This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

This data was developed to represent city of cape coral citizen action center issues and their associated attributes for the purpose of mapping, analysis, and planning. The accuracy of this data varies and should not be used for precise measurements or calculations.

No Publication Abstract is Available

Database created for replication of GeoStoryTelling. Our life stories evolve in specific and contextualized places. Although our homes may be our primarily shaping environment, our homes are themselves situated in neighborhoods that expose us to the immediate “real world” outside home. Indeed, the places where we are currently experiencing, and have experienced life, play a fundamental role in gaining a deeper and more nuanced understanding of our beliefs, fears, perceptions of the world, and even our prospects of social mobility. Despite the immediate impact of the places where we experience life in reaching a better understanding of our life stories, to date most qualitative and mixed methods researchers forego the analytic and elucidating power that geo-contextualizing our narratives bring to social and health research. From this view then, most research findings and conclusions may have been ignoring the spatial contexts that most likely have shaped the experiences of research participants. The main reason for the underuse of these geo-contextualized stories is the requirement of specialized training in geographical information systems and/or computer and statistical programming along with the absence of cost-free and user-friendly geo-visualization tools that may allow non-GIS experts to benefit from geo-contextualized outputs. To address this gap, we present GeoStoryTelling, an analytic framework and user-friendly, cost-free, multi-platform software that enables researchers to visualize their geo-contextualized data narratives. The use of this software (available in Mac and Windows operative systems) does not require users to learn GIS nor computer programming to obtain state-of-the-art, and visually appealing maps. In addition to providing a toy database to fully replicate the outputs presented, we detail the process that researchers need to follow to build their own databases without the need of specialized external software nor hardware. We show how the resulting HTML outputs are capable of integrating a variety of multi-media inputs (i.e., text, image, videos, sound recordings/music, and hyperlinks to other websites) to provide further context to the geo-located stories we are sharing (example https://cutt.ly/k7X9tfN). Accordingly, the goals of this paper are to describe the components of the methodology, the steps to construct the database, and to provide unrestricted access to the software tool, along with a toy dataset so that researchers may interact first-hand with GeoStoryTelling and fully replicate the outputs discussed herein. Since GeoStoryTelling relied on OpenStreetMap its applications may be used worldwide, thus strengthening its potential reach to the mixed methods and qualitative scientific communities, regardless of location around the world. Keywords: Geographical Information Systems; Interactive Visualizations; Data StoryTelling; Mixed Methods & Qualitative Research Methodologies; Spatial Data Science; Geo-Computation.

