USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Me ...

Rifts mapped through reviewing the location of dikes and vents on the USGS 2007 Geologic Map of the State of Hawaii, as well as our assessment of topography, and, to a small extent, gravity data. Data is in shapefile format. Hawaii rifts shapefile projection file

Outline of Hawaiian islands (Kauai, Oahu, Molokai, Kahoolawe, Lanai, Maui, Hawaii) generated from the Geologic Map of the State of Hawaii published by the USGS in 2007. Island boundaries shapefile projection file

Fourth of a set of four files which make up the ESRI shapefile format. (PRJ)

Download high-quality, up-to-date shapefile boundaries (SHP, projection system SRID 4326). Our Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Download high-quality, up-to-date Portugal shapefile boundaries (SHP, projection system SRID 4326). Our Portugal Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

This resource provides a Jupyter notebook demonstrating how to use GeoPandas and Shapely in Python to extract bounding box information from a shapefile or GeoJSON file. It ensures that the returned values are in a specified projection system, regardless of whether the original file uses a geographic or projected coordinate system. Users can adjust the parameters in the notebook to fit their specific use case. In this example, the parameters are based on the Kings River Watershed in California, with the target projection system being Lambert Conformal Conic, as used in National Water Model versions 1-3.

Download high-quality, up-to-date Japan shapefile boundaries (SHP, projection system SRID 4326). Our Japan Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

The shapefile format is intended for geographic information systems (GIS). It contains all the information related to the geometry of the objects described. It is composed of three files with the same name, with the extensions SHP, DBF (attributes), SHX (geometry index) and PRJ (data projection system).

The file contains the all the lines of the national rail network.

The MNEMO column shows the status of each line: project (PROJECT), operated (OPERATED), neutralized (NEUT), neutralized and preserved (NEUT DEF), transferred to service track (VS), closed not removed (CLOSED ND), closed with the track maintained in place (CLOSED MV), closed and removed (CLOSED D), closed (CLOSED), closed made available to third parties (CLOSED DT ), withdrawn (RETRANCHE), declassified not sold (DEC NV), declassified sold (DEC V).

A line section is a physically continuous part of a line, delimited by two end nodes (origin and end) of line section, and having a coherent identification system. When there is a discontinuity, the line is divided into several sections, with an increasing rank.

The precision of the shapefile of the lines is decametric: use on a medium scale (1/50,000th).

This game publishes the data as of 12/31/2021.

date updated 11/16/2022

Download high-quality, up-to-date Spain shapefile boundaries (SHP, projection system SRID 4326). Our Spain Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Faults combined from USGS 2007 Geologic Map of the State of Hawaii and the USGS Quaternary Fault and Fold database. This data is in shapefile format. Hawaii faults shapefile projection file

The shapefile format is intended for geographic information systems (GIS). It contains all the information related to the geometry of the objects described. It consists of three files with the same name, with the extensions SHP, DBF (attributes), SHX (geometry index) and PRJ (data projection system). The file contains all the lines of the national rail network. The MNEMO column shows the status of each line: project (PROJECT), operated (EXPLOITE), neutralised (NEUT), neutralised and conserved (NEUT DEF), transferred in service (VS), closed not deposited (FERME ND), closed with lane retention (FERME MV), closed and deposited (FERME D), closed (FERME), closed made available to third parties (FERME DT), entrenched (RETRANCHE), decommissioned not sold (DEC NV), decommissioned sold (DEC V). A section of line is a physically continuous part of a line, bounded by two end nodes (origin and end) of a section of line, and having a coherent tracking system. Where there is a discontinuity, the line is divided into several sections, with an increasing row. The accuracy of the shapefile of the lines is decametric: medium-scale use (1/50.000th). update date 28/03/2024

