The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

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

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.

The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.Manager Type provides a coarse level land manager description from the PAD-US "Agency Type" Domain, "Manager Type" Field (for example, Federal, State, Local Government, Private).PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.Dataset SummaryPhenomenon Mapped: This layer displays protected areas symbolized by manager type.Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, 3 or 4GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Cloud GIS Market size was valued at USD 890.81 Million in 2023 and is projected to reach USD 2298.38 Million by 2031, growing at a CAGR of 14.5% from 2024 to 2031.

Key Market Drivers
• Increased Adoption of Cloud Computing: Cloud computing provides scalable resources that can be adjusted based on demand, making it easier for organizations to manage and process large GIS datasets. The pay-as-you-go pricing models of cloud services reduce the need for significant upfront investments in hardware and software, making GIS more accessible to small and medium-sized enterprises.
• Growing Need for Spatial Data Integration: The ability to integrate and analyze large volumes of spatial and non-spatial data helps organizations make more informed decisions. The proliferation of Internet of Things (IoT) devices generates massive amounts of spatial data that can be processed and analyzed using Cloud GIS.
• Advancements in GIS Technology: User-friendly interfaces and visualization tools make it easier for non-experts to use GIS applications. Advanced analytical tools and machine learning algorithms available in cloud platforms enhance the capabilities of traditional GIS.
• Increased Demand for Real-Time Data: Industries like disaster management, transportation, and logistics require real-time data processing and analysis, which is facilitated by Cloud GIS. The need for up-to-date maps and spatial data drives the adoption of cloud-based GIS solutions.
• Collaboration and Sharing Needs: The ability to access GIS data and collaborate from anywhere enhances productivity and supports remote work environments. Cloud GIS supports simultaneous access by multiple users, facilitating better teamwork and data sharing.
• Urbanization and Smart Cities Initiatives: Cloud GIS is crucial for smart city initiatives, urban planning, and infrastructure development, providing the tools needed for efficient resource management. Supports planning and monitoring of sustainable development projects by providing comprehensive spatial analysis capabilities.
• Government and Policy Support: Increased government investment in geospatial technologies and smart infrastructure projects drives the adoption of Cloud GIS. Compliance with regulatory requirements for environmental monitoring and land use planning necessitates the use of advanced GIS tools.
• Industry-Specific Applications: Precision farming and land management benefit from the advanced analytics and data integration capabilities of Cloud GIS. Epidemiology and public health monitoring rely on spatial data analysis for tracking disease outbreaks and resource allocation.

This dataset identifies the project boundaries for registered Emissions Reduction Fund (ERF) area based projects. Area based projects are generally savanna burning and sequestration activities. These ERF Projects are registered across a declared project area identified by project proponents at the time of registration. These project areas generally encompass the entire cadastral boundaries for the properties for which the participants intend to conduct their project activities and for which they hold the legal rights. For sequestration projects the project area does not generally represent the actual extent of a project activity which is generally a subset of the project area. These subset areas are known as Carbon Estimation Areas (CEA) which are defined by rules set out in the individual ERF methods. A project can contain one or many CEAs. The dataset includes basic attribution including: Scheme Participant; Project Name; Project ID; Method; Method Type; Project Description; Date Project Registered; Project location (State); Project location (Postcode); Permanence Period; and, Project Status (Active or Revoked) The Clean Energy Regulator publishes and maintains a project register which contains further details about projects registered under the Emissions Reduction Fund. The project register is published on the Clean Energy Regulator website at http://www.cleanenergyregulator.gov.au/DocumentAssets/Pages/Emissions-Reduction-Fund-Register.aspx and is the point of truth for information about ERF projects. The project register contains attributes not in the spatial dataset, such as, the number of Australian carbon credit units (ACCUs) issued, whether any units have been relinquished, or if that land has a carbon maintenance obligation in place. However, the Project Id attribute (PROJ_ID) can be used to link the mapping data with the project register if analysis of those attributes is required. Notes: Users should be aware that the project register is updated on a weekly basis. The dataset does not contain the boundaries of ten projects which have had their location suppressed or partially suppressed.

