Global Geographic Information System GIS Software market size 2025 was XX Million. Geographic Information System GIS Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

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

Based on open access data, 79 Mediterranean passenger ports are analyzed to compare their infrastructure, hinterland accessibility and offered multi-modality categories. Comparative Geo-spatial analysis is also carried out by using the data normalization method in order to visualize the ports' performance on maps. These data driven comprehensive analytical results can bring added value to sustainable development policy and planning initiatives in the Mediterranean Region. The analyzed elements can be also contributed to the development of passenger port performance indicators. The empirical research methods used for the Mediterranean passenger ports can be replicated for transport nodes of any region around the world to determine their relative performance on selected criteria for improvement and planning.

The Mediterranean passenger ports were initially categorized into cruise and ferry ports. The cruise ports were identified from the member list of the Association for the Mediterranean Cruise Ports (MedCruise), representing more than 80% of the cruise tourism activities per country. The identified cruise ports were mapped by selecting the corresponding geo-referenced ports from the map layer developed by the European Marine Observation and Data Network (EMODnet). The United Nations (UN) Code for Trade and Transport Locations (LOCODE) was identified for each of the cruise ports as the common criteria to carry out the selection. The identified cruise ports not listed by the EMODnet were added to the geo-database by using under license the editing function of the ArcMap (version 10.1) geographic information system software. The ferry ports were identified from the open access industry initiative data provided by the Ferrylines, and were mapped in a similar way as the cruise ports (Figure 1).

Based on the available data from the identified cruise ports, a database (see Table A1–A3) was created for a Mediterranean scale analysis. The ferry ports were excluded due to the unavailability of relevant information on selected criteria (Table 2). However, the cruise ports serving as ferry passenger ports were identified in order to maximize the scope of the analysis. Port infrastructure and hinterland accessibility data were collected from the statistical reports published by the MedCruise, which are a compilation of data provided by its individual member port authorities and the cruise terminal operators. Other supplementary sources were the European Sea Ports Organization (ESPO) and the Global Ports Holding, a cruise terminal operator with an established presence in the Mediterranean. Additionally, open access data sources (e.g. the Google Maps and Trip Advisor) were consulted in order to identify the multi-modal transports and bridge the data gaps on hinterland accessibility by measuring the approximate distances.

A list of Place Names extracted from the ǂKhomani San | Hugh Brody Collection held by the University of Cape Town (UCT) Library.Effort has been made to geocode as many place names as possible with their geographic coordinates (Latitude & Longitude).The data set is available in three formats:• a comma separated values table (CSV); • a KMZ spatial data layer, compatible with Google Maps, Google Earth and most GIS packages; • a ZIP archive of an ESRI shapefile, compatible with most GIS packagesThis data set is incomplete. Not all resources in the collection have been processed, additional place names may be missing from the list. Geocoding was performed as accurately as our reference resources allowed, but some locations may have been misplaced.We would like to thank African Tongue and the communities of the region for their assistance with the creation of this data set.The ǂKhomani San are the first people of the southern Kalahari. They lived as hunters and gatherers in the immense desert in the northwest corner of South Africa. For them, it is a land rich in wildlife, plants, trees, great sand dunes and dry riverbeds. When the ǂKhomani San share their history, they tell a story of dispossession from their lands, erasure of their way of life, and disappearance of their language. To speak of their past is to search in memory for all that was taken from them in the colonial, apartheid and post-apartheid era. They also tell a story of reclamation and recovery of lands, language, and even of memory itself. Coordinate Reference System: Geographic Coordinate System WGS1984 (GCS WGS84)Fields - Due to software limitations diacritics were not used in field names:Place_Name: Name of placeLatitude: Latitude Ordinate GCS WGS84Longitude: Longitude Ordinate GCS WGS84Notes_Loc: Any extra information about the place name location, either from the collection or discovered by the authors.Source: The source of the geographic coordinatesLocal Name: This is the name as it may have changed locallyEng: English nameAfr: Afrikaans namekqz_Kora: Kora namenaq_Nama: Nama namengh_Nuu: Nuu nametsn_Tswana: Tswana namegla_Scottish_Gaelic: Gaelic namefra_French: French nameNotes_ling: notes of linguistic interest

