This dataset is a compilation of address point data for the City of Tempe. The dataset contains a point location, the official address (as defined by The Building Safety Division of Community Development) for all occupiable units and any other official addresses in the City. There are several additional attributes that may be populated for an address, but they may not be populated for every address. Contact: Lynn Flaaen-Hanna, Development Services Specialist Contact E-mail Link: Map that Lets You Explore and Export Address Data Data Source: The initial dataset was created by combining several datasets and then reviewing the information to remove duplicates and identify errors. This published dataset is the system of record for Tempe addresses going forward, with the address information being created and maintained by The Building Safety Division of Community Development.Data Source Type: ESRI ArcGIS Enterprise GeodatabasePreparation Method: N/APublish Frequency: WeeklyPublish Method: AutomaticData Dictionary

This is the official Street Centerline dataset for the County of Sacramento and the incorporated cities within. The Street Range Index table is a distinct list of street names within the Centerline dataset along with the existing address range for each street by zip code.The Street Name Index table is a distinct list of street names within the Centerline dataset.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The Digital Geologic-GIS Map of Everglades National Park and Vicinity, 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 (ever_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 (ever_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 (ever_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 (ever_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (ever_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 (ever_geology_metadata_faq.pdf). Please read the ever_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: Florida Geological Survey and 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 (ever_geology_metadata.txt or ever_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 1:675,000 and United States National Map Accuracy Standards features are within (horizontally) 342.9 meters or 1125 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).

This dataset consists of summer temperature metrics for Boston, MA. These heat metrics summarize six CAPA Urban Heat Watch program temperature and heat index datasets using geographical boundaries from the Census Tract (CT) layer. Heat datasets were created by Museum of Science, Boston, and the Helmuth Lab at Northeastern University. Heat metrics are presented in the attribute table as mean values of each Heat Watch program dataset for all hexagon features. The six heat values included in this table are July 2019 temperature and heat index in degrees Fahrenheit for each of 3 1-hour periods -- 6 a.m., 3 p.m., and 7 p.m. EDT. The geographic boundaries used to summarize the heat metrics are current as of 2019.

This dataset represents a water shortage vulnerability analysis performed by DWR using modified PLSS sections pulled from the Well Completion Report PLSS Section Summaries. The attribute table includes water shortage vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to modified PLSS sections. Several relevant summary statistics from the Well Completion Reports are included in this table as well. This data is from the 2024 analysis.

Water Code Division 6 Part 2.55 Section 8 Chapter 10 (Assembly Bill 1668) effectively requires California Department of Water Resources (DWR), in consultation with other agencies and an advisory group, to identify small water suppliers and “rural communities” that are at risk of drought and water shortage. Following legislation passed in 2021 and signed by Governor Gavin Newsom, the Water Code Division 6, Section 10609.50 through 10609.80 (Senate Bill 552 of 2021) effectively requires the California Department of Water Resources to update the scoring and tool periodically in partnership with the State Water Board and other state agencies. This document describes the indicators, datasets, and methods used to construct this deliverable.  This is a statewide effort to systematically and holistically consider water shortage vulnerability statewide of rural communities, focusing on domestic wells and state small water systems serving between 4 and 14 connections. The indicators and scoring methodology will be revised as better data become available and stake-holders evaluate the performance of the indicators, datasets used, and aggregation and ranking method used to aggregate and rank vulnerability scores. Additionally, the scoring system should be adaptive, meaning that our understanding of what contributes to risk and vulnerability of drought and water shortage may evolve. This understanding may especially be informed by experiences gained while navigating responses to future droughts.”

A spatial analysis was performed on the 2020 Census Block Groups, modified PLSS sections, and small water system service areas using a variety of input datasets related to drought vulnerability and water shortage risk and vulnerability. These indicator values were subsequently rescaled and summed for a final vulnerability score for the sections and small water system service areas. The 2020 Census Block Groups were joined with ACS data to represent the social vulnerability of communities, which is relevant to drought risk tolerance and resources. These three feature datasets contain the units of analysis (modified PLSS sections, block groups, small water systems service areas) with the model indicators for vulnerability in the attribute table. Model indicators are calculated for each unit of analysis according to the Vulnerability Scoring documents provided by Julia Ekstrom (Division of Regional Assistance).

