The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops bi-annually updated point locations (latitude and longitude) for private schools included in the NCES Private School Survey (PSS). The PSS is conducted to provide a biennial count of the total number of private schools, teachers, and students. The PSS school location and associated geographic area assignments are derived from reported information about the physical location of private schools. The school geocode file includes supplemental geographic information for the universe of schools reported in the most current PSS school collection, and they can be integrated with the survey files through use of institutional identifiers included in both sources. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations and https://nces.ed.gov/programs/edge/Geographic/LocaleBoundaries Previous collections are available for the following year: 2021-22 2019-20 2017-18 2015-16 All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

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

The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.While every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. Boundary data is most suitable for larger scale maps; point locations are better suited for mapping scales between 1:250,000 and 1:3,000,000.If a site is part of a Joint Base (effective/designated on 1 October, 2010) as established under the 2005 Base Realignment and Closure process, it is attributed with the name of the Joint Base. All sites comprising a Joint Base are also attributed to the responsible DoD Component, which is not necessarily the pre-2005 Component responsible for the site.

The datasets that are included in the composite layer making up the protected area layer are given below: DatasetExample DesignationsCitation or hyperlinkPAD-US (CBI Edition)National Parks, GAP Status 1 and 2, State Parks, Open Spaces, Natural Areas“PAD-US (CBI Edition) Version 2.1b, California”. Conservation Biology Institute. 2016. https://databasin.org/datasets/64538491f43e42ba83e26b849f2cad28.Conservation EasementsCalifornia Conservation Easement Database (CCED), 2022a. 2022. www.CALands.org. Accessed December 2022. Inventoried Roadless Areas“Inventoried Roadless Areas.” US Forest Service. Dec 12, 2022. https://www.fs.usda.gov/detail/roadless/2001roadlessrule/maps/?cid=stelprdb5382437BLM National Landscape Conservation SystemWilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivershttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areashttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areashttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-national-monuments-nca-forest-reserves-other-poly/Greater Sage Grouse Habitat Conservation Areas (BLM)For solar technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’)For wind technology: BLMP_Managm = ‘PHMA’“Nevada and Northeastern California Greater Sage-Grouse Approved Resource Management Plan Amendment.” US Department of the Interior Bureau of Land Management Nevada State Office. 2015. https://eplanning.blm.gov/public_projects/lup/103343/143707/176908/NVCA_Approved_RMP_Amendment.pdf Other BLM Protected AreasAreas of Critical Environmental Concern (ACECs), Recreation Areas (SRMA, ERMA, OHV Designated Areas), including Vinagre Wash Special Recreation Management Area, National Scenic Areas, including Alabama Hills National Scenic Areahttps://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations

The project lead for the collection of this data was Carrington Hilson. Elk (9 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2023-2024. The Potter-Redwood Valley herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 6.5 hour intervals in the dataset. To improve the quality of the data set, all points with DOP values greater than 5 and those points visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd's home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 8 elk, including 15 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

