Historical dataset of population level and growth rate for the Bristol, UK metro area from 1950 to 2025.

Output Areas (OAs) are the lowest level of geographical area for census statistics and were first created following the 2001 Census. Using Census 2021 data, some changes were made to 2011 OAs as a result of population and household changes since 2011. Additionally, around 200 OA boundaries were selectively realigned to ward boundaries.2011 OAs that were still made up of between 40 and 250 households and had a usually resident population between 100 and 625 persons did not change. Otherwise, new 2021 OAs were created by merging or splitting 2011 OAs to make sure they fitted within the population and household thresholds of the unchanged 2011 OAs.There are some exceptions where OAs may have more than 625 people, for example, where there is a large prison.Outputs from Census 2021 are for 2021 OAs and are made up of unchanged 2011 OAs and new 2021 OAs.

Bristol City Council is one of the largest employers in the West Country. We are committed to achieving equal opportunities and we are seeking to attract a workforce that reflects the diverse population of the city.

Each year a survey called 'Quality of Life in Your Neighbourhood' is carried out. Bristol residents are asked to help monitor the Quality of Life in Bristol. The information provided from this survey helps to support existing monitoring of Indicators of Quality of Life, the Sustainable Community Strategy, the Joint Strategic Needs Assessment and Local Area Agreement and national indicators.

This project will explore the impact of the economic recession on cities and households through a systematic comparison of the experiences of two English cities, Bristol and Liverpool.The research will use both quantitative and qualitative approaches. Interviews will be held in both cities with stakeholders from across the public, private and voluntary and community sectors. A social survey of 1000 households will also be conducted in the two cities covering 10 specific household types. A series of in-depth qualitative interviews will then be held with households drawn from the survey and chosen to illustrate the spectrum of experience.In the context of globalisation and the rescaling of cities and states, the research aims to develop our understanding of the relationship between economic crisis, global connectivity and the transnational processes shaping cities and the everyday lives of residents. It will explore the 'capillary-like' impact of the crisis and austerity measures on local economic development, and local labour and housing markets, as well as highlight the intersecting realities of everyday life for households across the life course.The research will document the responses and coping strategies developed across different household types and evaluate the impact and effectiveness of 'anti-recession' strategies and policies.

This dataset comprises polygon data showing the extent & location of all Common Land & Village Greens within Bristol. Commons are typically unfenced land in private ownership with development / land use protected by legislation, although commoners may be granted specific rights. Village Greens are generally areas where local residents go for exercise, lawful sports & pastimes. Some Village Greens also have rights of common over them but enjoy separate statutory protection.

The Avon Longitudinal Study of Parents and Children (ALSPAC, and also known as the 'Children of the 90s' study), which is based at the University of Bristol, is an ongoing longitudinal study of a population of children born to mothers resident in one geographic area in England. The overall objectives of the study are to understand the ways in which the physical and social environments interact over time with genetic inheritance to affect health, behaviour and development in infancy, childhood and then into adulthood. Information has been collected at regular and frequent intervals from pregnancy and throughout childhood concerning the child's physical environments, parental characteristics (including economic and educational indicators), social circumstances, and family relationships. ALSPAC recruited more than 14,000 pregnant women with estimated dates of delivery between April 1991 and December 1992, who were living in the Avon Health Authority area, to take part in the study. These women, the children arising from the index pregnancy and the women's partners have been followed up since then and detailed data collected throughout childhood.The datasets held at the UKDA are sampler datasets, and have been compiled using various questionnaire and assessment data from the ALSPAC study. Further information may be found in the documentation, and for the wider study, on the ALSPAC web site.

Middle-Layer Super Output Areas (MSOAs) 2011Middle-Layer Super Output Areas (MSOAs) are a statistical geography created for the Census of England and Wales. Each MSOA sits within a local authority, and each local authority is divided into between one and 132 MSOAs depending on how many people live there.Middle Layer Super Output Areas (MSOAs) are designed to improve the reporting of small area statistics in England and Wales. MSOAs are built from groups of contiguous LSOAs.The OA is the lowest geographical level at which census estimates are provided. Lower Super Output Areas (LSOAs) are a national geography for collecting, aggregating and reporting statistics. They were designed to improve the reporting of small area statistics and are built up from groups of Output Areas.

It provides the number and percentage by level of qualification of the resident population aged 16 years and over.