[Metadata] A database was developed in support of the conventional hydropower assessment that included more than 50 site-specific economic and environmental/social criteria. All of the data collected, along with the original source references, is also available as an electronic Excel spreadsheet that can be sorted depending on criteria. This data is cross-referenced as a GIS shapefile, which is available from the Honolulu USACE. The geospatial data allows users to geographically and visually sort projects based on any of the selected fields including island, size, scale, incremental energy cost, location, etc. The information provided allows the USACE, State, County, and private developers to analyze potential hydropower sites based on their needs and interests. This information is available as Appendix A, and provides complete site descriptions, additional caveats, and details about the original documents describing the sites. Please note that the geospatial locations of existing and proposed hydropower plants varying in accuracy. Certain sites are placed on known XY coordinate locations (e.g. existing powerplants) while others simply make reference to a certain valley or area on the island. Please review the 'Source of Geospatial Data' attribute column for more information. Please note that user-friendly aliases are available in the attribute table when opening the MXDs in this data package. These column aliases provide more information on the data entries as well as units of measurement. The user is also referred to Appendix A of the Technical Appendix which presents this database in an accessible MS Excel format.

The Digital Environmental Geologic-GIS Map for San Antonio Missions National Historical Park and Vicinity, Texas 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 (saan_environmental_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (saan_environmental_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (saan_environmental_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (saan_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (saan_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 (saan_environmental_geology_metadata_faq.pdf). Please read the saan_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: Texas Bureau of Economic Geology, University of Texas at Austin. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (saan_environmental_geology_metadata.txt or saan_environmental_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in 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). Purpose:

The Digital Geologic-GIS Map of Vicksburg National Military Park, Mississippi and Louisiana 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 (vick_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (vick_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (vick_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (vick_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (vick_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 (vick_geology_metadata_faq.pdf). Please read the vick_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: Mississippi State University, Department of Geosciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (vick_geology_metadata.txt or vick_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This layer contains the fire perimeters from the previous calendar year, and those dating back to 1878, for California. Perimeters are sourced from the Fire and Resource Assessment Program (FRAP) and are updated shortly after the end of each calendar year. Information below is from the FRAP web site. There is also a tile cache version of this layer.About the Perimeters in this LayerInitially CAL FIRE and the USDA Forest Service jointly developed a fire perimeter GIS layer for public and private lands throughout California. The data covered the period 1950 to 2001 and included USFS wildland fires 10 acres and greater, and CAL FIRE fires 300 acres and greater. BLM and NPS joined the effort in 2002, collecting fires 10 acres and greater. Also in 2002, CAL FIRE’s criteria expanded to include timber fires 10 acres and greater in size, brush fires 50 acres and greater in size, grass fires 300 acres and greater in size, wildland fires destroying three or more structures, and wildland fires causing $300,000 or more in damage. As of 2014, the monetary requirement was dropped and the damage requirement is 3 or more habitable structures or commercial structures.In 1989, CAL FIRE units were requested to fill in gaps in their fire perimeter data as part of the California Fire Plan. FRAP provided each unit with a preliminary map of 1950-89 fire perimeters. Unit personnel also verified the pre-1989 perimeter maps to determine if any fires were missing or should be re-mapped. Each CAL FIRE Unit then generated a list of 300+ acre fires that started since 1989 using the CAL FIRE Emergency Activity Reporting System (EARS). The CAL FIRE personnel used this list to gather post-1989 perimeter maps for digitizing. The final product is a statewide GIS layer spanning the period 1950-1999.CAL FIRE has completed inventory for the majority of its historical perimeters back to 1950. BLM fire perimeters are complete from 2002 to the present. The USFS has submitted records as far back as 1878. The NPS records date to 1921.About the ProgramFRAP compiles fire perimeters and has established an on-going fire perimeter data capture process. CAL FIRE, the United States Forest Service Region 5, the Bureau of Land Management, and the National Park Service jointly develop the fire perimeter GIS layer for public and private lands throughout California at the end of the calendar year. Upon release, the data is current as of the last calendar year.The fire perimeter database represents the most complete digital record of fire perimeters in California. However it is still incomplete in many respects. Fire perimeter database users must exercise caution to avoid inaccurate or erroneous conclusions. For more information on potential errors and their source please review the methodology section of these pages.The fire perimeters database is an Esri ArcGIS file geodatabase with three data layers (feature classes):A layer depicting wildfire perimeters from contributing agencies current as of the previous fire year;A layer depicting prescribed fires supplied from contributing agencies current as of the previous fire year;A layer representing non-prescribed fire fuel reduction projects that were initially included in the database. Fuels reduction projects that are non prescribed fire are no longer included.All three are available in this layer. Additionally, you can find related web maps, view layers set up for individual years or decades, and tile layers here.Recommended Uses There are many uses for fire perimeter data. For example, it is used on incidents to locate recently burned areas that may affect fire behavior (see map left).Other uses include:Improving fire prevention, suppression, and initial attack success.Reduce and track hazards and risks in urban interface areas.Provide information for fire ecology studies for example studying fire effects on vegetation over time. Download the Fire Perimeter GIS data hereDownload a statewide map of Fire Perimeters hereSource: Fire and Resource Assessment Program (FRAP)

