The Greater Manchester RIGS Group operates the RIGS register from within the offices of the Greater Manchester Ecology Unit (GMEU). This dataset contains all current RIGS boundaries, including the name of each site, the relevant Local Authority and its defining features. RIGS are non-statutory areas of substantive geological or geomorphological importance within the county of Greater Manchester. The RIGS system is designed to establish and highlight to planners, landowners and site managers where areas of high geodiversity interest occur so that appropriate decisions on planning applications, land use and land management can be made. Full RIGS citations including information and descriptions of features of interest on each site can be obtained from GMRIGS Group if required. This dataset is a copy of the master RIGS register for Greater Manchester, which is maintained by the GMRIGS Group as a MapInfo TAB file. This dataset is updated whenever a new site is designated, a site is de-designated or a boundary is altered.

Super Output Areas are a geographic hierarchy designed to improve the reporting of small-area statistics.

The Lower Super Output Areas and Data Zones list contains 42,619 areas of the following constituent geographies:

• 32,844 Lower Super Output Areas in England
• 890 Super Output Areas in Northern Ireland
• 6,976 Data Zones in Scotland
• 1,909 Lower Super Output Areas in Wales

Please visit ONS Beginner's Guide to UK Geography for more info.

The boundaries are available as either extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands) or

clipped to the coastline (Mean High Water mark).

Greater Manchester Ecology Unit (GMEU) operates the SBI register for and on behalf of the ten Local Authorities that comprise Greater Manchester. This dataset contains all current SBI boundaries, including the name of each site, its containing Local Authority and its defining features.

SBIs are non-statutory areas of substantive nature conservation importance within the county of Greater Manchester. The SBI system is designed to establish and highlight to planners, landowners and site managers where areas of high biodiversity interest occur so that appropriate decisions on planning applications, land use and land management can be made.

Full SBI citations including descriptions, statistics and grading information can be obtained from GMEU if required.

This is a collection of datasets and source code used for a crime prediction study based on POI locations.
[1] Crime data from data.police.uk for the following UK police forces:
-- Greater Manchester Police,
-- Merseyside Police,
-- Dorset Police,
-- West Yorkshire Police.
Time span: October 2016--September 2019.
License: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
Format: CVS (zipped).
Files:
GreaterManchesterPolice.zip
MerseysidePolice.zip
Dorset Police.zip
West Yorkshire Police.zip

[2] Data extracts from OpenStreetMap made available by Geofabrik.de for
the following UK counties:
-- Greater Manchester,
-- Merseyside,
-- Dorset,
-- West Yorkshire.
Data layers: pois, pois_a, transport, transport_a.
Downloaded on: November 14, 2019.
License: Open Database License 1.0.
Format: shapefile (zipped).
Files:
greater-manchester-latest-free.shp.zip
merseyside-latest-free.shp.zip
dorset-latest-free.shp.zip
west-yorkshire-latest-free.shp.zip

[3] UK administrative boundary data from ordnancesurvey.co.uk.
Downloaded on: September 23, 2019.
License: Open Government License.
Note: Contains OS data © Crown copyright and database right 2018
Format: shapefile (zipped)
File: district_borough_unitary_region.shp.zip

[4] Administrative boundary data for the following UK urban areas:
-- Manchester (Manchester District),
-- Liverpool (Liverpool District),
-- Bournemouth (Bournemouth, Christchurch and Poole District),
-- Wakefield (Wakefield District).
Obtained by extracting the corresponding districts from dataset [3] listed above.
Format: RDS.
File: adm-man-liv-bou-wak.RDS.

[5] Crime data aggregated to 300x300m grid for the following UK urban areas:
-- Manchester (Manchester District),
-- Liverpool (Liverpool District),
-- Bournemouth (Bournemouth, Christchurch and Poole District),
-- Wakefield (Wakefield District).
Obtained by geographical aggregation of crime data [1] listed above, limited to the corresponding district boundaries from [3].
Format: RDS.
File: crime-grid-man-liv-bou-wak.RDS.

[6] POI data aggregated to 300x300m grid for the following UK urban areas:
-- Manchester (Manchester District),
-- Liverpool (Liverpool District),
-- Wakefield (Wakefield District),
-- Bournemouth (Bournemouth, Christchurch and Poole District).
Obtained by geographical aggregation of POI data [2] listed above, limited to the corresponding district boundaries from [3].
Format: RDS.
File: poi-grid-man-liv-bou-wak.RDS.