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

This dataset is a modified version of the FWS developed data depicting “Highly Important Landscapes”, as outlined in Memorandum FWS/AES/058711 and provided to the Wildlife Habitat Spatial analysis Lab on October 29th 2014. Other names and acronyms used to refer to this dataset have included: Areas of Significance (AoSs - name of GIS data set provided by FWS), Strongholds (FWS), and Sagebrush Focal Areas (SFAs - BLM). The BLM will refer to these data as Sagebrush Focal Areas (SFAs). Data were provided as a series of ArcGIS map packages which, when extracted, contained several datasets each. Based on the recommendation of the FWS Geographer/Ecologist (email communication, see data originator for contact information) the dataset called “Outiline_AreasofSignificance” was utilized as the source for subsequent analysis and refinement. Metadata was not provided by the FWS for this dataset. For detailed information regarding the dataset’s creation refer to Memorandum FWS/AES/058711 or contact the FWS directly. Several operations and modifications were made to this source data, as outlined in the “Description” and “Process Step” sections of this metadata file. Generally: The source data was named by the Wildlife Habitat Spatial Analysis Lab to identify polygons as described (but not identified in the GIS) in the FWS memorandum. The Nevada/California EIS modified portions within their decision space in concert with local FWS personnel and provided the modified data back to the Wildlife Habitat Spatial Analysis Lab. Gaps around Nevada State borders, introduced by the NVCA edits, were then closed as was a large gap between the southern Idaho & southeast Oregon present in the original dataset. Features with an area below 40 acres were then identified and, based on FWS guidance, either removed or retained. Finally, guidance from BLM WO resulted in the removal of additional areas, primarily non-habitat with BLM surface or subsurface management authority. Data were then provided to each EIS for use in FEIS development. Based on guidance from WO, SFAs were to be limited to BLM decision space (surface/sub-surface management areas) within PHMA. Each EIS was asked to provide the limited SFA dataset back to the National Operations Center to ensure consistent representation and analysis. Returned SFA data, modified by each individual EIS, was then consolidated at the BLM’s National Operations Center retaining the three standardized fields contained in this dataset.Several Modifications from the original FWS dataset have been made. Below is a summary of each modification.1. The data as received from FWS: 16,514,163 acres & 1 record.2. Edited to name SFAs by Wildlife Habitat Spatial Analysis Lab:Upon receipt of the “Outiline_AreasofSignificance” dataset from the FWS, a copy was made and the one existing & unnamed record was exploded in an edit session within ArcMap. A text field, “AoS_Name”, was added. Using the maps provided with Memorandum FWS/AES/058711, polygons were manually selected and the “AoS_Name” field was calculated to match the names as illustrated. Once all polygons in the exploded dataset were appropriately named, the dataset was dissolved, resulting in one record representing each of the seven SFAs identified in the memorandum.3. The NVCA EIS made modifications in concert with local FWS staff. Metadata and detailed change descriptions were not returned with the modified data. Contact Leisa Wesch, GIS Specialist, BLM Nevada State Office, 775-861-6421, lwesch@blm.gov, for details.4. Once the data was returned to the Wildlife Habitat Spatial Analysis Lab from the NVCA EIS, gaps surrounding the State of NV were closed. These gaps were introduced by the NVCA edits, exacerbated by them, or existed in the data as provided by the FWS. The gap closing was performed in an edit session by either extending each polygon towards each other or by creating a new polygon, which covered the gap, and merging it with the existing features. In addition to the gaps around state boundaries, a large area between the S. Idaho and S.E. Oregon SFAs was filled in. To accomplish this, ADPP habitat (current as of January 2015) and BLM GSSP SMA data were used to create a new polygon representing PHMA and BLM management that connected the two existing SFAs.5. In an effort to simplify the FWS dataset, features whose areas were less than 40 acres were identified and FWS was consulted for guidance on possible removal. To do so, features from #4 above were exploded once again in an ArcMap edit session. Features whose areas were less than forty acres were selected and exported (770 total features). This dataset was provided to the FWS and then returned with specific guidance on inclusion/exclusion via email by Lara Juliusson (lara_juliusson@fws.gov). The specific guidance was:a. Remove all features whose area is less than 10 acresb. Remove features identified as slivers (the thinness ratio was calculated and slivers identified by Lara Juliusson according to https://tereshenkov.wordpress.com/2014/04/08/fighting-sliver-polygons-in-arcgis-thinness-ratio/) and whose area was less than 20 acres.c. Remove features with areas less than 20 acres NOT identified as slivers and NOT adjacent to other features.d. Keep the remainder of features identified as less than 40 acres.To accomplish “a” and “b”, above, a simple selection was applied to the dataset representing features less than 40 acres. The select by location tool was used, set to select identical, to select these features from the dataset created in step 4 above. The records count was confirmed as matching between the two data sets and then these features were deleted. To accomplish “c” above, a field (“AdjacentSH”, added by FWS but not calculated) was calculated to identify features touching or intersecting other features. A series of selections was used: first to select records 6. Based on direction from the BLM Washington Office, the portion of the Upper Missouri River Breaks National Monument (UMRBNM) that was included in the FWS SFA dataset was removed. The BLM NOC GSSP NLCS dataset was used to erase these areas from #5 above. Resulting sliver polygons were also removed and geometry was repaired.7. In addition to removing UMRBNM, the BLM Washington Office also directed the removal of Non-ADPP habitat within the SFAs, on BLM managed lands, falling outside of Designated Wilderness’ & Wilderness Study Areas. An exception was the retention of the Donkey Hills ACEC and adjacent BLM lands. The BLM NOC GSSP NLCS datasets were used in conjunction with a dataset containing all ADPP habitat, BLM SMA and BLM sub-surface management unioned into one file to identify and delete these areas.8. The resulting dataset, after steps 2 – 8 above were completed, was dissolved to the SFA name field yielding this feature class with one record per SFA area.9. Data were provided to each EIS for use in FEIS allocation decision data development.10. Data were subset to BLM decision space (surface/sub-surface) within PHMA by each EIS and returned to the NOC.11. Due to variations in field names and values, three standardized fields were created and calculated by the NOC:a. SFA Name – The name of the SFA.b. Subsurface – Binary “Yes” or “No” to indicated federal subsurface estate.c. SMA – Represents BLM, USFS, other federal and non-federal surface management 12. The consolidated data (with standardized field names and values) were dissolved on the three fields illustrated above and geometry was repaired, resulting in this dataset.

