at its disposal a minimum of rooms equal to: - one room for the household; - one room by couple in the household; - one room for each single person aged 18 and more; - one room by pair of single people of the same sex between 12 and 17 years of age; - one room for each single person between 12 and 17 years of age and not included in the previous category; - one room by pair of children under 12 years of age. The indicator is presented by sex.

Table shows the percentage of households that are defined as overcrowded - defined by the 'bedroom standard'.

'Overcrowded includes Basic Overcrowded and Severely Overcrowded. This includes households with at least 1 bedroom too few.

'Bedroom standard' is used as an indicator of occupation density. A standard number of bedrooms is allocated to each household in accordance with its age/sex/marital status composition and the relationship of the members to one another. A separate bedroom is allocated to each married or cohabiting couple, any other person aged 21 or over, each pair of adolescents aged 10 - 20 of the same sex, and each pair of children under 10. Any unpaired person aged 10 - 20 is paired, if possible with a child under 10 of the same sex, or, if that is not possible, he or she is given a separate bedroom, as is any unpaired child under 10. This standard is then compared with the actual number of bedrooms (including bed-sitters) available for the sole use of the household, and differences are tabulated. Bedrooms converted to other uses are not counted as available unless they have been denoted as bedrooms by the informants; bedrooms not actually in use are counted unless uninhabitable.

Please note, unfortunately the Department for Communities and Local Government, who sponsored a question relating to the number of bedrooms in a household no longer take part in the Integrated Household Survey, and therefore the question will not be included again. This means that annual overcrowding data at borough level will not be available in the future.

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.

Dataset contains counts and measures for households from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 1.

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The variables included in this dataset are for households in occupied private dwellings (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated):

• Count of households in occupied private dwellings
• Access to telecommunication systems (total responses)
• Household crowding index for levels 1 and 2
• Household composition
• Number of usual residents in household
• Average number of usual residents in household
• Number of motor vehicles
• Sector of landlord for households in rented occupied private dwellings
• Tenure of household
• Total household income
• Median ($) total household income
• Weekly rent paid by household for households in rented occupied private dwellings
• Median ($) weekly rent paid by household for households in rented occupied private dwellings.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Household crowding

Household crowding is based on the Canadian National Occupancy Standard (CNOS). It calculates the number of bedrooms needed based on the demographic composition of the household. The household crowding index methodology for 2023 Census has been updated to use gender instead of sex. Household crowding should be used with caution for small geographical areas due to high volatility between census years as a result of population change and urban development. There may be additional volatility in areas affected by the cyclone, particularly in Gisborne and Hawke's Bay. Household crowding index – 2023 Census has details on how the methodology has changed, differences from 2018 Census, and more.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.

