The digital map market is estimated to capture a valuation of US$ 18.3 billion in 2023 and is projected to reach US$ 73.1 billion by 2033. The market is estimated to secure a CAGR of 14.8% from 2023 to 2033.

Attributes	Details
Market CAGR (2023 to 2033)	14.8%
Market Valuation (2023)	US$ 18.3 billion
Market Valuation (2033)	US$ 73.1 billion

How are the Various Regions Affecting the Growth of Digital Map in the Market?

Countries	Current Market Share 2023
United States	16.5%
Germany	9.1%
Japan	7.1%
Australia	3.5%

Countries	Current Market CAGR 2023
China	16.7%
India	18.7%
United Kingdom	15.4%

Scope of Report

Attributes	Details
Forecast Period	2023 to 2033
Historical Data Available for	2018 to 2022
Market Analysis	US$ billion for Value
Key Countries Covered	United States, United Kingdom, Japan, India, China, Australia, Germany
Key Segments Covered	Type Application Region
Key Companies Profiled	TomTom NV HERE Technologies Apple Inc. Alibaba Group Navinfo Co., Ltd INRIX Inc. Baidu, Inc. MapBox Inc. Environmental Systems Research Institute (ESRI) Alphabet Inc.
Report Coverage	Market Forecast, Company Share Analysis, Competition Intelligence, DROT Analysis, Market Dynamics and Challenges, and Strategic Growth Initiatives
Customization & Pricing	Available upon Request

ZIP Code business counts data for Maptitude mapping software are from Caliper Corporation and contain aggregated ZIP Code Business Patterns (ZBP) data and Rural-Urban Commuting Area (RUCA) data.

Business location data for Maptitude mapping software are from Caliper Corporation and contain point locations for businesses.

The National Mine Map Repository (NMMR) maintains point locations for mines appearing on maps within its archive. This dataset is intended to help connect the Office of Surface Mining Reclamation and Enforcement, other federal, state, and local government agencies, private industry, and the general public with archived mine maps in the NMMR's collection. The coordinates for mine point locations represent the best information the NMMR has for the location of the mine. As much as possible, the NMMR strives to find precise locations for all historic mines appearing on mine maps. When this is not possible, another feature as close to the mine as is known is used. This information is reflected in the mine point symbols. However, the NMMR cannot guarantee the accuracy of mine point locations or any other information on or derived from mine maps. The NMMR is part of the United States Department of the Interior, Office of Surface Mining Reclamation and Enforcement (OSMRE). The mission of the NMMR is to preserve abandoned mine maps, to correlate those maps to the surface topography, and to provide the public with quality map products and services. It serves as a point of reference for maps and other information on surface and underground coal, metal, and non-metal mines from throughout the United States. It also serves as a location to retrieve mine maps in an emergency. Some of the information that can be found in the repository includes: Mine and company names, Mine plans including mains, rooms, and pillars, Man-ways, shafts, and mine surface openings. Geological information such as coal bed names, bed thicknesses, bed depths and elevations, bed outcrops, drill-hole data, cross-sections, stratigraphic columns, and mineral assays. Geographical information including historic railroad lines, roads, coal towns, surface facilities and structures, ponds, streams, and property survey lines, gas well and drill-hole locations. Please note: Map images are not available for download from this dataset. They can be requested by contacting NMMR staff and providing them with the desired Document Numbers. NMMR staff also have additional search capabilities and can fulfill more complex requests if necessary. See the NMMR website homepage for contact information: https://www.osmre.gov/programs/national-mine-map-repository. There is no charge for noncommercial use of the maps. Commercial uses will incur a $46/hour research fee for fulfilling requests.

This layer contains data on the number of establishments, total employment, and total annual payroll for for 20 selected 4- and 5-digit North American Industry Classification System (NAICS) codes. This is shown by county and state boundaries. The full CBP data set (available at census.gov) is updated annually to contain the most currently released CBP data. This layer is symbolized to show the total number of establishments depicted by size, and the average annual pay per employee, depicted by color.

