Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

This data set provides a count and percentage of the number of cars owned by households sampled in obtaining data for the ACT Household Travel Survey.

Households with cars

This layer shows household size by number of vehicles available. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of households with no vehicle available. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08201 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset provides Census estimates that classify households by the number of cars or vans owned or available for use by household members. Vehicles included: pick-ups, camper vans and motor homesvehicles that are temporarily not workingvehicles that have failed their MOTvehicles owned or used by a lodgercompany cars or vans if they're available for private use. Vehicles not included: motorbikes, trikes, quad bikes or mobility scootersvehicles that have a Statutory Off Road Notification (SORN)vehicles owned or used only by a visitorvehicles that are kept at another address or not easily accessed. Statistical disclose control would have been applied to the data if it would have been possible to identify a household.Data is Powered by LG Inform Plus

The percentage of households that do not have a personal vehicle available for use out of all households in an area. Source: American Community Survey Years Available: 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023

Utilising a regression analysis we created a correlation matrix utilising a number of demographic indicators from the Local Insight platform. This application is showing the distribution of the datasets that were found to have the strongest relationships, with the base comparison dataset of households with four cars. This app contains the following datasets: proportion of dwellings that are detached houses or bungalows, Index of Multiple Deprivation 2015 rank, Indices of Deprivation 2015 health deprivation and disability rank, proportion of dwellings with eight rooms, prevalence of loneliness probability for those aged 65 and over, male healthy life expectancy at birth, proportion of people in occupation group of managers and directors, Indices of Deprivation 2019 health deprivation and disability rank, proportion of households with no car and male disability-free life expectancy.

Dataset population: Households

Accommodation type

The type of accommodation used or available for use by an individual household. Examples include the whole of a terraced house, or a flat in a purpose-built block of flats.

Car or van availability

The number of cars or vans that are owned, or available for use, by one or more members of a household. This includes company cars and vans that are available for private use. It does not include motorbikes or scooters, or any cars or vans belonging to visitors.

Households with 10 to 20 cars or vans were counted as having only 1. Responses indicating a number of cars or vans greater than 20 were treated as invalid and a value was imputed.

The count of cars or vans in an area relates only to households. Cars or vans used by residents of communal establishments were not counted.

Number of usual residents aged 17 or over

This derived variable provides a count of the number of people aged 17 or over in the household.

A household is defined as:

• One person living alone
• A group of people (not necessarily related) living at the same address who share cooking facilities and share a living room, sitting room or dining area.

This includes:

• Sheltered accommodation units in an establishment where 50 per cent or more have their own kitchens (irrespective of whether there are other communal facilities)
• All people living in caravans on any type of site that is their usual residence (this will include anyone who has no other usual residence elsewhere in the UK)

A household must contain at least one person whose place of usual residence is at the address. A group of short-term residents living together is not classified as a household, and neither is a group of people at an address where only visitors are staying.

Daily vehicle miles traveled (VMT) is a distance- and volume-based measure of driving on roadways for all motorized vehicle types—car, bus, motorcycle, and truck—on an average day. Per capita VMT is the same measure divided by the same area's population for the same year. Per vehicle VMT divides VMT by the number of household vehicles available by residents of that geography in the same year. These three value types can be selected in the dropdown in the first chart below. Use the legend items to explore various geographies. The second chart below shows per capita and total personal vehicles available to the region’s households from the American Community Survey.

Normalizing VMT by a county or region's population, or household vehicles, is helpful for context, but does not have complete parity with what is measured in VMT estimates. People and vehicles come into the region from other places, just as people and vehicles leave the region to visit other places. VMT per capita compares all miles traveled on the region's roads to the region's population (for all ages) from the U.S. Census Bureau's latest population estimates. Vehicle counts for VMT are classified by vehicle types, but not by vehicle ownership. In 2017, statewide estimates for VMT by motorcycles, passenger cars, and two-axle single-unit trucks with four wheels made up 88% of Pennsylvania's VMT, and 95% of New Jersey's. These vehicle types are highly likely to be personal vehicles, owned by households, but a small percent could be fleet vehicles of companies or governments. The remaining VMT is made up of vehicle types like school and commercial buses and trucks with more than two axles so they are highly likely to be commercial vehicles.

Standard indexes of poverty and deprivation are rarely sensitive to how the causes and consequences of deprivation have different impacts depending upon where a person lives. More geographically minded approaches are alert to spatial variations but are also difficult to compute using desktop PCs.The aim of the ESRC sponsored project was to develop a method of spatial analysis known as 'geographically weighted regression' (GWR) to run in the high power computing environment offered by 'Grid computation' and e-social science. GWR, like many other methods of spatial analysis, is characterised by multiple repeat testing as the data are divided into geographical regions and also randomly redistributed many times to simulate the likelihood that the results obtained from the analysis are actually due to chance. Each of these tests requires computer time so, given a large dataset such as the UK Census statistics, running the analysis on a standard machine can take a long time! Fortunately, the computational grid is not standard but offers the possibility to speed up the process by running GWR's sequences of calibration, analysis and non-parametric simulation in parallel.An output is a model of the geographically varying correlates of car non-ownership fitted for the 165,665 Census Output Areas in England. Specifically, a geographically weighted regression of the relationship between the proportion of households without a car (or van) in 2001 (the dependent variable), and the following predictor variables: proportion of persons of working age unemployed; proportion of households in public housing; proportion of households that are lone parent households; proportion of persons 16 or above that are single; and proportion of persons that are white British.