This dataset contains a GIS database of Aids to Navigation in the Gulf of America and coastal waters of Alabama, Florida, Louisiana, Mississippi and Texas. These data were compiled on 1999-10-21. The term "Aids to Navigation" (ATONS or AIDS) refers to a device outside of a vessel used to assist mariners in determining their position or safe course, or to warn them of obstructions. AIDS to navigation include lighthouses, lights, buoy, sound signals, landmarks, RACONs, radio beacons, LORAN, and omega. These include AIDS which are installed and maintained by the Coast Guard as well as privately installed and maintained aids (permit required). This does not include unofficial AIDS (illegal) such as stakes, PVC pipes, and such placed without permission. Each USCG District Headquarters is responsible for updating their database on an "as needed" basis. When existing AIDS are destroyed or relocated and new AIDS are installed the database is updated. Each AID is assigned an official "light listing number". The light list is a document listing the current status of ATONS and it is published and distributed on a regular basis. Interim changes to the light list are published in local Notices to Mariners which are the official means which navigators are supposed to keep their charts current. In addition, the USCG broadcasts Notices to Mariners on the marine band radio as soon as changes of the status of individual AIDS are reported. The light list number and local Notices to Mariners reports are suggested ways to keep the database current on a regular or even "real time" basis. However, annual (or more frequent) updates of the entire dataset may be obtained from each USCG District Headquarters. Geographic Information System (GIS) software is required to display the data in this NCEI accession.

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GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. To produce the digital map, a combination of 1:12,000-scale true color digital ortho-imagery acquired in 2003, 1:12,000-scale true color ortho-rectified imagery acquired in 2005, and all of the GPS referenced ground data were used. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. All of the resulting map unit names, map unit codes, NVC information, and other relevant attributes were added to each polygon in the GIS layer.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

This wye pipes feature class represents current wastewater information connecting the sewer service to either side of the street in the City of Los Angeles. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most rigorous geographic information of the sanitary sewer system using a geometric network model, to ensure that its sewers reflect current ground conditions. The sanitary sewer system, pump plants, wyes, maintenance holes, and other structures represent the sewer infrastructure in the City of Los Angeles. Wye and sewer information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the wastewater Wye is entered into attributes. Principal attributes include:WYE_SUBTYPE: wye subtype is the principal field that describes various types of points as either Chimney, Chimney Riser, Offset Chimney Riser, Siphon, Special Case, Spur, Tap, Tee, Unclassified, Vertical Tee, Vertical Tee Riser, Wye, Wye Drawn as a Tap.For a complete list of attribute values, please refer to (TBA Wastewater data dictionary).Wastewater Wye pipes lines layer was created in geographical information systems (GIS) software to display the location of wastewater wye pipes. The wyes lines layer is a feature class in the LACityWastewaterData.gdb Geodatabase dataset. The layer consists of spatial data as a line feature class and attribute data for the features. The lines are entered manually based on wastewater sewer maps and BOE standard plans, and information about the lines is entered into attributes. The wye pipes lines features are sewer pipe connections for buildings. The features in the Wastewater connector wye points layer is a related structure connected with the wye pipe line. The WYE_ID field value is the unique ID. The WYE_ID field relates to the Sewer Permit tables. The annotation wye features are displayed on maps alongside features from the Wastewater Sewer Wye pipes lines layer. The wastewater wye pipes lines are inherited from a sewer spatial database originally created by the City's Wastewater program. The database was known as SIMMS, Sewer Inventory and Maintenance Management System. Wye pipe information should only be added to the Wastewater wye pipes layer if documentation exists, such as a wastewater map approved by the City Engineer. Sewers plans and specifications proposed under private development are reviewed and approved by by Bureau of Engineering. The Department of Public Works, Bureau of Engineering's, Brown Book (current as of 2010) outlines standard specifications for public works construction. For more information on sewer materials and structures, look at the Bureau of Engineering Manual, Part F, Sewer Design, F 400 Sewer Materials and Structures section, and a copy can be viewed at http://eng.lacity.org/techdocs/sewer-ma/f400.pdf.List of Fields:SPECIAL_STRUCT: This attribute is the basin number.TOP_: When a chimney is present, this is the depth at the top of the chimney.BOTTOM: When a chimney is present, this is the depth at the bottom of the chimney.PL_HUNDS: This value is the hundreds portion of the stationing at the property line.SHAPE: Feature geometry.USER_ID: The name of the user carrying out the edits of the wye pipes data.TYPE: This is the old wye status and is no longer referenced.REMARKS: This attribute contains additional comments regarding the wye line segment, such as a line through in all caps when lined out on wye maps.WYE_NO: This value is the number of the line segment for the wye structure located along the pipe segment. This is a 2 digit value. The number starts at 1 for the first wye connected to a pipe. The numbers increase sequentially with each wye being unique.WYE_ID: The value is a combination of PIPE_ID and WYE_NO fields, forming a unique number. This 19 digit value is a key attribute of the wye lines data layer. This field relates to the Permit tables.C_TENS: This value is the tens portion of the stationing at the curb line.C_HUNDS: This value is the hundreds portion of the stationing at the curb line.WYE_SUBTYPE: This value is the type of sewer connection. Values: • 2 - Tap. • 8 - Siphon. • 13 - Wye Drawn as a Tap. • 9 - Special Case. • 6 - Chimney riser. • 4 - Chimney. • 5 - Vertical Tee Riser. • 7 - Vertical tee. • 10 - Spur. • 11 - Unclassified. • 12 - Offset Chimney Riser. • 1 - Wye. • 3 - Tee.SIDE: The side of the pipe looking up stream to which structure attaches. Values: • U - Unknown. • L - Left. • R - Right. • C - Centered.ASSETID: User-defined unique feature number that is automatically generated.PL_DEPTH: This value is the depth of the service connection at the property line.DEPTH: This value is the depth of the Wye from the surface in feet.STAT_HUND: This value is the hundreds portion of the stationing.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District. • W - West LA Engineering District.PIPE_ID: The value is a combination of the values in the UP_STRUCT, DN_STRUCT, and PIPE_LABEL fields. This is the 17 digit identifier of each pipe segment and is a key attribute of the pipe line data layer. This field named PIPE_ID relates to the field in the Annotation Pipe and to the field named PIPE_ID in the Pipe line feature class data layers.OBJECTID: Internal feature number.ENABLED: Internal feature number.REHAB: This attribute indicates if the wye pipe has been rehabilitated.C_DEPTH: This value is the depth of the service connection at the curb line.STAT_TENS: This value is the tens portion of the stationing.BASIN: This attribute is the basin number.LAST_UPDATE: Date of last update of the point feature.STATUS: This value is the active or inactive status of the wye pipes. Values: • Capped - Capped. • INACTIVE - Inactive.PL_TENS: This value is the tens portion of the stationing at the property line.CRTN_DT: Creation date of the point feature.SERVICEID: User-defined unique feature number that is automatically generated.SHAPE_Length: Length of feature in internal units.

Abstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.
Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

This alley feature class represents current street centerline lines in the City of Los Angeles. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works. Alley Centerline layer was created in geographical information systems (GIS) software to display alley centerlines. The purpose of the layer is to show the location of the alleys on map products and web mapping applications. The Bureau of Street Services tracks the location of existing alleys that they maintain. Alleys are not dedicated streets, and the use of the road is determined by the Planning Department. Street features in the Street Centerline layer are dedicated. In the Alley Centerline layer, there is no naming convention stored in the corresponding records to identify the names of the alleys. Department of Transportation, LADOT, is working on a naming standard. Note there are Alleys features in the Street Centerline layer are Named Alleys. Since the line features for named alleys are stored in the Street Centerline layer, there are no line features for named alleys in those areas that are geographically coincident in the Alley layer. For a named alley line feature in the Street Centerline layer, the corresponding record contains the street designation field value of ST_DESIG = 20, and there is a name for the alley stored in the STNAME and STSFX fields. Currently the Alley line features in Alley layer do not split the street centerline layer features where they intersect, so there is no intersection point created or maintained in the Intersection layer.List of Fields:ZIP_R: Zip code right.ID: A unique numeric identifier of the line feature. This unique identifier is no longer used and ASSETID is used now instead.AL_DESIG: This value is currently not being used.MAPSHEET: The alpha-numeric mapsheet number, which refers to a valid B-map or A-map number on the Cadastral tract index map. Values: • B, A, -5A - Any of these alpha-numeric combinations are used, whereas the underlined spaces are the numbers.SHAPE: Feature geometry.USER_ID: The name of the user carrying out the edits.CRTN_DT: Creation date of the polygon feature.LST_MODF_DT: Last modification date of the feature.ZIP_L: Zip code left.STNUM: Street identification number. This field is currently not being used.OBJECTID: Internal feature number.LINE_ID: This value is currently not being edited. This used to be an automatically assigned unique ID of the alley centerline line segments.ASSETID: User-defined feature autonumber.AL_ID: This value is the ID of the alley as assigned by Bureau of Street Services, Department of Public Works. This is unique for each alley, but not unique for each alley line segment. This field has not been updated.SHAPE.LEN

