Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

The average for 2021 based on 196 countries was 656013 sq. km. The highest value was in Russia: 16376870 sq. km and the lowest value was in Monaco: 2 sq. km. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

The average for 2021 based on 192 countries was 14.4 percent. The highest value was in Bangladesh: 60.5 percent and the lowest value was in Djibouti: 0.1 percent. The indicator is available from 1961 to 2022. Below is a chart for all countries where data are available.

Use this global model layer when performing analysis across continents. This layer displays a global land cover map and model for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Use this country model layer when performing analysis within a single country. This layer displays a single global land cover map that is modeled by country for the year 2050 at a pixel resolution of 300m. ESA CCI land cover from the years 2010 and 2018 were used to create this prediction.Variable mapped: Projected land cover in 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer may be added to online maps and compared with the ESA CCI Land Cover from any year from 1992 to 2018. To do this, add Global Land Cover 1992-2018 to your map and choose the processing template (image display) from that layer called “Simplified Renderer.” This layer can also be used in analysis in ecological planning to find specific areas that may need to be set aside before they are converted to human use.Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and world) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between countries, use the country level. If mapping larger patterns, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasProvincesRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil TextureWere small countries modeled?Clark University modeled some small countries that had a few transitions. Only five countries were modeled with this procedure: Bhutan, North Macedonia, Palau, Singapore and Vanuatu.As a rule of thumb, the MLP neural network in the Land Change Modeler requires at least 100 pixels of change for model calibration. Several countries experienced less than 100 pixels of change between 2010 & 2018 and therefore required an alternate modeling methodology. These countries are Bhutan, North Macedonia, Palau, Singapore and Vanuatu. To overcome the lack of samples, these select countries were resampled from 300 meters to 150 meters, effectively multiplying the number of pixels by four. As a result, we were able to empirically model countries which originally had as few as 25 pixels of change.Once a selected country was resampled to 150 meter resolution, three transition potential images were calibrated and averaged to produce one final transition potential image per transition. Clark Labs chose to create averaged transition potential images to limit artifacts of model overfitting. Though each model contained at least 100 samples of "change", this is still relatively little for a neural network-based model and could lead to anomalous outcomes. The averaged transition potentials were used to extrapolate change and produce a final hard prediction and risk map of natural land cover conversion to Cropland and Artificial Surfaces in 2050.39 Small Countries Not ModeledThere were 39 countries that were not modeled because the transitions, if any, from natural to anthropogenic were very small. In this case the land cover for 2050 for these countries are the same as the 2018 maps and their vulnerability was given a value of 0. Here were the countries not modeled:AndorraAntigua and BarbudaBarbadosCape VerdeComorosCook IslandsDjiboutiDominicaFaroe IslandsFrench GuyanaFrench PolynesiaGibraltarGrenadaGuamGuyanaIcelandJan MayenKiribatiLiechtensteinLuxembourgMaldivesMaltaMarshall IslandsMicronesia, Federated States ofMoldovaMonacoNauruSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSamoaSan MarinoSeychellesSurinameSvalbardThe BahamasTongaTuvaluVatican CityIndex to land cover values in this dataset:The Clark University Land Cover 2050 projections display a ten-class land cover generalized from ESA Climate Change Initiative Land Cover. 1 Mostly Cropland2 Grassland, Scrub, or Shrub3 Mostly Deciduous Forest4 Mostly Needleleaf/Evergreen Forest5 Sparse Vegetation6 Bare Area7 Swampy or Often Flooded Vegetation8 Artificial Surface or Urban Area9 Surface Water10 Permanent Snow and Ice

Land area (sq. km) in World was reported at 129718826 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

Use this global model layer when performing analysis across continents. This layer displays predictions globally of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create this prediction.Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

The average for 2021 based on 194 countries was 31.8 percent. The highest value was in Suriname: 94.6 percent and the lowest value was in Egypt: 0 percent. The indicator is available from 1990 to 2022. Below is a chart for all countries where data are available.