Data Background:This layer displays the general areas of capital projects along with associated project data. It is maintained in accordance with section 10.22.160 of the Tacoma Municipal Code: "The Public Works Department may develop a capital projects layer on its GIS mapping system, entitled “Capital Improvement Projects,” where it will identify its capital improvement projects. Once established, all public and private Tacoma Municipal Code (Revised 4/2018) 10-44 City Clerk’s Office utilities and operators of any communications or cable system shall identify and update their capital projects on the Capital Improvement Projects map, in accordance with Local Law. The Public Works Department, all utilities, and all communications or cable system operators are responsible for updating their capital improvement projects on no less than a calendar quarterly basis."Public Works project data is updated monthly by project managers. Recommended Symbology:"cipstatus" field valuePolygon FillHex/TransparencyPolygon OutlineHex/Transparency/WidthDrawing OrderYes#0078BD/50%#0078BD/0%/2px SolidTopNoNo Fill/100%#999999/50%/1.5px DashedBottomSome projects do not have mappable work areas because they involve work throughout the city or have otherwise indeterminate work areas. For dataset integrity purposes, these projects are mapped as a polygon encompassing the city limits of Tacoma and given a value of "No" in the field "cipstatus". Selecting individual features is difficult if these features are not hidden, transparent, or drawn first. To improve functionality while viewing mapped features, the above symbology and drawing order is recommended. Depending on your use case, you might also simply choose to filter out features with a "cipstatus" value of "No".Unique Fields: projname Official project title used in documentation

websiteurl URL for the project's individual web page (if it has one)

project_type Primary type of asset involved

project_description Overview of project scope

project_rationale Description of justification for the work

current_phase Capital projects typically progress through some or all of the following phases in order:Unfunded: Bringing a construction idea to life requires funds. Projects marked as "Unfunded" are in the process of securing funding and approval. They are not considered active yet.Planning: The project has confirmed some or all funding, and a plan needs to be made to get it moving. The Planning phase involves gathering people and resources to map out the project's future.Design: If not already fully funded by this point, the project has at least enough funding to be completely designed. An engineering team decides how the work should be done and what the final result must include.Right-of-Way (ROW): At this stage, the project team secures the project area for construction. They find potential legal issues and solve them with things like securing permits, making negotiations, or notifying property owners/businesses.Ad-Award: Project plans are advertised so potential contractors can bid on performing the work. The City awards the project contract based on cost estimates and guidelines such as equity in contracting.Construction: The project is fully funded. The City's construction team and any contractors collaborate to perform and inspect the work.Closeout: After construction is substantially complete, documentation and finances are squared away.Complete: All processes to perform the work have been completed. The project is no longer active.Work might also be paused during any phase due to unforeseen issues. This marks the project phase as On Hold.

phase_notes Brief progress update to elaborate on the current phase

construction_start Month and Year in which construction is estimated to start. Projects in early phases may not have this estimate ready.

