Here we used remote sensing data from multiple sources (time-series of Landsat and Sentinel images) to map the impervious surface area (ISA) at five-year intervals from 1990 to 2015, and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more, which were listed in the United Nations (UN) World Urbanization Prospects (WUP) database (including Mainland China, Hong Kong, Macao and Taiwan). We employed a range of spectral indices to generate the 1990–2015 ISA maps in urban areas based on remotely sensed data acquired from multiple sources. In this process, various types of auxiliary data were used to create the desired products for urban areas through manual segmentation of peri-urban and rural areas together with reference to several freely available products of urban extent derived from ISA data using automated urban–rural segmentation methods. After that, following the well-established rules adopted by the UN, we carried out the conversion to the standardized built-up area products from the 1990–2015 ISA maps in urban areas, which conformed to the definition of urban agglomeration area (UAA). Finally, we implemented data postprocessing to guarantee the spatial accuracy and temporal consistency of the final product.The standardized urban built-up area dataset (SUBAD–China) introduced here is the first product using the same definition of UAA adopted by the WUP database for 433 county and higher-level cities in China. The comparisons made with contemporary data produced by the National Bureau of Statistics of China, the World Bank and UN-habitat indicate that our results have a high spatial accuracy and good temporal consistency and thus can be used to characterize the process of urban expansion in China.The SUBAD–China contains 2,598 vector files in shapefile format containing data for all China's cities listed in the WUP database that have different urban sizes and income levels with populations over 300,000. Attached with it, we also provided the distribution of validation points for the 1990–2010 ISA products of these 433 Chinese cities in shapefile format and the confusion matrices between classified data and reference data during different time periods as a Microsoft Excel Open XML Spreadsheet (XLSX) file.Furthermore, The standardized built-up area products for such cities will be consistently updated and refined to ensure the quality of their spatiotemporal coverage and accuracy. The production of this dataset together with the usage of population counts derived from the WUP database will close some of the data gaps in the calculation of SDG11.3.1 and benefit other downstream applications relevant to a combined analysis of the spatial and socio-economic domains in urban areas.

The rural-urban commuting area codes (RUCA) classify U.S. census tracts using measures of urbanization, population density, and daily commuting from the decennial census. The most recent RUCA codes are based on data from the 2000 decennial census. The classification contains two levels. Whole numbers (1-10) delineate metropolitan, micropolitan, small town, and rural commuting areas based on the size and direction of the primary (largest) commuting flows. These 10 codes are further subdivided to permit stricter or looser delimitation of commuting areas, based on secondary (second largest) commuting flows. The approach errs in the direction of more codes, providing flexibility in combining levels to meet varying definitional needs and preferences. The 1990 codes are similarly defined. However, the Census Bureau's methods of defining urban cores and clusters changed between the two censuses. And, census tracts changed in number and shapes. The 2000 rural-urban commuting codes are not directly comparable with the 1990 codes because of these differences. An update of the Rural-Urban Commuting Area Codes is planned for late 2013.

Important Note: This item is in mature support as of June 2023 and will retire in December 2025. A new version of this item is available for your use.The layers going from 1:1 to 1:1.5M present the 2010 Census Urbanized Areas (UA) and Urban Clusters (UC). A UA consists of contiguous, densely settled census block groups (BGs) and census blocks that meet minimum population density requirements (1000 people per square mile (ppsm) / 500 ppsm), along with adjacent densely settled census blocks that together encompass a population of at least 50,000 people. A UC consists of contiguous, densely settled census BGs and census blocks that meet minimum population density requirements, along with adjacent densely settled census blocks that together encompass a population of at least 2,500 people, but fewer than 50,000 people. The dataset covers the 50 States plus the District of Columbia within United States. The layer going over 1:1.5M presents the urban areas in the United States derived from the urban areas layer of the Digital Chart of the World (DCW). It provides information about the locations, names, and populations of urbanized areas for conducting geographic analysis on national and large regional scales. To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to USA Census Urban Areas.

The functional urban area (FUA) classification identifies small urban areas and rural areas that are integrated with major, large, and medium urban areas to create FUAs. This dataset is clipped to the coastline. This clipped version has been created for map creation/cartographic purposes and so does not fully represent the official full extent boundaries.