United States US: Land Area data was reported at 9,147,420.000 sq km in 2017. This stayed constant from the previous number of 9,147,420.000 sq km for 2016. United States US: Land Area data is updated yearly, averaging 9,158,960.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 9,161,920.000 sq km in 2007 and a record low of 9,147,420.000 sq km in 2017. United States US: Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Land Use, Protected Areas and National Wealth. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

The United States Geological Survey (USGS) - Science Analytics and Synthesis (SAS) - Gap Analysis Project (GAP) manages the Protected Areas Database of the United States (PAD-US), an Arc10x geodatabase, that includes a full inventory of areas dedicated to the preservation of biological diversity and to other natural, recreation, historic, and cultural uses, managed for these purposes through legal or other effective means (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/protected-areas). The PAD-US is developed in partnership with many organizations, including coordination groups at the [U.S.] Federal level, lead organizations for each State, and a number of national and other non-governmental organizations whose work is closely related to the PAD-US. Learn more about the USGS PAD-US partners program here: www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards. The United Nations Environmental Program - World Conservation Monitoring Centre (UNEP-WCMC) tracks global progress toward biodiversity protection targets enacted by the Convention on Biological Diversity (CBD) through the World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) available at: www.protectedplanet.net. See the Aichi Target 11 dashboard (www.protectedplanet.net/en/thematic-areas/global-partnership-on-aichi-target-11) for official protection statistics recognized globally and developed for the CBD, or here for more information and statistics on the United States of America's protected areas: www.protectedplanet.net/country/USA. It is important to note statistics published by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Center (www.marineprotectedareas.noaa.gov/dataanalysis/mpainventory/) and the USGS-GAP (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-statistics-and-reports) differ from statistics published by the UNEP-WCMC as methods to remove overlapping designations differ slightly and U.S. Territories are reported separately by the UNEP-WCMC (e.g. The largest MPA, "Pacific Remote Islands Marine Monument" is attributed to the United States Minor Outlying Islands statistics). At the time of PAD-US 2.1 publication (USGS-GAP, 2020), NOAA reported 26% of U.S. marine waters (including the Great Lakes) as protected in an MPA that meets the International Union for Conservation of Nature (IUCN) definition of biodiversity protection (www.iucn.org/theme/protected-areas/about). USGS-GAP released PAD-US 3.0 Statistics and Reports in the summer of 2022. The relationship between the USGS, the NOAA, and the UNEP-WCMC is as follows: - USGS manages and publishes the full inventory of U.S. marine and terrestrial protected areas data in the PAD-US representing many values, developed in collaboration with a partnership network in the U.S. and; - USGS is the primary source of U.S. marine and terrestrial protected areas data for the WDPA, developed from a subset of the PAD-US in collaboration with the NOAA, other agencies and non-governmental organizations in the U.S., and the UNEP-WCMC and; - UNEP-WCMC is the authoritative source of global protected area statistics from the WDPA and WD-OECM and; - NOAA is the authoritative source of MPA data in the PAD-US and MPA statistics in the U.S. and; - USGS is the authoritative source of PAD-US statistics (including areas primarily managed for biodiversity, multiple uses including natural resource extraction, and public access). The PAD-US 3.0 Combined Marine, Fee, Designation, Easement feature class (GAP Status Code 1 and 2 only) is the source of protected areas data in this WDPA update. Tribal areas and military lands represented in the PAD-US Proclamation feature class as GAP Status Code 4 (no known mandate for biodiversity protection) are not included as spatial data to represent internal protected areas are not available at this time. The USGS submitted more than 51,000 protected areas from PAD-US 3.0, including all 50 U.S. States and 6 U.S. Territories, to the UNEP-WCMC for inclusion in the WDPA, available at www.protectedplanet.net. The NOAA is the sole source of MPAs in PAD-US and the National Conservation Easement Database (NCED, www.conservationeasement.us/) is the source of conservation easements. The USGS aggregates authoritative federal lands data directly from managing agencies for PAD-US (https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup), while a network of State data-stewards provide state, local government lands, and some land trust preserves. National nongovernmental organizations contribute spatial data directly (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards). The USGS translates the biodiversity focused subset of PAD-US into the WDPA schema (UNEP-WCMC, 2019) for efficient aggregation by the UNEP-WCMC. The USGS maintains WDPA Site Identifiers (WDPAID, WDPA_PID), a persistent identifier for each protected area, provided by UNEP-WCMC. Agency partners are encouraged to track WDPA Site Identifier values in source datasets to improve the efficiency and accuracy of PAD-US and WDPA updates. The IUCN protected areas in the U.S. are managed by thousands of agencies and organizations across the country and include over 51,000 designated sites such as National Parks, National Wildlife Refuges, National Monuments, Wilderness Areas, some State Parks, State Wildlife Management Areas, Local Nature Preserves, City Natural Areas, The Nature Conservancy and other Land Trust Preserves, and Conservation Easements. The boundaries of these protected places (some overlap) are represented as polygons in the PAD-US, along with informative descriptions such as Unit Name, Manager Name, and Designation Type. As the WDPA is a global dataset, their data standards (UNEP-WCMC 2019) require simplification to reduce the number of records included, focusing on the protected area site name and management authority as described in the Supplemental Information section in this metadata record. Given the numerous organizations involved, sites may be added or removed from the WDPA between PAD-US updates. These differences may reflect actual change in protected area status; however, they also reflect the dynamic nature of spatial data or Geographic Information Systems (GIS). Many agencies and non-governmental organizations are working to improve the accuracy of protected area boundaries, the consistency of attributes, and inventory completeness between PAD-US updates. In addition, USGS continually seeks partners to review and refine the assignment of conservation measures in the PAD-US.

The Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid, Revision 11 is a raster representation of nation-states in GPWv4 for use in aggregating population data. This data set was produced from the input census Units which were used to create a raster surface where pixels that cover the same census data source (most often a country or territory) have the same value. Note that these data are not official representations of country boundaries; rather, they represent the area covered by the input data. In cases where multiple countries overlapped a given pixel (e.g. on national borders), the pixels were assigned the country code of the input data set which made up the majority of the land area. The data file was produced as a global raster at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research commUnities, the 30 arc-second data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1 degree resolutions. Each level of aggregation results in the loss of one or more countries with areas smaller than the cell size of the final raster. Rasters of all resolutions were also converted to polygon shapefiles.

This dataset, created by ODM, aggregates data from various sources to build out data relating to SDG indicators of interest for the Lower Mekong Region. It is updated on a regular basis. See also the following resources: - https://data.opendevelopmentmekong.net/dataset/sdg3-c-1-health-worker-density-by-type-of-occupation-per-10-000-population-for-lmcs - https://data.opendevelopmentmekong.net/dataset/sdg-3-8-2-proportion-of-population-with-large-household-expenditures-on-health-at-10-of-household-t - https://data.opendevelopmentmekong.net/dataset/sdg-8-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-12-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-4-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-10-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-5-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-1-available-data - https://data.opendevelopmentmekong.net/dataset/sdg-9-dataset - https://data.opendevelopmentmekong.net/dataset/sdg-2-available-data - https://data.opendevelopmentmekong.net/dataset/population-and-proportion-in-urban-slums-lmcs-sdg11-1-1 - https://data.opendevelopmentmekong.net/dataset/data-on-sdg-7-tier-i-indicators-lmcs-only-all-custodian-agencies - https://data.opendevelopmentmekong.net/dataset/wash-dataset-1990-215 - https://data.opendevelopmentmekong.net/dataset/proportion-of-urban-population-living-in-slums - https://data.opendevelopmentmekong.net/dataset/population-density-from-1995-to-2013-thailand-myanmar-laos-vietnam-and-cambodia - https://data.opendevelopmentmekong.net/dataset/sdg-index-dashboards-report - https://data.opendevelopmentmekong.net/dataset/sdg-indicator-15-1-1-forest-area-as-a-proportion-of-total-land-area?type=dataset