Abstract

The dataset is a geodatabase focusing on the distribution of freshwater fish species in Northern Greece. The study area encompasses various lakes and rivers within the regions of Thrace, Eastern, Central, and Western Macedonia, and Epirus. It classifies fish species into three categories based on their conservation status according to the IUCN Red List: Critically Endangered, Endangered, and Vulnerable. The data analysis reveals that the study area is characterized by high fish diversity, particularly in certain ecosystems such as the Evros River, Strymonas River, Aliakmonas River, Axios River, Volvi Lake, Nestos River, and Prespa Lake. These ecosystems serve as important habitats for various fish species. Mapping of the dataset shows the geographic distribution of threatened fish species, indicating that Northern Greece is a hotspot for species facing extinction risks. Overall, the dataset provides valuable insights for researchers, policymakers, and conservationists in understanding the status of fish fauna in Northern Greece and developing strategies for the protection and preservation of these important ecosystems.

Methods

Data Collection: The dataset was collected through a combination of field surveys, literature reviews, and the compilation of existing data from various reliable sources. Here's an overview of how the dataset was collected and processed:

• Freshwater Fishes and Lampreys of Greece: An Annotated Checklist
• The Red Book of Endangered Animals of Greece
• The "Red List of Threatened Species"
• The study "Monitoring and Evaluation of the Conservation Status of Fish Fauna Species of Community Interest in Greece"
• The international online fish database FishBase

Data Digitization and Georeferencing: To create a comprehensive database, we digitized and georeferenced the collected data from various sources. This involved converting information from papers, reports, and surveys into digital formats and associating them with specific geographic coordinates. Georeferencing allowed us to map the distribution of fish species within the study area accurately.

Data Integration: The digitized and georeferenced data were then integrated into a unified geodatabase. The geodatabase is a central repository that contains both spatial and descriptive data, facilitating further analysis and interpretation of the dataset.

Data Analysis: We analyzed the collected data to assess the distribution of fish species in Northern Greece, evaluate their conservation status according to the IUCN Red List categories, and identify the threats they face in their respective ecosystems. The analysis involved spatial mapping to visualize the distribution patterns of threatened fish species.

Data Validation: To ensure the accuracy and reliability of the dataset, we cross-referenced the information from different sources and validated it against known facts about the species and their habitats. This process helped to eliminate any discrepancies or errors in the dataset.

Interpretation and Findings: Finally, we interpreted the analyzed data and derived key findings about the diversity and conservation status of freshwater fish species in Northern Greece. The results were presented in the research paper, along with maps and visualizations to communicate the spatial patterns effectively.

Overall, the dataset represents a comprehensive and well-processed collection of information about fish fauna in the study area. It combines both spatial and descriptive data, providing valuable insights for understanding the distribution and conservation needs of freshwater fish populations in Northern Greece.

Usage notes

The data included with the submission is stored in a geodatabase format, specifically an ESRI Geodatabase (.gdb). A geodatabase is a container that can hold various types of geospatial data, including feature classes, attribute tables, and raster datasets. It provides a structured and organized way to store and manage geographic information.

To open and work with the geodatabase, you will need GIS software that supports ESRI Geodatabase formats. The primary software for accessing and manipulating ESRI Geodatabases is ESRI ArcGIS, which is a proprietary GIS software suite. However, there are open-source alternatives available that can also work with Geodatabase files.

Open-source software such as QGIS has support for reading and interacting with Geodatabase files. By using QGIS, you can access the data stored in the geodatabase and perform various geospatial analyses and visualizations. QGIS is a powerful and widely used open-source Geographic Information System that provides similar functionality to ESRI ArcGIS.

For tabular data within the geodatabase, you can export the tables as CSV files and open them with software like Microsoft Excel or the open-source alternative, LibreOffice Calc, for further analysis and manipulation.

Overall, the data provided in the submission is in a geodatabase format, and you can use ESRI ArcGIS or open-source alternatives like QGIS to access and work with the geospatial data it contains.