All three feature classes are DWR analysis zones that are based off existing GIS datasets. The spatial data for the sections feature class is extracted from the Well Completion Reports PLSS sections to be aligned with the work and analysis that SGMA is doing. These are not true PLSS sections, but a version of the projected section lines in areas where there are gaps in PLSS. The spatial data for the Census block group feature class is downloaded from the Census. ACS (American Communities Survey) data is joined by block group, and statistics calculated by DWR have been added to the attribute table. The spatial data for the small water systems feature class was extracted from the State Water Resources Control Board (SWRCB) SABL dataset, using a definition query to filter for active water systems with 3000 connections or less. None of these datasets are intended to be the authoritative datasets for representing PLSS sections, Census block groups, or water service areas. The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR.

These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the Drought and Water Shortage Vulnerability Scoring: California's Domestic Wells and State Smalls Systems documentation for more information on indicators and scoring. These estimated indicator scores may sometimes be calculated in several different ways, or may have been calculated from data that has since be updated. Counts of domestic wells may be calculated in different ways. In order to align with DWR SGMO's (State Groundwater Management Office) California Groundwater Live dashboards, domestic wells were calculated using the same query. This includes all domestic wells in the Well Completion Reports dataset that are completed after 12/31/1976, and have a 'RecordType' of 'WellCompletion/New/Production or Monitoring/NA'.

Please refer to the Well Completion Reports metadata for more information. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.4, dated September 14, 2022. DWR makes no warranties or guarantees — either expressed or implied— as to the completeness, accuracy, or correctness of the data.

DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

Parcel boundaries exported nightly from the Assessor's Office managed parcel fabric and joined with attributes related to owner information, values and size, the water source (Public Health database) and a link to the SmartGov public portal (permitting database). Most features are within 3 feet however some features can be up to 20 feet off. Please read the full data disclaimer when using this dataset.

A .docx and .odt showing what the different acronyms mean for BFE, BFC, BGC, BSC, BUC, GRE, BGG.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

See section 5 of the metadata for an attribute summary.

Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

Many communities in Canada depend to some extent on forestry and the forest sector. The importance of the forest industry to the regional economy can be assessed using the Canadian Forest Ecumene GIS (Geographic Information System) Database. “Ecumene” is a term used by geographers, meaning “inhabited lands.” A forest ecumene refers to areas where human settlement coincides with forested areas, including locations where people depend on the forest for their livelihood. Populated places in the ecumene database are referenced using natural boundaries, as opposed to administrative or census boundaries, and provide a more suitable means for integrating socio-economic data with ecological and environmental data in a region.

The Canadian Forest Ecumene GIS Database includes the custom boundaries for more than 4,200 populated areas across Canada, many of which were derived from remote-sensing “night-lights” imagery. Each ecumene place has a corresponding set of attributes pertaining to place name, province and other descriptive information, as well as an initial custom set of demographic and labour force variables derived from Statistics Canada Census and National Household Survey data for 2001, 2006 and 2011. An analysis of ecumene labour force data and location of mill facilities resulted in a generalized rendering showing regional dependency of the forest industry. The location of mill facilities layer includes information on mill type (i.e., pulp and saw) and ownership. The sensitivity to forest industry layer shows which forest communities and regions are more sensitive to economic impacts in the forest industry.

Provided layer: the Canadian Forest Ecumene GIS Database.

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 USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). 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 (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B 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.

CDFW BIOS GIS Dataset, Contact: PSMFC GIS, Description: Accurate mapping of tidal wetlands is vital for effective conservation and restoration of these valued habitats, and good mapping is key to strategic planning for coastal resilience. Tidal wetlands are defined by regular inundation by the tides; therefore, mapping of tidal wetlands should be based on knowledge of tidal water levels and the land areas inundated by the tides.