construction_end Month and Year in which construction is estimated to be completed. Projects in early phases may not have this estimate ready.

citywide Some projects do not have precise mapped locations and are given the value "citywide". This is most often because the project is actually an ongoing project fund that continuously affects many locations every year (example: Unfit/Unsafe Sidewalk Program) or because the project's goal is to conduct a study to determine future work locations.

business_districts City of Tacoma Business Districts containing any of the project area

city_council_districts City Council Districts containing any of the project area

neighborhood_councils City of Tacoma Neighborhood Councils containing any of the project area

total_estimated_cost Estimated combined cost of the project throughout its lifetime in dollars. Might be blank or very rough estimate for early-stage projects

confirmed_funds_so_far Dollar amount that has been secured toward the total cost of the project

associated_programs_6ytip "Yes" if the project is in the 6-Year Transportation Improvement Plan

associated_programs_cfp "Yes" if the project is in the Capital Facilities Plan

associated_programs_si "Yes" if the project is associated with the Tacoma Streets Initiative

lead_department Department/organization with primary ownership of the project

partners Other departments/organizations/entities that support the project, financially or otherwise

contact_name Subject Matter Expert of the project

contact_email Subject Matter Expert's email address to contact with questions about the project

contact_phone Subject Matter Expert's phone number to contact with questions about the project

cipstatus "Yes" if the precise project area is mapped; "No" if the project area is indeterminate and mapped as a city boundary polygon This is a layer view. The original dataset contains many non-viewer-friendly fields structured for HTML and Arcade functionality in various apps, maps, websites, and reports such as Capital Project Highlights, Capital Improvement Plan web app, Capital Facilities Plan documentation, and more. Omitted fields can be seen in the App View of this dataset.Data Owner:Natasha MillerAssociate Civil Engineer -- Asset Managementnmiller@cityoftacoma.org

Statewide AEM Surveys Project Overview

The Department of Water Resources’ (DWR’s) Statewide Airborne Electromagnetic (AEM) Surveys Project is funded through California’s Proposition 68 and the General Fund. The goal of the project is to improve the understanding of groundwater aquifer structure to support the state and local goal of sustainable groundwater management and the implementation of the Sustainable Groundwater Management Act (SGMA).

During an AEM survey, a helicopter tows electronic equipment that sends signals into the ground which bounce back. The data collected are used to create continuous images showing the distribution of electrical resistivity values of the subsurface materials that can be interpreted for lithologic properties. The resulting information will provide a standardized, statewide dataset that improves the understanding of large-scale aquifer structures and supports the development or refinement of hydrogeologic conceptual models and can help identify areas for recharging groundwater.

DWR is collecting AEM data in all of California’s high- and medium-priority groundwater basins, where data collection is feasible. Data are collected in a coarsely spaced grid, with a line spacing of approximately 2-miles by 8-miles. AEM data collection started in 2021 and will continue over the next several years. Visit the AEM Survey Schedule Webpage to get up-to-date information on the survey schedule: https://gis.water.ca.gov/app/AEM-schedule.

Additional information about the Statewide AEM Surveys can be found at the project website: https://water.ca.gov/Programs/SGMA/AEM.

Survey Areas

AEM data are being collected in groups of groundwater basins, defined as a Survey Area. See Survey Area Map for groundwater subbasins within a Survey Area: https://data.cnra.ca.gov/dataset/aem/resource/a6286b07-5597-49e6-9cac-6a3a98b904df