Current Vintage: 2017

CBP Table: CB1700CBP

Data downloaded from: Census Bureau's API for County Business Patterns

Date of API call: June 1, 2019

The United States Census Bureau's County Business Patterns Program (CBP):

About this Program Data Technical Documentation News & Updates

This 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. Please cite the Census Bureau and CBP when using this data.

Data Processing Notes: Boundaries come from the US Census Bureau 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 Bureau. These are Census Bureau 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 56 records - all US states, Washington D.C., Puerto Rico, and U.S. Island Areas Blank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.

The widespread influence of land use and natural disturbance on population, community, and landscape dynamics and the long-term legacy of disturbance on modern ecosystems requires that a historical, broad-scale perspective become an integral part of modern ecological studies and conservation assessment and planning. In previous studies, the Harvard Forest Long Term Ecological Research (LTER) program has developed an integrated approach of paleoecological and historical reconstruction, meteorological modeling, air photo interpretation, GIS analyses, and field studies of vegetation and soils, to address fundamental ecological questions concerning the rates, direction, and causes of vegetation change, to evaluate controls over modern species and community distributions and landscape patterns, and to provide critical background for conservation and restoration planning. In the current study, we extend this approach to investigate the link between landscape history and the abundance, distribution, and dynamics of species, communities and landscapes of the Cape Cod to Long Island coastal region, including the islands of Martha's Vineyard, Nantucket, and Block Island. The study region includes many areas of high conservation priority that are linked geographically, historically, and ecologically. This data package includes GIS layers digitized by Harvard Forest researchers from copies of the US Coastal Survey “T-Sheet” maps available from the National Archives in College Park, Maryland. The US Coastal Survey, and then the US Coast and Geodetic Survey mapped the region, or specific parts of it, several times between 1832 and the 1960s. In this project we digitized the earliest T-Sheet available for each location. The original maps were surveyed between 1832 and 1886, with most of them made between 1835 to 1855. The original maps showed features such as roads, farm walls, railroads, buildings, some industrial buildings, saltworks, wharfs, and land cover including woodlands, sandplains, grasslands, open agricultural fields, cultivated areas, fruit tree orchards, wetlands, etc. Many sheets had symbols which differentiated conifer trees from hardwoods. There were some inconsistencies in what features were mapped or how they were drawn between the original T-Sheets. Since we digitized the maps over the course of several different research projects, we did not always digitize all of the same features in each geographic area, therefore users of this data are encouraged to look at scans of the original T-Sheets for their specific areas of interest (links below). We always digitized land cover and roads and occasionally buildings and fences as mentioned in the datasets below.

Market Overview:

United States ammonia market size is projected to exhibit a growth rate (CAGR) of 2.18% during 2025-2033. The growing utilization in the agriculture sector as a key ingredient in fertilizers, rising development in logistics, including improved tank designs and safety protocols, and increasing adoption of precision agriculture techniques represent some of the key factors driving the market.

Report Attribute	Key Statistics
Base Year	2024
Forecast Years	2025-2033
Historical Years	2019-2024
Market Growth Rate (2025-2033)	2.18%

Ammonia is a colorless gas with a characteristic pungent, produced through the Haber-Bosch process, which synthesizes ammonia from nitrogen and hydrogen gases under high pressure and temperature in the presence of a catalyst. It comes in various forms, including anhydrous ammonia, aqueous ammonia, and ammonium salts, each offering different characteristics. It is a key component in fertilizers, providing a rich source of nitrogen, which is an essential nutrient for plant growth. It serves as a fundamental raw material in the production of various chemicals, including nitric acid, urea, and other nitrogenous fertilizers. It is a potent cleaning agent due to its ability to cut through grease and grime effectively. It is integral in the production of plastics, synthetic fibers, and other materials essential in everyday products.