This dataset contains documentation on the 146 global regions used to organize responses to the ArchaeGLOBE land use questionnaire between May 18 and July 31, 2018. The regions were formed from modern administrative regions (Natural Earth 1:50m Admin1 - states and provinces, https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-1-states-provinces/). The boundaries of the polygons represent rough geographic areas that serve as analytical units useful in two respects - for the history of land use over the past 10,000 years (a moving target) and for the history of archaeological research. Some consideration was also given to creating regions that were relatively equal in size. The regionalization process went through several rounds of feedback and redrawing before arriving at the 146 regions used in the survey. No bounded regional system could ever truly reflect the complex spatial distribution of archaeological knowledge on past human land use, but operating at a regional scale was necessary to facilitate timely collaboration while achieving global coverage. Map in Google Earth Format: ArchaeGLOBE_Regions_kml.kmz Map in ArcGIS Shapefile Format: ArchaeGLOBE_Regions.zip (multiple files in zip file) The shapefile format is a digital vector file that stores geographic location and associated attribute information. It is actually a collection of several different file types: .shp — shape format: the feature geometry .shx — shape index format: a positional index of the feature geometry .dbf — attribute format: columnar attributes for each shape .prj — projection format: the coordinate system and projection information .sbn and .sbx — a spatial index of the features .shp.xml — geospatial metadata in XML format .cpg — specifies the code page for identifying character encoding Attributes: FID - a unique identifier for every object in a shapefile table (0-145) Shape - the type of object (polygon) World_ID - coded value assigned to each feature according to its division into one of seventeen ‘World Regions’ based on the geographic regions used by the Statistics Division of the United Nations (https://unstats.un.org/unsd/methodology/m49/), with small changes to better reflect archaeological scholarly communities. These large regions provide organizational structure, but are not analytical units for the study. World_RG - text description of each ‘World Region’ Archaeo_ID - unique identifier (1-146) corresponding to the region code used in the ArchaeoGLOBE land use questionnaire and all ArchaeoGLOBE datasets Archaeo_RG - text description of each region Total_Area - the total area, in square kilometers, of each region Land-Area - the total area minus the area of all lakes and reservoirs found within each region (source: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/) PDF of Region Attribute Table: ArchaeoGLOBE Regions Attributes.pdf Excel file of Region Attribute Table: ArchaeoGLOBE Regions Attributes.xls Printed Maps in PDF Format: ArchaeoGLOBE Regions.pdf Documentation of the ArchaeoGLOBE Regional Map: ArchaeoGLOBE Regions README.doc

The outlines of the craters as a shapefile (PRJ file).

Download high-quality, up-to-date Germany shapefile boundaries (SHP, projection system SRID 4326). Our Germany Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

The Randolph Glacier Inventory (RGI) is a globally complete inventory of glacier outlines. It is supplemental to the database compiled by the Global Land Ice Measurements from Space initiative (GLIMS). While GLIMS is a multi-temporal database with an extensive set of attributes, the RGI is intended to be a snapshot of the world’s glaciers as they were near the beginning of the 21st century (although in fact its range of dates is still substantial). Production of the RGI was motivated by the preparation of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5).

Version 1.0 of the RGI was released in February 2012. It included a considerable number of unsubdivided ice bodies, which we refer to as glacier complexes, and a considerable number of nominal glaciers, which are glaciers for which only a location and an area are known; they are represented by circles of the appropriate area at the given location. Version 6.0, released in July 2017, has improved coverage of the conterminous US (regions 02-05 and 02-06), Scandinavia (region 08) and Iran (region 12-2). In Scandinavia several hundred smaller glaciers have been added and most glaciers now have exact dates. The flag attributes RGIFlag and GlacType were reorganized. Surging codes have been added from Sevestre and Benn (2015).

For version 1.0, we visualized the data in a geographic information system by overlaying outlines on modern satellite imagery, and assessed their quality relative to other available products. In several regions the outlines already in GLIMS were used for the RGI. Data from the World Glacier Inventory (WGI, http://nsidc.org/data/docs/noaa/g01130_glacier_inventory/; WGI, 1989) and the related WGI-XF (http://people.trentu.ca/~gcogley/glaciology; Cogley, 2009) were used for some nominal glaciers, mainly in the Pyrenees and in northern Asia. Where no other data were available we relied on data from the Digital Chart of the World (Danko, 1992).