This digital data release presents contour data from multiple subsurface geologic horizons as presented in previously published summaries of the regional subsurface configuration of the Michigan and Illinois Basins. The original maps that served as the source of the digital data within this geodatabase are from the Geological Society of America’s Decade of North American Geology project series, “The Geology of North America” volume D-2, chapter 13 “The Michigan Basin” and chapter 14 “Illinois Basin Region”. Contour maps in the original published chapters were generated from geophysical well logs (generally gamma-ray) and adapted from previously published contour maps. The published contour maps illustrated the distribution sedimentary strata within the Illinois and Michigan Basin in the context of the broad 1st order supercycles of L.L. Sloss including the Sauk, Tippecanoe, Kaskaskia, Absaroka, Zuni, and Tejas supersequences. Because these maps represent time-transgressive surfaces, contours frequently delineate the composite of multiple named sedimentary formations at once. Structure contour maps on the top of the Precambrian basement surface in both the Michigan and Illinois basins illustrate the general structural geometry which undergirds the sedimentary cover. Isopach maps of the Sauk 2 and 3, Tippecanoe 1 and 2, Kaskaskia 1 and 2, Absaroka, and Zuni sequences illustrate the broad distribution of sedimentary units in the Michigan Basin, as do isopach maps of the Sauk, Upper Sauk, Tippecanoe 1 and 2, Lower Kaskaskia 1, Upper Kaskaskia 1-Lower Kaskaskia 2, Kaskaskia 2, and Absaroka supersequences in the Illinois Basins. Isopach contours and structure contours were formatted and attributed as GIS data sets for use in digital form as part of U.S. Geological Survey’s ongoing effort to inventory, catalog, and release subsurface geologic data in geospatial form. This effort is part of a broad directive to develop 2D and 3D geologic information at detailed, national, and continental scales. This data approximates, but does not strictly follow the USGS National Cooperative Geologic Mapping Program's GeMS data structure schema for geologic maps. Structure contour lines and isopach contours for each supersequence are stored within separate “IsoValueLine” feature classes. These are distributed within a geographic information system geodatabase and are also saved as shapefiles. Contour data is provided in both feet and meters to maintain consistency with the original publication and for ease of use. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units referenced herein. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and accompanying nonspatial tables.

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.The statewide layer is only provided as a map image layer service. The data is available as feature layer services by Regional Board extract. To view all regional board feature layer extracts go to the Basin Plan GIS Data Library Group here.