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Symbol

-997 Not available

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Abstract copyright UK Data Service and data collection copyright owner.The Living in Wales (LIW) survey series, which ran from 2004-2008, was the main general source of statistical information about households and the condition of homes in Wales. The LIW survey was also referred to as the Welsh Household and Dwelling Survey and replaced the former Welsh House Condition Survey (WHCS), which was last conducted in 1997 and 1998, when a Household Survey was completed in 1997 and a Property Survey in 1998. The LIW survey had two separate but linked components: the Household Survey and the Property Survey. The Household Survey was completed annually from 2004 and was conducted as a face-to-face interview with the household reference person (HRP) or another appropriate adult. It aimed to provide additional information to complement the Property Survey, but also to provide information about the community, the use of the Welsh language, the health of the members of the household, the values and opinions of the respondent as well as demographic characteristics. The Property Survey was conducted in 2004 and 2008 (held under SNs 7201 and 7202 respectively) and comprised an internal and external assessment of the property which was completed by a qualified surveyor. The Living in Wales survey closed in 2008. From 2009/2010 onwards, it has been replaced by the National Survey for Wales (held at the Archive under GN 33435). Further information can be found on the Welsh Assembly Government Living in Wales web page. Main Topics: The Household Survey 2008 includes the following topics: household composition, ethnicity, religion, employmentdisability and long-term limiting illness heating, insulation, overcrowding transport equality issues environment volunteering quality of Life natural environment fire protection communication and internet usage housing history tenure/area features economic status income questions on usage of and satisfaction with the following services: local bus services; GP surgeries; hospital in-patients, out-patients and day cases; all health services; recycling facilities; sport and leisure provision; all local government services Multi-stage stratified random sample Face-to-face interview Self-completion 2008 ACCESS TO HEALTH SE... ACCESS TO INFORMATION ACCESS TO PUBLIC SE... AGE AIDS FOR THE DISABLED AMBULANCE SERVICES APARTMENTS ASSOCIATIONS ATTITUDES BATHROOMS BEDROOMS BICYCLES BROADBAND BUSES CARDIOVASCULAR DISE... CARE OF DEPENDANTS CARS CHILD BENEFITS CHILD DAY CARE COMMERCIAL BUILDINGS COMMUNITIES COMMUNITY ACTION COMMUNITY PARTICIPA... COMMUTING COMPUTERS CONSUMER ACTION COUNCIL TAX CULTURAL ASSOCIATIONS DEBILITATIVE ILLNESS DENTISTS DISABILITIES DISABILITY DISCRIMI... DISABLED FACILITIES DISCRIMINATION ECONOMIC ACTIVITY EMERGENCY AND PROTE... EMPLOYMENT EMPLOYMENT PROGRAMMES ENVIRONMENTAL CONSE... ENVIRONMENTAL ISSUES ENVIRONMENTAL MOVEM... ETHNIC GROUPS FAMILY BENEFITS FIRE FIRE PROTECTION EQU... FREEHOLD FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... FURTHER EDUCATION GENDER GENERAL PRACTITIONERS HEADS OF HOUSEHOLD HEALTH CONSULTATIONS HEARING IMPAIRMENTS HEATING SYSTEMS HIGH RISE FLATS HOME BUYING HOME OWNERSHIP HOME SHARING HOUSE PRICES HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSES HOUSING HOUSING AGE HOUSING BENEFITS HOUSING TENURE Housing INCOME INFORMATION NEEDS INFORMATION SOURCES INTERNET ACCESS INTERNET USE JOB SEEKER S ALLOWANCE KITCHENS KNOWLEDGE TRANSFER LANDLORDS LANGUAGES USED AT HOME LAVATORIES LEARNING DISABILITIES LEASEHOLD LIBRARY FACILITIES LIBRARY SERVICES LOANS LOCAL COMMUNITY FAC... LOCAL GOVERNMENT SE... LOCAL TAX BENEFITS MATERNITY BENEFITS MEMBERSHIP MENTAL HEALTH METHODS OF PAYMENT MORTGAGES NATIONAL IDENTITY NATIONALITY NEIGHBOURHOODS NEIGHBOURS NERVOUS SYSTEM DISE... NEWS NEWS TRANSMISSION OCCUPATIONAL PENSIONS OCCUPATIONS OLD AGE BENEFITS OLD PEOPLE S CLUBS OPEN SPACES AND REC... PARENT TEACHER ASSO... PART TIME EMPLOYMENT PATIENTS PAYMENTS PENSION BENEFITS PENSIONS PHYSICAL MOBILITY POLICE SERVICES PRIMARY SCHOOLS PRIVATE PENSIONS PRIVATE SECTOR PUBLIC SECTOR PUBLIC SERVICES PUBLIC TRANSPORT QUALIFICATIONS RECREATIONAL FACILI... RECYCLING RELIGIOUS AFFILIATION RENTED ACCOMMODATION RENTS RESIDENTIAL MOBILITY RESPIRATORY TRACT D... ROAD TOLL CHARGES ROOM SHARING ROOMS RURAL AREAS SATISFACTION SAVINGS SECONDARY SCHOOLS SELF EMPLOYED SHARED HOME OWNERSHIP SICK PAY SICKNESS AND DISABI... SOCIAL ATTITUDES SOCIAL CAPITAL SOCIAL HOUSING SOCIAL PARTICIPATION SOCIAL PROBLEMS SOCIAL SECURITY BEN... SOCIAL SERVICES SOCIO CULTURAL CLUBS SOCIO ECONOMIC STATUS SPORTS CLUBS SPORTS FACILITIES SPOUSES SQUATS STATE RETIREMENT PE... STATUS IN EMPLOYMENT Social behaviour an... Social conditions a... THERMAL INSULATION TIED HOUSING TRAINS TRANSPORT TRANSPORT FARES TRAVEL TRAVEL TIMETABLES Transport and travel UNEMPLOYED UNFURNISHED ACCOMMO... URBAN AREAS VISION IMPAIRMENTS VOLUNTARY ORGANIZAT... VOLUNTARY WORK WAGES WALKING WELSH LANGUAGE WOMEN S ORGANIZATIONS