The CalFish Abundance Database contains a comprehensive collection of anadromous fisheries abundance information. Beginning in 1998, the Pacific States Marine Fisheries Commission, the California Department of Fish and Game, and the National Marine Fisheries Service, began a cooperative project aimed at collecting, archiving, and entering into standardized electronic formats, the wealth of information generated by fisheries resource management agencies and tribes throughout California.Extensive data are currently available for chinook, coho, and steelhead. Major data categories include adult abundance population estimates, actual fish and/or carcass counts, counts of fish collected at dams, weirs, or traps, and redd counts. Harvest data has been compiled for many streams, and hatchery return data has been compiled for the states mitigation facilities. A draft format has been developed for juvenile abundance and awaits final approval. This CalFish Abundance Database shapefile was generated from fully routed 1:100,000 hydrography. In a few cases streams had to be added to the hydrography dataset in order to provide a means to create shapefiles to represent abundance data associated with them. Streams added were digitized at no more than 1:24,000 scale based on stream line images portrayed in 1:24,000 Digital Raster Graphics (DRG).These features generally represent abundance counts resulting from stream surveys. The linear features in this layer typically represent the location for which abundance data records apply. This would be the reach or length of stream surveyed, or the stream sections for which a given population estimate applies. In some cases the actual stream section surveyed was not specified and linear features represent the entire stream. In many cases there are multiple datasets associated with the same length of stream, and so, linear features overlap. Please view the associated datasets for detail regarding specific features. In CalFish these are accessed through the "link" that is visible when performing an identify or query operation. A URL string is provided with each feature in the downloadable data which can also be used to access the underlying datasets.The coho data that is available via the CalFish website is actually linked directly to the StreamNet website where the database's tabular data is currently stored. Additional information about StreamNet may be downloaded at http://www.streamnet.org. Complete documentation for the StreamNet database may be accessed at http://http://www.streamnet.org/def.html

This is a collection of 124 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to Lindersson et al. (accepted february 2020 in WIREs Water) for further information about review methodology.

The collection is a descriptive list, holding the following information for each dataset:

• Category - as structured in Lindersson et al. (in preparation).
• Sub-category- as structured in Lindersson et al. (in preparation).
• Abbreviation - official or as specified in Lindersson et al. (in preparation).
• Title - full title of dataset.
• Product(s) - type of product(s) offered by the dataset.
• Period - time period covered by the dataset, not defined for all datasets.
• Temporal resolution - not defined for static datasets.
• Angular spatial resolution - only defined for gridded datasets.
• Metric spatial resolution - only defined for gridded datasets.
• Map scale
• Extent - geographic coverage of dataset given in latitude limits.
• Description
• Creating institute(s)
• Data type - raster, vector or tabular.
• File format
• Primary EO type - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.
• Data sources - lists the data sources behind the dataset, to the extent this is feasible.
• Data sources also in this table - data sources that are also included as datasets in this collection.
• Intentionally compatible with - defines other datasets in this collection that the dataset is intentinoally compatible with.
• Citation - dataset reference or credit.
• Documentation - dataset documentation.
• Web address - dataset access link.

NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.