Geospatial data can provide valuable visualization and analytical abilities to Facility and Resource Managers in regards to maintained landscapes throughout the NPS. Maintained landscapes are records in the Facility Management Software System (FMSS) and can include battlefields, ornamental gardens, picnic areas, and other types. To map a maintained area and the features within it at the enterprise level, a geospatial data service is needed to ensure consistency, accuracy, and thorough documentation of data lineage. The Maintained Landscape Spatial Data Service will structure maintained landscape data into a common format that will enable GIS data to be easily integrated, traced, analyzed and shared across the park. Such a structure will increase users’ ability to discern the quality and accuracy of the data enabling the user to make better data driven decisions. This schema is designed to match the structure and hierarchy of FMSS so that should this system become spatially enabled this data could be utilized. Within the FMSS database, features are organized in locations records and assets records. A location record could be thought of as a bin, within which component assets records are stored. Park Facilities Management Division (PFMD) Employees of the National Park Service are tasked with managing facilities such as roads, trails, buildings, and landscapes. To properly manage these assets PFMD must make management decisions based on spatial and non-spatial data. This service allows the accurate geographic representation of maintained landscapes in a common service-wide schema. Furthermore, the establishment of a maintained landscapes spatial data service will allow for the integration of several NPS managed databases. These include (but are not limited to) the Facilities Management Software System (FMSS).The corresponding Integration of Resource Management Applications (IRMA) NPS Data Store reference is Great Smoky Mountains National Park Maintained Landscapes.

The coastal recreation study is designed to establish a baseline characterization of participation rates and the economic value of coastal recreation and provide a spatial baseline of coastal recreation use patterns in the North Central Coast region. Please see the project report for full details on the data collection and analysis methods as well as a listing of survey questions. Included in this data package is a summary of all survey data as well as a PDF map depicting intensity of use across all coastal recreation activities data collected in the region. We also have spatial data for individual activities, including: Scenic enjoyment; Beach going (dog-walking, kite-flying, jogging, etc.); Photography; Watching birds and/or other marine life from shore; Sitting in your car watching the scene; Biking or hiking; Collection of non-living resources/beachcombing (agates, fossils, driftwood); and Swimming or body surfing in the ocean. If you would like to access the GIS data from this project please contact us. This dataset was originally uploaded to Oceanspaces (http://oceanspaces.org/) in 2013 as part of the North Central Coast baseline monitoring program. In 2020 the baseline data and reports were uploaded to the California Ocean Protection Council Data Repository by Mike Esgro (Michael.Esgro@resources.ca.gov) and Rani Gaddam (gaddam@ucsc.edu). Every attempt has been made to include all of the original data, metadata, and reports submitted in 2013, but please contact the Data Set Contacts with any questions. The long-term California MPA boundary and project info tables referenced in this dataset can be found as a separate dataset here: https://opc.dataone.org/view/doi:10.25494/P64S3W

Geospatial data can provide valuable visualization and analytical abilities to Facility and Resource Managers in regards to maintained landscapes throughout the NPS. Maintained landscapes are records in the Facility Management Software System (FMSS) and can include battlefields, ornamental gardens, picnic areas, and other types. To map a maintained area and the features within it at the enterprise level, a geospatial data service is needed to ensure consistency, accuracy, and thorough documentation of data lineage. The Maintained Landscape Spatial Data Service will structure maintained landscape data into a common format that will enable GIS data to be easily integrated, traced, analyzed and shared across the park. Such a structure will increase users’ ability to discern the quality and accuracy of the data enabling the user to make better data driven decisions. This schema is designed to match the structure and hierarchy of FMSS so that should this system become spatially enabled this data could be utilized. Within the FMSS database, features are organized in locations records and assets records. A location record could be thought of as a bin, within which component assets records are stored. Park Facilities Management Division(PFMD) Employees of the National Park Service are tasked with managing facilities such as roads, trails, buildings, and landscapes. To properly manage these assets PFMD must make management decisions based on spatial and non-spatial data. This service allows the accurate geographic representation of maintained landscapes in a common service-wide schema. Furthermore, the establishment of a maintained landscapes spatial data service will allow for the integration of several NPS managed databases. These include (but are not limited to) the Facilities Management Software System (FMSS), the Cultural Resources Enterprise Geographic Information System (CRGIS), the Cultural Landscapes Inventory (CLI), and the List of Classified Structures (LCS). The Cultural Resource Enterprise GIS dataset contains the cultural landscapes inventory spatial data, list of classified structures spatial data, National Register spatial data and links to all of these databases, as well as other partner programs

