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. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx 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. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

A set of three estimates of land-cover types and annual transformations of land use are provided on a global 0.5 x0.5 degree lat/lon grid at annual time steps. The longest of the three estimates spans 1770-2010. The dataset presented here takes into account land-cover change due to four major land-use/management activities: (1) cropland expansion and abandonment, (2) pastureland expansion and abandonment, (3) urbanization, and (4) secondary forest regrowth due to wood harvest. Due to uncertainties associated with estimating historical agricultural (crops and pastures) land use, the study uses three widely accepted global reconstruction of cropland and pastureland in combination with common wood harvest and urban land data set to provide three distinct estimates of historical land-cover change and underlying land-use conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and extent to which different ecosystem have undergone changes. The three estimates use a consistent methodology, and start with a common land-cover map during pre-industrial conditions (year 1765), taking different courses as determined by the land-use/management datasets (cropland, pastureland, urbanization and wood harvest) to attain forest area distributions close to satellite estimates of forests for contemporary period. The satellite based estimates of forest area are based on MODIS sensor. All data uses the WGS84 spatial coordinate system for mapping.

Download In State Plane Projection HereClick here to access additional data directly from the Illinois State Geospatial Data Clearinghouse.The contours are created from a bare earth DEM derived from the source lidar and 3D breaklines. The contours are created using controlled and tested methods to limit the amount of error introduced during contour production but smoothing applied to the contours does introduce some deviation from the original source lidar and bare earth DEM. The contours were produced from the raster bare earth DEM and inherit the accuracy of the DEM.The project specifications require the accuracy (ACCz) of the derived DEM be calculated and reported in two ways: 1. The required NVA is: 19.6 cm (0.64 ft) at a 95% confidence level, derived according to NSSDA, i.e., based on RMSE of 10 cm (0.33 ft) in the “bare earth” and "urban" land cover classes. This is a required accuracy. The NVA was tested with 116 checkpoints located in bare earth and urban (non-vegetated) areas. 2. Vegetated Vertical Accuracy (VVA): VVA shall be reported for "brushlands/low trees" and "tall weeds/crops" land cover classes. The target VVA is: 29.4 cm (0.96 ft) at the 95th percentile, derived according to ASPRS Guidelines, Vertical Accuracy Reporting for Lidar Data, i.e., based on the 95th percentile error in all vegetated land cover classes combined. This is a target accuracy. The VVA was tested with 79 checkpoints located in tall weeds/crops and brushlands/low trees (vegetated) areas. The checkpoints were distributed throughout the project area and were surveyed using GPS techniques. See survey report for additional survey methodologies. AccuracyZ has been tested to meet 19.6 cm (0.64 ft) or better Non-Vegetated Vertical Accuracy at 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA); assessed and reported using National Digital Elevation Program (NDEP)/ASRPS Guidelines.

Spatial data on soils, land use, and topography, combined with knowledge of conservation effectiveness can be used to identify alternatives to reduce nutrient discharge from small watersheds. This database was developed to be used in conjunction with the Agricultural Conservation Planning Framework Toolkit. Data comprise soil survey information and land use. Soil characterization data were extracted from the Natural Resources Conservation Service (NRCS) Web Soil Survey (Soil Survey Staff, 2013). Land use coverages were developed to represent agricultural fields and the types and rotations of agricultural crops and other land cover types. Land use boundaries were produced by editing a publicly available USDA field boundaries dataset (pre-2008), with all ownership and county-level attributes removed. To ensure these field polygons were consistent with recent land use, the 2009 Cropland Data Layer (USDA-NASS, 2013) was examined for all fields larger than 16 ha. For those fields with multiple cover types, 2009 National Agricultural Imagery Program (NAIP) aerial photography was used as a basis to manually edit field boundaries. A field was considered to have multiple cover types and was edited if the dominant cover occupied <75% of the field, as indicated by the 2009 Cropland Data Layer. Updated field boundaries were then overlaid with data from USDA-National Agricultural Statistics Service (2013) Cropland Data Layer for 2000 – 2014, and each field was classified to represent crop rotations and land cover using the most recent six-year (2009-2014) sequence of land cover. Six-year land-cover strings (e.g., corn-corn-soybean-corn-soybean-corn) generated for each field were classified to represent major crop rotations, which were dominantly comprised of corn (Zea mays L.) and soybean (Glycine max (L.) Merr) annual row crops. The database does not include high-resolution digital elevation models (DEMs) derived from LiDAR (light detection and ranging) survey data, although these are needed by the Agricultural Conservation Planning Framework Toolkit and must be obtained independently. Database is scheduled to become available on October 1, 2015. Resources in this dataset:Resource Title: Land Use and Soils data, viewing and downloading page. File Name: Web Page, url: https://www.nrrig.mwa.ars.usda.gov/st40_huc/dwnldACPF.html Recent land use, field boundary, and soil survey information for individual HUC12 watersheds in Iowa, Illinois, and southern Minnesota. With this land use viewer web page, users may navigate to individual HUC12 watersheds, view land-use maps, and download land use and soils data that can be directly used as input data for the ACPF toolbox. Before developing information on conservation priorities and opportunities using the ACPF toolbox, users will need to obtain elevation data for their watershed, which is usually available from your state government.