Abstract copyright UK Data Service and data collection copyright owner. The purpose of this study was to explore the way of life of ordinary urban residents in cities of varying sizes and types in various parts of Nigeria, especially in regard to social networks and activities, occupational and migration careers, and attitudes toward urban life, education and members of ethnic groups. Main Topics: Variables Dr Peil's study was designed as a comparative study of the daily life of people living in cities in various parts of Nigeria, these cities differing in size and composition. Data were collected by oral interviewing, supplemented by observation, mapping, recording of schools, churches, health facilities, government services, craftsmen, etc. Only the interviewing data (from cities Ajegunle, Kikuri, Abeokuta and Aba) have been supplied to the Archive. To quote from Dr Peil's report: 'Investigation of social networks provides a framework for testing hypotheses about social change and modernity, adjustment to urban life and the social effects of various types of housing and various kinds of employment. Identical studies of several cities permit analysis of the effects of city size, heterogeneity and social structure on the lives of the inhabitants. . . . The 'quality of life' measured in this study is concerned with items which can be easily reported by individuals rather than with official statistics. . . . It was also hoped that this study would be useful to urban planners, who generally have very little information on what the average family makes of its life in town. What are their expectations and their aspirations? What amenities do they most appreciate and most miss? How much urban experience have they had and how long can they be expected to stay, especially in the face of unemployment? How much unemployment is there and how are the unemployed supported?. . .' Hence, there is detailed demographic information for a general picture of the population of the four cities. The housing section collects details on household composition, overcrowding, landlords and inter-ethnic mixing. Marriage and kinship information indicates the numbers and location of wives and children, attitudes and practices in educating children, contacts with relatives in town and at home, and plans for returning home. A social life section deals with membership in associations, and contacts with co-tenants, workmates and friends. Information is also collected on how urban dwellers handle problems, and there is detailed occupational and migration career data. Approximately 100 houses were taken in each city, by systematic sampling from a series of random starts, designed to represent all parts of the community being studied. About 200 interviews were completed in each city, the individuals being chosen from census sheets on a quota basis to ensure the inclusion of men and women in various age, occupational, educational, ethnic and religious and migratory categories Face-to-face interview

This dataset provides Census 2021 estimates that classify households in England and Wales by four dimensions of deprivation: Employment, education, health and disability, and household overcrowding. The estimates are as at Census Day, 21 March 2021.

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.

Coverage

Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

• 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

Household deprivation

The dimensions of deprivation used to classify households are indicators based on four selected household characteristics.

Education

A household is classified as deprived in the education dimension if no one has at least level 2 education and no one aged 16 to 18 years is a full-time student.

Employment

A household is classified as deprived in the employment dimension if any member, not a full-time student, is either unemployed or economically inactive due to long-term sickness or disability.

Health

A household is classified as deprived in the health dimension if any person in the household has general health that is bad or very bad or is identified as disabled

People who have assessed their day-to-day activities as limited by long-term physical or mental health conditions or illnesses are considered disabled. This definition of a disabled person meets the harmonised standard for measuring disability and is in line with the Equality Act (2010).

Housing

A household is classified as deprived in the housing dimension if the household's accommodation is either overcrowded, in a shared dwelling, or has no central heating.

This dataset provides detailed information on the 2019 Index of Multiple Deprivation (IMD) for Birmingham, UK. The data is available at the postcode level and includes the Lower Layer Super Output Area (LSOA) information.Data is provided at the LSOA 2011 Census geography.The decile score ranges from 1-10 with decile 1 representing the most deprived 10% of areas while decile 10 representing the least deprived 10% of areas.The IMD rank and decile score is allocated to the LSOA and all postcodes within it at the time of creation (2019).Note that some postcodes cross over LSOA boundaries. The Office for National Statistics sets boundaries for LSOAs and allocates every postcode to one LSOA only: this is the one which contains the majority of residents in that postcode area (as at 2011 Census).

The English Indices of Deprivation 2019 provide detailed measures of relative deprivation across small areas in England. The Barriers to Housing and Services dataset is a key component of this index, measuring the physical and financial accessibility of housing and local services. This dataset includes indicators such as household overcrowding, homelessness, housing affordability, and the distance to key services like primary schools, general stores, and GP surgeries. It helps identify areas where residents face significant barriers to accessing adequate housing and essential services, guiding policy interventions and resource allocation to improve living conditions and accessibility.