This parcels polygons feature class represents current city parcels within the City of Los Angeles. It shares topology with the Landbase parcel lines feature class. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way, ownership and land record information. The legal boundaries are determined on the ground by license surveyors in the State of California, and by recorded documents from the Los Angeles County Recorder's office and the City Clerk's office of the City of Los Angeles. Parcel and ownership information are available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Associated information about the landbase parcels is entered into attributes. Principal attributes include:PIN and PIND: represents the unique auto-generated parcel identifier and key to related features and tables. This field is related to the LA_LEGAL, LA_APN and LA_HSE_NBR tables. PIN contains spaces and PIND replaces those spaces with a dash (-).LA_LEGAL - Table attributes containing legal description. Principal attributes include the following:TRACT: The subdivision tract number as recorded by the County of Los AngelesMAP_REF: Identifies the subdivision map book reference as recorded by the County of Los Angeles.LOT: The subdivision lot number as recorded by the County of Los Angeles.ENG_DIST: The four engineering Districts (W=Westla, C=Central, V= Valley and H=Harbor).CNCL_DIST: Council Districts 1-15 of the City of Los Angeles. OUTLA means parcel is outside the City.LA_APN- Table attributes containing County of Los Angeles Assessors information. Principal attributes include the following:BPP: The Book, Page and Parcel from the Los Angeles County Assessors office. SITUS*: Address for the property.LA_HSE_NBR - Table attributes containing housenumber information. Principal attributes include the following:HSE_ID: Unique id of each housenumber record.HSE_NBR: housenumber numerical valueSTR_*: Official housenumber addressFor a complete list of attribute values, please refer to Landbase_parcel_polygons_data_dictionary.Landbase parcels polygons data layer was created in geographical information systems (GIS) software to display the location of the right of way. The parcels polygons layer delineates the right of way from Landbase parcels lots. The parcels polygons layer is a feature class in the LACityLandbaseData.gdb Geodatabase dataset. The layer consists of spatial data as a polygon feature class and attribute data for the features. The area inside a polygon feature is a parcel lot. The area outside of the parcel polygon feature is the right of way. Several polygon features are adjacent, sharing one line between two polygons. For each parcel, there is a unique identifier in the PIND and PIN fields. The only difference is PIND has a dash and PIN does not. The types of edits include new subdivisions and lot cuts. Associated legal information about the landbase parcels lots is entered into attributes. The landbase parcels layer is vital to other City of LA Departments, by supporting property and land record operations and identifying legal information for City of Los Angeles. The landbase parcels polygons are inherited from a database originally created by the City's Survey and Mapping Division. Parcel information should only be added to the Landbase Parcels layer if documentation exists, such as a Deed or a Plan approved by the City Council. When seeking the definitive description of real property, consult the recorded Deed or Plan.List of Fields:ID: A unique numeric identifier of the polygon. The ID value is the last part of the PIN field value.ASSETID: User-defined feature autonumber.MAPSHEET: The alpha-numeric mapsheet number, which refers to a valid B-map or A-map number on the Cadastral grid index map. Values: • B, A, -5A - Any of these alpha-numeric combinations are used, whereas the underlined spaces are the numbers. An A-map is the smallest grid in the index map and is used when there is a large amount of spatial information in the map display. There are more parcel lines and annotation than can fit in the B-map, and thus, an A-map is used. There are 4 A-maps in a B-map. In areas where parcel lines and annotation can fit comfortably in an index map, a B-map is used. The B-maps are at a scale of 100 feet, and A-maps are at a scale of 50 feet.OBJECTID: Internal feature number.BPPMAP_REFTRACTBLOCKMODLOTARBCNCL_DIST: LA City Council District. Values: • (numbers 1-15) - Current City Council Member for that District can be found on the mapping website http://navigatela.lacity.org/navigatela, click Council Districts layer name, under Boundaries layer group.SHAPE: Feature geometry.BOOKPAGEPARCELPIND: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. The D in the field name PIND, means "dash", and there is a dash between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.ENG_DIST: LA City Engineering District. The boundaries are displayed in the Engineering Districts index map. Values: • H - Harbor Engineering District. • C - Central Engineering District. • V - Valley Engineering District. • W - West LA Engineering District.PIN: The value is a combination of MAPSHEET and ID fields, creating a unique value for each parcel. There are spaces between the MAPSHEET and ID field values. This is a key attribute of the LANDBASE data layer. This field is related to the APN and HSE_NBR tables.