Statbel, the Belgian statistical office, examines for the first time the possession of cars by Belgian households, determined on the basis of administrative sources. Previously, the number of Belgian households with or without one or more cars could only be estimated on the basis of surveys. To overcome this, Statbel has developed an experimental coupling that gives a more precise though still incomplete view of the number of cars owned by households. Four indicators are presented here, the share of private households without cars, the share of private households owning exactly one car, the share of private households with exactly two cars and the share of private households with three or more cars.

Number of households with cars by Local Authorities. (Census 2022 Theme 15 Table 1 )Census 2022 table 15.1 is number of households with cars. Attributes include a breakdown of households by number of cars owned. Census 2022 theme 15 is Motor Car Availability, and Internet Access. The country is divided into 31 administrative counties/cities. Outside Dublin, there are 23 administrative counties and four cities: Cork, Limerick, Waterford and Galway. There are four local authority areas in Dublin: Dublin City and the three administrative counties of Dún Laoghaire-Rathdown, Fingal and South Dublin. The Local Government Reform Act 2014 Section 9 provided for the amalgamation of the city and county councils in Limerick, Waterford, and North Tipperary and South Tipperary County Councils.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. This dataset is provided by Tailte Éireann, Administrative Counties 2019

This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

Abstract copyright UK Data Service and data collection copyright owner. Standard indexes of poverty and deprivation are rarely sensitive to how the causes and consequences of deprivation have different impacts depending upon where a person lives. More geographically minded approaches are alert to spatial variations but are also difficult to compute using desktop PCs. The aim of the ESRC sponsored project was to develop a method of spatial analysis known as ‘geographically weighted regression’ (GWR) to run in the high power computing environment offered by ‘Grid computation’ and e-social science. GWR, like many other methods of spatial analysis, is characterised by multiple repeat testing as the data are divided into geographical regions and also randomly redistributed many times to simulate the likelihood that the results obtained from the analysis are actually due to chance. Each of these tests requires computer time so, given a large dataset such as the UK Census statistics, running the analysis on a standard machine can take a long time! Fortunately, the computational grid is not standard but offers the possibility to speed up the process by running GWR’s sequences of calibration, analysis and non-parametric simulation in parallel. An output is a model of the geographically varying correlates of car non-ownership fitted for the 165,665 Census Output Areas in England. Specifically, a geographically weighted regression of the relationship between the proportion of households without a car (or van) in 2001 (the dependent variable), and the following predictor variables: proportion of persons of working age unemployed; proportion of households in public housing; proportion of households that are lone parent households; proportion of persons 16 or above that are single; and proportion of persons that are white British. Note - the file does not contain Census 2001 data, only National Grid references and regression coefficients. Further information is available from the Grid Enabled Spatial Regression Models (With Application to Deprivation Indices) web page.

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation. The HIECS 2010/2011 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2010/2011, among a long series of similar surveys that started back in 1955. The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands.

• To define average household and per-capita income from different sources.

• To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey.

• To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas.

• To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure.

• To study the relationships between demographic, geographical, housing characteristics of households and their income.

• To provide data necessary for national accounts especially in compiling inputs and outputs tables.

• To identify consumers behavior changes among socio-economic groups in urban and rural areas.

• To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services.

• To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

Compared to previous surveys, the current survey experienced certain peculiarities, among which :

1- The total sample of the current survey (26.5 thousand households) is divided into two sections:

a- A new sample of 16.5 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, etc.

b- A panel sample with 2008/2009 survey data of around 10 thousand households was selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2- The number of enumeration area segments is reduced from 2526 in the previous survey to 1000 segments for the new sample, with decreasing the number of households selected from each segment to be (16/18) households instead of (19/20) in the previous survey.

3- Some additional questions that showed to be important based on previous surveys results, were added, such as:

a- Collect the expenditure data on education and health on the person level and not on the household level to enable assessing the real level of average expenditure on those services based on the number of beneficiaries.

b- The extent of health services provided to monitor the level of services available in the Egyptian society.

c- Smoking patterns and behaviors (tobacco types- consumption level- quantities purchased and their values).

d- Counting the number of household members younger than 18 years of age registered in ration cards.

e- Add more details to social security pensions data (for adults, children, scholarships, families of civilian martyrs due to military actions) to match new systems of social security.

f- Duration of usage and current value of durable goods aiming at estimating the service cost of personal consumption, as in the case of imputed rents.

