Cumulative number of specimens tested by Irish laboratories for SARS-CoV - number and percentage positive. Data is available for all laboratories, hospitals and other labs (NVRL and Cherry Orchard) - total tests and total positive results. Data is provided to the HPSC by the HSE COVID-19 Daily lab tracker system. Based on data reported to HSE by 15:00 (Date_HPSC) but refers to data collected as of midnight the previous day.

Introduction The dataset provides detailed information about UK Power Networks' Grid and Primary Sites. It includes key characteristics such as:

Spatial coordinates of each site Year commissioned Asset counts against each site Power transformer count Local authority information Winter and summer demand Transformer ratings

This data is useful for understanding the infrastructure and capacity of the electricity network across its regions.

Methodological Approach

Source: Various internal data domains - geospatial, asset, long term development statement; as well as openly available data from the Ordnance Survey and Office of National Statistics Manipulation: Various data characteristics were combined together using Functional Locations (FLOCs)

Quality Control Statement The data is provided "as is".

Assurance Statement The Open Data team has checked the data against source to ensure data accuracy and consistency. The data domain owners have checked their respective data aspects.

Other Contains data from Office for National Statistics licensed under the Open Government Licence v.3.0. Local Authority District (2022) to Grouped Local Authority District (2022) Lookup for EW - data.gov.uk

Contains Ordnance Survey data Crown copyright and database right [2019-]. Free OS OpenData Map Downloads | Free Vector & Raster Map Data | OS Data Hub

Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

This file contains the digital vector boundaries for built up areas in England and Wales as at 2024. The built up area boundaries are generalised and created using an automated approach based on a 25m grid squares (BGG).Only the latest versions of the products are available on the OS DataHub whereas we have the 2011 built-up areas, 2022 built up areas and these ones.This file has been created from OS Open Built Up Areas. Further information about this product can be found in our FAQ document or from Ordnance Survey’s product information page.Please note that this product contains both Ordnance Survey and ONS Intellectual Property Rights.REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/main_ONS_BUA_2024_EW/FeatureServerREST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Built_Up_Areas_2024_Boundaries_EW_BGG/WFSServer?service=wfs&request=getcapabilitiesREST URL of MapServer – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Built_Up_Areas_(2024)_Boundaries_EW_BGG/MapServer

The Cloud Computing Center Operating System (CCOS) market is experiencing robust growth, driven by the increasing adoption of cloud computing across various sectors. While precise market size figures for 2025 aren't provided, a reasonable estimation, considering the current market trends and the involvement of major players like Microsoft, Alibaba, and Huawei, places the 2025 market value at approximately $15 billion. This signifies a substantial increase from previous years, fueled by the ongoing digital transformation initiatives and the need for efficient, scalable, and secure operating systems for cloud data centers. The Compound Annual Growth Rate (CAGR) of the CCOS market, let's assume to be around 15% over the forecast period (2025-2033), reflects the consistently high demand. This growth is further bolstered by factors such as the rising adoption of virtualization technologies, the increasing need for hybrid and multi-cloud environments, and the expanding deployment of Artificial Intelligence (AI) and Machine Learning (ML) applications within data centers. Key trends include the development of containerization technologies, serverless computing, and edge computing, all of which are shaping the future of CCOS. However, market growth is not without its challenges. Restraints such as the complexity of managing large-scale cloud environments, the need for specialized expertise, and security concerns related to cloud-based systems pose significant hurdles. Despite these, the long-term outlook remains positive, with a projected market size exceeding $50 billion by 2033. This projection takes into account continued innovation in CCOS technologies, ongoing improvements in security protocols, and the growing reliance on cloud-based infrastructure globally. The competitive landscape is dominated by established players like Microsoft and Alibaba, along with emerging players such as QingCloud and Huawei, vying for market share through technological advancements and strategic partnerships. Geographic expansion, particularly in regions with developing digital infrastructures, presents a significant opportunity for future market expansion.

Author:Buro HappoldCreation date:November 2024Date of source data harvest:March 2024Temporal coverage of source data:Up to March 2024Spatial Resolution:Lower Super Output Area (LSOA)Geometry:PolygonSource data URL:OS MasterMap Topography Layer | Data Products | OS (ordnancesurvey.co.uk)Data terms of use:- Dataset can be shared openly for reuse for non-commercial purposes, with appropriate attribution. Data contains Ordnance Survey mapping and is published under Ordnance Survey's 'presumption to publish'Data attribution:- Dataset created by Buro Happold as part of the CIEN & South London sub-regional LAEPs, 2024. - Contains OS data © Crown copyright and database right 2025 (0100032216 GLA).- Office for National Statistics licensed under Open Government Licence v3.0.Workflow Diagram:Available: pngComments:The data and analysis developed for the sub-regional LAEP was undertaken using data available at the time and will need to be refined for a full Phase 2 LAEP. Please check here for more detailed background on the data.Whilst every effort has been made to ensure the quality and accuracy of the data, the Greater London Authority is not responsible for any inaccuracies and/or mistakes in the information provided.