Bristol City Council individual payments to suppliers with a value over £500 made within the month. Publication of these lists forms part of the Council's commitment to be open and transparent with its residents. Each report shows: •supplier name •amount •description of what was bought •purchasing department •payment date •transaction number The amounts shown exclude VAT. Where payments under £500 appear in a report, this is either because once the VAT element is included the total is more than £500, or because an invoice for more than £500 has been broken down to reflect different items of purchase which could also include credits such as discounts. Payments made using purchase cards are presented as a separate report. The reports do not include: •banking investments •salaries •refunds •member allowances •employee remuneration payments •benefits and homelessness payments Individual's names are not shown and are replaced with the word "redacted".

This dataset comprises polygon data showing Smoke Control areas within Bristol.The dataset is based on paper plans created as a consequence of the Clean Air Act 1956. Smoke Control Areas are areas where people can only use smokeless fuels in domestic chimneys. The paper maps were originally digitised in 1995 and have since been cleaned in 2010.

Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

Provisional counts of the number of deaths registered in England and Wales, by age, sex, region and Index of Multiple Deprivation (IMD), in the latest weeks for which data are available.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains county area measurements in squareKilometers units and were aggregated to a yearly timescale.

Lower layer Super Output Areas (LSOAs) are made up of groups of OAs, usually four or five. They comprise between 400 and 1,200 households and have a usually resident population between 1,000 and 3,000 persons.Using Census 2021 data, some changes were made to 2011 LSOAs as a result of population and household changes since 2011. New 2021 LSOAs were created by merging or splitting 2011 LSOAs to ensure that population and household thresholds were met.Outputs from Census 2021 are for 2021 LSOAs and are made up of unchanged 2011 LSOAs and new 2021 LSOAs.There are 33,755 LSOAs in England and 1,917 in Wales.

Future fine particulate matter (PM2.5) concentrations and health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographics. In this study, a human-earth system model is used to estimate US state-level PM2.5 mortality costs from 2015 to 2050 considering current major air quality and energy regulations. The Logarithmic Mean Divisia Index is applied to quantify the contributions of socioeconomic and energy factors to future changes in PM2.5 mortality costs. National PM2.5 mortality costs are estimated to decrease by 25% from 2015 to 2050, primarily driven by decreases in energy intensity and decreases in PM2.5 mortality cost per unit consumption of electric sector coal and transportation liquids. These factors together contribute to 68% of the net decrease, primarily because of technology improvements and air pollutant emission regulations. Furthermore, the results suggest that states with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PM2.5 mortality costs. In contrast, states with larger projected decreases in mortality costs have smaller increases in population and per capita GDP and greater decreases in electric sector coal share and PM2.5 mortality cost per unit fuel consumption. This dataset includes source code, input data, and model output from the Global Change Assessment Model (GCAM-USA) human-earth system model used in this study. It also includes Excel workbooks and R scripts used in producing the figures in the manuscript. This dataset is associated with the following publication: Ou, Y., S. Smith, J.J. West, C. Nolte, and D. Loughlin. State-level drivers of future fine particulate matter mortality in the United States.. Environmental Research Letters. IOP Publishing LIMITED, Bristol, UK, 14(12): 124071, (2019).

Context

This dataset comes from a study into the movement of bats by researchers at the University of Bristol, UK. I found it whilst exploring the open datasets at the Movebank Data Repository, a site dedicated to animal tracking data.

Content

The datasets contain information on the position and timestamps for multiple bats. The type of movement (individual or paired) is also included. See the readme.txt file for much more information.

Acknowledgements

I did not create this data. Full citations are below,

Data, Holderied M, Giuggioli L, McKetterick TJ (2015) Data from: Delayed response and biosonar perception explain movement coordination in trawling bats. Movebank Data Repository. doi:10.5441/001/1.62h1f7k9

Associated paper (open access), Giuggioli L, McKetterick TJ, Holderied M (2015) Delayed response and biosonar perception explain movement coordination in trawling bats. PLOS Computational Biology. doi:10.1371/journal.pcbi.1004089.t001

Conducted between 1987 - 1989, this study aimed to document and interpret conceptions of health and illness used by doctors and patients; and to show that the way in which people think about their health affects their consultation with a doctor. This study concentrated on patients of South-Asian ancestry where misunderstandings in the medical situation are often compounded by the lack of a shared language and little appreciation of differences in custom and outlook. Some of the misunderstandings result from very basic differences in the way doctors and patients feel about such concepts as 'the body', 'hot' and 'cold', and 'feeling well'.

Lower layer Super Output Areas (LSOAs) are made up of groups of OAs, usually four or five. They comprise between 400 and 1,200 households and have a usually resident population between 1,000 and 3,000 persons.

It tops up a pensioners weekly income to ensure they live on an income above the 'guaranteed amount' set by government. Therefore, the data does not cover all people who receive a pension. Data is collected from the Department for Works and Pensions.