• Survey Area 1: 180/400 Foot Aquifer (partial), East Side (partial), Upper Valley, Forebay Aquifer, Paso Robles, Atascadero (limited), Adelaida (limited), Cuyama Valley.
• Survey Area 2: Scott River Valley, Shasta Valley, Butte Valley, Tulelake, Fall River Valley (limited), Big Valley (Modoc/Lassen County).
• Survey Area 3: Big Valley (Lake County), Ukiah Valley, Santa Rosa Plain, Petaluma Valley, Sonoma Valley.
• Survey Area 4: White Wolf, Kern County, Tulare Lake, Tule, Kaweah.
• Survey Area 5: Pleasant Valley, Westside, Kings, Madera, Chowchilla, Merced, Turlock, Modesto, Delta-Mendota
• Survey Area 6: Cosumnes, Tracy, Eastern San Joaquin, East Contra Costa, Solano, Livermore, South American, North American, Yolo, Sutter, South Yuba, North Yuba
• Survey Area 7: Colusa, Butte, Wyandotte Creek, Vina, Los Molinos, Corning, Red Bluff, Antelope, Bowman, Bend, Millville, South Battle Creek, Anderson, Enterprise, Eel River, Sierra Valley
• Survey Area 8: Seaside, Monterey, 180/400 (partially surveyed Summer 2021), Eastside (partially surveyed Summer 2021), Langley, Pajaro, Santa Cruz Mid-County, Santa Margarita, San Benito, and Llagas (partial).
• Survey Area 9: Basin Characterization Pilot Study 1 - Madera and Kings.
• Survey Area 10: San Antonio Creek Valley, Arroyo Grande, Santa Maria, San Luis Obispo, Los Osos Area, Warden Creek, Chorro Valley (limited), Morro Valley (limited)
• Survey Area 11: Indian Wells Valley, Rose Valley, Owens Valley, Fish Slough, Indio, Mission Creek, West Salton Sea (limited), East Salton Sea (limited), Ocotillo-Clark Valley (limited), Imperial Valley (limited),Chocolate Valley (limited), Borrego Springs, and San Jacinto

Data Reports

Data reports detail the AEM data collection, processing, inversion, interpretation, and uncertainty analyses methods and procedures. Data reports also describe additional datasets used to support the AEM surveys, including digitized lithology and geophysical logs. Multiple data reports may be provided for a single Survey Area, depending on the Survey Area coverage.

Data Availability and Types

All data collected as a part of the Statewide AEM Surveys will be made publicly available, by survey area, approximately six to twelve months after individual surveys are complete (depending on survey area size). Datasets that will be publicly available include:

AEM Datasets

• Raw AEM Data
• Processed AEM Data
• Inverted AEM Data
• Inverted AEM Data Uncertainty Analysis
• Interpreted AEM Data (for coarse fraction)
• Interpreted AEM Data Uncertainty Analysis

Supporting Datasets

• Flown Survey Lines
• Digitized Lithology Logs
• Digitized Geophysical Logs

AEM Data Viewers

DWR has developed AEM Data Viewers to provides a quick and easy way to visualize the AEM electrical resistivity data and the AEM data interpretations (as texture) in a three-dimensional space. The most recent data available are shown, which my be the provisional data for some areas that are not yet finalized. The Data Viewers can be accessed by direct link, below, or from the Data Viewer Landing Page: https://data.cnra.ca.gov/dataset/aem/resource/29c4478d-fc34-44ab-a373-7d484afa38e8

AEM 3D Viewer (Beta) (computer only): https://dwr.maps.arcgis.com/apps/instant/3dviewer/index.html?appid=f781b14f42ab45e5b72f32cf07af899c

AEM Profile Viewer: https://dwr.maps.arcgis.com/apps/instant/attachmentviewer/index.html?appid=65f0aa6db8124aeda54e1f33c5dfe66c

SGMA Data Viewer (Basin Characterization tab): https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#basincharacter

AEM Depth Slice and Shallow Subsurface Average Maps

As a part of DWR’s upcoming Basin Characterization Program, DWR will be publishing a series of maps and tools to support advanced data analyses. The first of these maps have now been published and provide analyses of the Statewide AEM Survey data to support the identification of potential recharge areas. The maps are located on the SGMA Data Viewer (under the Hydrogeologic Conceptual Model tab) and show the AEM electrical resistivity and AEM-derived texture data as the following:

• Shallow Subsurface Average: Maps showing the average electrical resistivity and AEM-derived texture in the shallow subsurface (the top approximately 50 feet below ground surface). These maps support identification of potential recharge areas, where the top 50 feet is dominated by high resistivity or coarse-grained materials.

• Depth Slices: Depth slice automations showing changes in electrical resistivity and AEM-derived texture with depth. These maps aid in delineating the geometry of large-scale features (for example, incised valley fills).