Description

The Correlates of War project hosts a variety of datasets related to the study of inter-state conflict.

Details

As of 2007-09-22 the following datasets were listed:

State System Membership (v2004.1): This data set records the fluctuating composition of the state system since 1816. It also identifies countries corresponding to the standard Correlates of War country codes. Access the system membership data here.

Inter-, Extra- and Intra-State War (v3.0): War takes many forms in the contemporary era, including serious military conflicts between states (inter-state war), between states and non-state actors (extra-state war), and within states (intra-state war). This data set records such events over the 1816-1997 period. Access the Interstate War data here. Access the Extrastate War data here. Access the Intrastate War data here.

Militarized Interstate Disputes (v3.02): This data set records all instances of when one state threatened, displayed, or used force against another. Version 3.0 covers the 1816-2001 period, and can be downloaded from this page.

National Material Capabilities (v3.02): Power is considered by many to be a central concept in explaining conflict, and six indicators—military expenditure, military personnel, energy consumption, iron and steel production, urban population, and total population—are included in this data set. It serves as the basis for the most widely used indicator of national capability, CINC (Composite Indicator of National Capability) and covers the period 1816-2001. Access the capabilities data here.

Formal Alliances (v3.03): Alliances have been credited with preventing wars and provoking wars, and they have been important instruments of statecraft for centuries. This data set records all formal alliances among states between 1816 and 2000, including mutual defense pacts, non-aggression treaties, and ententes. This data set is hosted by Douglas Gibler, University of Kentucky. It may be downloaded here.

Territorial Change (v3.0): Territory has played an important role in interstate conflict, and this data set records all peaceful and violent changes of territory from 1816-2000. This data set is hosted by Paul Diehl, University of Illinois. Access the territorial change data here.

Direct Contiguity (v3.0): Geographic factors are known to play an important role in conflict. The Direct Contiguity data set registers the land and sea borders of all states since the Congress of Vienna, and covers 1816-2000. This data set is hosted by Paul Diehl, University of Illinois. Access the direct contiguity data here.

Colonial/Dependency Contiguity (v3.0): The Colonial/Dependency Contiguity data set registers contiguity relationships between the colonies/dependencies of states (by land and by sea up to 400 miles) from 1816-2002. Access the colonial/dependency contiguity data here.

Intergovernmental Organizations (v2.1): Although the number of intergovernmental organizations (IGOs) grew dramatically during the late 20th century, they have been part of the world scene for much longer. This data set tracks the status and membership of such organizations from 1815-2000. Access information about this data here. This data set is hosted by Timothy Nordstrom, University of Mississippi, and John Pevehouse, University of Wisconsin.

Diplomatic Exchange (v2006.1): The Diplomatic Exchange data set tracks diplomatic representation at the level of chargé d'affaires, minister, and ambassador between states from 1817-2005. Access information about this data here. This data set is hosted by Reşat Bayer, Koç University.

Bilateral Trade: Trade is considered by many to have a pacifying effect on the relations of states. This collection of bilateral trade data begins in 1870 and covers most members of the interstate system. Access trade data here.

Openness: Open (?)

• Access: all datasets available for direct download in convenient formats (e.g. zipped csv)
• License: no license specified but does state on the datasets page: "The data sets listed on this page are all available for download." Some datasets require citation as a condition of use (with the exact form stated on the website).