In 2023, almost nine million people lived in Greater London, making it the most populated ceremonial county in England. The West Midlands Metropolitan County, which contains the large city of Birmingham, was the second-largest county at 2.98 million inhabitants, followed by Greater Manchester and then West Yorkshire with populations of 2.95 million and 2.4 million, respectively. Kent, Essex, and Hampshire were the three next-largest counties in terms of population, each with around 1.89 million people. A patchwork of regions England is just one of the four countries that compose the United Kingdom of Great Britain and Northern Ireland, with England, Scotland and Wales making up Great Britain. England is therefore not to be confused with Great Britain or the United Kingdom as a whole. Within England, the next subdivisions are the nine regions of England, containing various smaller units such as unitary authorities, metropolitan counties and non-metropolitan districts. The counties in this statistic, however, are based on the ceremonial counties of England as defined by the Lieutenancies Act of 1997. Regions of Scotland, Wales, and Northern Ireland Like England, the other countries of the United Kingdom have their own regional subdivisions, although with some different terminology. Scotland’s subdivisions are council areas, while Wales has unitary authorities, and Northern Ireland has local government districts. As of 2022, the most-populated Scottish council area was Glasgow City, with over 622,000 inhabitants. In Wales, Cardiff had the largest population among its unitary authorities, and in Northern Ireland, Belfast was the local government area with the most people living there.

This dataset is part of the Geographical repository maintained by Opendatasoft. This dataset contains data for counties and unitary authorities in the United Kingdom.In 1974 a two-tier administrative structure of (shire and metropolitan) counties and non-metropolitan districts was set up across England and Wales, except for the Isles of Scilly and Greater London. Council functions were divided according to the level at which they could be practised most efficiently. As a consequence, counties took on functions including education, transport, strategic planning, fire services, consumer protection, refuse disposal, smallholdings, social services and libraries, whereas each LAD had responsibility for local planning, housing, local highways, building, environmental health, refuse collection and cemeteries. Responsibility for recreation and cultural matters was divided between the two tiers. Following the Local Government Reorganisation in the 1990s, major changes were implemented to create administrations most appropriate to the needs of the area concerned. The key feature of this change was the introduction of unitary authorities: single-tier administrations with responsibility for all areas of local government. Between 1995 and 1998 these were established in a number of areas across the country, especially in medium-sized urban areas, whilst other areas retained a two-tier structure. Further local government reorganisation occurred in 2009 and there are currently 57 unitary authorities (UA) in England, and 25 shire counties split into 188 (non-metropolitan) districts. Note that due to the changes in Cornwall, the Isles of Scilly are considered a UA for coding purposes.Processors and tools are using this data.EnhancementsAdd ISO 3166-3 codes.Simplify geometries to provide better performance across the services.Add administrative hierarchy.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

UK residents by broad country of birth and citizenship groups, broken down by UK country, local authority, unitary authority, metropolitan and London boroughs, and counties. Estimates from the Annual Population Survey.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of cancer (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to cancer (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with cancer was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with cancer was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with cancer, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have cancerB) the NUMBER of people within that MSOA who are estimated to have cancerAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have cancer, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from cancer, and where those people make up a large percentage of the population, indicating there is a real issue with cancer within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of cancer, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of cancer.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

The OSNI Large-scale boundaries is a polygon dataset consisting of County Boundaries.The data has been extracted from OSNI Largescale database and has been topologically cleansed and attributed to create a seamless dataset. This service is published for OpenData. By download or use of this dataset you agree to abide by the LPS Open Government Data Licence.Please Note for Open Data NI Users: Esri Rest API is not Broken, it will not open on its own in a Web Browser but can be copied and used in Desktop and Webmaps

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of hypertension (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to hypertension (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with hypertension was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with hypertension was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with hypertension , within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have hypertension B) the NUMBER of people within that MSOA who are estimated to have hypertension An average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have hypertension , compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from hypertension, and where those people make up a large percentage of the population, indicating there is a real issue with hypertension within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of hypertension, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of hypertension .TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by highest level of qualification and by sex. The estimates are as at Census Day, 21 March 2021.

There are quality considerations about higher education qualifications, including those at Level 4+, responses from older people and international migrants, and comparability with 2011 Census data. Read more about this quality notice.

Area type

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

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

Lower tier local authorities

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

Coverage

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

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

Highest level of qualification

The highest level of qualification is derived from the question asking people to indicate all qualifications held, or their nearest equivalent.

