This dataset contains estimates of proportional area of 18 major crops for each county in the United States at roughly decadal time steps between 1840 and 2017, and was used for analyses of historical changes in crop area, diversity, and distribution published in:Crossley, MS, KD Burke, SD Schoville, VC Radeloff. (2020). Recent collapse of crop belts and declining diversity of US agriculture since 1840. Global Change Biology (in press).The original data used to curate this dataset was derived by Haines et al. (ICPSR 35206) from USDA Agricultural Census archives (https://www.nass.usda.gov/AgCensus/). This dataset builds upon previous work in that crop values are georeferenced and rectified to match 2012 county boundaries, and several inconsistencies in the tabular-formatted data have been smoothed-over. In particular, smoothing included conversion of values of production (e.g. bushels, lbs, typical of 1840-1880 censuses) into values of area (using USDA NASS yield data), imputation of missing values for certain crop x county x year combinations, and correcting values for counties whose crop totals exceeded the possible land area.Please contact the PI, Mike Crossley, with any questions or requests: mcrossley3@gmail.com

United States US: GDP: Growth: Gross Value Added: Services data was reported at 2.621 % in 2015. This records an increase from the previous number of 2.221 % for 2014. United States US: GDP: Growth: Gross Value Added: Services data is updated yearly, averaging 2.335 % from Dec 1998 (Median) to 2015, with 18 observations. The data reached an all-time high of 4.456 % in 1999 and a record low of -1.772 % in 2009. United States US: GDP: Growth: Gross Value Added: Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Gross Domestic Product: Annual Growth Rate. Annual growth rate for value added in services based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Services correspond to ISIC divisions 50-99. They include value added in wholesale and retail trade (including hotels and restaurants), transport, and government, financial, professional, and personal services such as education, health care, and real estate services. Also included are imputed bank service charges, import duties, and any statistical discrepancies noted by national compilers as well as discrepancies arising from rescaling. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The industrial origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted Average; Note: Data for OECD countries are based on ISIC, revision 4.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The Coastline Shapefile includes all features within the MTDB Class "Coastline" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB is L4150. The coastline included in this shapefile was delineated by the Census Bureau in the MAF/TIGER database based on water measurement class for display of statistical information only; its depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement and it is not a legal land description. This shapefile should be used for data presentation purposes only. It is not the official source for the coastline feature. The name assigned to each Coastline feature is a short form of the name of the large body of water bordered by this Coastline feature.

This digital data set describes surficial geology of the conterminous United States. The data set was generated from a U.S. Geological Survey 1:7,500,000-scale map of surficial geology published as part of the U.S. Geological Survey National Atlas map series.

Consumption as percent of GDP in the USA, June, 2025 The most recent value is 68.2 percent as of Q2 2025, a decline compared to the previous value of 68.42 percent. Historically, the average for the USA from Q1 1960 to Q2 2025 is 64.16 percent. The minimum of 58.52 percent was recorded in Q1 1967, while the maximum of 69.06 percent was reached in Q1 2011. | TheGlobalEconomy.com

Graph and download economic data for Advance U.S. International Trade in Goods: Imports: Industrial Supplies (AITGIIS) from Aug 2025 to Aug 2025 about supplies, imports, trade, goods, industry, and USA.

Retirement Notice: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations. This map displays locations from the PAD-US version 3.0 symbolized with the Public Access field. This map includes two filtered and renamed copies of a view layer at scales of 1:1,000,000 and larger and a vector tile layer at scales smaller than 1:1,000,000. Two layers were used to create the different symbology of marine and terrestrial areas. PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.

CAP Geofences: Precision & Accuracy for Business Success

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The USA: Trade balance as percent of GDP: The latest value from 2024 is -3.09 percent, a decline from -2.88 percent in 2023. In comparison, the world average is -3.07 percent, based on data from 134 countries. Historically, the average for the USA from 1960 to 2024 is -1.81 percent. The minimum value, -5.69 percent, was reached in 2006 while the maximum of 1.01 percent was recorded in 1964.