This is a GIS file set of the Gede ruins. The data was generated from laser scans, photogrammetric techniques and GPS data. The data maps the site of the Gede ruins in Kilifi County in Kenya. All data is in either the unprojected Geographic (GCS WGS84) or the projected Universal Transverse Mercator 37 South (UTM37S WGS84) coordinate system.The data is packaged as an ESRI Map Package (.mpk). If you are not an ESRI user and wish to unpack the package please rename the file extension to .zip and use a programme, such as 7-Zip, to unpack the package. The package contains shapefiles and images which are compatible with most GIS software. The ruins of Gede (also Gedi), a traditional Arab-African Swahili town, are located just off Kenya’s coastline, some 90km north of Mombasa. Gede was a small town built entirely from stones and rocks, and most of the original foundations are still visible today. Remaining structures at the site include coral stone buildings, mosques, houses and a palace. The town was abandoned in the early 17th century, and Gede’s buildings date back to the 15th century, although it is believed that the site could have been inhabited as early as the 11th or 12th century. The Zamani Project spatially documented the Gede ruins in 2010. In addition to the three principal structures of the Great Mosque, the Small Mosque and the Palace, remains of other structures in the immediate vicinity were also documented.The Zamani Project seeks to increase awareness and knowledge of tangible cultural heritage in Africa and internationally by creating metrically accurate digital representations of historical sites. Digital spatial data of cultural heritage sites can be used for research and education, for restoration and conservation and as a record for future generations. The Zamani Project operates as a non-profit organisation within the University of Cape Town.This text has been adapted from the UNESCO website (https://whc.unesco.org/en/tentativelists/5501/).The Zamani Project received funding from the Andrew W Mellon Foundation at the time of the project.

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.The statewide layer is only provided as a map image layer service. The data is available as feature layer services by Regional Board extract. To view all regional board feature layer extracts go to the Basin Plan GIS Data Library Group here.

This U.S. Geological Survey (USGS) data release presents a digital database of geospatially enabled vector layers and tabular data transcribed from the geologic map of the Lake Owen quadrangle, Albany County, Wyoming, which was originally published as U.S. Geological Survey Geologic Quadrangle Map GQ-1304 (Houston and Orback, 1976). The 7.5-minute Lake Owen quadrangle is located in southeastern Wyoming approximately 25 miles (40 kilometers) southwest of Laramie in the west-central interior of southern Albany County, and covers most of the southern extent of Sheep Mountain, the southeastern extent of Centennial Valley, and a portion of the eastern Medicine Bow Mountains. This relational geodatabase, with georeferenced data layers digitized at the publication scale of 1:24,000, organizes and describes the geologic and structural data covering the quadrangle's approximately 35,954 acres and enables the data for use in spatial analyses and computer cartography. The data types presented in this release include geospatial features (points, lines, and polygons) with matching attribute tables, nonspatial descriptive and reference tables, and ancillary resource files for correct symbolization, in formats that conform to the Geologic Map Schema (GeMS) developed and released by the U.S. Geological Survey's National Cooperative Geologic Mapping Program (GeMS, 2020). When reconstructed from the geodatabase's vector layers and tabular data that has been symbolized according to specifications encoded in the accompanying style file, and using the supplied Federal Geographic Data Committee (FGDC) GeoAge font for labeling formations and GeoSym fonts for structural line decorations and orientation measurement symbols, this data release presents the Geologic Map as shown on the published GQ-1304 map sheet. These GIS data augment but do not supersede the information presented on GQ-1304. References: Houston, R.S., and Orback, C.J., 1976, Geologic Map of the Lake Owen Quadrangle, Albany County, Wyoming: U.S. Geological Survey Geologic Quadrangle Map GQ-1304, scale 1:24,000, https://doi.org/10.3133/gq1304. U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema)- A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133/tm11B10.