Shapefiles for the formatted AEM electrical resistivity data and AEM derived texture data as depth slices and the shallow subsurface average can be downloaded here:

Electrical Resistivity Depth Slices and Shallow Subsurface Average Maps: https://data.cnra.ca.gov/dataset/aem/resource/7d115ac3-d7b8-47fa-ab8b-a078b2525bbe

Texture Interpretation (Coarse Fraction) Depth Slices and Shallow Subsurface Average Maps: https://data.cnra.ca.gov/dataset/aem/resource/0952506a-1ad8-4c04-9372-ded45148e6a6

SGMA Data Viewer (Basin Characterization tab): https://sgma.water.ca.gov/webgis/?appid=SGMADataViewer#basincharacter

Technical Memos

Technical memos are developed by DWR's consultant team (Ramboll Consulting) to describe research related to AEM survey planning or data collection. Research described in the technical memos may also be formally published in a journal publication.

• AEM Test Flights to Evaluate the Bias Signal Caused by Vineyard Trellises Containing Metal: https://data.cnra.ca.gov/dataset/aem/resource/42e5798e-c633-4a7a-8398-fc96c2afaced

• SkyTEM Instrument Comparison for Airborne EM:https://data.cnra.ca.gov/dataset/aem/resource/d38f1284-71f3-45e3-9af5-676ebe22f61b

2018-2020 AEM Pilot Studies

Three pilot studies were conducted in California from 2018-2020 to support the development of the Statewide AEM Survey Project. The AEM Pilot Studies were conducted in the Sacramento Valley in Colusa and Butte county groundwater basins, the Salinas Valley in Paso Robles groundwater basin, and in the Indian Wells Valley groundwater basin. All pilot study reports and data are available on the California Natural Resources Agency Open Data Portal: https://data.cnra.ca.gov/dataset/aem-pilot-studies.

Provisional Statement

Data Reports and datasets labeled as provisional may be incomplete and are subject to revision until they have been thoroughly reviewed and received final approval. Provisional data and reports may be inaccurate and subsequent review may result in revisions to the data and reports. Data users are cautioned to consider carefully the provisional nature of the information before using it for decisions that concern personal or public safety or the conduct of business that involves substantial monetary or operational consequences.

This record contains the data used by the North Slope Science Initiative (NSSI) scenario process. These data sets are listed in the Alaska DataCatalog. The data sets are grouped thematically and can be downloaded along with the DataCatalog using the links below. The NSSI Scenarios reports can be downloaded from a separate listing using the link below. The North Slope Science Initiation (NSSI) commissioned a scenario project as a means to provide NSSI member agencies with guidance for moving forward on implementing research and monitoring recommendations and priorities. The NSSI partnered with a research consortium, formed by the University of Alaska Fairbanks and GeoAdaptive, LLC, a scenario-specialist consulting group, to develop the Scenarios Project. These scenarios for energy and resource development helped envision the potential future state of the socio-ecological systems of the North Slope and adjacent seas, and can thereby inform and help resource management agencies to develop appropriate research and monitoring strategies for the future. The scenarios identified through this collaborative effort reflect a plausible range of potential future conditions in the region through 2040. However, these scenarios do not represent a development plan for the region; they were designed to be used as the basis for discussion on the future of the region to help best identify future research and monitoring priorities. As noted above, while the outcomes of this project provide a powerful and widely vetted tool to inform research and monitoring priorities, they do not set those priorities for NSSI or its individual member entities. This is not a policy document.