5m resolution digital terrain model (DTM) of Great Britain. The elevation values are calculated at the centre of the cell and they are given to the nearest 0.01 m. Due to local tidal conditions, the height of the mean high and low water mark varies continuously around the coast of Britain. The mean high and low water lines have been derived from large-scale mapping and assigned constant height values, based on the average for each tile. This average value has been determined from local tide tables. The mean high and low water lines were used as heighted breaklines when creating the grid to ensure the grid product is consistent with the contour product. This means that there may be a small discrete step in the height of tidal water between adjacent tiles. For areas of permanent tidal water the height of the mean low water has been extended out to the tile edge to ensure that the tile is complete. Heights in the foreshore area are interpolated between the mean high and low water heights.

Article 4 Direction (A4D) is part of planning legislation that allows the Council to remove permitted development rights including changes of use from an area or a particular property. This dataset shows the Flexible Town Centre Use.Created over OS MasterMap data. Full decision made by the Council.

This modelled fluvial flood depth data with climate change was created for the 1% annual chance of flooding situations and was produced as a by-product from the 2004 generalised modelling project. The purpose of the generalised modelling project was to fill the gaps where there was no detailed local modelled data in 2004, in order to define the extents of Flood Zones for spatial planning.

The purpose of this climate change data was to provide a high-level sensitivity analysis of the possible effects of climate change based on a 20% increase in peak flows in the fluvial modelling.

A two-dimensional hydrodynamic model called JFlow was used to produce this modelled fluvial flood depth data on a 5x5m grid.

Since 2004, some local detailed modelling projects have included scenarios for climate change however this climate change dataset has not been updated.

INFORMATION WARNING: This data is not suitable for identifying whether an individual property will flood due to climate change, for detailed decision making or for use in site specific Flood Risk or Strategic Flood Risk Assessments. Where this data is used further evidence, verification and studies should be undertaken. Climate change allowances have changed since this work was completed in 2004.

More recent, accurate and local detailed modelling depth data with climate change is available for some places. Please contact your local Environment Agency office to see if detailed modelling is available for your area of interest.

This metadata record is for Approval for Access product AfA480 2004 Climate Change 1 in 100 Fluvial Flood Depth Grids

Modelled fluvial flood depth data with climate change are available for the whole of England, however this data is for the 100x100km squared Ordnance Survey National Grid reference NY. If you are interested in data for another grid reference refer to this Ordnance Survey National Grid document to find the relevant referencing code and search on Data.gov.uk again to download the data. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown Copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

This modelled fluvial flood depth data with climate change was created for the 0.1% annual chance of flooding situations and was produced as a by-product from the 2004 generalised modelling project. The purpose of the generalised modelling project was to fill the gaps where there was no detailed local modelled data in 2004, in order to define the extents of Flood Zones for spatial planning. The purpose of this climate change data was to provide a high-level sensitivity analysis of the possible effects of climate change based on a 20% increase in peak flows in the fluvial modelling. A two-dimensional hydrodynamic model called JFlow was used to produce this modelled fluvial flood depth data on a 5x5m grid. Since 2004, some local detailed modelling projects have included scenarios for climate change however this climate change dataset has not been updated. INFORMATION WARNING: This data is not suitable for identifying whether an individual property will flood due to climate change, for detailed decision making or for use in site specific Flood Risk or Strategic Flood Risk Assessments. Where this data is used further evidence, verification and studies should be undertaken. Climate change allowances have changed since this work was completed in 2004. More recent, accurate and local detailed modelling depth data with climate change is available for some places. Please contact your local Environment Agency office to see if detailed modelling is available for your area of interest. This metadata record is for Approval for Access product AfA480 2004 Climate Change 1 in 1000 Fluvial Flood Depth Grids Modelled fluvial flood depth data with climate change are available for the whole of England, however this data is for the 100x100km squared Ordnance Survey National Grid reference NU. If you are interested in data for another grid reference refer to this Ordnance Survey National Grid document to find the relevant referencing code and search on Data.gov.uk again to download the data. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown Copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

Government Office (GO) Regions in Shape format derived from OS Boundary-Line data. The Regions were created from Digimap datasets downloaded from Boundary Download of 'County' Boundaries and the 'District Borough Unitary Authority' boundaries'. These were loaded into ArcMap as Shape files and using the map at http://www.gos.gov.uk/common/docs/239408/442543 (which is accessed from http://www.gos.gov.uk/aboutusnat/) as the guide the Regions were identified and merged together from individual Counties, Unitary Authorites and Metropoliatain Districts. The Revision Date of the OS Boundary-Line data is April 2008. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-20 and migrated to Edinburgh DataShare on 2017-02-21.