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.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.The statewide layer is only provided as a map image layer service. The data is available as feature layer services by Regional Board extract. To view all regional board feature layer extracts go to the Basin Plan GIS Data Library Group here.

description: This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific 'production' or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys), and the Bureau of Census 2015 Cartographic State Boundaries. The Entity-Attribute section of this metadata describes these components in greater detail. Please note that the data on this site, although published at regular intervals, may not be the most current PLSS data that is available from the BLM. Updates to the PLSS data at the BLM State Offices may have occurred since this data was published. To ensure users have the most current data, please refer to the links provided in the PLSS CadNSDI Data Set Availability accessible here: https://gis.blm.gov/EGISDownload/Docs/PLSS_CadNSDI_Data_Set_Availability.pdf or contact the BLM PLSS Data Set Manager.; abstract: This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific 'production' or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys), and the Bureau of Census 2015 Cartographic State Boundaries. The Entity-Attribute section of this metadata describes these components in greater detail. Please note that the data on this site, although published at regular intervals, may not be the most current PLSS data that is available from the BLM. Updates to the PLSS data at the BLM State Offices may have occurred since this data was published. To ensure users have the most current data, please refer to the links provided in the PLSS CadNSDI Data Set Availability accessible here: https://gis.blm.gov/EGISDownload/Docs/PLSS_CadNSDI_Data_Set_Availability.pdf or contact the BLM PLSS Data Set Manager.

AbstractThe spatial and temporal distribution patterns of the livestock health status in the developing countrieslike India are complex. In this regards, the application of Geographical Information System (GIS) isvaluable as it has many features that make it an ideal tool for use in animal health surveillance, monitoring,prediction and its management strategy. The goal of the present study is to find out the effect of ambienttemperature on goat health in two different agro-climatic zones in West Bengal, India with the additionalhelp of GIS technology. The highest mean value of temperature (42.6 ± 1.5 ºC) has been reported duringthe month of April or May in the season of pre-monsoon in Purulia. Survey of India (SOI) topographicalsheets (73 I/3 and 79 B/5) are used to map the study areas. Top sheets are scanned, geo-referenced andthen digitized with the help of GIS software. The biochemical and meteorological data are entered to thenewly prepared digitized map as the non-spatial data or attributes. Moreover, the present work aims toconfer an indication of the potential applications and usages of a GIS in the field of animal health foradvancing the knowledge about this innovative approach of goat heath surveillance and monitoring.Keywords: Goats; GIS; Pre-Monsoon; Post-Monsoon; Purulia; Nadia.

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 Galilee dataset contains v13+V12 of the GAL Asset database (GAL_asset_database_20160104.mdb), a Geodatabase version for GIS mapping purposes (GAL_asset_database_20160104_GISONLY.gdb), the draft Water Dependent Asset Register spreadsheet (BA-LEB-GAL-130-WaterDependentAssetRegister-AssetList-v20160104.xlsx), the draft Receptor Register spreadsheet (BA-LEB-GAL-140-ReceptorRegister-v20160104.xlsx), a data dictionary (GAL_asset_database_doc_20160104.doc), a folder (Indigenous_doc) containing documentation associated with Indigenous water asset project, and a folder (NRM_DOC) containing documentation associated with the Water Asset Information Tool (WAIT) process as outlined below

This database supersedes Asset database for the Galilee subregion on 10 September 2015 (GUID: c22a13bf-07ea-4eaa-960d-79d488a50496).

The updating in this V13+V12 GAL asset database 201600104 includes:

(1) Total number of registered water assets was increased by 79 due to: (a) The 9 assets changed their M2 test to "Yes" from the review done by Ecologist group. (b) 69 indigenous water assets from OWS were added.

(2) GAL receptor was included

The Asset database is registered to the BA repository as an ESRI personal goedatabase (.mdb - doubling as a MS Access database) that can store, query, and manage non-spatial data while the spatial data is in a separated file geodatabase joined by AID/Element ID/BARID. 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 "GAL_asset_database_doc_20160104.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 "GAL_asset_database_doc_20160104.doc" located in the zip file.