Workplace address and usual residence address data from the 2018 Census of Population and Dwellings were used to identify satellite urban areas (1,000–4,999 residents), and rural statistical area 1s (SA1s) from which at least 40 percent of workers commuted to urban areas with more than 5,000 residents.

An FUA includes Urban rural (UR) 2018 urban areas, rural settlements and rural SA1s where there is: an urban core, one or more secondary urban cores, one or more satellite urban areas, and rural hinterland (rural settlements or rural SA1s).

The FUA indicator (IFUA) classifies UR2018 urban areas and rural SA1s according to their character within their FUA, e.g., urban core, satellite urban area. The information from the Stats NZ classification can be accessed using the classification tool Ariā.

The 53 FUAs are classified by population size. The urban core’s population rather than the entire FUA’s population is used to maintain consistency between the descriptions of UR2018 urban area and FUA type (TFUA).

FUAs that have more than 100,000 residents living in their urban core are known as metropolitan areas, while smaller FUAs are divided into large (core population 30,000–99,999), medium (core population 10,000–29,999), and small regional centres (core population 5,000–9,999).

Names are provided with and without tohutō/macrons. The name field without macrons is suffixed ‘ascii’.

For more detail, and classifications, please refer to Ariā.

Digital boundary data became freely available on 1 July 2007.

We reconstruct the walled cities for China that extend back from 15th century to 19th century based on multiple historical documents. Cities in late imperial China (the Ming and the Qing Dynasties, 1368-1911) generally had city walls, and these walls were usually built around the urban built-up area. By restoring the scope of the city walls, it is helpful to explore the urban extend in this period Firstly, we collected the years of construction or reconstruction of city walls from the historical data. Specifically, the period in which the scope of the city wall keeps unchanged is recorded as a lifetime of it. Secondly, specialization of the scope of the city wall could be conducted based on the urban morphology method, and variety of documentation, including the historical literature materials, the military topographic maps of the first half of the 20th century, and the remote sensing images of the 1970s. Correlation and integration of the lifetime and the spatial data would produce China City Wall Areas Dataset (CCWAD) in late imperial. Based on the proximity to the time of most of the city walls, we selected six representative years (i.e., 1400, 1537, 1648, 1708, 1787, and 1866) from CCWAD to produce China Urban Extent Dataset (CUED) in the 15th-19th centuries.

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

The Urban Planning Code defines four types of areas regulated in the local planning plan (R.123-5 to 8): urban areas (U), areas to be urbanised (AU), agricultural areas (A) and natural and forest areas (N). These areas shall be demarcated on one or more graphic documents. A regulation is attached to each area. The by-law may lay down different rules, depending on whether the purpose of the construction relates to housing, hotel accommodation, offices, commerce, crafts, industry, agricultural or forestry operations or warehouse functions. These categories are restrictive (Art. R.123-9). Areas already urbanised are classified as U areas where existing or under construction public facilities have sufficient capacity to serve the buildings to be installed. The areas of a natural nature of the municipality intended to be opened for urbanisation depending on whether or not the existing facilities on the periphery are sufficient to serve the buildings to be installed may be classified as AU zones. There are two types of AU zone: “constructible” and “inconstructible” AU zones. Areas A may be classified as areas of the municipality, whether or not equipped, to be protected due to the agronomic, organic or economic potential of agricultural land. Areas of the municipality equipped or not may be classified as N zones, to be protected either by reason of the quality of the sites, natural habitats, landscapes and their interest, in particular from the aesthetic, historical or ecological point of view, the existence of forestry or their nature as natural areas. — Within zones N, may be demarcated: areas within which the right to be built can be transferred (transfer of COS), — areas of limited size and capacity where construction is possible under the condition of location and density.

About this dataset

Context

The dataset tabulates the Homer City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Homer City. The dataset can be utilized to understand the population distribution of Homer City by age. For example, using this dataset, we can identify the largest age group in Homer City.