United States Ammonia Market Trends:

At present, rising innovations in catalysis and process efficiency for enhancing the Haber-Bosch method, reducing energy consumption and operational costs represent one of the key factors impelling the market growth in the United States. Moreover, the growing adoption of green ammonia production methods, using renewable energy sources like solar, wind, and hydroelectric power, is positively influencing the market. These advancements not only improve the sustainability of ammonia production but also align with global efforts to reduce carbon emissions. Additionally, the increasing utilization of ammonia in the agriculture sector as a key ingredient in fertilizers for enhancing crop yields is bolstering the market growth. The US, with its vast agricultural lands, experiences a high demand for ammonia-based fertilizers to meet both domestic and global food requirements. Besides this, the rising development in logistics for facilitating the safe handling of ammonia is supporting the market growth in the country. In addition, there is a growing adoption of precision agriculture techniques, which involves the use of advanced technologies, such as satellite imagery, global positioning system (GPS), and internet of things (IoT) sensors to optimize farming practices, including ammonia- based fertilizer application. This shift towards more precise and efficient use of ammonia-based fertilizers is helping to enhance crop yields and reduce environmental impacts in the country. Furthermore, the increasing integration of digital technologies, such as machine learning (ML) and artificial intelligence (AI), in ammonia production to optimize production processes, enhance supply chain management, and predict market trends is contributing to the market growth. This digital transformation allows for more efficient operations, reduced waste, and better decision-making based on real-time data. Apart from this, the rising employment of ammonia in the textile industry for the treatment and dyeing of fibers and fabrics and helping in processes like mercerization that strengthens and gives luster to cotton fabrics is propelling the market growth.

United States Ammonia Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on physical form, application, and end use industry.

Physical Form Insights:

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• Liquid
• Powder
• Gas
  

The report has provided a detailed breakup and analysis of the market based on the physical form. This includes liquid, powder, and gas.

Application Insights:

• MAP and DAP
• Urea
• Nitric Acid
• Ammonium Sulfate
• Ammonium Nitrate
• Others
  

A detailed breakup and analysis of the market based on the application have also been provided in the report. This includes MAP and DAP, urea, nitric acid, ammonium sulfate, ammonium nitrate, and others.

End Use Industry Insights:

• Agrochemical
• Industrial Chemical
• Mining
• Pharmaceutical
• Textiles
• Others
  

The report has provided a detailed breakup and analysis of the market based on the end use industry. This includes agrochemical, industrial chemical, mining, pharmaceutical, textiles, and others.

Regional Insights:

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• Northeast
• Midwest
• South
• West
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northeast, Midwest, South, and West.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

United States Ammonia Market Report Coverage:

Report Features	Details
Base Year of the Analysis	2024
Historical Period	2019-2024
Forecast Period	2025-2033
Units	US$ Million
Scope of the Report	Exploration of Historical and Forecast Trends, Industry Catalysts and Challenges, Segment-Wise Historical and Predictive Market Assessment: Physical Form Application End Use Industry Region
Physical Forms Covered	Liquid, Powder, Gas
Applications Covered	MAP and DAP, Urea, Nitric Acid, Ammonium Sulfate, Ammonium Nitrate, Others
End Use Industries Covered	Agrochemical, Industrial Chemical, Mining, Pharmaceutical, Textiles, Others
Regions Covered	Northeast, Midwest, South, West
Customization Scope	10% Free Customization
Report Price and Purchase Option	Single User License: US$ 3699 Five User License: US$ 4699 Corporate License: US$ 5699
Post-Sale Analyst

We used Landsat satellite imagery and forest inventory plot measurements to develop a time series of annual maps representing potential forest harvest events for the state of Maine in the Northeastern US for the years 1986 to 2019. We first generated a set of LandTrendr temporal segmentation results for three different spectral indices. Change results were filtered to remove events greater than two years in duration, then results were combined using a seven-parameter degenerate decision trees model that determined a set of thresholds on disturbance patch size, magnitude of spectral change, and change “votes” across indices. We found that we were able to detect harvest events that removed at least 30% of total basal area with a mean F1 score of 0.72 (σ = 0.02) with a mean false negative error rate (omission) of 0.32 (σ = 0.02) and mean false positive error rate (commission) of 0.23 (σ = 0.03), and these scores further improve when maps are masked to remove human land use (built and agriculture) and water based on National Land Cover Dataset and JRC Global Surface Water classifications (mean F1 = 0.73, σ = 0.02). Comparisons with an out-of-sample reference dataset and an existing national forest disturbance dataset indicate our forest harvest maps are a locally accurate source of information for characterizing spatial and temporal variability in long-term harvest patterns across the industrial forests of northern Maine. Here, we provide annual ensemble-based maps of potential harvest events; cross-validated results, which give an indication of detection agreement across subsets of our forest inventory reference datasets; and ancillary datasets that can be used to mask false detections in urban and agricultural land uses and water.

