Managing field operations can be a pretty daunting task. Not only do you have to manage workers in the field, coordinate schedules, keep track of equipment, and respond to unplanned events, you also need to maintain level of service commitments and do so in compliance with health and safety regulations.Organizations are moving away from traditional paper and pen approaches and embracing apps deployed to their smartphones and tablets. Esri provides a suite of apps designed for field operations._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Asset management is key to successful mobile operations, and the use of Geographic Information Systems (GIS) can ensure that you can achieve this simply and efficiently.

An ArcGIS Field Maps map used by public works and mosquito control personnel to address mosquito activity reports submitted by the general public.

The Geospatial Analytics Market size was valued at USD 98.93 billion in 2023 and is projected to reach USD 227.04 billion by 2032, exhibiting a CAGR of 12.6 % during the forecasts period. The Geospatial Analytics Market describes an application of technologies and approaches processing geographic and spatial data for intelligence and decision-making purposes. This market comprises of mapping tools and software, spatial data and geographic information systems (GIS) used in various fields including urban planning, environmental, transport and defence. Use varies from inventory tracking and control to route optimization and assessment of changes in environment. Other trends are the growth of big data and machine learning to improve the predictive methods, the improved real-time data processing the use of geographic data in combination with other technologies, for example, IoT and cloud. Some of the factors that are fuelling the need to find a marketplace for GIS solutions include; Increasing importance of place-specific information Increasing possibilities for data collection The need to properly manage spatial information in a high stand environment. Recent developments include: In May 2023, Google launched Google Geospatial Creator, a powerful tool that allows users to create immersive AR experiences that are both accurate and visually stunning. It is powered by Photorealistic 3D Tiles and ARCore from Google Maps Platform and can be used with Unity or Adobe Aero. Geospatial Creator provides a 3D view of the world, allowing users to place their digital content in the real world, similar to Google Earth and Google Street View. , In April 2023, Hexagon AB launched the HxGN AgrOn Control Room. It is a mobile app that allows managers and directors of agricultural companies to monitor all field operations in real time. It helps managers identify and address problems quickly, saving time and money. Additionally, the app can help to improve safety by providing managers with a way to monitor the location and status of field workers. , In December 2022, ESRI India announced the availability of Indo ArcGIS offerings on Indian public clouds and services to provide better management, collecting, forecasting, and analyzing location-based data. , In May 2022, Trimble announced the launch of the Trimble R12i GNSS receiver, which has a powerful tilt adjustment feature. It enables land surveyors to concentrate on the task and finish it more quickly and precisely. , In May 2021, Foursquare purchased Unfolded, a US-based provider of location-based services. This US-based firm provides location-based services and goods, including data enrichment analytics and geographic data visualization. With this acquisition, Foursquare aims to provide its users access to various first and third-party data sets and integrate them with the geographical characteristics. , In January 2021, ESRI, a U.S.-based geospatial image analytics solutions provider, introduced the ArcGIS platform. ArcGIS Platform by ESRI operates on a cloud consumption paradigm. App developers generally use this technology to figure out how to include location capabilities in their apps, business operations, and goods. It aids in making geospatial technologies accessible. .

Submarine groundwater discharge (SGD) into Maryland's Corsica River Estuary was investigated as part of a larger study to determine the importance of nutrient delivery to Chesapeake Bay via this pathway. Resource managers are concerned about nutrients that are entering the estuary via submarine groundwater discharge from this primarily agricultural watershed that may be contributing to eutrophication, harmful algal blooms, and fish kills. An interdisciplinary U.S. Geological Survey (USGS) science team conducted field operations in the estuary in April and May 2007. Techniques used included continuous resistivity profiling (CRP), piezometer sampling, seepage meter measurements, and collection of a radon tracer time series. Better understanding of the style, locations, and rates of groundwater discharge could lead to improved models and mitigation strategies for estuarine nutrient over-enrichment in the Corsica River Estuary, and other similar settings. More information on the field work can be accessed from the Woods Hole Coastal and Marine Science Center Field Activity webpage: https://cmgds.marine.usgs.gov/fan_info.php?fan=2007-005-FA.

