Looking for information on a construction project near you? Project Portal offers a comprehensive view of all current, funded, and planned projects occurring across the State of Maryland. You can quickly and easily access specific project information, including a general overview, interactive map, news, schedule, pictures and video, supporting documents, and upcoming public meetings. It’s easy to search by location for a specific project, or by county for a list of all projects in your jurisdiction.(MDOT SHA Project Portal Individual Project Page Web Map)MDOT SHA WebsiteContact Us

The overview map (UEK250MV) of Mecklenburg-Western Pomerania is a topographic map depicting the entire territory of the state of Mecklenburg-Western Pomerania on a scale of 1:250 000 on a map sheet. Localities and districts, traffic networks, highways and federal roads with ribbon, water network, relief, forest and borders to the county level are shown. In cooperation with the State Office for Road Construction and Transport Mecklenburg-Western Pomerania, the road map (SK250MV) is drawn up. The road classification is highlighted by different ribbons. In addition, the map contains the details of distances on motorways, federal and rural roads and the inscription of the roads with numbers up to the Kreisstraße. The administrative map (VK250MV) focuses on the representation of all administrative boundaries up to the municipal level. The monochrome administrative card (VKE250MV) has a map content reduced to the representation of the administrative boundaries and the lettering of the administrative units.

Looking for information on a construction project near you? Project Portal offers a comprehensive view of all current, funded, and planned projects occurring across the State of Maryland. You can quickly and easily access specific project information, including a general overview, interactive map, news, schedule, pictures and video, supporting documents, and upcoming public meetings. It’s easy to search by location for a specific project, or by county for a list of all projects in your jurisdiction.(MDOT SHA Project Portal Individual Project Page Web Map)MDOT SHA WebsiteContact Us

Overview maps (PDF format) showing the results of our aerial surveys are available for the region (Arizona & New Mexico) and for specific areas. Note that as of 2012 we have stopped producing many of the map products shown below as we switched to a national system. For access to data and the ability to generate maps, please visit the Insect & Disease Survey Explorer hosted by our national office through the Forest Health portal. Resources in this dataset:Resource Title: Insect & Disease Survey Maps and Data. File Name: Web Page, url: https://www.fs.usda.gov/wps/portal/fsinternet/cs/detail/!ut/p/z1/04_Sj9CPykssy0xPLMnMz0vMAfIjo8zijQwgwNHCwN_DI8zPyBcqYKAfjlVBmA9cQRQx-g1wAEci9eNREIXf-HD9KKxWIPuAkBkFuaGhEQaZjgCVqf1Y/dz/d5/L2dBISEvZ0FBIS9nQSEh/?position=Not Yet Determined.Html&pname=Region 3- Insects &navtype=BROWSEBYSUBJECT&ss=1103&pnavid=140000000000000&navid=140110000000000&ttype=detail&cid=stelprdb5228467 Regional Maps and GIS data (AZ and NM)

The data set contains information on the distance between the groundwater surface and the lower bound of the effective root space (We) (in dm) of the soils in Germany. The basis for the compilation of the data set is the land overview map on a scale of 1:200,000 (BÜK 200), which is available in a harmonised manner throughout Germany, provided by the BGR (2021); (https://www.bgr.bund.de/EN/topics/ground/information bases/soil_maps_databases/BUEK200/buek200_node.html). These are averages derived from the profiles of a BÜK polygon present in the BÜK200 for specific land use purposes. The data are not absolutely valid results, but are in the context of the methodological assumptions in the creation and processing of the source data. The soil characteristic value was derived on the basis of the soil mapping instructions (KA5; Ad hoc Working Group on Soil (2005): Soil mapping instructions (KA 5). Ad hoc working group soil of the geological state offices and the Federal Institute for Geosciences and Natural Resources of the Federal Republic of Germany, Hanover). A basic description of the methodological approach can be found in (publication of the final report). This parameter is currently used for the further development of water balance modelling with the model LARSIM (the BfG).

This vector web map features outline maps of the World. The maps can be used for coloring and other fun activities by budding cartographers. These outline maps are great for teaching children about our World. Have them color and label countries, regions and bodies of water. Limited labels appear on the map at large scales. After coloring the city maps, children can do further research to learn more about these places. These maps are also available in a printable PDF format. See this blog with more details on how to work with the vector maps in ArcGIS Pro.For other creatively designed Esri vector basemaps, see the ArcGIS Living Atlas of the World gallery.

With the map, a comprehensive floor overview map of the state of Brandenburg was created. The base map provides an overview of the country’s main soils. Despite the medium scale and the aggregation of content, it is usually more detailed than other countries’ soil maps.

Map that shows the current weather conditions for North and Central TX. The intended audience is the local partners across the NWS Fort Worth county warning area (CWA). This is an experimental service.