This layer contains data on the number of establishments, total employment, and total annual payroll for for 20 selected 4- and 5-digit North American Industry Classification System (NAICS) codes. This is shown by county and state boundaries. The full CBP data set (available at census.gov) is updated annually to contain the most currently released CBP data. This layer is symbolized to show the total number of establishments depicted by size, and the average annual pay per employee, depicted by color.

Current Vintage: 2017

CBP Table: CB1700CBP

Data downloaded from: Census Bureau's API for County Business Patterns

Date of API call: June 1, 2019

The United States Census Bureau's County Business Patterns Program (CBP):

About this Program Data Technical Documentation News & Updates

This 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. Please cite the Census Bureau and CBP when using this data.

Data Processing Notes: Boundaries come from the US Census Bureau 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 Bureau. These are Census Bureau 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 56 records - all US states, Washington D.C., Puerto Rico, and U.S. Island Areas Blank values represent industries where there either were no businesses in that industry and that geography OR industries where the data had to be withheld to avoid disclosing data for individual companies. Users should visit data.census.gov or Census Business Builder for more details on these withheld records.

Snapshot img

Photogrammetry Software Market Size 2024-2028

The photogrammetry software market size is forecast to increase by USD 1.16 billion at a CAGR of 14.3% between 2023 and 2028.

The market is experiencing significant growth due to the increasing adoption of 3D mapping and modeling in various industries, particularly in building and construction. This technology enables the creation of geo-referenced maps and orthomosaic images from drone images, which are essential for 3D visualizations and 3D reconstruction. Additionally, the use of 3D scanning and computer visualization in applications such as 3D modeling and 3D printing of models for drones and quadcopters is driving market growth. However, challenges persist, including the inadequate infrastructure in developing and underdeveloped countries, which hampers the market's expansion. Key software solutions in this market include VisualSFM and OpenMVG, which offer advanced features for processing Images and Video to generate 3D models.

What will be the Size of the Market During the Forecast Period?

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Photogrammetry software has emerged as a critical tool in various industries, particularly in defense and security, engineering, architecture, and surveyor sectors. This software utilizes advanced imaging technologies, such as high-resolution cameras, Lidar, and drones, to capture data in the form of a series of photographs. These images are then processed using artificial intelligence (AI) and machine learning algorithms to generate 3D models in real time. Data collection technologies have significantly evolved in recent years, with the integration of AI and machine learning enabling faster and more accurate processing of large datasets.

Moreover, the AI-driven photogrammetry software uses pixels and reference points from the images to create 3D meshes, which are essential for various applications, including emergency management and object recognition in space. The market is segmented into cloud-based Software as a Service (SaaS) and on-premises commercial/proprietary software. The SaaS model offers benefits such as cost savings, flexibility, and scalability, while on-premises software provides greater control and security. The use of AI-driven photogrammetry software is not limited to specific industries. It is widely adopted by surveyors, architects, engineers, and contractors to streamline their workflows and improve accuracy. For instance, Autodesk REMake, an AI-driven photogrammetry software, enables users to create 3D models from images, which can be used for various applications, including architectural design and construction planning.

