The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

(Link to Metadata) The BNDHASH dataset depicts Vermont village, town, county, and Regional Planning Commission (RPC) boundaries. It is a composite of generally 'best available' boundaries from various data sources (refer to ARC_SRC and SRC_NOTES attributes). However, this dataset DOES NOT attempt to provide a legally definitive boundary. The layer was originally developed from TBHASH, which was the master VGIS town boundary layer prior to the development and release of BNDHASH. By integrating village, town, county, RPC, and state boundaries into a single layer, VCGI has assured vertical integration of these boundaries and simplified maintenance. BNDHASH also includes annotation text for town, county, and RPC names. BNDHASH includes the following feature classes: 1) BNDHASH_POLY_VILLAGES = Vermont villages 2) BNDHASH_POLY_TOWNS = Vermont towns 3) BNDHASH_POLY_COUNTIES = Vermont counties 4) BNDHASH_POLY_RPCS = Vermont's Regional Planning Commissions 5) BNDHASH_POLY_VTBND = Vermont's state boundary 6) BNDHASH_LINE = Lines on which all POLY feature classes are built The master BNDHASH data is managed as an ESRI geodatabase feature dataset by VCGI. The dataset stores village, town, county, RPC, and state boundaries as seperate feature classes with a set of topology rules which binds the features. This arrangement assures vertical integration of the various boundaries. VCGI will update this layer on an annual basis by reviewing records housed in the VT State Archives - Secretary of State's Office. VCGI also welcomes documented information from VGIS users which identify boundary errors. NOTE - VCGI has NOT attempted to create a legally definitive boundary layer. Instead the idea is to maintain an integrated village/town/county/RPC/state boundary layer which provides for a reasonably accurate representation of these boundaries (refer to ARC_SRC and SRC_NOTES). BNDHASH includes all counties, towns, and villages listed in "Population and Local Government - State of Vermont - 2000" published by the Secretary of State. BNDHASH may include changes endorsed by the Legislature since the publication of this document in 2000 (eg: villages merged with towns). Utlimately the Vermont Secratary of State's Office and the VT Legislature are responsible for maintaining information which accurately describes the locations of these boundaries. BNDHASH should be used for general mapping purposes only. * Users who wish to determine which boundaries are different from the original TBHASH boundaries should refer to the ORIG_ARC field in the BOUNDARY_BNDHASH_LINE (line feature with attributes). Also, updates to BNDHASH are tracked by version number (ex: 2003A). The UPDACT field is used to track changes between versions. The UPDACT field is flushed between versions.

The Surface Management Agency (SMA) Geographic Information System (GIS) dataset depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. The SMA feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details. The SMA Withdrawals feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA Withdrawal is defined by formal actions that set aside, withhold, or reserve Federal land by statute or administrative order for public purposes. A withdrawal creates a title encumbrance on the land. Withdrawals must accomplish one or more of the following: A. Transfer total or partial jurisdiction of Federal land between Federal agencies. B. Close (segregate) Federal land to operation of all or some of the public land laws and/or mineral laws. C. Dedicate Federal land to a specific public purpose. There are four major categories of formal withdrawals: (1) Administrative, (2) Presidential Proclamations, (3) Congressional, and (4) Federal Power Act (FPA) or Federal Energy Regulatory Commission (FERC) Withdrawals. These SMA Withdrawals will include the present total extent of withdrawn areas rather than all of the individual withdrawal actions that created them over time. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details.

This is series-level metadata for the USGS Protected Areas Database of the United States (PAD-US) data released by the United States Geological Survey (USGS). PAD-US is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas. Starting with version 1.4 PAD-US was identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://ngda-cadastre-geoplatform.hub.arcgis.com/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling "best available" data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/programs/gap-analysis-project/science/protected-areas/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/programs/gap-analysis-project/pad-us-data-history for more information): - Current Version - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B - Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT - Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE - Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ - Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD - Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov) - Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov) - First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov) Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Ranger Station, White House, and City/Town Hall. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. Included is a feature class of preliminary building polygons provided by FEMA, USA Structures. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/ngp-standards-and-specifications/national-map-structures-content.

The Michigan Department of Transportation (MDOT) manages several Linear Referencing Systems (LRS) for the State including the Physical Reference (PR), Control Section (CS) and Route networks These networks include Michigan's Roads, Rails, Trails & more. MDOT also partners with the Michigan State Police (MSP), Department of Natural Resources (DNR), Department of Technology Management & Budged (DTMB) and others to manage the Michigan LRS.For more information, please view the Michigan Linear Referencing Home Page.