The RGI is provided as shapefiles containing the outlines of glaciers in geographic coordinates (longitude and latitude, in degrees) which are referenced to the WGS84 datum. Data are organized by first-order region. For each region there is one shapefile (.SHP with accompanying .DBF, .PRJ and .SHX files) containing all glaciers and one ancillary .CSV file containing all hypsometric data. The attribute (.DBF) and hypsometric files contain one record per glacier. Each object in the RGI conforms to the data-model conventions of ESRI ArcGIS shapefiles. That is, each object consists of an outline encompassing the glacier, followed immediately by outlines representing all of its nunataks (ice-free areas enclosed by the glacier). In each object successive vertices are ordered such that glacier ice is on the right. This data model is not the same as the current GLIMS data model, in which nunataks are independent objects. The outlines of the RGI regions are provided as two shapefiles, one for first-order and one for second-order regions. A summary file containing glacier counts, glacierized area and a hypsometric list for each first-order and each second-order region is also provided. The 0.5°×0.5° grid is provided as a plain-text .DAT file in which zonal records of blank-separated glacierized areas in km2 are ordered from north to south. Information about RGI glaciers that are present in the mass balance tables of the WGMS database Fluctuations of Glaciers is provided as an ancillary .CSV file. The 19 regional attribute (.DBF) files are also provided in .CSV format.ReferencesRGI Consortium, 2017, Randolph Glacier Inventory (RGI) – A Dataset of Global Glacier Outlines: Version 6.0. Technical Report, Global Land Ice Measurements from Space, Boulder, Colorado, USA. Digital Media. DOI: https://doi.org/10.7265/N5-RGI-60 Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner, A. S., Hagen, J-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S., Moholdt, G., Molg, N., Paul, F., Radic, V., Rastner, P., Raup, B. H., Rich, J., Sharp, M. J. Glasser, N. (2014). The Randolph Glacier Inventory: A globally complete inventory of glaciers. Journal of Glaciology, 60 (221), 537-552.https://www.cambridge.org/core/services/aop-cambridge-core/content/view/730D4CC76E0E3EC1832FA3F4D90691CE/S002214300020600Xa.pdf/randolph_glacier_inventory_a_globally_complete_inventory_of_glaciers.pdf

Download high-quality, up-to-date South Korea shapefile boundaries (SHP, projection system SRID 4326). Our South Korea Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

If you use the SWORD Database in your work, please cite: Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. Water Resources Research. https://doi.org/10.1029/2021WR030054 1. Summary: The upcoming Surface Water and Ocean Topography (SWOT) satellite mission, planned to launch in 2022, will vastly expand observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission will provide a range of relevant data products. One product the SWOT mission will provide are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The SWOT vector data products will be most broadly useful if they allow multitemporal analysis of river nodes and reaches covering the same river areas. Doing so requires defining SWOT reaches and nodes a priori, so that SWOT data can be assigned to them. The SWOt River Database (SWORD) combines multiple global river- and satellite-related datasets to define the nodes and reaches that will constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater. 2. Data Formats: The SWORD database is provided in netCDF and shapefile formats. All files start with a two-digit continent identifier (“af” – Africa, “as” – Asia / Siberia, “eu” – Europe / Middle East, “na” – North America, “oc” – Oceania, “sa” – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats. NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v2.nc (i.e. na_sword_v2.nc). SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v2.shp (i.e. na_sword_nodes_hb74_v2.shp; na_sword_reaches_hb74_v2.shp). 3. Attribute Description: This list contains the primary attributes contained in the SWORD netCDFs and shapefiles. x: Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees). y: Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees). node_id: ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node). node_length (node files only): Node length measured along the GRWL centerline points (units: meters). reach_id: ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach). reach_length (reach files only): Reach length measured along the GRWL centerline points (units: meters). wse: Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters). wse_var: WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters). width: Average width for a node or reach (units: meters). width_var: Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters). max_width: Maximum width value across the channel for each node or reach that includes island and bar areas (units: meters). facc: Maxim...