Similarly, the geospatial technology plays a crucial role in the effective implementation of photogrammetry software. Real-time data processing and analysis are essential for various applications, including emergency management and infrastructure monitoring. The integration of AI and machine learning algorithms in photogrammetry software enables faster and more accurate processing of geospatial data, making it an indispensable tool for various industries. In conclusion, the advancement of imaging technologies, AI, and machine learning algorithms has significantly impacted the market. The software's ability to generate 3D models from a series of photographs in real-time makes it an essential tool for various industries, including defense and security, engineering, architecture, and surveyor sectors. The integration of geospatial technology further enhances the software's capabilities, making it an indispensable tool for data-driven decision-making.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  3D printing
  Drones and robots
  Films and games
  Others


Deployment

  On-premises
  Cloud


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  South America



  Middle East and Africa

By Application Insights

The 3D printing segment is estimated to witness significant growth during the forecast period. In the realm of advanced imaging technologies, the market is witnessing significant growth. This expansion is driven by various sectors, including Defense and Security, where high-precision 3D models are essential for mission planning and analysis. Artificial Intelligence (AI) and Machine Learning (ML) are also playing a pivotal role in the market's growth, enabling real-time data processing and analysis. Data collection technologies, such as LiDAR and high-resolution cameras, integrated with drones, are revolutionizing the way data is captured and processed. Geospatial technology, a critical component of photogrammetry, is enabling the creation

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

Land cover describes the surface of the earth. This time-enabled service of the National Land Cover Database groups land cover into 20 classes based on a modified Anderson Level II classification system. Classes include vegetation type, development density, and agricultural use. Areas of water, ice and snow and barren lands are also identified.The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.Time Extent: 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021 for the conterminous United States. The layer displays land cover for Alaska for the years 2001, 2011, and 2016. For Puerto Rico there is only data for 2001. For Hawaii, Esri reclassed land cover data from NOAA Office for Coastal Management, C-CAP into NLCD codes. These reclassed C-CAP data were available for Hawaii for the years 2001, 2005, and 2011. Hawaii C-CAP land cover in its original form can be used in your maps by adding the Hawaii CCAP Land Cover layer directly from the Living Atlas.Units: (Thematic dataset)Cell Size: 30m Source Type: Thematic Pixel Type: Unsigned 8 bitData Projection: North America Albers Equal Area Conic (102008)Mosaic Projection: North America Albers Equal Area Conic (102008)Extent: 50 US States, District of Columbia, Puerto RicoSource: National Land Cover DatabasePublication date: June 30, 2023Time SeriesThis layer is served as a time series. To display a particular year of land cover data, select the year of interest with the time slider in your map client. You may also use the time slider to play the service as an animation. We recommend a one year time interval when displaying the series. If you would like a particular year of data to use in analysis, be sure to use the analysis renderer along with the time slider to choose a valid year.North America Albers ProjectionThis layer is served in North America Albers projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into Web Mercator projection, if that is the destination projection of the service.Processing TemplatesCartographic Renderer - The default. Land cover drawn with Esri symbols. Each year's land cover data is displayed in the time series until there is a newer year of data available.Cartographic Renderer (saturated) - This renderer has the same symbols as the cartographic renderer, but the colors are extra saturated so a transparency may be applied to the layer. This renderer is useful for land