Abstract copyright UK Data Service and data collection copyright owner.

The 1831 Census was the fourth national census to be undertaken in Great Britain. Although the amount of information collected in this census was far less than was to be collected in later years, that of 1831 was the first in which detailed occupational statistics were collected on the employment of males aged 20 and over. The census was also the first in which detailed instructions were given to the enumerators on how they were to count the population.

Main Topics:

This dataset is comprise by a complete transcription of the 1831 census abstracts for the whole of Great Britain and the offshore islands of Jersey, Guernsey and the Isle of Man; re-organised according to 1851 registration districts. It forms part of the wider Victorian Census project which aims to digitise nineteenth century census documents and related material, such as vital registration and crime statistics, pertaining to Great Britain and Ireland.

This dataset will not be available until January 2005, but a simplified version of the database can be downloaded from the Victorian Census Project web site:

http://www.staffs.ac.uk/schools/humanities_and_soc_sciences/census/vichome.htm


Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

Aim: The Island Rule – i.e. the tendency for body size to decrease in large mammals and increase in small mammals on islands has been commonly evaluated through macroecological or macroevolutionary, pattern-orientated approaches, which generally fail to model the microevolutionary processes driving either dwarfing or gigantism. Here, we seek to identify which microevolutionary process could have driven extreme insular dwarfism in the extinct dwarf red deer population on the island of Jersey.

Location: Jersey, UK (Channel Islands).

Taxon: Red deer ( Cervus elaphus)

Methods: We applied an individual-based quantitative genetics model parameterized with red deer life-history data to study the evolution of dwarfism in Jersey's deer, considering variations in island area and isolation through time due to sea level changes.

Results: The body size of red deer on Jersey decreased fast early on, due to phenotypic plasticity, then kept decreasing almost linearly over time down to the actual body size of the Jersey deer (36 kg on average). Only 1% out of 10,000 replicates failed to reach that size in our simulations. The distribution of time to adaptation in these simulations was right-skewed, with a median of 395 generations (equivalent to roughly 4 ky years), with complete dwarfism effectively occurring in less than 6 ky 84.6% of times. About 72% of the variation in the time to adaptation between simulations was collectively explained by higher mutational variance, the number of immigrants from the continent after isolation, available genetic variance, heritability, and phenotypic plasticity.

Main Conclusions: The extreme dwarfing of red deer on Jersey is an expected outcome of high mutational variance, high immigration rate, a wide adaptive landscape, low levels of inbreeding, and high phenotypic plasticity (in the early phase of dwarfing), all occurring within a time window of around 6 ky. Our model reveals how extreme dwarfism is a plausible outcome of common, well-known evolutionary processes.

This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK and crown dependencies postcode districts (e.g. 'AB') for the years 2016-2019, inclusive.

The paper describing this dataset is available here: https://www.nature.com/articles/s41597-022-01135-6

The data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring. As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence.

The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at this Zenodo archive. The data there contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model.

The code used to make the estimations is available at this Zenodo archive.

The postcode data in postcode_district_data.csv are collated from several sources:

https://www.doogal.co.uk/UKPostcodes.php (population figures for the UK (UK Census 2011))

https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm (postcode boundary polygons for UK and crown dependancies)

https://www.gov.gg/population (Guernsey (GY) population data for end June 2020)

https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx (Jersey (JE) population data for end 2019)

https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf (Isle of Man (IM) population data for April 2016)

The data-set is presented in CSV format, as six files:

postcode_district_data.csv: location metadata (region_id, geometry, description, population, country)

regional_site_counts.csv: a table showing the number of sites for each measurement (columns), for each region_id (rows). region_id's match those in the postcode_district_data.csv file.

turing_regional_estimates_aq_daily_met_pollen_pollution_imputed_data.csv: uses imputed site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)

turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)

turing_regional_estimates_aq_loc_type_daily_imputed_data.csv: uses imputed site data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)

turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)

• Air quality site types:

Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)

'Rural background' (14 sites)

'Urban background' (48 sites)

'Urban traffic' (47 sites)

New Jersey has the highest rate of millionaires, with 9.76% of households showing a net worth of $1 million or above. That means that 246,058 New Jersey households are millionaires.