The global field data collection software market is experiencing robust growth, driven by increasing adoption across diverse sectors like construction, oil & gas, and environmental management. The market's expansion is fueled by several key factors. Firstly, the rising need for real-time data capture and analysis to enhance operational efficiency and decision-making is a major catalyst. Secondly, advancements in mobile technology and cloud computing are enabling the development of more sophisticated and user-friendly field data collection applications. Thirdly, the growing demand for improved safety and compliance across various industries is boosting the adoption of these solutions to manage safety protocols and track regulatory compliance. The market is segmented by application (environmental, construction, oil & gas, transportation, mining, others) and deployment type (cloud-based, on-premises). Cloud-based solutions are witnessing faster growth due to their scalability, accessibility, and cost-effectiveness. While North America currently holds a significant market share, regions like Asia-Pacific are showing promising growth potential driven by rapid industrialization and infrastructure development. Competition is intense, with established players like SafetyCulture and ArcGIS vying for market share alongside emerging technology providers. However, challenges remain, including data security concerns, integration complexities with existing systems, and the need for robust training and support to ensure widespread user adoption. The projected Compound Annual Growth Rate (CAGR) suggests a significant expansion of the market over the forecast period (2025-2033). While precise figures are unavailable, based on industry analysis and considering a conservative estimate, the market size in 2025 is likely around $5 billion, reaching approximately $8 billion by 2033. This growth trajectory is fueled by the increasing digitization of field operations, rising demand for data-driven insights, and the ongoing development of innovative features in field data collection software, including advanced analytics, AI-powered automation, and improved integration with other enterprise systems. The on-premises segment is expected to experience steady growth, although cloud-based deployments will likely dominate the market share in the long term due to their inherent flexibility and scalability advantages. The continued expansion of the market across various geographic regions points towards a bright future for this technology.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows: Green (fast): 85 - 100% of free flow speeds Yellow (moderate): 65 - 85% Orange (slow); 45 - 65% Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.The color-coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation, and field operations. A color-coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes.The map also includes dynamic traffic incidents showing the location of accidents, construction, closures, and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis.Data sourceEsri’s typical speed records and live and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds.Data coverageThe service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.

IMPORTANT NOTICE This item has moved to a new organization and will enter Mature Support on May 7th, 2025. This item is scheduled to be Retired and removed from ArcGIS Online on July 7th, 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here. This dataset provides information related to the principal producing oil and gas fields operating in Canada during the given reference year. The dataset is maintained by the National Energy Board.See Principal Mineral Areas, Producing Mines, and Oil and Gas Fields (900A) for additional resources, formats, services, and contact information.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The delineation of agricultural field boundaries has a wide range of applications, such as for crop management, precision agriculture, land use planning and crop insurance, etc. Manually digitizing agricultural fields from imagery is labor-intensive and time-consuming. This deep learning model automates the process of extracting agricultural field boundaries from satellite imagery, thereby significantly reducing the time and effort required. Its ability to adapt to varying crop types, geographical regions, and imaging conditions makes it suitable for large-scale operations.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputSentinel-2 L2A 12-bands multispectral imagery using Bottom of Atmosphere (BOA) reflectance product in the form of a raster, mosaic or image service.OutputFeature class containing delineated agricultural fields.Applicable geographiesThe model is expected to work well in agricultural regions of USA.Model architectureThis model uses the Mask R-CNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.64 for fields.Training dataThis model has been trained on an Esri proprietary agricultural field delineation dataset.LimitationsThis model works well only in areas having farmlands and may not give satisfactory results in areas near water bodies and hilly regions. The results of this pretrained model cannot be guaranteed against any other variation of the Sentinel-2 data.Sample resultsHere are a few results from the model.

This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B08303Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

HSIP Local Emergency Operations Centers in the United States "The physical location at which the coordination of information and resources to support domestic incident management activities normally takes place. An Emergency Operations Center may be a temporary facility or may be located in a more central or permanently established facility, perhaps at a higher level of organization within a jurisdiction. Emergency Operations Centers may be organized by major functional disciplines (e.g., fire, law enforcement, and medical services), by jurisdiction (e.g., Federal, State, regional, county, city, tribal), or some combination thereof." (Excerpted from the National Incident Management System) The GFI source for this layer contains State and Federal Emergency Operations Centers in addition to local Emergency Operations Centers. This dataset contains these features as well. In cases where an Emergency Operations Center has a mobile unit, TechniGraphics captured the location of the mobile unit as a separate record. This record represents where the mobile unit is stored. If this location could not be verified, a point was placed in the approximate center of the Emergency Operations Centers service area. Effort was made by TechniGraphics to verify whether or not each Emergency Operations Center has a generator on-site and whether or not the Emergency Operations Center is located in a basement. This information is indicated by the values in the [GENERATOR] and [BASEMENT] fields respectively. In cases where more than one record existed for a geographical area (e.g., county, city), TechniGraphics verified whether or not one of the records represented an alternate location. This was indicated by appending "-ALTERNATE" to the value in the [NAME] field. Some Emergency Operations Centers are located at private residences. The [TYPE] field was manually evaluated during the delivery process to compare the records in which the [NAME] field contained "-ALTERNATE". In cases where these values contradicted information that was verified by TechniGraphics (e.g. [NAME] contained "-ALTERNATE" and [TYPE] = "PRIMARY"), the value in the [TYPE] field was changed to match the type indicated by the [NAME] of the verified record. TechniGraphics did not change values in this field if the type was not verified. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields that TechniGraphics populated. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this attribute, the oldest record dates from 08/28/2009 and the newest record dates from 11/18/2009.