cover over a basemap or relief. MRLC Cartographic Renderer - Cartographic renderer using the land cover symbols as issued by NLCD (the same symbols as is on the dataset when you download them from MRLC).Analytic Renderer - Use this in analysis. The time series is restricted by the analytic template to display a raster in only the year the land cover raster is valid. In a cartographic renderer, land cover data is displayed until a new year of data is available so that it plays well in a time series. In the analytic renderer, data is displayed for only the year it is valid. The analytic renderer won't look good in a time series animation, but in analysis this renderer will make sure you only use data for its appropriate year.Simplified Renderer - NLCD reclassified into 10 broad classes. These broad classes may be easier to use in some applications or maps.Forest Renderer - Cartographic renderer which only displays the three forest classes, deciduous, coniferous, and mixed forest.Developed Renderer - Cartographic renderer which only displays the four developed classes, developed open space plus low, medium, and high intensity development classes.Hawaii data has a different sourceMRLC redirects users interested in land cover data for Hawaii to a NOAA product called C-CAP or Coastal Change Analysis Program Regional Land Cover. This C-CAP land cover data was available for Hawaii for the years 2001, 2005, and 2011 at the time of the latest update of this layer. The USA NLCD Land Cover layer reclasses C-CAP land cover codes into NLCD land cover codes for display and analysis, although it may be beneficial for analytical purposes to use the original C-CAP data, which has finer resolution and untranslated land cover codes. The C-CAP land cover data for Hawaii is served as its own 2.4m resolution land cover layer in the Living Atlas.Because it's a different original data source than the rest of NLCD, different years for Hawaii may not be able to be compared in the same way different years for the other states can. But the same method was used to produce each year of this C-CAP derived land cover to make this layer. Note: Because there was no C-CAP data for Kaho'olawe Island in 2011, 2005 data were used for that island.The land cover is projected into the same projection and cellsize as the rest of the layer, using nearest neighbor method, then it is reclassed to approximate the NLCD codes. The following is the reclass table used to make Hawaii C-CAP data closely match the NLCD classification scheme:C-CAP code,NLCD code0,01,02,243,234,225,216,827,818,719,4110,4211,4312,5213,9014,9015,9516,9017,9018,9519,3120,3121,1122,1123,1124,025,12USA NLCD Land Cover service classes with corresponding index number (raster value):11. Open Water - areas of open water, generally with less than 25% cover of vegetation or soil.12. Perennial Ice/Snow - areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover.21. Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes.22. Developed, Low Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover. These areas most commonly include single-family housing units.23. Developed, Medium Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include single-family housing units.24. Developed High Intensity - highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover.31. Barren Land (Rock/Sand/Clay) - areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.41. Deciduous Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in response to seasonal change.42. Evergreen Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.43. Mixed Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. 51. Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation.52. Shrub/Scrub - areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions.71. Grassland/Herbaceous - areas dominated by gramanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing.72. Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra.73. Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation.74. Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.Planted/Cultivated 81. Pasture/Hay - areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20% of total vegetation.82. Cultivated Crops - areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20% of total vegetation. This class also includes all land being actively tilled.90. Woody Wetlands - areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