Key observations

The largest age group in Homer City, PA was for the group of age 65 to 69 years years with a population of 167 (9.73%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Homer City, PA was the 25 to 29 years years with a population of 22 (1.28%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Homer City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Homer City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Homer City Population by Age. You can refer the same here

This dataset contains global, city-level population data from 2250 BC - AD 1975 for 1,587 cities in .csv form.Version 2 removes spaces after some city names which can also be done programmatically after the datasets are combined. It also corrects/combines the spelling of one city - Magdeburg, Germany and updates Kiev's country name to Ukraine.

Abstract copyright UK Data Service and data collection copyright owner.

The English Housing Survey 2008-2009 Household Data Teaching Dataset is based on the English Housing Survey, 2008-2009: Household Data (available from the UK Data Archive under SN 6613) and constitutes real data which are used by the Department for Communities and Local Government and are behind many headlines. The teaching dataset is a subset which has been subjected to certain simplifications and additions for the purpose of learning and teaching.

The main differences are:

• the number of variables has been reduced
• weighting has been simplified
• a reduced codebook is provided

Further information is available in the study documentation (below) which includes a dataset user guide. Information about other teaching resources can be found on the Teaching resources webpage.



Main Topics:

The main topics covered are:

• housing characteristics
• household characteristics
• satisfaction with the home and local area

INSEE zoning comprising a commune or a group of municipalities which includes in its territory a built-up area of at least 2,000 inhabitants where no dwelling is separated from the nearest to more than 200 metres. In addition, each municipality concerned has more than half of its population in this built-up area. The concept of urban unity is based on the continuity of the building and the number of inhabitants. An urban unit is a municipality or group of municipalities with a continuous building area (no cut-off of more than 200 metres between two buildings) with at least 2,000 inhabitants. If the urban unit is located in a single municipality, it is referred to as an isolated city. If the urban unit extends over several municipalities, and each of these municipalities concentrates more than half of its population in the continuous built-up area, it is referred to as a multi-communal agglomeration.

The Law of 14 November 1996 implementing the City Recovery Pact (PRV) distinguishes three levels of intervention: sensitive urban areas, urban revitalisation zones (ZRUs), urban free zones (ZFU). These three levels of intervention ZUS, ZRU and ZFU, characterised by schemes of increasing importance, aim to respond to different degrees of difficulties encountered in these neighbourhoods.The sensitive urban areas are suburban areas defined by the public authorities to be the priority target of city policy, depending on local considerations linked to the difficulties faced by the inhabitants of these territories. Sensitive urban areas are defined in the PRV Act as areas “characterised by the presence of large settlements or degraded areas of habitat and by an increased imbalance between housing and employment”. The SEZs were determined on qualitative criteria (large sets, employment/habitat imbalance) through a joint community-state analysis. These areas are now part of the priority areas of urban social cohesion contracts (CUCS).In addition, the PRV law states that “urban revitalisation areas correspond to those of sensitive urban areas [...] which face particular difficulties, assessed according to their situation in the agglomeration, their economic and commercial characteristics and a summary index. taking into account the number of inhabitants of the neighbourhood, the unemployment rate, the proportion of young people under the age of 25, the proportion of people leaving the school system without a diploma, and the fiscal potential of the municipalities concerned.”The former ZUS are archived and are no longer included in this dataset.

About this dataset

Context

The dataset tabulates the Junction City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Junction City. The dataset can be utilized to understand the population distribution of Junction City by age. For example, using this dataset, we can identify the largest age group in Junction City.

Key observations

The largest age group in Junction City, WI was for the group of age 30 to 34 years years with a population of 39 (12.96%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Junction City, WI was the 85 years and over years with a population of 3 (1%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Junction City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Junction City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Junction City Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Clay City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Clay City. The dataset can be utilized to understand the population distribution of Clay City by age. For example, using this dataset, we can identify the largest age group in Clay City.

Key observations

The largest age group in Clay City, IN was for the group of age 15-19 years with a population of 168 (14.12%), according to the 2021 American Community Survey. At the same time, the smallest age group in Clay City, IN was the 85+ years with a population of 17 (1.43%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Clay City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Clay City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Clay City Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Plum City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Plum City. The dataset can be utilized to understand the population distribution of Plum City by age. For example, using this dataset, we can identify the largest age group in Plum City.