Land area (sq. km) in United States was reported at 9147420 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Use this country model layer when performing analysis within a single country. This layer displays predictions within each country of relative vulnerability to modification by humans by the year 2050. ESA CCI land cover maps from the years 2010 and 2018 were used to create these predictions.Variable mapped: Vulnerability of land cover to anthropogenic change by 2050.Data Projection: Cylindrical Equal AreaMosaic Projection: Cylindrical Equal AreaExtent: Global Cell Size: 300mSource Type: ThematicVisible Scale: 1:50,000 and smallerSource: Clark UniversityPublication date: April 2021What you can do with this layer?This layer can be used in analysis, to estimate and compare vulnerability to land cover change globally due to expansion of human activity, by 2050. This layer is useful in ecological planning, helping to prioritize areas for conservation. Links to the six Clark University land cover 2050 layers in ArcGIS Living Atlas of the World:There are three scales (country, regional, and global) for the land cover and vulnerability models. They’re all slightly different since the country model can be more fine-tuned to the drivers in that particular area. Regional (continental) and global have more spatially consistent model weights. Which should you use? If you’re analyzing one country or want to make accurate comparisons between proximate countries, use the country level. If mapping larger patterns or vastly separated countries, use the global or regional extent (depending on your area of interest). Land Cover 2050 - GlobalLand Cover 2050 - RegionalLand Cover 2050 - CountryLand Cover Vulnerability to Change 2050 GlobalLand Cover Vulnerability to Change 2050 RegionalLand Cover Vulnerability to Change 2050 CountryWhat these layers model (and what they don’t model)The model focuses on human-based land cover changes and projects the extent of these changes to the year 2050. It seeks to find where agricultural and urban land cover will cover the planet in that year, and what areas are most vulnerable to change due to the expansion of the human footprint. It does not predict changes to other land cover types such as forests or other natural vegetation during that time period unless it is replaced by agriculture or urban land cover. It also doesn’t predict sea level rise unless the model detected a pattern in changes in bodies of water between 2010 and 2018. A few 300m pixels might have changed due to sea level rise during that timeframe, but not many.The model predicts land cover changes based upon patterns it found in the period 2010-2018. But it cannot predict future land use. This is partly because current land use is not necessarily a model input. In this model, land set aside as a result of political decisions, for example military bases or nature reserves, may be found to be filled in with urban or agricultural areas in 2050. This is because the model is blind to the political decisions that affect land use.Quantitative Variables used to create ModelsBiomassCrop SuitabilityDistance to AirportsDistance to Cropland 2010Distance to Primary RoadsDistance to RailroadsDistance to Secondary RoadsDistance to Settled AreasDistance to Urban 2010ElevationGDPHuman Influence IndexPopulation DensityPrecipitationRegions SlopeTemperatureQualitative Variables used to create ModelsBiomesEcoregionsIrrigated CropsProtected AreasContinentCountryRainfed CropsSoil ClassificationSoil DepthSoil DrainageSoil pHSoil Texture

Abstract

Most people in Lao PDR live in rural areas and make their living from agriculture. The Government needs detailed and up-to-date statistics on agriculture to help develop the agricultural sector and improve the welfare of the people. The Government already has statistics on the area and production of rice and other major crops, as well as livestock numbers. However, there is little information available on such things as: the different types of rice grown, the number of rice farmers, the area planted to minor crops, the use of different inputs, the use of farm machinery, farm size, farm labour, and the age/sex structure of livestock. The Lao Agricultural Census will provide these and many other data. The Lao Agricultural Census is part of a world-wide programme of agricultural censuses, which started in the 1930’s. Over 120 countries are now participating in that programme; many of these undertake agricultural censuses every ten years. The Lao Agricultural Census is the first such census undertaken in Lao PDR. It is being conducted in all 141 districts and is one of the largest and most important statistical collections ever undertaken in the country.

Objectives of Lao Agricultural Census:

1. To provide data on the structure of agriculture, agricultural land (land use for agricultural crops; livestock).

   Land issue: there is strong need for land use and other related data to guide land policy formulation .

2. To obtain community-level data (at the village level) for examining the infrastructure and services available to farm holdings.