The Federal Outer Continental Shelf (OCS) Marine Mineral (e.g. sand and gravel) Lease Areas layer is maintained by the Bureau of Ocean Energy Management (BOEM), part of the U.S. Department of the Interior. Each lease area polygon defines an area where entities that have entered into or have formally requested a Negotiated Non-Competitive Lease or Memorandum of Agreement with BOEM can dredge sand, gravel, or shell resources from the OCS. Section 8 (k) of the OCS Lands Act (OCSLA), as amended by Public Law 103-426 (enacted in 1994), gives BOEM the authority to negotiate an agreement for the use of OCS sand, gravel, and shell resources for use in: (1) a project for shore protection, beach restoration, or coastal restoration undertaken by a Federal, State, or local government agency; or (2) for use in a construction project funded in whole or in part by, or authorized by, the Federal government. This dataset is a collection of active, completed, expired, and proposed lease areas under BOEM's purview. Attribution consists of OCS lease number, state, project ID, volume of material allocated, borrow area ID, leases status, and administrative chronology of the lease term (e.g. execution and expiration dates). Revisions to this dataset are made on a regular basis by BOEM staff to reflect the current status of OCS marine mineral leasing activity.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in table 1. Distances indicate the minimum distance from each feature for commercial scale wind developmentAttributes: Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 area Urban areas: defined by the U.S. Census. Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping Tool Major highways: available from ESRI Living Atlas Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping Tool Active mines: Active Mines and Mineral Processing Plants in the United States in 2003Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center, or installation. Table 1 Wind Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <1000 m Water bodies <250 m Railways <250 m Major highways <125 m Airports <5000 m Active mines <1000 m Military Lands <3000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cyclesFootnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8]https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9]https://ezmt.anl.gov/[10]https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11]https://mrdata.usgs.gov/mineplant/Credits Title: Techno-economic screening criteria for utility-scale wind energy installations for Integrated Resource Planning Purpose for creation: These site suitability criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning. Keywords: wind energy, resource potential, techno-economic, IRP Extent: western states of the contiguous U.S. Use Limitations The geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts.Confidentiality: Public ContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.govOluwafemi Sawyerr femi@ethree.com

We started with ABCA's definition of “Full-Service Grocery Stores” (https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0)– pulled from the Food System Assessment below), and using those criteria, determined locations that fulfilled the categories in section 1 of the definition.Then, we reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a few more examples that meet or come very close to the full-service grocery store criteria. Here’s the explanation from OP regarding how they came to create their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”We also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

The Digital Geologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sahi_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sahi_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sahi_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sahi_geology_metadata_faq.pdf). Please read the sahi_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sahi_geology_metadata.txt or sahi_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The data are designed for strategic analyses at a national or regional scale which require spatially explicit information regarding the extent, distribution, and prevalence of the ownership types represented. The data are not recommended for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of these data can vary geographically under different ownership patterns.

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The datasets that are included in the composite layer making up the protected area layer are given below: Dataset Example Designations Citation or hyperlink PAD-US (CBI Edition) National Parks, GAP Status 1 and 2, State Parks, Open Spaces, Natural Areas “PAD-US (CBI Edition) Version 2.1b, California”. Conservation Biology Institute. 2016. https://databasin.org/datasets/64538491f43e42ba83e26b849f2cad28. Conservation Easements California Conservation Easement Database (CCED), 2022a. 2022. www.CALands.org. Accessed December 2022. Inventoried Roadless Areas “Inventoried Roadless Areas.” US Forest Service. Dec 12, 2022. https://www.fs.usda.gov/detail/roadless/2001roadlessrule/maps/?cid=stelprdb5382437 BLM National Landscape Conservation System Wilderness Areas, Wilderness Study Areas, National Monuments, National Conservation Lands, Conservation Lands of the California Desert, Scenic Rivers https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-areas https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-wilderness-study-areas https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-national-monuments-nca-forest-reserves-other-poly/ Greater Sage Grouse Habitat Conservation Areas (BLM) For solar technology: BLM_Managm IN (‘PHMA’, ‘GHMA’, ‘OHMA’) For wind technology: BLMP_Managm = ‘PHMA’ “Nevada and Northeastern California Greater Sage-Grouse Approved Resource Management Plan Amendment.” US Department of the Interior Bureau of Land Management Nevada State Office. 2015. https://eplanning.blm.gov/public_projects/lup/103343/143707/176908/NVCA_Approved_RMP_Amendment.pdf Other BLM Protected Areas Areas of Critical Environmental Concern (ACECs), Recreation Areas (SRMA, ERMA, OHV Designated Areas), including Vinagre Wash Special Recreation Management Area, National Scenic Areas, including Alabama Hills National Scenic Area https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-off-highway-vehicle-designations https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-ca-areas-of-critical-environmental-concern https://gbp-blm-egis.hub.arcgis.com/datasets/BLM-EGIS::blm-az-area-of-critical-environmental-concern-polygon [Big Marias ACEC and Beale Slough Riparian and Cultural ACEC] BLM, personal communication, November 2, 2022. Mono Basin NFSA https://pcta.maps.arcgis.com/home/item.html?id=cf1495f8e09940989995c06f9e290f6b#overview Terrestrial 30x30 Conserved Areas Gap Status 1 and 2 CA Nature. 30x30 Conserved Areas, Terrestrial. 2021. https://www.californianature.ca.gov/datasets/CAnature::30x30-conserved-areas-terrestrial/ Accessed September 2022. CPAD Open Spaces and Parks under city or county level California Protected Areas Database (CPAD), 2022b. 2022. https://www.calands.org/cpad/. Accessed February 22, 2023. USFS Special Interest Management Areas https://data-usfs.hub.arcgis.com/datasets/usfs::special-interest-management-areas-feature-layer/about Proposed Protected Area Molok Luyuk Extension (Berryessa Mtn NM Expansion) CalWild, personal communication, January 19, 2023. This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library. Change Log: Version 1.1 (January 22, 2024 10:40 AM) Layer revised to allow for gaps to remain when combining all components of the protected area layer.