The Vietnam data center storage market, currently valued at $0.2 billion in 2025, exhibits robust growth potential. A compound annual growth rate (CAGR) of 4.87% projects significant expansion through 2033, driven by several key factors. The increasing adoption of cloud computing and big data analytics within the IT & telecommunication, BFSI (Banking, Financial Services, and Insurance), and government sectors fuels the demand for advanced storage solutions. Furthermore, the expanding media and entertainment industry in Vietnam contributes to this growth, requiring robust and scalable storage infrastructure to manage large volumes of data. The market's segmentation reveals a preference towards All-Flash storage, reflecting the need for high-speed data access and reduced latency. While traditional storage remains a significant segment, the transition towards faster and more efficient All-Flash and hybrid storage solutions is evident. This trend is further amplified by the increasing adoption of Network Attached Storage (NAS) and Storage Area Network (SAN) technologies over Direct Attached Storage (DAS) for improved scalability and centralized management. Competitive landscape features major players like Dell, NetApp, Kingston, and Seagate, all vying for market share through innovation and strategic partnerships. The continuous development of Vietnam's digital infrastructure and the government's push for digital transformation initiatives further bolster the market's optimistic outlook. The growth trajectory is expected to continue, with increasing demand from various end-user segments. However, potential restraints include infrastructural limitations in certain regions and concerns around data security and privacy. Despite these challenges, the long-term forecast for the Vietnam data center storage market remains positive, with continued growth driven by technological advancements, increasing digitalization, and government support. The market is poised to benefit from the ongoing expansion of data centers across Vietnam and the rise of data-intensive applications, thereby fostering significant opportunities for both established players and new entrants in the coming years. Recent developments include: August 2023: Lenovo unveiled the Lenovo ThinkSystem D4390 Direct, featuring storage expansion capabilities enriched with powerful 24Gbps SAS direct-attached drives. This design is crafted to offer density, speed, scalability, security, and high availability for high-capacity applications., October 2023: Dell Technologies enhanced its PowerFlex software-defined infrastructure platform to assist customers in the ongoing modernization of the data center storage experience. Dell PowerFlex 4.5 incorporates a broader range of supported operating systems (OS), improved alerting capabilities, a single-capacity namespace, and unified storage pool management.. Key drivers for this market are: Expansion of IT Infrastructure to Increase Market Growth, Increased Investments in Hyperscale Data Centers To Increase Market Growth. Potential restraints include: Expansion of IT Infrastructure to Increase Market Growth, Increased Investments in Hyperscale Data Centers To Increase Market Growth. Notable trends are: IT & Telecommunication Segment to Hold Major Share in the Market.

Author:Buro HappoldCreation date:November 2024Date of source data harvest:March to June 2024Temporal coverage of source data:Multiple inputs, see workflow diagramsSpatial Resolution:Lower Super Output Area (LSOA)Geometry:PolygonSource data URL:MultipleData terms of use:Data freely available for download and reuse, with appropriate attribution.Data attribution:- Dataset created by Buro Happold as part of the CIEN & South London sub-regional LAEPs, 2024. Contains data derived from multiple sources including: Ordnance Survey; London Building Stock Models; and the Department for Levelling Up, Communities and Housing (See workflow diagrams for a full list).- Contains OS data © Crown copyright and database right 2025.- Office for National Statistics licensed under Open Government Licence v3.0.Workflow Diagram:Available: pdf / pngComments:The data and analysis developed for the sub-regional LAEP was undertaken using data available at the time and will need to be refined for a full Phase 2 LAEP. Please check here for more detailed background on the data.Whilst every effort has been made to ensure the quality and accuracy of the data, the Greater London Authority is not responsible for any inaccuracies and/or mistakes in the information provided.

10K Grid of UK showing areas where surveys have been carried out under contract for Bacterial Leaf Scorch. Attributes give the year(s) surveys have been carried out and the year the disease was found and confirmed.

Surveyed - 'Y' indicates surveys have been conducted in grid square

Svy2015 - 'Y' indicates survey was carried out in grid square in 2015

YrFound - Gives the year that the surveys first found a positive result in grid square Attribution statement: Contains OS data © Crown copyright [and database right] [year].

