A zipped download package of an Esri File Geodatabase (River Environment Classification, REC 2 v 5, June 2019). This is a large download (>450 MB).REC2 (River Environment Classification, v2.5) - June 2019 The River Environment Classification (REC) is a database of catchment spatial attributes, summarised for every segment in New Zealand's network of rivers. The attributes were compiled for the purposes of river classification, while the river network description has been used to underpin models. Typically, models (e.g. CLUES and TopNet) would use the dendritic (branched) linkages of REC river segments to perform their calculations. Since its release and use over the last decade, some errors in the location and connectivity of these linkages have been identified. The current revision corrects those errors, and updates a number of spatial attributes with the latest data. REC2 provides a recut framework of rivers for modelling and classification. It is built on a newer version of the 30m digital elevation model, in which the original 20m contours were supplemented with, for example, more spot elevation data and a better coastline contour. Boundary errors were minimised by processing contiguous areas (such as the whole of the North Island) together, which wasn't possible a decade ago. Major updates include the revision of catchment land use information, by overlaying with the latest land cover database (LCDB3, current as at 2008), and the update of river and rainfall statistics with data from 1960-2006. The river network and associated attributes have been assembled within an ArcGIS geodatabase as a Geometric Network. Topological connectivity has been established to allow upstream and downstream tracing within the network. NIWA acknowledges funding from the Terrestrial and Freshwater Biodiversity Information System (TFBIS) towards the preparation of REC v2.5NIWA acknowledges funding from the MBIE SSIF towards the preparation of REC v2.5Coordinate Reference System: NZTM (New Zealand Transverse Mercator, EPSG: 2193)Geometric Representation of Rivers: LinesExtent (Bounding Box):

Top (Latitude) -33.9534 Bottom (Latitude) -47.4867 Left (Longitude) 166.2634

Right (Longitude) 178.9733

Riverlines table Attributes associated directly with network:

Field Type Description

Catarea Real Watershed area in m2 CUM_Area Real Area upstream of a reach (and including this reach area) in m2. Nzsegment Integer Reach identifier to be used with REC2 (supercedes nzreach in REC1).

Lengthdown Real The distance to coast from any reach to its outlet reach, where the river drains (m). Headwater Integer Number (0) denoting whether a stream is a “source” (headwater) stream. Non-zero for non-headwater streams.

Hydseq Integer A unique number denoting the hydrological processing order of a river segment relative to others in the network.

StreamOrder Integer A number describing the Strahler order a reach in a network of reaches.

euclid_dist Real The straight line distance of a reach from the reach “inlet” to its “outlet”. upElev Real Height (asl) of the upstream end of a reach section in a watershed (m). downElev Real Height (asl) of the downstream end of a reach section in a watershed (m).

upcoordX Real Easting of the upstream end of a river segment in m (NZTM2000). upcoordY Real Northing of the upstream end of a river segment in m (NZTM2000). downcoordX Real Easting of the downstream end of a river segment in m (NZTM2000).

downcoordY Real Northing of the downstream end of a river segment in m (NZTM2000). sinuosity Real Actual distance divided by the straight line distance giving the degree of curvature of the stream nzreach_re Integer The REC1 identifiying number for the corresponding\closest reach from REC1 (can be used to retrieve the REC management classes) headw_dist Integer Distance of the furthermost “source” or headwater reach from any reach (m). Shape_leng Real The length of the reach (vector) as calculated by ArcGIS. Segslpmean Real Mean segment slope along length of reach.

LID Integer Lake Identifier number(LID) of overlapping lake.

Reachtype

Integer A value of 2 is assigned if the segment is an outlet to the lake, otherwise 0 or null. nextdownid integer segment number of the most downstream reach

_Item Page Created: 2021-06-08 04:08 Item Page Last Modified: 2025-03-15 19:08Owner: steinmetzt_NIWA

Wetlands in California are protected by several federal and state laws, regulations, and policies. This layer was extracted from the broader vegetation raster from the CA Nature project which was recently enhanced to include a more comprehensive definition of wetland. This wetlands dataset is used as an exclusion as part of the biological planning priorities in the CEC 2023 Land-Use Screens.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

DescriptionThis dataset is available for download from: Parcelization (File Geodatabase)Parcelization, a measure of size and density of parcels in a localized area, is a development feasibility factor that is used in evaluating substations’ ability to support new utility-scale resources in long-term energy planning. A statewide dataset of parcel boundaries are used to develop this index. The parcels are converted into a 90-meter raster, containing values of a unique identifier reflective of Parcel APN. A focal statistics tool is used to count the number of unique parcels within a 0.5 mile radius of each parcel. This output is provided here and is an intermediate output to the final parcelization map. Users who wish to use this information to produce the final map should overlay parcel boundary data and extract the mean raster value within each parcel. The map is limited to the area considered with solar technical resource potential after a minimum set of land-use screens (referred to as the Base Exclusions) has been applied. More information on the methods developing this dataset as well as the main use of this dataset in state electric system planning processes can be found in a recent CEC staff report and workshops supporting the resource-to-busbar mapping methodology for the 2024-2025 Transmission Planning Process.