The Spaceports dataset was compiled on August 08, 2023 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This layer is meant to be a reference layer and features public and private spaceport facilities in the United States. The information found on FAA’s Office of Spaceports was used in creating this layer by the Bureau of Transportation Statistics (BTS), found here https://www.faa.gov/space/office_spaceports. These facilities support the launching and receiving of spacecraft into and from space. As a result, U.S. spaceports have a critical role in the growing global commercial space transportation industry. The FAA Office of Spaceports is responsible for development of policies that promote infrastructure improvements and strengthen the competitiveness of U.S. spaceports, supporting launch and reentry site licensing activities, providing technical assistance and guidance to existing and proposed new spaceports, and the domestic and global promotion of U.S. spaceports. The mission of the FAA’s Office of Spaceports is to enable the safest, most efficient network of launch and reentry spaceports in the world, along with a vision to advance a robust, innovative national system of spaceports supporting the U.S. as a global leader in the commercial space transportation industry.

National Risk Index Version: March 2023 (1.19.0)The National Risk Index Counties feature layer contains county-level data for the Risk Index, Expected Annual Loss, Social Vulnerability, and Community Resilience.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

Important Note: This item is in mature support as of June 2022 and will be retired in December 2025.This shows the market opportunity for food services and drinking places in the U.S. in 2017 (in 2021 geography) in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The map uses the Leakage/Surplus Factor, an indexed value that represents opportunity (leakage), saturation (surplus), or balance within a market. This map focuses on the opportunity for food services and drinking places (NAICS 722).The pop-up is configured to include the following information for each geography level:Count of food services businesses and drinking places - NAICS 722 Total annual NAICS 722 sales (supply)Total annual NAICS 722 sales potential (demand)Market Opportunity for NAICS 722 (expressed as an index)Total annual supply and demand for various food industries:Special Food Services - NAICS 7223Drinking Places (Alcohol) - NAICS 7224Restaurants/Other Eating Places - NAICS 7225Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest 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 efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA FIRMS - Active Fire Data - for WorldScale/Resolution: 1kmUpdate Frequency: 1/2 Hour (every 30 minutes) using the Aggregated Live Feed MethodologyArea Covered: WorldWhat can I do with this layer?The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Additional InformationMODIS stands for MODerate resolution Imaging Spectroradiometer. The MODIS instrument is on board NASA’s Earth Observing System (EOS) Terra (EOS AM) and Aqua (EOS PM) satellites. The orbit of the Terra satellite goes from north to south across the equator in the morning and Aqua passes south to north over the equator in the afternoon resulting in global coverage every 1 to 2 days. The EOS satellites have a ±55 degree scanning pattern and orbit at 705 km with a 2,330 km swath width.It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.Attribute InformationLatitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.Acquisition Date: Derived Date/Time field combining Date and Time attributes.Satellite: Whether the detection was picked up by the Terra or Aqua satellite.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.RevisionsJune 22, 2022: Added 'HOURS_OLD' field to enhance Filtering data. Added 'Last 7 days' Layer to extend data to match time range of VIIRS offering. Added Field level descriptions.This map 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!

In the past four centuries, the population of the United States has grown from a recorded 350 people around the Jamestown colony of Virginia in 1610, to an estimated 331 million people in 2020. The pre-colonization populations of the indigenous peoples of the Americas have proven difficult for historians to estimate, as their numbers decreased rapidly following the introduction of European diseases (namely smallpox, plague and influenza). Native Americans were also omitted from most censuses conducted before the twentieth century, therefore the actual population of what we now know as the United States would have been much higher than the official census data from before 1800, but it is unclear by how much. Population growth in the colonies throughout the eighteenth century has primarily been attributed to migration from the British Isles and the Transatlantic slave trade; however it is also difficult to assert the ethnic-makeup of the population in these years as accurate migration records were not kept until after the 1820s, at which point the importation of slaves had also been illegalized. Nineteenth century In the year 1800, it is estimated that the population across the present-day United States was around six million people, with the population in the 16 admitted states numbering at 5.3 million. Migration to the United States began to happen on a large scale in the mid-nineteenth century, with the first major waves coming from Ireland, Britain and Germany. In some aspects, this wave of mass migration balanced out the demographic impacts of the American Civil War, which was the deadliest war in U.S. history with approximately 620 thousand fatalities between 1861 and 1865. The civil war also resulted in the emancipation of around four million slaves across the south; many of whose ancestors would take part in the Great Northern Migration in the early 1900s, which saw around six million black Americans migrate away from the south in one of the largest demographic shifts in U.S. history. By the end of the nineteenth century, improvements in transport technology and increasing economic opportunities saw migration to the United States increase further, particularly from southern and Eastern Europe, and in the first decade of the 1900s the number of migrants to the U.S. exceeded one million people in some years. Twentieth and twenty-first century The U.S. population has grown steadily throughout the past 120 years, reaching one hundred million in the 1910s, two hundred million in the 1960s, and three hundred million in 2007. In the past century, the U.S. established itself as a global superpower, with the world's largest economy (by nominal GDP) and most powerful military. Involvement in foreign wars has resulted in over 620,000 further U.S. fatalities since the Civil War, and migration fell drastically during the World Wars and Great Depression; however the population continuously grew in these years as the total fertility rate remained above two births per woman, and life expectancy increased (except during the Spanish Flu pandemic of 1918).

Since the Second World War, Latin America has replaced Europe as the most common point of origin for migrants, with Hispanic populations growing rapidly across the south and border states. Because of this, the proportion of non-Hispanic whites, which has been the most dominant ethnicity in the U.S. since records began, has dropped more rapidly in recent decades. Ethnic minorities also have a much higher birth rate than non-Hispanic whites, further contributing to this decline, and the share of non-Hispanic whites is expected to fall below fifty percent of the U.S. population by the mid-2000s. In 2020, the United States has the third-largest population in the world (after China and India), and the population is expected to reach four hundred million in the 2050s.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.