3. To provide data to use as benchmarks for current agricultural statistics.

4. To strengthen national capacity and provide frames for future agricultural sample surveys.

Geographic coverage

National coverage

Analysis unit

Households

Universe

The statistical unit was the farm household (holding), defined as an economic unit of agricultural production under single management, comprising all livestock raised and all agricultural land operated, regardless of ownership, which engages agricultural operation above certain established thresholds of land, livestock or aquaculture.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Sample design and selection

The sample for the sample farm household component was selected using two-stage sampling: a sample of villages was first selected, and then a sample of farm households was selected in each sample village. In most districts, a sample of between 16 and 22 villages was selected, with 16 farm households selected in each sample village; that is, a sample of between 256 and 352 sample farm households in each district. The more villages or farm households in a district, the bigger the sample that was taken. A smaller sample was taken in urban districts and districts containing few villages or households. In each district, the sample of villages was selected using stratified systematic probability proportional to size (PPS) sampling. A list of all villages in Lao PDR was prepared. Villages were divided into urban and rural strata, with rural strata being sampled more heavily than urban strata because of their agricultural importance. The estimated number of households in each village was used as the size measure for PPS sampling. The sample of farm households in each sample village was selected using stratified systematic random sampling based on a list of all farm households in each village prepared following the household component of the census. Altogether, 2,620 villages and 41,660 sample farm households were selected in the sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the CA 2010/2011, as follows:

(i) a form for listing the households in the village, Form 1;

(ii) a questionnaire for the household component, Form 4;

(iii) a questionnaire for the farm household (holding) component, Form 5;

(iv) a questionnaire for the village component, Form 3.

The 2010/2011 CA census questionnaires covered all 16 core items recommended for the WCA 2010 roundn namely;

0001 Identification and location of agricultural holding 0002+ Legal status of agricultural holder 0003 Sex of agricultural holder 0004 Age of agricultural holder 0005 Household size 0006 Main purpose of production of the holding 0007 Area of holding according to land use types 0008 Total area of holding 0009 Land tenure types on the holding 0010 Presence of irrigation on the holding 0011 Types of temporary crops on the holding 0012 Types of permanent crops on the holding and whether in compact plantation 0013 Number of animals on the holding for each livestock type 0014 Presence of aquaculture on the holding 0015+ Presence of forest and other wooded land on the holding 0016 Other economic production activities of the holding's enterprise

See questionnnares in external materials tab

Cleaning operations

Census processing

Completed questionnaires were returned to ACO in Vientiane for processing. Processing involved:

• checking that the census enumeration was complete;
• manually checking that questionnaires had been correctly filled out; coding of descriptive responses (such as crop types);
• entering data into the computer (using keyboard methods); running computer checks to identify and correct errors; and producing tabulations of census data.

There were nearly 1.2 million questionnaires and therefore it took some for the processing to be completed. Preliminary checking and coding was done from May-September 2011; data entry was done from June-December 2011; and error checking was done from August 2011-February 2012. Tabulations were prepared by April 2012.

Response rate

village component- 99 percent

household component - 99. 4 percent

sample farm household componen - 99.9 percent

Sampling error estimates

The census data presented from the sample farm household component are based on a sample and are therefore subject to sampling errors. Because of the sample design used, sampling errors on provincial and national estimates are generally quite small.

Data appraisal

To ensure census data quality, supervision was done at the central, provincial and district level. The data were confronted with external sources, such as the data from the previous census and current agricultural surveys. For the sampling component, statistical errors were computed.

Sweden SE: Population Density: People per Square Km data was reported at 24.718 Person/sq km in 2017. This records an increase from the previous number of 24.362 Person/sq km for 2016. Sweden SE: Population Density: People per Square Km data is updated yearly, averaging 20.697 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 24.718 Person/sq km in 2017 and a record low of 18.326 Person/sq km in 1961. Sweden SE: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

This raster includes Ecological System or Land Use Classes from the National GAP Land Cover Data (v2) that represent Chihuahuan Desert Scrub in the Chihuahuan Desert REA Analysis Extent. See Process Steps.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe#8217;s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe#8217;s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.