This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2021-2022 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. Complete field and attribute information is available in the "Entities and Attributes" metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 4,910 new records and modifications to the spatial location and/or attribution of 17,329 records.

The Digital Geologic-GIS Map of the Mammoth Cave Quadrangle, Kentucky is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (macv_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (macv_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (macv_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (maca_abli_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (maca_abli_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (macv_geology_metadata_faq.pdf). Please read the maca_abli_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (macv_geology_metadata.txt or macv_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This dataset is flattened and multicounty communities are unsplit by county lines. Flattened means that there are no overlaps; larger shapes like counties are punched out or clipped where smaller communities are contained within them. This allows for choropleth shading and other mapping techniques such as calculating unincorporated county land area. Multicounty cities like Houston are a single feature, undivided by counties. This layer is derived from Census, State of Maine, and National Flood Hazard Layer political boundaries.rnrnThe Community Layer datasets contain geospatial community boundaries associated with Census and NFIP data. The dataset does not contain personal identifiable information (PII). The Community Layer can be used to tie Community ID numbers (CID) to jurisdiction, tribal, and special land use area boundaries.rnrnA geodatabase (GDB) link is Included in the Full Data section below. The compressed file contains a collection of files that can store, query, and manage both spatial and nonspatial data using software that can read such a file. It bcontains all of the community layers/b, not just the layer for which this dataset page describes. rnThis layer can also be accessed from the FEMA ArcGIS viewer online: https://fema.maps.arcgis.com/home/item.html?id=8dcf28fc5b97404bbd9d1bc6d3c9b3cfrnrnrnCitation: FEMA's citation requirements for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions.rnrnFor answers to Frequently Asked Questions (FAQs) about the OpenFEMA program, API, and publicly available datasets, please visit: https://www.fema.gov/about/openfema/faq.rnIf you have media inquiries about this dataset, please email the FEMA News Desk at FEMA-News-Desk@fema.dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open Government program, please email the OpenFEMA team at OpenFEMA@fema.dhs.gov.

A zipped set of CSV files containing a snapshot of personal property assessment data.

The project lead for the collection of this data was Erin Zulliger. Elk (3 adult females) were captured and equipped with GPS collars (Litetrack/Pinpoint Iridium collars, Lotek Wireless Inc., Newmarket, Ontario, Canada or Vectronic Aerospace) transmitting data from 2021-2023. The Sam’s Neck herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 6-hour intervals in the dataset. To improve the quality of the data set, the GPS data locations fixed in 2D space and visually assessed as a bad fix by the analyst were removed.The methodology used for this migration analysis allowed for the mapping of the herd's annual range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 3 elk, including 6 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Annual home range designations for this herd may expand with a larger sample.

Private schools located in Cook County. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS, is required.