Groups of geographically defined attendance areas. They include elementary and middle-level schools that feed into particular high schools and consortium schools.For further details: https://www2.montgomeryschoolsmd.org/departments/Clusteradmin/Clusters/indexFor more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

The conflicted areas feature class is a depiction of known overlaps or gaps resulting from two or more different surveys of the same area within the BLM Administrative State of Wyoming. It may also contain areas with discrepancies in area, identification, or survey condition. The reason for the conflict should be noted in the description. This feature class may also include an indication of canceled or suspended surveys.

esmtrow_poly: This data set represents areal ROWs and Easements. Rights-of-Way and Easements that are linear in nature, such as roads or power lines, have associated widths that can be used to buffer the linear features and create polygon areas. Area entities include these linear buffer features as well as ROWs and Easements described by parcels. These are a portion of the total ENCUMBRANCE data category which includes entities about rights and restrictions. The rights and restrictions relate to the use of federal public land or to the use of non-federal land by the federal and public entities. Example uses might be construction or simply crossing the land. Rights-of-way in this data set include ROW and other land use authorizations issued by the United States under the authorities of Title V and Sec. 302(b) of Federal Land Policy and Management Act (FLPMA) (and other ROW authorities repealed by FLPMA) ,O Act of August 28, 1937, plus the Federal Highway Act and the Mineral Leasing Act. Easements in this data set are the spatial representation of partial interests in non-federal land acquired or reserved by the United States. In general, ROWs are rights granted by BLM and Easements are rights granted to BLM, but there are exceptions. The data set includes both linear and area entities. This data set is not intended to include all ROWs and Easements, but only those most important for common GIS spatial analysis. In addition, only basic information about the ROWs and Easements is provided. Details and the complete rights and restrictions history are found in the authoritative sources: Master Title Plats, case file records, and the LR2000 database.

State Geologic Map Compilation – StructureThis feature layer, utilizing data from the U.S. Geological Survey, portrays the State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States. The SGMC represents a seamless, spatial database of 48 State geologic maps. Per USGS, "A national digital geologic map database is essential in interpreting other datasets that support numerous types of national-scale studies and assessments, such as those that provide geochemistry, remote sensing, or geophysical data. The SGMC is a compilation of the individual USGS releases of the Preliminary Integrated Geologic Map Databases for the United States."A full discussion of the procedures and methodology used to create this dataset is available in the accompanying report: Horton, J.D., San Juan, C.A., and Stoeser, D.B, 2017, The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (ver. 1.1, August 2017): U.S. Geological Survey Data Series 1052, 46 p.SGMC: Fault StructuresData currency: June 30, 2017Data source: The State Geologic Map Compilation (SGMC) Geodatabase of the Conterminous United States.Data modification: NoneFor more information: The State Geologic Map Compilation (SGMC) Geodatabase of the Conterminous United StatesFor feedback please contact: ArcGIScomNationalMaps@esri.comU.S. Geological SurveyPer USGS, "The USGS provides science about the natural hazards that threaten lives and livelihoods; the water, energy, minerals, and other natural resources we rely on; the health of our ecosystems and environment; and the impacts of climate and land-use change."

Version: GOGI_V10_2This data was downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database.Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.Link to SourcePoint of Contact: Jennifer Bauer email:jennifer.bauer@netl.doe.govMichael D Sabbatino email:michael.sabbatino@netl.doe.gov

Some records delivered to BLM were sensitive and restricted from publication. This index contains those titles in addition to the published records, making this file a complete index. BLM plays a critical part in the management and ultimate recovery of the Desert Tortoise. As an agency, we manage significant portions of the species range. Without BLM's active participation in the management and improvement of the tortoise and its habitat, the species will not recover or become self sustaining. This spreadsheet is record that pulls together the desert tortoise data from across BLM states, districts, and field offices. It is to provide a clearing house of accumulated information to reduce duplicative efforts and create a synergy of knowledge to improve our ability to manage this iconic desert dwelling species.