This may include foreign qualifications where they were matched to the closest UK equivalent.

Sex

This is the sex recorded by the person completing the census. The options were “Female” and “Male”.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical illnesses that are linked with obesity and inactivity. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:- The percentage of the MSOA area that was covered by each GP practice’s catchment area- Of the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illnessThe estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 7 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.LIMITATIONS1. GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices. This dataset should be viewed in combination with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset to identify where there are areas that are covered by multiple GP practices but at least one of those GP practices did not provide data. Results of the analysis in these areas should be interpreted with caution, particularly if the levels of obesity/inactivity-related illnesses appear to be significantly lower than the immediate surrounding areas.2. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).3. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.4. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of obesity/inactivity-related illnesses, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of these illnesses. TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:- Health and wellbeing statistics (GP-level, England): Missing data and potential outliersDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of diabetes mellitus in persons (aged 17+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to diabetes mellitus in persons (aged 17+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (aged 17+) with diabetes mellitus was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with diabetes mellitus was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with depression, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have diabetes mellitusB) the NUMBER of people within that MSOA who are estimated to have diabetes mellitusAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have diabetes mellitus, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from diabetes mellitus, and where those people make up a large percentage of the population, indicating there is a real issue with diabetes mellitus within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of diabetes mellitus, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of diabetes mellitus.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of asthma (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to asthma (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with asthma was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with asthma was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with asthma, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have asthmaB) the NUMBER of people within that MSOA who are estimated to have asthmaAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have asthma, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from asthma, and where those people make up a large percentage of the population, indicating there is a real issue with asthma within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of asthma, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of asthma.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Abstract copyright UK Data Service and data collection copyright owner.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. This study assembles historical data from the National Insurance system, plus some data from trade union welfare systems gathered and published by the Board of Trade Labour Department. The data were computerised by the Great Britain Historical GIS Project. 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. Most of the data here was originally published by the Ministry of Labour, either in the Labour Gazette, later the Employment Gazette, or in the specialised Local Unemployment Index (LUI), published between 1927 and 1939. The largest dataset here is a complete transcription of the LUI data for each January, April, July and October from January 1927 to July 1939 inclusive, the most detailed information that exists on the geography of the inter-war depression, other than the 1931 census. Unlike census data, these data concern a wide range of regions, "divisions", "districts", towns and sometimes areas within towns, seldom defined (the LUI data do list counties). The study therefore also includes two specially constructed gazetteers which attempt to provide towns and areas within towns with point coordinates. Another limitation is that these data generally provide counts of the unemployed, but not counts of the insured, or numbers in work, so calculation of rates often requires data from other sources such as the census. The study also includes two transcriptions from unpublished tabulations in the National Archives, relating to unemployment in 1928 and 1933. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.For the second edition (February 2024), the data was updated; data running up to 1974 has been added and the former study 3711 has been incorporated.

Police recorded crime figures by Police Force Area and Community Safety Partnership areas (which equate in the majority of instances, to local authorities).

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of stroke and transient ischaemic attack (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to stroke and transient ischaemic attack (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) to have suffered a stroke or transient ischaemic attack was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population to have suffered a stroke or transient ischaemic attack was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA who have suffered a stroke or transient ischaemic attack, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have had a stroke or transient ischaemic attackB) the NUMBER of people within that MSOA who are estimated to have had a stroke or transient ischaemic attackAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have had a stroke or transient ischaemic attack, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from stroke and transient ischaemic attack, and where those people make up a large percentage of the population, indicating there is a real issue with stroke and transient ischaemic attack within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of stroke and transient ischaemic attack, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of stroke and transient ischaemic attack.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

This dataset contains gridded population with a spatial resolution of 1 km x 1 km for the UK based on Census 2011 and Land Cover Map 2007 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies . While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2011 UK Census population data on Output Area level with Land Cover Map 2007 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum).

http://www.openstreetmap.org/images/osm_logo.png" alt=""> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

These profiles help paint a general picture of an area by presenting a range of headline indicator data in both spreadsheet and map form to help show statistics covering demographic, economic, social and environmental datasets for each borough, alongside relevant comparator areas.

The full datasets and more information for each of the indicators are usually available on the London Datastore. A link to each of the datasets is contained in the spreadsheet and map.

Borough Profiles - Excel

On opening the spreadsheet a simple drop down box allows you to choose which borough profile you are interested in. Selecting this will display data for that borough, plus either Inner or Outer London, London and a national comparator (usually England where data is available).