The Fabric Data Center market is experiencing robust growth, projected to reach $2.31 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 28.90% from 2025 to 2033. This expansion is driven by the increasing demand for high-bandwidth, low-latency network infrastructure to support cloud computing, big data analytics, and the Internet of Things (IoT). Businesses across various sectors, including IT & Communications, Banking & Financial Services, and Healthcare, are adopting fabric data centers to improve agility, scalability, and operational efficiency. The shift towards software-defined networking (SDN) and network function virtualization (NFV) is further fueling market growth, enabling greater automation and centralized management of network resources. Key players like Cisco, Huawei, and Arista Networks are leading the innovation, offering advanced solutions that cater to evolving enterprise needs. The market segmentation reveals strong growth across solutions (routers, switches, storage area networking), applications (IT & Communications, Banking & Financial Services), and end-users (cloud service providers, telecom service providers). The Asia-Pacific region is expected to witness particularly rapid expansion due to increasing digitalization and infrastructure investments. However, despite the significant growth trajectory, the market faces certain challenges. High initial investment costs associated with implementing fabric data center solutions can be a barrier for some organizations, particularly smaller businesses. Furthermore, the complexity of managing and integrating these advanced systems requires skilled personnel, creating a potential talent shortage. Despite these restraints, the long-term benefits of enhanced performance, scalability, and reduced operational costs are expected to outweigh these challenges, ensuring continued market expansion throughout the forecast period. The competitive landscape is marked by both established players and emerging vendors, leading to ongoing innovation and price competition which benefits end-users. Recent developments include: July 2023: Huawei's announced three innovative data center facility solutions as unveiled the next-generation indirect evaporative cooling solution EHU and the mobile intelligent management solution iManager-M. These scenario-based data center solutions promise optimal reliability throughout the lifecycle and aim to drive the high-quality development of the data center industry., December 2022: The Nokia 7220 IXR D2/D3 interconnect routers will be used as core switching datacentre leaf platforms for North's datacentre fabric, running the Nokia SR Linux network operating system (NOS). The data center is built on bare metal servers running OpenStack Ironic, which interfaces with NOS using open-source upstream code., October 2022: Cloudera, the hybrid data startup announced new hybrid data capabilities that will allow enterprises to more easily migrate data, metadata, data workloads, and data applications between clouds and on-premises in order to optimize for performance, cost, and security.. Key drivers for this market are: Increasing Demand for Data Storage and Adoption of Cloud Computing, Need for High Speed Data Transfer; Increasing Demand of Fabric Switches. Potential restraints include: Increasing Demand for Data Storage and Adoption of Cloud Computing, Need for High Speed Data Transfer; Increasing Demand of Fabric Switches. Notable trends are: Increasing Demand of Fabric Switches is Driving the Market.

This modelled fluvial flood depth data with climate change was created for the 1% annual chance of flooding situations and was produced as a by-product from the 2004 generalised modelling project. The purpose of the generalised modelling project was to fill the gaps where there was no detailed local modelled data in 2004, in order to define the extents of Flood Zones for spatial planning.

The purpose of this climate change data was to provide a high-level sensitivity analysis of the possible effects of climate change based on a 20% increase in peak flows in the fluvial modelling.

A two-dimensional hydrodynamic model called JFlow was used to produce this modelled fluvial flood depth data on a 5x5m grid.

Since 2004, some local detailed modelling projects have included scenarios for climate change however this climate change dataset has not been updated.

INFORMATION WARNING: This data is not suitable for identifying whether an individual property will flood due to climate change, for detailed decision making or for use in site specific Flood Risk or Strategic Flood Risk Assessments. Where this data is used further evidence, verification and studies should be undertaken. Climate change allowances have changed since this work was completed in 2004.

More recent, accurate and local detailed modelling depth data with climate change is available for some places. Please contact your local Environment Agency office to see if detailed modelling is available for your area of interest.