Key information about Morocco Total Imports from USA

• Morocco Total Imports from USA recorded 607.047 USD mn in Sep 2024, compared with 604.627 USD mn in the previous month
• Morocco Total Imports from USA data is updated monthly, available from Jan 1980 to Sep 2024, with an averaged value of 56.221 USD mn
• The data reached an all-time high of 712.611 USD mn in Jul 2024 and a record low of 6.450 USD mn in Sep 1982

CEIC converts monthly Total Imports from USA into USD. Foreign Exchange Office provides Total Imports from USA, CIF, in local currency. The Bank Al-Maghrib average market exchange rate is used for currency conversions. Total Imports from USA prior to January 1996 is sourced from the International Monetary Fund.

United States Louisiana: GR: OS: Taxes data was reported at 18,448,039.000 USD th in 2015. This records an increase from the previous number of 18,078,599.000 USD th for 2014. United States Louisiana: GR: OS: Taxes data is updated yearly, averaging 5,512,986.000 USD th from Jun 1957 (Median) to 2015, with 57 observations. The data reached an all-time high of 18,448,039.000 USD th in 2015 and a record low of 498,233.000 USD th in 1957. United States Louisiana: GR: OS: Taxes data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.F027: Revenue & Expenditure: State and Local Government: Louisiana.

This layer displays change in US land cover between 2001 and 2011. Pixels that changed during this period display the land cover value that they changed to. Pixels with no change are transparent.The National Land Cover Database 2011 (NLCD 2011) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data.The 2001/2011 land cover change layer is one of five primary data products produced as part of the NLCD 2011: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover.Land cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies

GapMaps Crime Risk Location data sourced from Applied Geographic Solutions (AGS) includes the latest crime risk indexes and projections available at census block level. Understand the relative crime risk across any location across the USA and Canada so you can make more informed business decisions.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).

This dataset explores the school breakfast program participation by state for the fiscal years 2003-2007. Participation data are nine-month averages; summer months (June-August) are excluded. Participation is based on average daily meals divided by an attendance factor of 0.927

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 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 Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. 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 and apply filters. 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.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse 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 theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

Cores from living coral colonies were collected from Dry Tortugas National Park, Florida, to obtain skeletal records of past coral growth and allow geochemical reconstruction of environmental variables during the corals’ centuries-long lifespans. The samples were collected as part of the U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies project (http://coastal.er.usgs.gov/crest/) that provides science to assist resource managers tasked with the stewardship of coral reef resources. Three colonies each of the coral species Orbicella faveolata and Siderastrea siderea were collected in May 2012 as approved under National Park Service (NPS) scientific collecting permit number DRTO-2012-SCI-0001. These coral samples can be used to retroactively construct sea-surface temperature records by measuring the elemental ratio of strontium (Sr) to calcium (Ca), and are valuable for measuring additional paleoproxies as new methods are developed. Flannery and others (2017) includes temperature reconstructions using samples from one of the six (coral CG2) collected in this study. The core slabs described here, as well as others (see http://olga.er.usgs.gov/coreviewer/), can be requested on loan for further scientific study. Here we provide photographic images for each core depicting 1) the coral in its ocean environment, 2) the core as curated and slabbed, and 3) the X-rays of the slabs. More information on coring methods can be found in the associated U.S. Geological Survey Open-File Report 2016-1182 (Weinzierl and others, 2016). These coral samples are presently on loan from the NPS, stored at the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) in St. Petersburg, Florida, and cataloged under accession number DRTO-353.

New Covid cases per million people in the USA, March, 2023 The most recent value is 2004 new Covid cases per million people as of March 2023, a decline compared to the previous value of 3208 new Covid cases per million people. Historically, the average for the USA from February 2020 to March 2023 is 7989 new Covid cases per million people. The minimum of 0 new Covid cases per million people was recorded in February 2020, while the maximum of 60436 new Covid cases per million people was reached in January 2022. | TheGlobalEconomy.com

United States Pennsylvania: GR: OS: CM: Charges: Air Transportation data was reported at 568,083.000 USD th in 2015. This records a decrease from the previous number of 618,178.000 USD th for 2014. United States Pennsylvania: GR: OS: CM: Charges: Air Transportation data is updated yearly, averaging 305,879.000 USD th from Jun 1977 (Median) to 2015, with 37 observations. The data reached an all-time high of 618,178.000 USD th in 2014 and a record low of 52,560.000 USD th in 1977. United States Pennsylvania: GR: OS: CM: Charges: Air Transportation data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.F047: Revenue & Expenditure: State and Local Government: Pennsylvania.