The Ontario government, generates and maintains thousands of datasets. Since 2012, we have shared data with Ontarians via a data catalogue. Open data is data that is shared with the public. Click here to learn more about open data and why Ontario releases it. Ontario’s Open Data Directive states that all data must be open, unless there is good reason for it to remain confidential. Ontario’s Chief Digital and Data Officer also has the authority to make certain datasets available publicly. Datasets listed in the catalogue that are not open will have one of the following labels: If you want to use data you find in the catalogue, that data must have a licence – a set of rules that describes how you can use it. A licence: Most of the data available in the catalogue is released under Ontario’s Open Government Licence. However, each dataset may be shared with the public under other kinds of licences or no licence at all. If a dataset doesn’t have a licence, you don’t have the right to use the data. If you have questions about how you can use a specific dataset, please contact us. The Ontario Data Catalogue endeavors to publish open data in a machine readable format. For machine readable datasets, you can simply retrieve the file you need using the file URL. The Ontario Data Catalogue is built on CKAN, which means the catalogue has the following features you can use when building applications. APIs (Application programming interfaces) let software applications communicate directly with each other. If you are using the catalogue in a software application, you might want to extract data from the catalogue through the catalogue API. Note: All Datastore API requests to the Ontario Data Catalogue must be made server-side. The catalogue's collection of dataset metadata (and dataset files) is searchable through the CKAN API. The Ontario Data Catalogue has more than just CKAN's documented search fields. You can also search these custom fields. You can also use the CKAN API to retrieve metadata about a particular dataset and check for updated files. Read the complete documentation for CKAN's API. Some of the open data in the Ontario Data Catalogue is available through the Datastore API. You can also search and access the machine-readable open data that is available in the catalogue. How to use the API feature: Read the complete documentation for CKAN's Datastore API. The Ontario Data Catalogue contains a record for each dataset that the Government of Ontario possesses. Some of these datasets will be available to you as open data. Others will not be available to you. This is because the Government of Ontario is unable to share data that would break the law or put someone's safety at risk. You can search for a dataset with a word that might describe a dataset or topic. Use words like “taxes” or “hospital locations” to discover what datasets the catalogue contains. You can search for a dataset from 3 spots on the catalogue: the homepage, the dataset search page, or the menu bar available across the catalogue. On the dataset search page, you can also filter your search results. You can select filters on the left hand side of the page to limit your search for datasets with your favourite file format, datasets that are updated weekly, datasets released by a particular organization, or datasets that are released under a specific licence. Go to the dataset search page to see the filters that are available to make your search easier. You can also do a quick search by selecting one of the catalogue’s categories on the homepage. These categories can help you see the types of data we have on key topic areas. When you find the dataset you are looking for, click on it to go to the dataset record. Each dataset record will tell you whether the data is available, and, if so, tell you about the data available. An open dataset might contain several data files. These files might represent different periods of time, different sub-sets of the dataset, different regions, language translations, or other breakdowns. You can select a file and either download it or preview it. Make sure to read the licence agreement to make sure you have permission to use it the way you want. Read more about previewing data. A non-open dataset may be not available for many reasons. Read more about non-open data. Read more about restricted data. Data that is non-open may still be subject to freedom of information requests. The catalogue has tools that enable all users to visualize the data in the catalogue without leaving the catalogue – no additional software needed. Have a look at our walk-through of how to make a chart in the catalogue. Get automatic notifications when datasets are updated. You can choose to get notifications for individual datasets, an organization’s datasets or the full catalogue. You don’t have to provide and personal information – just subscribe to our feeds using any feed reader you like using the corresponding notification web addresses. Copy those addresses and paste them into your reader. Your feed reader will let you know when the catalogue has been updated. The catalogue provides open data in several file formats (e.g., spreadsheets, geospatial data, etc). Learn about each format and how you can access and use the data each file contains. A file that has a list of items and values separated by commas without formatting (e.g. colours, italics, etc.) or extra visual features. This format provides just the data that you would display in a table. XLSX (Excel) files may be converted to CSV so they can be opened in a text editor. How to access the data: Open with any spreadsheet software application (e.g., Open Office Calc, Microsoft Excel) or text editor. Note: This format is considered machine-readable, it can be easily processed and used by a computer. Files that have visual formatting (e.g. bolded headers and colour-coded rows) can be hard for machines to understand, these elements make a file more human-readable and less machine-readable. A file that provides information without formatted text or extra visual features that may not follow a pattern of separated values like a CSV. How to access the data: Open with any word processor or text editor available on your device (e.g., Microsoft Word, Notepad). A spreadsheet file that may also include charts, graphs, and formatting. How to access the data: Open with a spreadsheet software application that supports this format (e.g., Open Office Calc, Microsoft Excel). Data can be converted to a CSV for a non-proprietary format of the same data without formatted text or extra visual features. A shapefile provides geographic information that can be used to create a map or perform geospatial analysis based on location, points/lines and other data about the shape and features of the area. It includes required files (.shp, .shx, .dbt) and might include corresponding files (e.g., .prj). How to access the data: Open with a geographic information system (GIS) software program (e.g., QGIS). A package of files and folders. The package can contain any number of different file types. How to access the data: Open with an unzipping software application (e.g., WinZIP, 7Zip). Note: If a ZIP file contains .shp, .shx, and .dbt file types, it is an ArcGIS ZIP: a package of shapefiles which provide information to create maps or perform geospatial analysis that can be opened with ArcGIS (a geographic information system software program). A file that provides information related to a geographic area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open using a GIS software application to create a map or do geospatial analysis. It can also be opened with a text editor to view raw information. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format for sharing data in a machine-readable way that can store data with more unconventional structures such as complex lists. How to access the data: Open with any text editor (e.g., Notepad) or access through a browser. Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A text-based format to store and organize data in a machine-readable way that can store data with more unconventional structures (not just data organized in tables). How to access the data: Open with any text editor (e.g., Notepad). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. A file that provides information related to an area (e.g., phone number, address, average rainfall, number of owl sightings in 2011 etc.) and its geospatial location (i.e., points/lines). How to access the data: Open with a geospatial software application that supports the KML format (e.g., Google Earth). Note: This format is machine-readable, and it can be easily processed and used by a computer. Human-readable data (including visual formatting) is easy for users to read and understand. This format contains files with data from tables used for statistical analysis and data visualization of Statistics Canada census data. How to access the data: Open with the Beyond 20/20 application. A database which links and combines data from different files or applications (including HTML, XML, Excel, etc.). The database file can be converted to a CSV/TXT to make the data machine-readable, but human-readable formatting will be lost. How to access the data: Open with Microsoft Office Access (a database management system used to develop application software). A file that keeps the original layout and