The purpose of the American Indian and Alaska Native Land Area Representation (AIAN-LAR) Geographic Information System (GIS) dataset is to depict the external extent of federal Indian reservations and the external extent of associated land held in “trust” by the United States, “restricted fee” or “mixed ownership” status for federally recognized tribes and individual Indians. This dataset includes other land area types such as Public Domain Allotments, Dependent Indian Communities and Homesteads. This GIS Dataset is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes. No warranty is made by the Bureau of Indian Affairs (BIA) for the use of the data for purposes not intended by the BIA. This GIS Dataset may contain errors. There is no impact on the legal status of the land areas depicted herein and no impact on land ownership. No legal inference can or should be made from the information in this GIS Dataset. The GIS Dataset is to be used solely for illustrative, reference and statistical purposes and may be used for government to government Tribal consultation. Reservation boundary data is limited in authority to those areas where there has been settled Congressional definition or final judicial interpretation of the boundary. Absent settled Congressional definition or final judicial interpretation of a reservation boundary, the BIA recommends consultation with the appropriate Tribe and then the BIA to obtain interpretations of the reservation boundary. The land areas and their representations are compilations defined by the official land title records of the Bureau of Indian Affairs (BIA) which include treaties, statutes, Acts of Congress, agreements, executive orders, proclamations, deeds and other land title documents. The trust, restricted, and mixed ownership land area shown here, are suitable only for general spatial reference and do not represent the federal government’s position on the jurisdictional status of Indian country. Ownership and jurisdictional status is subject to change and must be verified with plat books, patents, and deeds in the appropriate federal and state offices. Included in this dataset are the exterior extent of off reservation trust, restricted fee tracts and mixed tracts of land including Public Domain allotments, Dependent Indian Communities, Homesteads and government administered lands and those set aside for schools and dormitories. There are also land areas where there is more than one tribe having an interest in or authority over a tract of land but this information is not specified in the AIAN-LAR dataset. The dataset includes both surface and subsurface tracts of land (tribal and individually held) “off reservation” tracts and not simply off reservation “allotments” as land has in many cases been subsequently acquired in trust. These data are public information and may be used by various organizations, agencies, units of government (i.e., Federal, state, county, and city), and other entities according to the restrictions on appropriate use. It is strongly recommended that these data be acquired directly from the BIA and not indirectly through some other source, which may have altered or integrated the data for another purpose for which they may not have been intended. Integrating land areas into another dataset and attempting to resolve boundary differences between other entities may produce inaccurate results. It is also strongly recommended that careful attention be paid to the content of the metadata file associated with these data. Users are cautioned that digital enlargement of these data to scales greater than those at which they were originally mapped can cause misinterpretation. The BIA AIAN-LAR dataset’s spatial accuracy and attribute information are continuously being updated, improved and is used as the single authoritative land area boundary data for the BIA mission. These data are available through the Bureau of Indian Affairs, Office of Trust Services, Division of Land Titles and Records, Branch of Geospatial Support.

This dataset includes processed satellite-based LST products and land use data for analyzing the diurnal variation in the surface urban heat island effect in southern metropolitan cities of Korea.

(1) ECOSTRESS LST data_after Daily Adjustment (2020-22): This dataset contains the results of daily adjustments performed on all ECOSTRESS LST observations with minimal data loss due to clouds, covering the study areas from 2020 to 2022.

(2) GK-2A LST data_Aligned with ECOSTRESS Grid (2020-22): This dataset consists of GK-2A LST data with similar observation areas to the ECOSTRESS LST images used in the study and acquisition times within 15 minutes. To facilitate smooth research, these data have been aligned to the ECOSTRESS LST grid.

(3) Seasonal Mean GK-2A LST data (2020-22): This dataset contains the results of calculating the mean LST data at 30-minute intervals throughout the day, based on GK-2A LST data for each season from 2020 to 2022.

(4) Land use data (2022): (4-1) Medium-level land use data: 2022 land use raster data categorized according to the medium-level classification system provided by the Ministry of Environment of Korea. It includes a classification code table. (4-2) Urban and Rural boundaries code: Urban and rural area boundaries defined based on the land use data.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Aeronautical Information (mAIS) is the official Swedish GIS dataset covering Air Traffic Management spatial information. The data set defined by approximately 40 layers covering the different feature types from Sweden AIP (Aeronautical Information Package), including Controlled Airspaces, Restricted Airspaces, Routes, Significant Points, Navigational Aids, Aerodrome facilities, En-route obstacles etc. The dataset does not follow the Feature Type definition of INSPIRE. The dataset is namespaced as "mais".