These data were collected under a cooperative agreement between the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center (WHCMSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of seafloor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters of coastal Massachusetts, primarily in water depths of 2-30 meters. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (http://woodshole.er.usgs.gov/project-pages/coastal_mass/html/current_map.html). The data collected in this study area are located in both Buzzards Bay and Vineyard Sound and are primarily in the shallow water areas around the eastern Elizabeth Islands and Martha's Vineyard, Massachusetts. The data include high resolution bathymetry, acoustic-backscatter intensity, sound velocity in water, seismic-reflection profiles, and navigation data. These data were collected during several cruises between 2007 and 2011 onboard the R/V Rafael using the following equipment: an SEA Ltd SwathPlus interferometric sonar (234 kHz), Klein 3000 dual frequency sidescan sonar, a boomer source and Geometrics 8-channel GeoEel streamer, a Knudsen 3200 subbottom profiling system, and 4 GPS antennae. More information about the cruises conducted as part of the project: Geologic Mapping of the Seafloor Offshore of Massachusetts can be found on the Woods Hole Coastal and Marine Science Center Field Activity webpages: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2011-013-FA http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2009-068-FA http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2007-039-FAhttp://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2010-100-FAhttp://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2010-047-FA

Water quality in the Barnegat Bay-Little Egg Harbor estuary along the New Jersey coast is the focus of a multidisciplinary research project begun in 2011 by the U.S. Geological Survey (USGS) in partnership with the New Jersey Department of Environmental Protection. This narrow estuary is the drainage for the Barnegat Watershed and flushed by just three inlets connecting it to the Atlantic Ocean, is experiencing degraded water quality, algal blooms, loss of seagrass, and increases in oxygen -depletion events, seaweed, stinging nettles, and brown tide. The scale of the estuary and the scope of the problems within it necessitate a multidisciplinary approach that includes characterizing its physical characteristics (for example, depth, magnitude and direction of tidal currents, distribution of seafloor and subseafloor sediment) and modeling how the physical characteristics interact to affect the estuary's water quality. Scientists from USGS Coastal and Marine Geology Program offices in Woods Hole, Massachusetts, and St. Petersburg, Florida, began mapping the seafloor of the Barnegat Bay-Little Egg Harbor estuary in November 2011 and completed in September 2013. With funding from the New Jersey Department of Environmental Protection and logistical support from the USGS New Jersey Water Science Center, they collected data with a suite of geophysical tools, including swath bathymetric sonar for measuring seafloor depth, a sidescan sonar for collecting acoustic-backscatter data (which provides information about seafloor texture and sediment type), subbottom profiler for imaging sediment layers beneath the floor of the estuary, and sediment samples with bottom photographs for ground validation of the acoustic data. 2011-041-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2011-041-FA 2012-003-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2012-003-FA 2013-014-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2013-014-FA 2013-030-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2013-030-FA

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:As a service that is subject to Viral loads (very high usage), avoid adding Filters that use a Date/Time type field. These queries are not cacheable and WILL be subject to Rate Limiting by ArcGIS Online. To accommodate filtering events by Date/Time, we encourage using the included "Age" fields that maintain the number of Days or Hours since a record was created or last modified compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be supplied to many users without adding load on the service.When ingesting this service in your applications, avoid using POST requests, these requests are not cacheable and will also be subject to Rate Limiting measures.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident ComplexArea Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes. Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

These data were collected in a collaboration between the Woods Hole Oceanographic Institution and the U.S. Geological Survey (USGS). The primary objective of this program was to collect baseline bathymetry for Muskeget Channel, Massachusetts, and identify areas of morphologic change within and around the channel. Repeat surveys in select areas were collected one month apart to monitor change. These data were collected to support an assessment of the effect on sediment transport that a tidal instream energy conversion facility would have within Muskeget Channel. Accurate data and maps of sea-floor topography are important first steps in monitoring bedform migration, fish habitat, marine resources, and environmental changes due to natural or human impacts. The data include high-resolution bathymetry, acoustic-backscatter intensity, sound velocity in water, and navigation data. These data were collected during two surveys between October 2010 and November 2011 onboard the research vessel (RV) Rafael using an SEA Ltd. SwathPlus interferometric sonar (234 kilohertz). More information about the cruise can be found on the Woods Hole Coastal and Marine Science Center field activity Web page at http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2010-072-FA

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA), is producing detailed geologic maps of the coastal sea floor. Bathymetric and sidescan-sonar data, originally collected by NOAA for charting purposes, provide a fundamental framework for research and management activities along this part of Block Island Sound, show the composition and terrain of the seabed, and provide information on sediment transport and benthic habitat. During June 2012, bottom photographs and surficial sediment data were acquired as part of a ground-truth reconnaissance survey of this area. Interpretations were derived from the multibeam-echosounder, sidescan-sonar, sedimentary, and photographic data collected in Block Island Sound. For more information on the ground-truth survey see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2012-002-FA.

An ArcGIS Workforce project used by addressing staff to manage address field operations.