[7] R functions for data preparation.File: crime-poi-functions-data.R.
[8] R functions for model creation and evaluation.File: crime-poi-functions-model.R.
[9] R script for data preparation.File: crime-poi-data.R.
[10] R script for model creation and evaluation.File: crime-poi-model.R.
The code assumes that:-- source code files are placed in the current working directory,-- original unprocessed data files [1-3] are placed in the data subdirectory of the current working directory,
-- the RDS subdirectory exists in the current working directory (this is where RDS files are saved),
-- the Plots subdirectory exists in the current working directory (this is where plot files are saved).
The provided RDS files [4-6] may be optionally placed in the RDS subdirectory to avoid repeating the time-consuming data preparation process -- they will be used if available or re-created from the original data otherwise (but the latter may take several hours).

Living England is a multi-year project which delivers a broad habitat map for the whole of England, created using satellite imagery, field data records and other geospatial data in a machine learning framework. The Living England habitat map shows the extent and distribution of broad habitats across England aligned to the UKBAP classification, providing a valuable insight into our natural capital assets and helping to inform land management decisions. Living England is a project within Natural England, funded by and supports the Defra Natural Capital and Ecosystem Assessment (NCEA) Programme and Environmental Land Management (ELM) Schemes to provide an openly available national map of broad habitats across England.This dataset includes very complex geometry with a large number of features so it has a default viewing distance set to 1:80,000 (City in the map viewer).Process Description:A number of data layers are used to develop a ground dataset of habitat reference data, which are then used to inform a machine-learning model and spatial analyses to generate a map of the likely locations and distributions of habitats across England. The main source data layers underpinning the spatial framework and models are Sentinel-2 and Sentinel-1 satellite data from the ESA Copernicus programme, Lidar from the EA's national Lidar Programme and collected data through the project's national survey programme. Additional datasets informing the approach as detailed below and outlined in the accompanying technical user guide.Datasets used:OS MasterMap® Topography Layer; Geology aka BGS Bedrock Mapping 1:50k; Long Term Monitoring Network; Uplands Inventory; Coastal Dune Geomatics Mapping Ground Truthing; Crop Map of England (RPA) CROME; Lowland Heathland Survey; National Grassland Survey; National Plant Monitoring Scheme; NE field Unit Surveys; Northumberland Border Mires Survey; Sentinel-2 multispectral imagery; Sentinel-1 backscatter imagery; Sentinel-1 single look complex (SLC) imagery; National forest inventory (NFI); Cranfield NATMAP; Agri-Environment HLS Monitoring; Living England desktop validation; Priority Habitat Inventory; Space2 Eye Lens: Ainsdale NNR, State of the Bog Bowland Survey, State of the Bog Dark Peak Condition Survey, State of the Bog Manchester Metropolitan University (MMU) Mountain Hare Habitat Survey Dark Peak, State of the Bog; Moors for the Future Dark Peak Survey; West Pennines Designation NVC Survey; Wetland Annex 1 inventory; Soils-BGS Soil Parent Material; Met Office HadUK gridded climate product; Saltmarsh Extent and Zonation; EA LiDAR DSM & DTM; New Forest Mires Wetland Survey; New Forest Mires Wetland Survey; West Cumbria Mires Survey; England Peat Map Vegetation Surveys; NE protected sites monitoring; ERA5; OS Open Built-up Areas; OS Boundaries dataset; EA IHM (Integrated height model) DTM; OS VectorMap District; EA Coastal Flood Boundary: Extreme Sea Levels; AIMS Spatial Sea Defences; LIDAR Sand Dunes 2022; EA Coastal saltmarsh species surveys; Aerial Photography GB (APGB); NASA SRT (Shuttle Radar Topography Mission) M30; Provisional Agricultural Land Classification; Renewable Energy Planning Database (REPD); Open Street Map 2024.Attribute descriptions: Column Heading Full Name Format Description

SegID SegID Character (100) Unique Living England segment identifier. Format is LEZZZZ_BGZXX_YYYYYYY where Z = release year (2223 for this version), X = BGZ and Y = Unique 7-digit number

Prmry_H Primary_Habitat Date Primary Living England Habitat

Relblty Reliability
Character (12) Reliability Metric Score

Mdl_Hbs Model_Habs Interger List of likely habitats output by the Random Forest model.

Mdl_Prb Model_Probs Double (6,2) List of probabilities for habitats listed in ‘Model_Habs’, calculated by the Random Forest model.

