The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a best available map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers,

"HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous."

Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. "

HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas."

These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality."

Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled land cover classes for each year. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

This is a superseded dataset, the most recent Australia's Indigenous land and forest estate spatial dataset can be found at: https://www.agriculture.gov.au/abares/forestsaustralia/forest-data-maps-and-tools/spatial-data/indigenous-land-and-forest.\r \r Australia’s Indigenous forest estate (2020) is a continental spatial dataset that identifies and reports separately the individual attributes of Australia’s Indigenous estate, namely the extent of land and forest over which Indigenous peoples and communities have ownership, management or co-management, or other special rights.\r \r The dataset was compiled by the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) for the National Forest Inventory (NFI), a collaborative partnership between the Australian and state and territory governments. The role of the NFI is to collate, integrate and communicate information on Australia's forests. The NFI applies a national classification to state and territory data to allow seamless integration of these datasets. Multiple independent sources of external data are used to fill data gaps and improve the quality of the final dataset.\r \r Australia’s National Forest Inventory has previously used the four derived category combinations of Dillon et al. (2015) (https://www.agriculture.gov.au/sites/default/files/documents/IndigenousForestEstate_20150828_v1.0.0.pdf) for reporting the Indigenous estate in Australia’s State of the Forests Report 2013 and Australia’s State of the Forests Report 2018. These four categories combined in various ways the individual attributes of the Indigenous estate, with an area of land or forest being allocated to only one of the four categories. However, the categories did not allow separate reporting on Indigenous ownership, management or co-management, or other special rights.​\r \r The methodology described in Australia’s Indigenous land and forest estate: separate reporting of Indigenous ownership, management and other special rights (Jacobsen et al. 2020) (https://www.agriculture.gov.au/abares/forestsaustralia/publications/indigenous-estate-report) disentangles the four categories of Dillon et al. (2015), and allows separate reporting of each of Indigenous ownership of land or forest, Indigenous management or co‑management of land or forest, and land or forest over which Indigenous peoples and communities have other special rights. Separate spatial coverages were created for each of these attributes, and this new dataset can form the basis for subsequent reporting on the relationships between Indigenous peoples and land or forest.\r \r The methods and data in this data package use the same sources of data used for assembling the Australia’s Indigenous forest estate (2018) spatial dataset, but presents the information on the Indigenous land estate by the separate attributes ownership, management of co-management, and other special rights, according to the new methodology of Jacobsen et al. (2020). The Indigenous land dataset is also combined (intersected) with forest cover information from the Forests of Australia (2018) dataset.​ The resulting output dataset provides information on the Indigenous estate for both land and forest.\r \r This dataset is updated every five years for the Australia's State of the Forests Report Series. Further information can be found on the Forests Australia website: http://www.agriculture.gov.au/abares/forestsaustralia/sofr/sofr-2018

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled land use classes for each year. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

This geospatial dataset depicts ownership patterns of forest land across the conterminous United States. Eight ownership categories are modeled, including three public ownerships: federal, state, and local; four private categories: family, corporate, Timber Investment Management Organization (TIMO) and Real Estate Investment Trust (REIT), and other private (including conservation organizations and unincorporated associations); and Native American tribal land. The data are modeled from Forest Inventory and Analysis (FIA) points from 2012-2017 and the most up-to-date publicly available boundaries of federal, state, and tribal lands.These data are intended to support national- and regional-scale planning and analyses involving spatially explicit distribution and patterns of forest ownership. These data are not intended or recommended for subregional- or local-scale planning or analyses. Map accuracy varies between ownership categories and regions, limiting its use for local or specific decision making.A corresponding Research Map (RMAP) has been produced to cartographically portray this dataset (Sass et al. 2020; https://doi.org/10.2737/NRS-RMAP-11).

Three previous data publications also model forest ownership types across the conterminous United States. Nelson et al. (2010) depicts public and private forest ownership, and differentiates corporate from other private ownership. Hewes et al. (2014) differentiates three public ownership categories (federal, state, and local) and three private ownership categories (family, corporate, and other private). Hewes et al. (2017) depicts these six categories as well as tribal lands. This dataset is updated with recently available data and differentiates a new private ownership category: Timber Investment Management Organizations (TIMOs) and Real Estate Investment Trusts (REITs), which are presented as a combined category.