Residential and non-residential development Pipeline for Montgomery County. Inventory of development projects that have been approved by the Planning Board but not completely built. This inventory covers unbuilt dwellings units and unbuilt non-residential building gross square footage.Laytonsville, Poolesville, Gaithersburg and Rockville projects are periodically included in this database. Due to the nature of gathering information from these various sources, the Montgomery County Pipeline of development is only updated three times per year.The pipeline of development is maintained at the ITI division of the Montgomery County Planning Department.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

Bulk exports, in file-geodatabase format, of data that is shared via the VT EGC (Enterprise GIS Consortium) Geospatial Data Exchange Protocol.

Abstract

This dataset was derived by the Bioregional Assessment Programme from the GEODATA TOPO 250K Series 3 dataset (GUID: a0650f18-518a-4b99-a553-44f82f28bb5f). The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset is a copy of the original Geodata Topo 250k Series 3 data, converted from Personal (Microsoft Access) Databases, to ESRI File Geodatabases. This was done to ensure .mdb lock files would not restrict map makers from using the topographic data in their cartographic products. The data and folders are structured the same as the original dataset.

Dataset History

A new file geodatabase schema was created in the same structure as the original .mdb data (including database and feature dataset names and projections). Feature Classes were then copied from the .mdb format to the .gdb format, using ArcCatalog 10.0.

Dataset Citation

Bioregional Assessment Programme (2014) GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb). Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/96ebf889-f726-4967-9964-714fb57d679b.

Dataset Ancestors

• Derived From GEODATA TOPO 250K Series 3

The term "Smartline" refers to a GIS line map format which can allow rapid capture of diverse coastal data into a single consistently classified map, which in turn can be readily analysed for many purposes. This format has been used to create a detailed nationally-consistent coastal geomorphic map of Australia, which is currently being used for the National Coastal Vulnerability Assessment (NCVA) as part of the underpinning information for understanding the vulnerability to sea level rise and other climate change influenced hazards such as storm surge. The utility of the Smartline format results from application of a number of key principles. A hierarchical form- and fabric-based (rather than morpho-dynamic) geomorphic classification is used to classify coastal landforms in shore-parallel tidal zones relating to but not necessarily co-incident with the GIS line itself. Together with the use of broad but geomorphically-meaningful classes, this allows Smartline to readily import coastal data from a diversity of differently-classified prior sources into one consistent map. The resulting map can be as spatially detailed as the available data sources allow, and can be used in at least two key ways: Firstly, Smartline can work as a source of consistently classified information which has been distilled out of a diversity of data sources and presented in a simple format from which required information can be rapidly extracted using queries. Given the practical difficulty many coastal planners and managers face in accessing and using the vast amount of primary coastal data now available in Australia, Smartline can provide the means to assimilate and synthesise all this data into more usable forms.

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

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


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


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


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


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


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


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

This File Geodatabase download, (last updated September 25, 2024), contains all the feature classes within the Land and Property Information group. The City of Langley has compiled all the Land and Property Information feature classes into one file geodatabase. Data is updated on a regular basis; however, lot sizes, legal descriptions and encumbrances must be confirmed at the Land Title Office.File Geodatabase Feature Classes:Address (Anno)Address PointsCity BoundaryEasement AnnoEasementsFacilitiesFolioLegal DescriptionsLot LinesParcelsRoad AllowanceSchools

The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that 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 Residential Schools Settlement Agreement are included in this dataset, 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 dataset 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 dataset,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. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

This dataset provides a representation of ground elevation (in NAVD 88 feet). Spot elevations are evenly spaced every 50 feet. Points on a regular grid were derived from LiDAR LAS files (flown in 2007 as part of the Statewide Regional Evacuation Study) using QChoherent's LP360 software. ASCII XYZ coordinates exported from a "Surface " using Triangulation (TIN) and a cell size of 50 feet were used to generate the points in this dataset.

This layer shows the ZIP Code level boundaries of Montgomery County in 2020. This polygon layer includes only ZIP Codes with a delivery area. It does not include Post Office locations or ZIP Codes for large volume single customers.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

Spot elevations generated from Lee County 1998 Digital Orthophotography project performed by EarthData International Elevations are in NAVD88. February-March 1998

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

The points in this dataset represent the location of a site or structure in Lee County, FL to which an address has been assigned by Lee County Department of Public Safety/E911 Addressing Division.

Public and Private Colleges and Universities.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

The Geocoded National Address File - File Geodatabase is an ESRI file format of PSMA’s version of G-NAF. The data has been transformed by Geoscience Australia from a Pipe Separated Value (PSV) format to support users of ESRI file-geodatabase format.

G-NAF is Australia’s authoritative, geocoded address file and contains the physical address records for all states and territories of Australia, including Christmas Island, Cocos (Keeling) Islands, Jervis Bay and Norfolk Island.

The Next-Generation 911 file geodatabase is a comprehensive dataset that contains various Geographic Information Systems (GIS) data layers benefiting public safety agencies within state, regional, and local government. The core layers in this product are the Site & Structure Address Points, Road Centerlines, and Emergency Service Zones. These core layers form the foundation for The State of Tennessee's Next-Generation 911 Telephone System. Click Here For Detailed Information About the Product

Water bodies are defined as being any area of water, including streams, greater than 500 square feet in size; includes ponds, rivers and lakes. For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

The Digital Geohazards-GIS Map of Everglades National Park and Vicinity (2005 Mapping), Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (ever_geohazard.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (ever_geohazard.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (ever_geohazard.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (ever_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (ever_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (ever_geohazard_metadata_faq.pdf). Please read the ever_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Florida Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (ever_geohazard_metadata.txt or ever_geohazard_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).