4- Quality control procedures especially for fieldwork, are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

National

Analysis unit

1- Household/family

2- Individual/Person

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of HIECS, 2010-2011 is a self-weighted two-stage stratified cluster sample, of around 26500 households. The main elements of the sampling design are described in the following:

1- Sample Size It has been deemed important to collect a smaller sample size (around 26.5 thousand households) compared to previous rounds due to the convergence in the time period over which the survey is conducted to be every two years instead of five years because of its importance. The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 26500 households has been considered, and was distributed between urban and rural with the percentages of 47.1 % and 52.9, respectively. This sample is divided into two parts: a- A new sample of 16.5 thousand households selected from main enumeration areas. b- A panel sample with 2008/2009 survey data of around 10 thousand households.

2- Cluster size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 16 households (that was increased to 18 households in urban governorates and Giza, in addition to urban areas in Helwan and 6th of October, to account for anticipated non-response in those governorates: in view of past experience indicating that non-response may almost be nil in rural governorates). While the cluster size for the panel sample was 4 households.

3- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2010 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area

Current (2021) and projected numbers of Plug-in Electrical Vehicles (PEVs) at the census block group level for the Delaware Valley region. The projected PEV distribution is based on a scenario in which 5 percent of passenger vehicles in the Greater Philadelphia region (or about 200,000 vehicles) are PEVs.

Also includes data projecting workplace charging demand in number of charging events and kilowatt-hours of demand by census block group based on the aforementioned PEV projections for the following three scenarios:

- workplace charging is free (free charging),

- workplace charging is twice the cost of home charging (paid charging).

The following datasets were used to obtain these results using the EV Planning Toolkit:

- 2021 vehicle registration data provided by the Pennsylvania Department of Transportation

- 2021 vehicle registration data from the New Jersey Motor Vehicle Commission provided by the New Jersey Department of Environmental Protection

• 2015-2019 American Community Survey 5-year Estimates

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• LEHD Origin-Destination Employment Statistics (LODES), version 7

- Block group-to-block group commuting distances from DVRPC's Travel Improvement Model 2.1, accessed December 2017.

• Block group-to-block group commuting distances calculated from Esri’s ArcGIS Network Analyst, used in 2017.

If you have any questions regarding this analysis or datasets used in the analysis, please contact: Sean Greene, Manager, Air Quality Programs | sgreene@dvrpc.org | (215) 238-2860

Dataset shows an individual’s statistical area 3 (SA3) of usual residence and the SA3 of their workplace address, for the employed census usually resident population count aged 15 years and over, by main means of travel to work from the 2018 and 2023 Censuses.

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The main means of travel to work categories are:

• Work at home
• Drive a private car, truck, or van
• Drive a company car, truck, or van
• Passenger in a car, truck, van, or company bus
• Public bus
• Train
• Bicycle
• Walk or jog
• Ferry
• Other.

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Main means of travel to work is the usual method which an employed person aged 15 years and over used to travel the longest distance to their place of work.

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Workplace address refers to where someone usually works in their main job, that is the job in which they worked the most hours. For people who work at home, this is the same address as their usual residence address. For people who do not work at home, this could be the address of the business they work for or another address, such as a building site.

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Workplace address is coded to the most detailed geography possible from the available information. This dataset only includes travel to work information for individuals whose workplace address is available at SA3 level. The sum of the counts for each region in this dataset may not equal the total employed census usually resident population count aged 15 years and over for that region. Workplace address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA3 code values:

• Statistical area 3 2023 (generalised)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

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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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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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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).

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Workplace address time series

Workplace address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Workplace address – 2023 Census: Information by concept has more information.

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Working at home

In the census, working at home captures both remote work, and people whose business is at their home address (e.g. farmers or small business owners operating from their home). The census asks respondents whether they ‘mostly’ work at home or away from home. It does not capture whether someone does both, or how frequently they do one or the other.

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Rows excluded from the dataset

Rows show SA3 of usual residence by SA3 of workplace address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA3-SA3 combinations have commuter flows.

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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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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Main means of travel to work quality rating

Main means of travel to work is rated as moderate quality.

Main means of travel to work – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Workplace address quality rating

Workplace address is rated as moderate quality.

Workplace address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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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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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

-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.

The Center for Disease Control and Prevention (CDC) has developed the Social Vulnerability Index to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, or after a hazardous event. SVI indicates the relative vulnerability of every U.S. Census tract, which are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts of 15 social factors and further groups them into four related themes. Tracts in the top 10% or at the 90th percentile of values are given a value of 1 to indicate high vulnerability. Tracts below the 90th percentile are given a value of zero. For each tract, we have calculated the number of flags for the fifteen individual variables, the flags for the themes, and the overall estimated percentage of the fifteen individual factors.See complete documentation here: https://gis.cdc.gov/grasp/svi/SVI2018Documentation.pdf. For additional questions, contact the SVI Lead at SVI_Coordinator@cdc.gov.