To see the full set of data for all 33 local authorities in London plus the comparator areas in Excel, click the 'Data' worksheet.

A chart and a map are also available to help visualise the data for all boroughs (macros must be enabled for the Excel map to function).

The data is set out across 11 themes covering most of the key indicators relating to demographic, economic, social and environmental data. Sources are provided in the spreadsheet. Notes about the indicator are provided in comment boxes attached to the indicator names.

Profiles using interactive mapping

For a geographical and bar chart representation of the profile data, open this interactive report. Choose indicators from the left hand side. Click on the comparators to make them appear on the chart and map.

Sources, links to data, and notes are all contained in the box in the bottom right hand corner.

These profiles include data relating to: Population, Households (census), Demographics, Migrant population, Ethnicity, Language, Employment, NEET, Benefits, Qualifications, Earnings, Volunteering, Jobs density, Business Survival, Crime, Fires, House prices, New homes, Tenure, Greenspace, Recycling, Carbon Emissions, Cars, Public Transport Accessibility (PTAL), Indices of Multiple Deprivation, GCSE results, Children looked after, Children in out-of-work families, Life Expectancy, Teenage conceptions, Happiness levels, Political control, and Election turnout.

Data is correct as of September 2014.

London Borough Atlas

To access even more data at local authority level, use the London Borough Atlas. It contains data about the same topics as the profiles but provides further detailed breakdowns and time-series data for each borough.

The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster.

You may also find our small area profiles useful - Ward, LSOA, and MSOA.

This layer identifies some of the strategic opportunities in your area for positive change and work out plans with land managers to enhance carbon, whatever the current land use. It will help you understand whether you are aiming to increase carbon by a smaller amount over a large area or concentrate on a couple of smaller schemes where there will be a large enhancement such as tree planting on low intensity (species poor) grassland. In this layer we have followed the IPCC methodology for reporting carbon emissions . Emissions are recorded as a positive value as they are adding to the carbon burden in the atmosphere. Sequestration is recorded as a negative value as it is removing carbon from the atmosphere.Carbon sequestration maps shows where the environment is actively capturing carbon dioxide and binding it in plants and soils. What is being captured now with the current land use, habitats and crops. As sequestration is much more dependent on land use and management practices which vary more widely this data is only a guide for broad trends not local differences in management. i.e. where woodland or grassland cover is consistent not variation between farming practices in fields or woodland management at a local area. It is measured as tonnes of carbon dioxide equivalent per hectare per year (t CO2e ha-1 y-1 ).Many areas in agricultural production will have a neutral carbon balance where land management is sufficient to replace carbon lost in cropping or grazing from the vegetation and tillage from the soil. However, some soil types are very vulnerable to losing carbon when actively managed, these includes the very fertile but deeplowland agricultural peats. The figures for habitat sequestration of carbon are taken from the Natural England Report NERR094 (Gregg et.al. 2021). This report identified some key gaps. Each habitat type was assigned a likely score for sequestration. Carbon sequestration is less researched and harder to measure and therefore the confidence in this dataset is lower than in the carbon storage dataset.

Three data component layers were collated together to form a continuous habitat data layer for England: The National Forest Inventory (2016); NE priority habitat Inventory (PHI) dataset (various dated); Living England habitat map from satellite imagery (2020). Each of the habitats was assigned a likely sequestration value. Management influences sequestration, additional data sets adjust the figures and hence outputs spatially this included: - the protected site data given a slight uplift to the scores; - woodland sites on very steep slopes a slight reduction was given; - if mineral soils had native vegetation designated by the PHI, values were slightly uplifted; - soil type is important to sequestration with peatland soils losing carbon under arable and intensive grasslands at an extremely fast rate. - The peatland maps were combined with the vegetation maps to highlight these areas. Management of soil in intensive pastoral and arable peat systems has a profound effect on soil carbon values. - It is easy to lose carbon repeatedly from a system due to ploughing. For this report we have therefore assumed that these productive systems have a neutral carbon sequestration result. This is an over simplification and for more detailed studies information about the types of management regimes and more detailed soil information would be needed to understand if these areas are a carbon source or sink. NE PHI/ Ancient Woodland - OGL NE Living England - OGL NE Peatmap [2008] - Non- comercial licence NE SSSI data NFI-National Forest Inventory (NFI) Forest Reserach- OGL Soilscapes - Cranfield University/ HMSO- NE Bespoke Licence SRTM- NASA ShuttleRadar Topography- Open Topography