Key observations

The largest age group in Plum City, WI was for the group of age 5-9 years with a population of 63 (10.41%), according to the 2021 American Community Survey. At the same time, the smallest age group in Plum City, WI was the 35-39 years with a population of 7 (1.16%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Plum City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Plum City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Plum City Population by Age. You can refer the same here

Urban is one of the most widely used hyperspectral data used in the hyperspectral unmixing study. There are 307x307 pixels, each of which corresponds to a 2x2 m2 area. In this image, there are 210 wavelengths ranging from 400 nm to 2500 nm, resulting in a spectral resolution of 10 nm. After the channels 1-4, 76, 87, 101-111, 136-153 and 198-210 are removed (due to dense water vapor and atmospheric effects), we remain 162 channels (this is a common preprocess for hyperspectral unmixing analyses). There are three versions of ground truth, which contain 4, 5 and 6 endmembers respectively, which are introduced in the ground truth.

Linda S. Kalman and Edward M. Bassett III "Classification and material identification in an urban environment using HYDICE hyperspectral data", Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); https://doi.org/10.1117/12.283843

Hosted at: - https://rslab.ut.ac.ir/data - http://lesun.weebly.com/hyperspectral-data-set.html - https://erdc-library.erdc.dren.mil/jspui/handle/11681/2925

This concept makes it possible to know which type of urban area 2010 is linked to each municipality in the department. This corresponds to the municipal base of urban areas 2010.

The Law of 14 November 1996 implementing the City Recovery Pact (PRV) distinguishes three levels of intervention: sensitive urban areas, urban revitalisation zones (ZRUs), urban free zones (ZFU). These three levels of intervention ZUS, ZRU and ZFU, characterised by devices of increasing importance, aim to respond to different degrees of difficulties encountered in these neighbourhoods. Sensitive urban areas are infra-urban areas defined by the public authorities to be the priority target of city policy, depending on local considerations related to the difficulties faced by the inhabitants of these territories. Sensitive urban areas are defined in the PRV Act as areas “characterised by the presence of large settlements or degraded areas of habitat and by an increased imbalance between housing and employment”. The SEZs were determined on qualitative criteria (large sets, employment/habitat imbalance) through a joint community-state analysis. These areas are now among the priority areas of urban social cohesion contracts (CUCS). In addition, the PRV Law states that ‘urban revitalisation areas correspond to those in sensitive urban areas... which face particular difficulties, assessed on the basis of their situation in the agglomeration, their economic and commercial characteristics and a summary index. This is established, under conditions laid down by decree, taking into account the number of inhabitants of the district, the rate of unemployment, the proportion of young people under 25 years of age, the proportion of persons leaving the school system without a diploma and the fiscal potential of the municipalities concerned’. The old ZUS are archived and are no longer included in this dataset.

Abstract copyright UK Data Service and data collection copyright owner.

The British Crime Survey 2007-2008: Unrestricted Access Teaching Dataset is based on the British Crime Survey, 2007-2008 (available from the UK Data Archive under SN 6066) and constitutes real data which are used by the Home Office and are behind many media headlines. The teaching dataset is a subset which has been subjected to certain simplifications and additions for the purpose of learning and teaching. (Note that the main British Crime Survey has now become the Crime Survey for England and Wales, but titles of older studies in the series remain the same.)

The main differences are:

• only respondents who completed Module B (attitudes to the Criminal Justice System) of the BCS, 2007-2008 are included
• the number of variables has been reduced
• weighting has been simplified
• a reduced codebook is provided
• additional continuous variables have been created (using factor analysis of pre-existing variables) in order to facilitate their use in quantitative methods classes

Further information is available in the study documentation (below) which includes a dataset user guide and additional notes for teachers.

Differences between the unrestricted dataset and the standard access BCS teaching dataset:
Note that SN 6561, the British Crime Survey 2007-2008: Teaching Dataset contains a larger subset of BCS 2007-2008 variables than the unrestricted version, and is only available to registered users of the UK Data Service.


Main Topics:

The variables cover:

• fear of crime
• opinions about crime and anti-social behaviour in the area
• experience of crime
• socio-demographics
• accommodation and area characteristics