This dataset contains recreation demand maps for the UK based on weekly, monthly and yearly visit frequencies. Recreation includes activities such as walking, hiking, cycling, etc, i.e., ‘outdoor non-vehicular recreation’. Recreation demand was calculated as the number of projected visits for local recreation, estimated using the universal law of human mobility (Schläpfer et al., 2021, Nature). Recreation demand maps are supplied at 250 m resolution in a British National Grid transverse Mercator projection (EPSG 27700). For each visit frequency (weekly, monthly and yearly), there is a map with and without attractiveness included in the calculation, where protected areas are used a proxy for attractiveness. This research was funded by the Natural Environment Research Council (NERC) under research programme NE/W005050/1 AgZero+ : Towards sustainable, climate-neutral farming. AgZero+ is an initiative jointly supported by NERC and the Biotechnology and Biological Sciences Research Council (BBSRC).

The Rural-Urban Classification is a Government Statistical Service product developed by the Office for National Statistics; the Department for Environment, Food and Rural Affairs; and the Welsh Assembly Government.Source: Office for National Statistics licensed under the Open Government Licence v.3.0.Contains OS data © Crown copyright 2025Links below to FAQ, Methodology and User GuideFAQ https://geoportal.statistics.gov.uk/documents/f359d48424664a1584dca319f3dac97f/aboutMethodology https://geoportal.statistics.gov.uk/documents/833a35f2a1ec49d98466b679ae0a0646/aboutUser Guide https://geoportal.statistics.gov.uk/documents/c8e8e6db38e04cb8937569d74bce277a/about

Thematic map of foreign guests from the Netherlands, Belgium, Great Britain and the USA in counties and associations. The share of all guests in %.:Guests from the United Kingdom (share of all guests in %) in Rhineland-Palatinate at district level.

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At Driver Technologies, we specialize in collecting high-quality, highly-anonymized, driving data crowdsourced using our dash cam app. Our Traffic Light Map Video Data is built from the millions of miles of driving data captured and is optimized to be trained for whatever computer vision models you need and enhancing various applications in transportation and safety.

What Makes Our Data Unique? What sets our Traffic Light Map Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes road signs, pedestrians, vehicles, traffic signs, and road conditions, resulting in rich, annotated datasets that can be used for a range of applications.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.

Primary Use-Cases and Verticals The Traffic Light Map Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:

Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.

Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic light placement. Our data can also aid in making sure municipalities have an accurate count of signs in their area.

Integration with Our Broader Data Offering The Traffic Light Map Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and computer vision models.

In summary, Driver Technologies' Traffic Light Map Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Traffic Light Map Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

Explore the interactive maps showing the average delay and average speed on the Strategic Road Network and local ‘A’ roads in England, in 2022.

On the Strategic Road Network (SRN) for 2022, the average delay is estimated to be 9.3 seconds per vehicle per mile (spvpm), compared to free flow, a 9.4% increase on 2021 and a 2.1% decrease on 2019.

The average speed is estimated to be 58.1 mph, down 1.4% from 2021 and up 0.2% from 2019.

On local ‘A’ roads for 2022, the average delay was estimated to be 45.5 seconds per vehicle per mile compared to free flow, up 2.5% from 2021 and down 2.8% from 2019 (pre-coronavirus)

The average speed is estimated to be 23.7 mph, down 1.7% from 2021 and up 2.2% from 2019 (pre-coronavirus).

Average speeds in 2022 have stabilised towards similar trends observed before the effects of the coronavirus pandemic.

The outbreak of coronavirus (COVID-19) has had a marked impact on everyday life, including on congestion on the road network. As some of these data are affected by the coronavirus pandemic in the UK, caution should be taken when interpreting these statistics and comparing them with other time periods. Additional http://bit.ly/COVID_Congestion_Analysis" class="govuk-link">analysis on the impact of the coronavirus pandemic on road journeys in 2020 is also available. This Storymap contains charts and interactive maps for road journeys in England in 2020.

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Thematic map of foreign guests from the Netherlands, Belgium, Great Britain and the USA in counties and associations. The share of all guests in %.:Guests from the United Kingdom (share of all guests in %) in Rhineland-Palatinate is proven at the association community level.

Two map files in ARC GIS PRO showing the main roads in England and Wales mapped by John Cary ca 1825

Roads in central southern England c.1675, constructed from John Ogilby's strip maps.The .kml file gives a crude preview; please download the shapefiles for discrimination between major routes, minor routes, and speculative spurs.