Mixd_Sg Mixed_Segment Character (50) Indication of the likelihood a segment contains a mixture of dominant habitats. Either Unlikely or Probable.

Source Source

Description of how the habitat classification was derived. Options are: Random Forest; Vector OSMM Urban; Vector Classified OS Water; Vector EA saltmarsh; LE saltmarsh & QA; Vector RPA Crome, ALC grades 1-4; Vector LE Bare Ground Analysis; LE QA Adjusted

SorcRsn Source_Reason

Reasoning for habitat class adjustment if ‘Source’ equals ‘LE QA Adjusted’

Shap_Ar Shape_Area

Segment area (m2) Full metadata can be viewed on data.gov.uk.

This experiment was one of a series of experiments aimed at investigating the role of social identity as an alternative explanation to 'automatic' contagion for of the spread of aggression, since as an account it fails to adequately explain social group boundaries of ‘passive’ social influence.This experiment used a 1 (student) x 2 (aggressive noise vs non-aggressive noise) between subjects design. In the aggressive noise condition, self-relevance of the crowd source significantly predicted explicit aggression (controlling for gender). Explicit and implicit aggression do not correlate with each other.How and why do behaviours spread from person to person? In particular, how does aggression and violent behaviour spread? When, as in 2011, riots began in London, why did they then occur in Birmingham, Manchester, and Liverpool? One of the most common ways of addressing such issues is through the notion of 'contagion'. The core idea is that, particularly in crowds, mere exposure to the behaviour of others leads observers to behave in the same way. 'Contagion' is now used to explain everything from 'basic' responses such as smiling and yawning (where the mere act of witnessing someone yawn or smile can invoke the same response in another) to complex phenomena like the behaviour of financial markets and, of course, rioting. What is more, laboratory experiments on the 'contagion' of simple responses (such as yawning) serve to underpin the plausibility of 'contagion' accounts as applied to complex phenomena (such as rioting). Despite this widespread acceptance, the 'contagion' account has major problems in explaining the spread of behaviours. In particular, there are boundaries to such spread. If men smile at a sexist joke, will feminists also smile in response to the men's smiles? If people riot in one town, why is it that they also riot in some towns but not others? For example, in 2011, disturbances spread from London to Birmingham, Manchester and Liverpool but they did not spread to Sheffield, Leeds or Glasgow. 'Contagion' explanations cannot answer such questions because they assume that transmission is automatic. They do not take account of the social relations between the transmitter and receiver. We propose a new account of behavioural transmission based on the social identity approach in social psychology. This suggests that influence processes are limited by group boundaries and group content: we are more influenced by ingroup members than by outgroup members, and we are more influenced by that which is consonant with rather than contradictory to group norms. The social identity approach is therefore ideally suited to explaining the social limits to influence, both for 'basic' phenomena and rioting. In order to advance both theoretical understanding and practical interventions, our research will develop a social identity analysis of transmission processes at multiple levels. Accordingly, the aims and objectives of this research project are as follows: First, we will conduct a series of experimental studies on 'basic' behaviours (yawning, itching) to examine whether the effects of being exposed to a behaviour depend on observers and actors being fellow ingroup members. We will also examine 'complex' behaviours (aggression and rioting) to see if (1) observers are more influenced when the actors are ingroup members; (2) observers are more influenced by the responses of other observers when these are also ingroup members; (3) willingness to copy others depends upon whether their behaviour is consonant with observer group norms. Second, we will examine the spread of urban disorder during the 2011 English riots. We have been granted special access to the full data-set from the Guardian/LSE 'Reading the Riots' study (270 interviews with participants carried out immediately following the events). This, along with other secondary sources (such as detailed crime figures), will allow us to examine the extent to which the spread of these riots was linked to a sense of shared identity with those who had rioted previously (that is, those who rioted 'saw themselves' in those who rioted before them, and those who lacked such a sense were less likely to riot). Third, we will use our findings to generate a wider debate about the nature of psychological transmission and the practicalities of addressing them. Activities will include workshops which will bring together researchers, practitioners (e.g., the police) and policy-makers in local and national government to address how we can mitigate against the spread of riots and violence. A total of 60 undergraduate students from the University of Sussex participated in this research. Subjects were recruited and approached through various Facebook pages used by Sussex University students, as well as in person on Sussex University campus. Subjects were randomly allocated to two conditions (aggressive crowd noise vs non-aggressive crowd noise) using a random number generator. They first listened to the crowd noise recording and then filled out a questionnaire that measured implicit and explicit aggression.