Note: This dataset has been updated with transmission lines for the MENA region. This is the most complete and up-to-date open map of Africa's electricity grid network. This dataset serves as an updated and improved replacement for the Africa Infrastructure Country Diagnostic (AICD) data that was published in 2007. Coverage This dataset includes planned and existing grid lines for all continental African countries and Madagascar, as well as the Middle East region. The lines range in voltage from sub-kV to 700 kV EHV lines, though there is a very large variation in the completeness of data by country. An interactive tool has been created for exploring this data, the Africa Electricity Grids Explorer. Sources The primary sources for this dataset are as follows: Africa Infrastructure Country Diagnostic (AICD) OSM © OpenStreetMap contributors For MENA: Arab Union of Electricity and country utilities. For West Africa: West African Power Pool (WAPP) GIS database World Bank projects archive and IBRD maps There were many additional sources for specific countries and areas. This information is contained in the files of this dataset, and can also be found by browsing the individual country datasets, which contain more extensive information. Limitations Some of the data, notably that from the AICD and from World Bank project archives, may be very out of date. Where possible this has been improved with data from other sources, but in many cases this wasn't possible. This varies significantly from country to country, depending on data availability. Thus, many new lines may exist which aren't shown, and planned lines may have completely changed or already been constructed. The data that comes from World Bank project archives has been digitized from PDF maps. This means that these lines should serve as an indication of extent and general location, but shouldn't be used for precisely location grid lines.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The OZ (Australian) SEEBASE™ compilation represents many years of work by FROGTECH in the Australian petroleum, mineral and coal sectors.

During this time FROGTECH has undertaken numerous projects in Australia with both the private and government sectors.

These projects have resulted in the development of a model of the geological evolution of the Phanerozoic Basins that is summarised in a GIS and a report. The model is consistent with a wide range of data-sets including airborne and satellite remote sensing, seismic, well and outcrop observations.

The Phanerozoic basins of Australia is formed by the repeated reactivation of long-lived basement structures. By understanding the genesis and geometry of the old basement structures, we have produced a consistent, testable model for the evolution of the basins that explains their structural framework and architecture. The SEEBASE™ model and structural interpretation can now be used as the basis for a new understanding of the sequence stratigraphy and petroleum systems of the Late Proterozoic to Recent basins of Australia.

OZ SEEBASE™ Version 2 includes updated OZ SEEBASE™ and SEEBASE™ Derivative files (sediment thickness and basement thickness - originally called "Crustal thickness" in Version 1). Geophysical and DEM images have been changed from ecws to jpgs to avoid ArcGIS ecw compatibility issues. Updated regions include: Darling Basin (NSW), Sydney Basin (NSW), Renmark Trough (SA), Stuart Shelf (SA), and the Neoproterozoic Redcliff Pound Group (WA/NT).

Dataset History

OZ SEEBASE was supplied to Geoscience Australia by Frog Tech, the creators and collaborators of this data. Full metadata for each data element (feature class, raster, etc) can be viewed in the description tab of ArcCatalog.

This data was supplied with the following caveat: "OZSEEBASE was a project funded by Shell and supported by GA but, at the time, the exact licensing wasn't clear and hasn't been addressed since. However, it is freely available and there are no restrictions on useage. We just ask for Attribution where it is used"

For more information see: http://www.frogtech.com.au/products/oz-seebase

Dataset Citation

FROGTECH (2014) Phanerozoic OZ SEEBASE v2 GIS. Bioregional Assessment Source Dataset. Viewed 22 June 2018, http://data.bioregionalassessments.gov.au/dataset/26e0fbd9-d8d0-4212-be52-ca317e27b3bd.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

[Metadata] A database was developed in support of the conventional hydropower assessment that included more than 50 site-specific economic and environmental/social criteria. All of the data collected, along with the original source references, is also available as an electronic Excel spreadsheet that can be sorted depending on criteria. This data is cross-referenced as a GIS shapefile, which is available from the Honolulu USACE. The geospatial data allows users to geographically and visually sort projects based on any of the selected fields including island, size, scale, incremental energy cost, location, etc. The information provided allows the USACE, State, County, and private developers to analyze potential hydropower sites based on their needs and interests. This information is available as Appendix A, and provides complete site descriptions, additional caveats, and details about the original documents describing the sites. Please note that the geospatial locations of existing and proposed hydropower plants varying in accuracy. Certain sites are placed on known XY coordinate locations (e.g. existing powerplants) while others simply make reference to a certain valley or area on the island. Please review the 'Source of Geospatial Data' attribute column for more information. Please note that user-friendly aliases are available in the attribute table when opening the MXDs in this data package. These column aliases provide more information on the data entries as well as units of measurement. The user is also referred to Appendix A of the Technical Appendix which presents this database in an accessible MS Excel format. This data is a SUBSET of the entire dataset delivered by the USACE. At the direction of the State Energy Office, the Statewide GIS Program extracted those studied hydro projects identified as meeting the criteria outlined in the USACE report (https://energy.hawaii.gov/wp-content/uploads/2011/10/HydroelectricPowerAssess.pdf).