Kenya Tourism Map

Explore the interactive maps showing the average delay and average speed on the Strategic Road Network and Local ‘A’ Roads in England, in 2020.

Additional http://bit.ly/COVID_Congestion_Analysis" class="govuk-link">Analysis on the impact of the Coronavirus (COVID-19) pandemic on the road journeys is also available. This story map contains charts and interactive maps for road journeys in England.

On the Strategic Road Network (SRN) for 2020, the average delay is estimated to be 6.7 seconds per vehicle per mile compared to speed limits travel times, a 29.5% decrease compared to 2019.

The average speed is estimated to be 61.8mph, 5.1% up on 2019.

In 2020, on average 42.1% of additional time was needed compared to speed limits travel times, on individual road sections of the SRN to ensure on time arrival. This is down 25.2 percentage points compared to 2019, so on average a lower proportion of additional time is required.

On local ‘A’ roads for 2020, the average delay is estimated to be 33.9 seconds per vehicle per mile compared to free flow travel times. This is a decrease of 22.8% on 2019.

The average speed is estimated to be 27.3 mph. This is an increase of 8.2% on 2019.

The outbreak of coronavirus (COVID-19) has had a marked impact on everyday life, including on congestion on the road network. As these data are affected by the coronavirus pandemic in the UK, caution should be taken when interpreting these statistics and comparing them with previous time periods. While values had previously been moving towards their pre-lockdown levels, this trend appears to have reversed in the months following September 2020.

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The Forestry Commission has developed a series of sensitivity maps, based on nationally available and consistent datasets, to indicate where there are likely to be fewer sensitivities to woodland creation.

These maps will help to indicate to landowners whether there is likely to be potential to establish new woodland on their land, and where there may be sensitivities that would preclude woodland creation.

The maps do not indicate that, where there is a low sensitivity to new woodland being created, that planting will be agreed by the Forestry Commission, the regulator for woodland and forestry projects in England. However, the low sensitivity areas have fewest identified constraints to address, and it should be easier to agree creating new woodland here than in other areas.

Likewise, creation of new woodland, particularly of native woodland, may be appropriate outside ‘low sensitivity’ areas, but the appropriateness of proposals in these areas will again be determined by the Forestry Commission, including through responding to the views of Natural England, Environment Agency, Historic England, the Local Authority and other stakeholders, where relevant.

The Sensitivity Maps exclude all land that is unsuitable for planting, including urban areas, existing (and assumed) woodland and habitats that are considered too wet, too rocky, and too salty to support the growth of trees. This is achieved through restricting the Sensitivity Maps to the following land covers, based on the most recent update of Landcover Map:

Acid grassland Arable and horticulture Calcareous grassland Heather Heather grassland Improved grassland Neutral grassland

The spatial datasets and individual layers of those datasets that define land as unsuitable and low, medium, or high sensitivity for woodland creation in the Full Sensitivity Map version 3.0, the Low Sensitivity Map and their variants are set out in the document here:

www.gov.uk/guidance/a-guide-to-forestry-commissions-sensitivity-maps-for-woodland-creation

Attributes:

‘Sensitivity’ = the sensitivity to woodland creation level the land has been assigned.

‘Area (Hectares)’ = the area in hectares of the polygon.

Lineage:

This is version 3.0 of these layers, having gone through several iterations where new data inputs were added and changes made to how these were treated as sensitivities for woodland creation. These are the first versions of the layers to be published as Open Data.

An ARC GIS PRO shapefile mapping the turnpike roads in England and Wales for the 18th and early 19th century. The data includes details of the Turnpike Acts, years of operation, the quality of the road and the routes used by Mail coaches. The data forms the basis of the paper "Government, trusts, and the making of better roads in early nineteenth century England & Wales by Rosevear, Bogart & Shaw-Taylor.

This layer of the map based index (GeoIndex) shows the location of waste sites within England and Wales. The information is taken from an index listing of some 3500 waste sites in England and Wales identified by BGS as part of a survey carried out on behalf of the Department of the Environment in 1973. The index has been corrected and updated to a limited extent, but the data itself has not been changed. The data was collected in 1972 and the information reflects the knowledge at that time. It does not reflect current interpretation. Not all authorities made returns and there are not records for all of the sites listed. However, the data is an invaluable source of information about pre-1974 sites. The records themselves contain interpretations of the geology, ground and surface water risk assessments and information about the quantities and types of waste. Data visible at all map scales.

These maps will help to indicate to landowners whether there is likely to be potential to establish new woodland on their land, and where there may be sensitivities that would preclude woodland creation.