MSB has the Government’s mission to support municipalities and county administrative boards with overviews of soil stability in built-up areas where there are conditions for soil movement. The aim is to identify built-up areas that cannot be categorised as stable. The mapping shall constitute support in the municipality’s risk inventory and risk management and can be used as a basis for the municipality’s master plans. The intention is for the municipality itself to proceed and carry out detailed investigations in designated areas. Data Reports • Areas identified as having satisfactory stability. • Areas which cannot be classified as satisfactorily stable or insufficiently investigated. Due to the lack of buildings, detailed investigation is not recommended. • Areas which cannot be categorised as satisfactory stable or which are insufficiently investigated and detailed investigation is recommended. • Areas where detailed investigation is deemed particularly urgent. • Areas that have previously been classified as satisfactorily stable or which have been reinforced but have not complied with the instructions of the Commission. • Areas where review of previous investigations and stabilisation measures is considered particularly urgent. In addition, the data set includes files that appear in the data set that are displayed in the data set, filling, stability measures and stability zones. Map files in digital form are available for mapping carried out after 2001. The mapping has been carried out in limited areas that are built and where conditions for walking are deemed to exist. Over time, erosion and human activity can affect conditions. The overview stability mapping shows the prevailing stability conditions at the time of mapping. The content and reliability of the overview stability maps vary as the methods have been refined over the years. MSB does not make any updates to the material based on changed circumstances or conducted investigations or reinforcements.

GIS project files and imagery data required to complete the Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro tutorial. These data cover the area in and around Jezero crater, Mars.

Thematic maps with different soil content in different scales (1:10,000 to 1:500,000). 1. Geological Map of Mecklenburg-Western Pomerania — Soils in scale 2. 1:750.000 scale soils (from GDR Atlas) 3. Ground map of the GDR in scale 1:500.000 Additional information Data collection: analog, available as: Map, retrieval: analogous

Description of the INSPIRE Download Service (predefined Atom): Geological Map of Rhineland Palatinate 1:300.000 (GÜK 300) The GÜK 300 has been reassembled on the basis of published geological maps of different scales. It replaces the previous official map of Rhineland-Palatinate in scale 1: 500 000. The new map is based on the geological overview maps issued by the Federal Institute for Geosciences and Raw Materials in Hannover in cooperation with the State Geological Services of the Länder in scale 1: 200 000 (CC 5502 Köln, CC 5510 Siegen, CC 6302 Trier, CC 6310 Frankfurt a.M. West, CC 7102 Saarbrücken and CC 7110 Mannheim), the geological maps published by the State Office for Geology and Mining Rhineland-Palatinate in scale 1: 100 000, 1: 50 000 and 1: 25 000 as well as the geological (and vulcanological) maps of external agents. Based on scale, stratigraphic formations were merged into larger units and boundary lines were generalised. The area-wise very complex disorder pattern has been reduced (strongly generalised and) to the representation of significant disturbances. — The link(s) for downloading the records is/are generated dynamically from getFeature Requests to a WFS 1.1.0

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Coal Oil Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Coal Oil Point map area data layers. Data layers are symbolized as shown on the associated map sheets.

Work in progress: data might be changed

The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies.

Vehicle Detections

Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock.

The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/807618b6-5c38-4456-88a1-cb47500081ff/download/detection_map.png" alt="Overview map of detected vehicles" title="Overview map of detected vehicles"> Figure 1: Overview map of detected vehicles

Parking Areas

The public parking areas were digitized manually using aerial images and the detected vehicles in order to exclude irregular parking spaces as far as possible. They were also tagged as to whether they were aligned parallel to the road and assigned to a use at the time of recording, as some are used for construction sites or outdoor catering, for example. Depending on the intended use, they can be filtered individually.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/16b14c61-d1d6-4eda-891d-176bdd787bf5/download/parking_area_example.png" alt="Example parking area occupation pattern" title="Visualization of example parking areas on top of an aerial image [by LGLN]"> Figure 2: Visualization of example parking areas on top of an aerial image [by LGLN]

Parking Occupancy

For modelling the parking occupancy, single slots are sampled as center points every 5 m from the parking areas. In this way, they can be integrated into a street/routing graph, for example, as prepared in Wage et al. (2023). Own representations can be generated from the parking area and vehicle detections. Those parking points were intersected with the vehicle boxes to identify occupancy at the respective epochs.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/ca0b97c8-2542-479e-83d7-74adb2fc47c0/download/datenpub-bays.png" alt="Overview map of parking slots' average load" title="Overview map of parking slots' average load"> Figure 3: Overview map of average parking lot load

However, unoccupied spaces cannot be determined quite as trivially the other way around, since no detected vehicle can result just as from no measurement/observation. Therefore, a parking space is only recorded as unoccupied if a vehicle was detected at the same time in the neighborhood on the same parking lane and therefore it can be assumed that there is a measurement.