Abstract

A population census is defined as the total process of collecting, compiling, evaluating, analyzing and publishing or otherwise disseminating demographic, economic and social data pertaining, at a specified time, to all persons in a country or in a well-delimited part of the country. A housing census is the total process of collecting, compiling, evaluating, analyzing and publishing or otherwise disseminating statistical data pertaining at a specified time, to all living quarters and occupants thereof in a country or in a well-delimited part of a country.

The 2003 Population and Housing Census of The Gambia was in accordance with these definitions. Further, it contained all the essential features of a census, namely individual enumeration, universality within the country and simultaneity.

Objectives of the Census The objectives of the Census are to count all the people in the country and to provide the Government with their number in each Local Government Area and District, by age, sex and several other characteristics. These figures are required for various aspects of economic and development planning. The ultimate aim of such planning is to provide a better way of life for the people of The Gambia, and to conquer what have been called the Five Giants: Disease, Ignorance, Squalor, Idleness and Want.

Planning for education obviously requires a knowledge of the number of children of school age who are likely to require schooling at various levels. The Government cannot know where to build the necessary schools or how many school teachers must be trained unless it knows where the need is great in terms of the number of children who should be going to school. 1.5 Housing is a major problem, particularly in urban areas where people are often living in terribly crowded conditions. If new houses are to be built in order to relieve this overcrowding, the Government must know the number of people living in these conditions who will be requiring such houses.

The Government wishes to improve and extend the medical services of the country so as to eliminate diseases and to reduce the number of children dying in infancy and early childhood. But if medical services are to be planned properly, the Government must know the number of people involved, the number of children being born and the rate at which they are dying.

For all these purposes, it is not enough just to know how many people there are at the time of the Census because figures of this sort get out-of-date very quickly. We must know also how fast the population is increasing, so that we can tell the Government how many people there will be, not only this year but also next year, in five years, in ten years time, etc. We therefore wish to obtain information not only of people now living, but also of the number of children being born and the number of children who have died.

Since the last Census there might have been changes in the structure of the population. The 2003 Census will thus help us up-date the Census data thereby ascertaining the specific changes in the structure of the population since 1993.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

All individuals in the country.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaires were used to collect census information:

Form A - Normal Household Form B - Institutional Population Form C - Building and Compound Particulars Form G - Graduate Card

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

The Census of Population first gathered data on housing "density", i.e. the number of persons in each household relative to the number of rooms, in 1891, although the first year included here is 1901. In 1891, over-crowding was defined as over 2 persons per room; by 1931 this threshold had dropped to 1.5 persons; and by 1961 to 1 person per room. Up to 1931, the data for each locality and date form a table of numbers of persons against numbers of rooms, and these transcriptions sometimes exclude the rows/columns for the very largest households (see the documentation for individual tables). From 1951 onwards, simpler tables simply list numbers of households in each density category (e.g. over 1 person per room and not more than 1.5 persons).

The Greater London Authority's ‘Housing in London’ report sets out the evidence base for the Mayor's housing policies, summarising key patterns and trends across a wide range of topics relevant to housing in the capital. The report is the evidence base for the Mayor’s London Housing Strategy, the latest edition of which was published in May 2018.

The 2024 edition of Housing in London can be viewed here. It includes monitoring indicators for the London Housing Strategy, and five thematic chapters:

• 1. Demographic, economic and social context
• 2. Housing stock and supply
• 3. Housing costs and affordability
• 4. Housing needs, including homelessness and overcrowding
• 5. Mobility and decent homes

Where possible, the data behind each year's report's charts and maps is made available below.

To provide feedback or request the document in an accessible format, please email housing.analysis@london.gov.uk

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

Occupancy rating

This variable calculates the difference between the actual number of bedrooms and the required number of bedrooms for the household.

The number of bedrooms required in each household is calculated using the bedroom standard which was introduced in the Housing (Overcrowding) Bill 46 (2003)

The bedroom standard indicates that for each household, each of the following groups or individuals requires a separate bedroom:

• any couple
• any person aged 21 years or more
• two people of the same sex aged between 10 and 20
• two children (whether of the same sex or not) under 10 years
• two people of the same sex where one person is aged between 10 years and 20 years and the other is aged less than 10 years
• any further person who cannot be paired appropriately.

An occupancy rating of -1 implies that a household has one fewer bedroom than required, whereas +1 implies that they have one more bedroom than required.

Details of classification can be found here

The quality assurance report can be found here