The maps do not indicate that, where there is a low sensitivity to new woodland being created, that planting will be agreed by the Forestry Commission, the regulator for woodland and forestry projects in England. However, the low sensitivity areas have fewest identified constraints to address, and it should be easier to agree creating new woodland here than in other areas.

Likewise, creation of new woodland, particularly of native woodland, may be appropriate outside ‘low sensitivity’ areas, but the appropriateness of proposals in these areas will again be determined by the Forestry Commission, including through responding to the views of Natural England, Environment Agency, Historic England, the Local Authority and other stakeholders, where relevant.

The Sensitivity Maps exclude all land that is unsuitable for planting, including urban areas, existing (and assumed) woodland and habitats that are considered too wet, too rocky, and too salty to support the growth of trees. This is achieved through restricting the Sensitivity Maps to the following land covers, based on the most recent update of Landcover Map:Acid grasslandArable and horticultureCalcareous grasslandHeatherHeather grasslandImproved grasslandNeutral grasslandThe spatial datasets and individual layers of those datasets that define land as unsuitable and low, medium, or high sensitivity for woodland creation in the Full Sensitivity Map version 3.0, the Low Sensitivity Map and their variants are set out in the document here:www.gov.uk/guidance/a-guide-to-forestry-commissions-sensitivity-maps-for-woodland-creationAttributes:‘Sensitivity’ = the sensitivity to woodland creation level the land has been assigned.‘Area (Hectares)’ = the area in hectares of the polygon. Lineage:This is version 3.0 of these layers, having gone through several iterations where new data inputs were added and changes made to how these were treated as sensitivities for woodland creation. These are the first versions of the layers to be published as Open Data.

This dataset consists of tick sampling and microclimate data from Exmoor, Richmond and New Forest study sites; as well as ARCGIS risk maps that model tick abundance driven by climate surfaces and host abundance. Tick sampling data (91 files, each representing a day of sampling) indicate tick abundance (distinguishing larvae, nymphs, adult males and adult females), vegetation height, soil moisture, temperature and relative humidity. Static risk map files indicate modeled tick abundance: 251 landcover files for the three sites, as well as 36 ArcView map files. The study is part of the NERC Rural Economy and Land Use (RELU) programme. Many people take pleasure from activities in forests and wild lands in the UK and others are being encouraged to participate. Unfortunately, there are risks and one of the most insidious is the possibility (albeit tiny) of acquiring a disease from wild animals; for example, ticks can be vectors of the bacterial infection leading to Lyme Disease. Both diagnosis and treatment can be problematic so prevention of acquiring such disease is highly desirable. Surprisingly little is known about how best to warn countryside users about the potential for disease without scaring them away or spoiling their enjoyment. Answering such questions was the goal of this project, and required the integration of a diverse set of scientific skills, and an understanding of the views of those who manage countryside, those who have contracted zoonotic diseases and those who access the land. This project combined knowledge from three strands of work, namely risk assessment, risk perception and communication, and scenario analysis. The study sites were selected to provide a range of environmental conditions and countryside use. Peri-urban parkland, accessible lowland forest and heath and remote upland forest were chosen as represented by Richmond Park on the fringe of Greater London, the New Forest in Southern England, and Exmoor in South West England. The following additional data from this same research project are available at the UK Data Archive under study number 6892 (see online resources): Lyme disease risk perception data resulting from tick imagery vignette experiments, Lyme disease patient interviews and surveys, residents and countryside staff focus groups, forest manager interviews, and multiple scoring procedures of animal social representation; as well as Lyme and tick risk communication data resulting from interviews with organisations and content analysis of risk warning information leaflets, Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This document sets out the recommended standard presentation of statistics for 2011 travel to work areas in the UK.

The Network Model digitally represents England’s Strategic Road Network. The model contains critical information about our road’s location, names, lanes and widths.The Network Model was derived from Ordnance Survey (OS) Highways data and enriched with internal datasets. It reflects National Highways roads that are open for traffic and have been validated against our Operational Highway Boundary (RedLine).To ensure the model remains accurate, we have implemented processes to track changes across the network. However, if you have noticed any inaccuracies in the data, please report it here. This form is to be used to report data issues only.In this initial release, speed limit and smart motorway information has been removed pending data validation.To download a file geodatabase containing all layers of the network model and their relationships please use this link.For more information about the Network Model please visit our landing page and technical hub.For maintenance issues on the network please report here. For non-emergency incidents please contact our Customer Contact Centre on 0300 123 5000.The data is published under an Open Government Licence.