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are 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 database is an initial Asset database for the Central West subregion on 29 April 2015. This dataset contains the spatial and non-spatial (attribute) components of the Central West subregion Asset List as one .mdb files, which is readable as an MS Access database and a personal geodatabase. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Central West are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Central West subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "CEN_asset_database_doc_20150429.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "CEN_asset_database_doc_20150429.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted.

Dataset History

This is initial asset database.

The Bioregional Assessments methodology (Barrett et al., 2013) defines a water-dependent asset as a spatially distinct, geo-referenced entity contained within a bioregion with characteristics having a defined cultural indigenous, economic or environmental value, and that can be linked directly or indirectly to a dependency on water quantity and/or quality.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. Elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2) - assets considered to be water dependent.

Elements may be represented by a single, discrete spatial unit (polygon, line or point), or a number of spatial units occurring at more than one location (multipart polygons/lines or multipoints). Spatial features representing elements are not clipped to the preliminary assessment extent - features that extend beyond the boundary of the assessment extent have been included in full. To assist with an assessment of the relative importance of elements, area statements have been included as an attribute of the spatial data. Detailed attribute tables contain descriptions of the geographic features at the element level. Tables are organised by data source and can be joined to the spatial data on the "ElementID" field

Elements are grouped into Assets, which are the objects used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy.

The "Element_to_asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the project team leader and incorporated into the Assetlist table in the Asset database. The Asset database is then re-registered into the BA repository.

The Asset database dataset (which is registered to the BA repository) contains separate spatial and non-spatial databases.

Non-spatial (tabular data) is provided in an ESRI personal geodatabase (.mdb - doubling as a MS Access database) to store, query, and manage non-spatial data. This database can be accessed using either MS Access or ESRI GIS products. Non-spatial data has been provided in the Access database to simplify the querying process for BA project teams. Source datasets are highly variable and have different attributes, so separate tables are maintained in the Access database to enable the querying of thematic source layers.

Spatial data is provided as an ESRI file geodatabase (.gdb), and can only be used in an ESRI GIS environment. Spatial data is represented as a series of spatial feature classes (point, line and polygon layers). Non-spatial attribution can be joined from the Access database using the AID and ElementID fields, which are common to both the spatial and non-spatial datasets. Spatial layers containing all the point, line and polygon - derived elements and assets have been created to simplify management of the Elementlist and Assetlist tables, which list all the elements and assets, regardless of the spatial data geometry type. i.e. the total number of features in the combined spatial layers (points, lines, polygons) for assets (and elements) is equal to the total number of non-spatial records of all the individual data sources.

Dataset Citation

Department of the Environment (2013) Asset database for the Central West subregion on 29 April 2015. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/5c3f9a56-7a48-4c26-a617-a186c2de5bf7.

Dataset Ancestors

• Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Ramsar Wetlands of Australia

• Derived From Native Vegetation Management (NVM) - Manage Benefits

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Climate Change Corridors (Dry Habitat) for North East NSW

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From [New South Wales NSW Regional CMA Water Asset

The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole.

Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model".

The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals - Please note that this dataset has been moved to a separate catalog post (https://doi.org/10.5878/t8s9-6y15) and unpublished due to licensing restrictions on its source dataset. • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals

In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city.

The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’.

The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives.

1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press.
2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

12 GIS-layers of the street network in Gothenburg, from 1960 to 2015, in 5-year intervals. File format: shapefile (.shp), MapinfoTAB (.TAB). The coordinate system used is SWEREF 99TM, EPSG:3006.

See the attached Technical Documentation for the description and further details on the production of the datasets. See the attached Report for the description of the related research project.