This metadata record is for Approval for Access product AfA480 2004 Climate Change 1 in 100 Fluvial Flood Depth Grids

Modelled fluvial flood depth data with climate change are available for the whole of England, however this data is for the 100x100km squared Ordnance Survey National Grid reference SS. If you are interested in data for another grid reference refer to this Ordnance Survey National Grid document to find the relevant referencing code and search on Data.gov.uk again to download the data. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown Copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

This dataset is not suitable for identifying whether an individual property will flood. GIS layer showing the extent of flooding from surface water that could result from a flood with a 3.3% chance of happening in any given year. This dataset is one output of our Risk of Flooding from Surface Water (RoFSW) mapping, previously known as the updated Flood Map for Surface Water (uFMfSW). It is one of a group of datasets previously available as the uFMfSW Complex Package. Further information on using these datasets can be found at the Resource Locator link below. Information Warnings: Risk of Flooding from Surface Water is not to be used at property level. If the Content is displayed in map form to others we recommend it should not be used with basemapping more detailed than 1:10,000 as the data is open to misinterpretation if used as a more detailed scale. Because of the way they have been produced and the fact that they are indicative, the maps are not appropriate to act as the sole evidence for any specific planning or regulatory decision or assessment of risk in relation to flooding at any scale without further supporting studies or evidence. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

The data shows Ordnance Survey pond locations, where they match with the surveyed location of a priority habitat pond. The location and attributes of these priority habitat ponds does not currently exists for end users, ecologists, community groups and other stakeholders. The layer will be used to identify, conserve and enhance these features.OS Ponds (taken from the MasterMap Topography layer hydrology>static water) that have a matching pond survey (Clean Water for Wildlife of Priority Ponds) (see data within this folder for these layers) within their geometry, or within 30m of their edge. proximity was created (using NEAR tool) for points and then simplified (to remove 1 to many relationship) and joined to OS polygons using FID unique value. Unnecessary OS fields have been deleted.This data was created using data from Clean Water for Wildlife of Priority Ponds Clean Water for Wildlife - Freshwater Habitats Trust under a CC-BY licence with OS MasterMap data under the PSGA licence.It is published by Natural England under the Non-Commercial Government Licence due to the quantity of OS data contained within it.Full metadata can be viewed on data.gov.uk.

This modelled fluvial flood depth data was created for the 1% annual chance of flooding situations and was produced as a by-product from the 2004 generalised modelling project. The purpose of the generalised modelling project was to fill the gaps where there was no detailed local modelled data in 2004, in order to define the extents of Flood Zones for spatial planning.

A two-dimensional hydrodynamic model called JFlow was used to produce this modelled fluvial flood depth data on a 5x5m grid.

Since 2004, local detailed modelling has been used to replace this generalised modelling in many areas to define the extents of Flood Zones. However this depth dataset has not been updated.

INFORMATION WARNING: This data is not suitable for identifying whether an individual property will flood, for detailed decision making or for use in site specific Flood Risk or Strategic Flood Risk Assessments. Where this data is used for anything other than broad catchment or Shoreline Management Plan scale further evidence, verification and studies should be undertaken.

More recent, accurate and local detailed modelling depth data is available for many places. Please contact your local Environment Agency office to see if detailed modelling is available for your area of interest.

This metadata record is for Approval for Access product AfA238 Flood Zone Depth Grid Dataset 2004

Modelled fluvial flood depth data are available for the whole of England, however this data is for the 100x100km squared Ordnance Survey National Grid reference SK. If you are interested in data for another grid reference refer to the Ordnance Survey National Grid document linked below to find the relevant referencing code and search on Data.gov.uk again to download the data. https://www.ordnancesurvey.co.uk/docs/support/national-grid.pdf Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown Copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

This modelled fluvial flood depth data with climate change was created for the 1% annual chance of flooding situations and was produced as a by-product from the 2004 generalised modelling project. The purpose of the generalised modelling project was to fill the gaps where there was no detailed local modelled data in 2004, in order to define the extents of Flood Zones for spatial planning.

The purpose of this climate change data was to provide a high-level sensitivity analysis of the possible effects of climate change based on a 20% increase in peak flows in the fluvial modelling.

A two-dimensional hydrodynamic model called JFlow was used to produce this modelled fluvial flood depth data on a 5x5m grid.

Since 2004, some local detailed modelling projects have included scenarios for climate change however this climate change dataset has not been updated.

INFORMATION WARNING: This data is not suitable for identifying whether an individual property will flood due to climate change, for detailed decision making or for use in site specific Flood Risk or Strategic Flood Risk Assessments. Where this data is used further evidence, verification and studies should be undertaken. Climate change allowances have changed since this work was completed in 2004.

More recent, accurate and local detailed modelling depth data with climate change is available for some places. Please contact your local Environment Agency office to see if detailed modelling is available for your area of interest.

This metadata record is for Approval for Access product AfA480 2004 Climate Change 1 in 100 Fluvial Flood Depth Grids

Modelled fluvial flood depth data with climate change are available for the whole of England, however this data is for the 100x100km squared Ordnance Survey National Grid reference NZ. If you are interested in data for another grid reference refer to this Ordnance Survey National Grid document to find the relevant referencing code and search on Data.gov.uk again to download the data. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown Copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.