To close temporal gaps, interpolations were made by hour for each parking slot, assuming that between two consecutive observations with an occupancy the space was also occupied in between - or if both times free also free in between. If there was a change, this is indicated by a proportional value. To close spatial gaps, unobserved spaces in the area are drawn randomly from the ten closest occupation patterns around.

This results in an exemplary occupancy pattern of a synthetic day. Depending on the application, the value could be interpreted as occupancy probability or occupancy share.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/184a1f75-79ab-4d0e-bb1b-8ed170678280/download/occupation_example.png" alt="Example parking area occupation pattern" title="Example parking area occupation pattern"> Figure 4: Example parking area occupation pattern

References

• F. Bock, D. Eggert and M. Sester (2015): On-street Parking Statistics Using LiDAR Mobile Mapping, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 2812-2818. https://doi.org/10.1109/ITSC.2015.452
• A. Leichter, U. Feuerhake, and M. Sester (2021): Determination of Parking Space and its Concurrent Usage Over Time Using Semantically Segmented Mobile Mapping Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
• O. Wage, M. Heumann, and L. Bienzeisler (2023): Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios. In 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (SuMob ’23), November 13, 2023, Hamburg, Germany. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3615899.3627930

👨‍💻 Author: Azhar Saleem

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Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

Mapping incident locations from a CSV file in a web map (YouTube video).

Overview

This dataset contains all DOMI Street Closure Permit data in the Computronix (CX) system from the date of its adoption (in May 2020) until the present. The data in each record can be used to determine when street closures are occurring, who is requesting these closures, why the closure is being requested, and for mapping the closures themselves. It is updated hourly (as of March 2024).

Preprocessing/Formatting

It is important to distinguish between a permit, a permit's street closure(s), and the roadway segments that are referenced to that closure(s).

• The CX system identifies a street in segments of roadway. (As an example, the CX system could divide Maple Street into multiple segments.)

• A single street closure may span multiple segments of a street.

• The street closure permit refers to all the component line segments.

• A permit may have multiple streets which are closed. Street closure permits often reference many segments of roadway.

The roadway_id field is a unique GIS line segment representing the aforementioned segments of road. The roadway_id values are assigned internally by the CX system and are unlikely to be known by the permit applicant. A section of roadway may have multiple permits issued over its lifespan. Therefore, a given roadway_id value may appear in multiple permits.

The field closure_id represents a unique ID for each closure, and permit_id uniquely identifies each permit. This is in contrast to the aforementioned roadway_id field which, again, is a unique ID only for the roadway segments.

City teams that use this data requested that each segment of each street closure permit be represented as a unique row in the dataset. Thus, a street closure permit that refers to three segments of roadway would be represented as three rows in the table. Aside from the roadway_id field, most other data from that permit pertains equally to those three rows. Thus, the values in most fields of the three records are identical.

Each row has the fields segment_num and total_segments which detail the relationship of each record, and its corresponding permit, according to street segment. The above example produced three records for a single permit. In this case, total_segments would equal 3 for each record. Each of those records would have a unique value between 1 and 3.

The geometry field consists of string values of lat/long coordinates, which can be used to map the street segments.

All string text (most fields) were converted to UPPERCASE data. Most of the data are manually entered and often contain non-uniform formatting. While several solutions for cleaning the data exist, text were transformed to UPPERCASE to provide some degree of regularization. Beyond that, it is recommended that the user carefully think through cleaning any unstructured data, as there are many nuances to consider. Future improvements to this ETL pipeline may approach this problem with a more sophisticated technique.

Known Uses

These data are used by DOMI to track the status of street closures (and associated permits).

Further Documentation and Resources

An archived dataset containing historical street closure records (from before May of 2020) for the City of Pittsburgh may be found here: https://data.wprdc.org/dataset/right-of-way-permits

The Digital Geologic Map of the U.S. Geological Survey Mapping in the Western Portion of Amistad National Recreation Area, Texas is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Eddie Collins, Amanda Masterson and Tom Tremblay (Texas Bureau of Economic Geology); Rick Page (U.S. Geological Survey); Gilbert Anaya (International Boundary and Water Commission). Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (wpam_metadata.txt; available at http://nrdata.nps.gov/amis/nrdata/geology/gis/wpam_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (wpam_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 14N. The data is within the area of interest of Amistad National Recreation Area.

Daily maps to show the number of confirmed Foot and Mouth Disease (FMD) cases and Form A (Suspected FMD premises) within each infected area for GB.

Daily maps to show the number of confirmed Foot and Mouth Disease (FMD) cases and Form A (Suspected FMD premises) within each infected area for GB. Attribution statement: ©Crown Copyright, APHA 2016