This data represents the map extent for current and historical USGS topographic maps for New Mexico, Cell Grid 1 X 2 Degree. The grid was generated using ESRI ArcInfo GIS software.

The Geographic Information Retrieval and Analysis System (GIRAS) was developed in the mid 70s to put into digital form a number of data layers which were of interest to the USGS. One of these data layers was the Hydrologic Units. The map is based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging unit. The hydrologic units are encoded with an eight- digit number that indicates the hydrologic region (first two digits), hydrologic subregion (second two digits), accounting unit (third two digits), and cataloging unit (fourth two digits). The data produced by GIRAS was originally collected at a scale of 1:250K. Some areas, notably major cities in the west, were recompiled at a scale of 1:100K. In order to join the data together and use the data in a geographic information system (GIS) the data were processed in the ARC/INFO GUS software package. Within the GIS, the data were edgematched and the neatline boundaries between maps were removed to create a single data set for the conterminous United States. NOTE: A version of this data theme that is more throughly checked (though based on smaller-scale maps) is available here: https://water.usgs.gov/lookup/getspatial?huc2m HUC, GIRAS, Hydrologic Units, 1:250 For the most current data and information relating to hydrologic unit codes (HUCs) please see http://water.usgs.gov/GIS/huc.html. The Watershed Boundary Dataset (WBD) is the most current data available for watershed delineation. See http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/water/watersheds/dataset

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

The Residential Schools Locations Dataset in shapefile format contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this data set, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The data set was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this data set,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School. When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. The geographic coordinate system for this dataset is WGS 1984. The data in shapefile format [IRS_locations.zip] can be viewed and mapped in a Geographic Information System software. Detailed metadata in xml format is available as part of the data in shapefile format. In addition, the field name descriptions (IRS_locfields.csv) and the detailed locations descriptions (IRS_locdescription.csv) should be used alongside the data in shapefile format.

Abstract: The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Use the metadata link, http://nhdgeo.usgs.gov/metadata/nhd_high.htm, for additional information. Purpose: The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.