This dataset was created to monitor the status, condition and trend of national BLM resources in accordance with BLM policies. It focuses on the BLM terrestrial core indicators, which include measures of vegetation and soil condition such as plant species cover and composition, plant height, and soil stability. The BLM terrestrial core indicators and methods were identified through a multi-disciplinary process and are described in BLM Technical Note 440 (https://ia800701.us.archive.org/6/items/blmcoreterrestri00mack/BlmCoreTerrestrialIndicatorsAndMethods_88072539.pdf). The Landscape Monitoring Framework (LMF) dataset was collect using the Natural Resource Conservation Services (NRCS) National Resource Inventory (NRI) methodology which mirrors the data collected by the BLM using the Monitoring Manual for Grassland, Shrubland, and Savannah Ecosystems (2nd edition; https://www.landscapetoolbox.org/methods-manuals/monitoring-manual-2nd-edition/). Specific instructions for data collectors each year the data were collected can be found at https://grazingland.cssm.iastate.edu/. Also see Interpreting Indicators of Rangeland Health (version 5; https://www.landscapetoolbox.org/manuals/iirhv5/).The monitoring locations were selected using spatially balanced, random sampling approaches and thus provide an unbiased representation of land conditions. However, these data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design.General DefinitionsNoxious: Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Each state’s noxious list can be found in tblStateSpecies Table, where the Noxious field is ‘YES’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Non-Noxious: Non-Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Non-Noxious status can be found in tblStateSpecies Table, where the Noxious field is ‘NO’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Sagebrush: Sagebrush species are designated for each BLM Administrative State using local botany expertise. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘Sagebrush’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Non-Sagebrush Shrub: Non Sagebrush Shrub species are designated for each BLM Administrative State as the plants determined to be shrubs that are not also Sagebrush. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘NonSagebrushShrub’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Tall Stature Perennial Grass: Tall Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘TallStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Short Stature Perennial Grass: Short Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘ShortStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Preferred Forb: Preferred forb for Sage Grouse status was determined for each state by Sage Grouse biologist and other local experts and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘PreferredForb’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Live: The NRI Methods measure Live vs Dead plant cover – i.e. if a pin drop hits a plant part and that plant part is dead (even if it’s on a living plant) that hit is considered a dead hit. Any occurrence of Live Sagebrush calculations indicates that the measurement is only hits that were live plant parts. If a pin hits both a live and a dead plant part in the same pin drop – that hit is considered live.Growth Habit: The form of a plant, in this dataset the options are Forb, Graminoid, Sedge, Succulent, Shrub, SubShrub, Tree, NonVascular. The most common growth habit for each state was determined by local botany expertise often after consulting the USDA plants database. The growth habit for each species is a state can be found in tblStateSpecies in the GrowthHabitSub field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc.Duration: The life length of a plant. In this dataset we consider plants to be either Perennial or Annual – Biennial plants are classified as Annuals. The most common duration for each state was determined by local botany expertise often after consulting the USDA plants database. The duration for each species is a state can be found in tblStateSpecies in the Duration field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc.tblStateSpecies: This table in the database contains the Species Lists for each state. In the instance where a species code does not have a Growth Habit, Growth Habit Sub, or Duration – any occurrence of that code will not be included in calculations that require that information – for example a code that has NonWoody Forb but no information about annual or perennial will not be included in any of the calculations that are perennial or annual forb calculations. Most codes with no information will have the following in the notes – indicating that the only calculation it will be included in is Total Foliar which doesn’t require any growth habit and duration information – “Not used for calculations except Total Foliar.”

This dataset includes one file for each of the 51 counties that were collected, as well as a CA_Merged file with the parcels merged into a single file.Note – this data does not include attributes beyond the parcel ID number (PARNO) – that will be provided when available, most likely by the state of California.DownloadA 1.6 GB zipped file geodatabase is available for download - click here.DescriptionA geodatabase with parcel boundaries for 51 (out of 58) counties in the State of California. The original target was to collect data for the close of the 2013 fiscal year. As the collection progressed, it became clear that holding to that time standard was not practical. Out of expediency, the date requirement was relaxed, and the currently available dataset was collected for a majority of the counties. Most of these were distributed with minimal metadata.The table “ParcelInfo” includes the data that the data came into our possession, and our best estimate of the last time the parcel dataset was updated by the original source. Data sets listed as “Downloaded from” were downloaded from a publicly accessible web or FTP site from the county. Other data sets were provided directly to us by the county, though many of them may also be available for direct download. Â These data have been reprojected to California Albers NAD84, but have not been checked for topology, or aligned to county boundaries in any way. Tulare County’s dataset arrived with an undefined projection and was identified as being California State Plane NAD83 (US Feet) and was assigned by ICE as that projection prior to reprojection. Kings County’s dataset was delivered as individual shapefiles for each of the 50 assessor’s books maintained at the county. These were merged to a single feature class prior to importing to the database.The attribute tables were standardized and truncated to include only a PARNO (APN). The format of these fields has been left identical to the original dataset. The Data Interoperablity Extension ETL tool used in this process is included in the zip file. Where provided by the original data sources, metadata for the original data has been maintained. Please note that the attribute table structure changes were made at ICE, UC Davis, not at the original data sources.Parcel Source InformationCountyDateCollecDateCurrenNotesAlameda4/8/20142/13/2014Download from Alamenda CountyAlpine4/22/20141/26/2012Alpine County PlanningAmador5/21/20145/14/2014Amador County Transportation CommissionButte2/24/20141/6/2014Butte County Association of GovernmentsCalaveras5/13/2014Download from Calaveras County, exact date unknown, labelled 2013Contra Costa4/4/20144/4/2014Contra Costa Assessor’s OfficeDel Norte5/13/20145/8/2014Download from Del Norte CountyEl Dorado4/4/20144/3/2014El Dorado County AssessorFresno4/4/20144/4/2014Fresno County AssessorGlenn4/4/201410/13/2013Glenn County Public WorksHumboldt6/3/20144/25/2014Humbodt County AssessorImperial8/4/20147/18/2014Imperial County AssessorKern3/26/20143/16/2014Kern County AssessorKings4/21/20144/14/2014Kings CountyLake7/15/20147/19/2013Lake CountyLassen7/24/20147/24/2014Lassen CountyLos Angeles10/22/201410/9/2014Los Angeles CountyMadera7/28/2014Madera County, Date Current unclear likely 7/2014Marin5/13/20145/1/2014Marin County AssessorMendocino4/21/20143/27/2014Mendocino CountyMerced7/15/20141/16/2014Merced CountyMono4/7/20144/7/2014Mono CountyMonterey5/13/201410/31/2013Download from Monterey CountyNapa4/22/20144/22/2014Napa CountyNevada10/29/201410/26/2014Download from Nevada CountyOrange3/18/20143/18/2014Download from Orange CountyPlacer7/2/20147/2/2014Placer CountyRiverside3/17/20141/6/2014Download from Riverside CountySacramento4/2/20143/12/2014Sacramento CountySan Benito5/12/20144/30/2014San Benito CountySan Bernardino2/12/20142/12/2014Download from San Bernardino CountySan Diego4/18/20144/18/2014San Diego CountySan Francisco5/23/20145/23/2014Download from San Francisco CountySan Joaquin10/13/20147/1/2013San Joaquin County Fiscal year close dataSan Mateo2/12/20142/12/2014San Mateo CountySanta Barbara4/22/20149/17/2013Santa Barbara CountySanta Clara9/5/20143/24/2014Santa Clara County, Required a PRA requestSanta Cruz2/13/201411/13/2014Download from Santa Cruz CountyShasta4/23/20141/6/2014Download from Shasta CountySierra7/15/20141/20/2014Sierra CountySolano4/24/2014Download from Solano Couty, Boundaries appear to be from 2013Sonoma5/19/20144/3/2014Download from Sonoma CountyStanislaus4/23/20141/22/2014Download from Stanislaus CountySutter11/5/201410/14/2014Download from Sutter CountyTehama1/16/201512/9/2014Tehama CountyTrinity12/8/20141/20/2010Download from Trinity County, Note age of data 2010Tulare7/1/20146/24/2014Tulare CountyTuolumne5/13/201410/9/2013Download from Tuolumne CountyVentura11/4/20146/18/2014Download from Ventura CountyYolo11/4/20149/10/2014Download from Yolo CountyYuba11/12/201412/17/2013Download from Yuba County

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

Mapped polygons of where civic environmental stewardship groups work in the city of Baltimore, MD, based upon the 2019 Baltimore STEW-MAP survey. Polygons were either self-mapped by stewardship groups or derived from an open-ended question of where a group has engaged in stewardship activities in the last year. Attribute data include other survey responses by the corresponding stewardship group.The Stewardship Mapping and Assessment Project (STEW-MAP) is a national USDA Forest Service research program designed to answer the questions: Which environmental stewardship groups are working across landscapes? Where, why, how, and to what effect? STEW-MAP defines a “stewardship group” as a civic organization or group that works to conserve, manage, monitor, advocate for, and/or educate the public about their local environments. STEW-MAP data are shared without the inclusion of personally identifiable information of individual respondents. This dataset includes results from the 2019 Baltimore, MD, STEW-MAP survey. Contact information is provided at the organizational (e.g., civic environmental stewardship group) level. This public version of the dataset only includes those groups in the database that agreed to be publicly listed. For more information on STEW-MAP, including methods, see https://www.fs.usda.gov/research/projects/stew-map.

GRA_ALLOTMENT: This is the feature dataset for the Livestock grazing allotments and associated attributes describing some basic characteristics of the allotments for allotments on BLM lands in Oregon and Washington.