The public version of this asset database can be accessed via the following dataset: Asset database for the Galilee subregion on 04 January 2016 Public (https://data.gov.au/dataset/eb4cf797-9b8f-4dff-9d7a-a5dfbc8d2bed)

Purpose

For creation of asset list for bioregional assessment

The public version of this asset database can be accessed via the following dataset: Asset database for the Galilee subregion on 04 January 2016 Public (https://data.gov.au/dataset/eb4cf797-9b8f-4dff-9d7a-a5dfbc8d2bed)

Dataset History

VersionID Date Notes

1.0 23/12/2013 Initial database

1.01 3/02/2014 updated 207 Names in table AssetList using AssetName in table NRM_Water_Asset for those recodes from source WAIT_Burdekin

1.01 3/02/2014 removed the space at the beginning of Unnamed)_South Australian Arid Lands_66329 and (Unnamed)_South Australian Arid Lands_57834

1.01 20/02/2014 The database is not changed. About 36 self intersect polygons in spatial data were fixed. New shapefile name for polygon is Galilee_AssetList_geoPolygon20140220.shp

3.0 23/04/2014 "Updated universally changing ""AssetID"" to ""ElementID"" and changing the name of the ""AssetList"" table to ""ElementList"". A table to include Queensland threatened species data has also been added, and ElementIDs added to the ""ElementList"" table."

2.0 23/04/2014 Errors found after handover to CSIRO. Updated immediately to v3.0.

4.0 24/04/2014 Queensland threatened species data updated to new sequence of ElementIDs. New spatial data provided [NAME]

8 5/05/2014 It is generally ready except calcification and asset area

9.0 28/11/2014 "Add additional datasets such as QLD_DERM PR Waterbodies, QLD_DERM PR Waterbodies QLD RegionalEcosystems as request

Update GDEsub, GDEsur, QLD_ DNRM_ECON_GW QLD_ DNRM_ECON_SW as request"

10 22/05/2015 Updated database tables of AssetDecisions and AssetList for M2 and M3 test results

11 10/09/2015

(1) AID 70360 added for potential distribution of Largetooth Sawfish (Pristis pristis (Pristis microdon)). Attributes in additional attribute look-up table tbl_Species_EPBC_PristisPristis.

(2) The (brief) explanation for M2 decisions has been updated based on advice from the project team, replacing detailed explanations which were truncated in the Assetlist and AssetDecisions tables. The detailed explanation is retained in the DecisionReason field of the AssetDecisions table. Note there are no changes to decision outcomes or numbers of assets on the asset register;

(3) The draft BA-LEB-GAL-130-WaterDependentAssetRegister-AssetList-V20150910.xlsx as has been updated as an output of this database. The brief M2_decision replaces the extended decision rationale that was included in the last version of the spreadsheet.

(4) x15 elements associated with the (single) asset named "No_Asset" (AID = 0) were removed from the database (deleted from the AssetList, Element_to_asset and ElementList tables, and also from the element and asset polygon layers). These polygons were exact duplicates of other elements from the same source dataset and had been previously grouped as "No_Asset". This action will not affect the asset count for the asset list or the water dependent asset register.

12 24/12/2015 "Area calculations were removed from the spatial data and added to the assetList and elementList tables in

                    this .mdb database. Area calculations were included for assets and elementlist line features.

                    A total of 69 Indigenous elements were added to the ElementList table in the database, translating into an 

                     additional 69 indigenous assets which were added to the AssetList table. 

                     Of these 69 indigenous assets: 

                         40 intersect the PAE and are included in the ""asset list"".

                         5 did not intersect the PAE, so did not pass ""M1"". These are retained in the AssetList table but 

                          are ""switched off"" at M1 (i.e. M1 = 'No'). These are not considered part of the ""asset list"".

                         24 have no meaningful spatial component. These were added to the AssetList table, but ""switched 

                          off"" at ""M0"" (i.e. not fit for purpose, M0 = 'No') and therefore are not consodered part of 

                          the ""asset list""."

13 4/01/2016 "(1)(a) Added table ReceptorList in GAL_asset_database_20160104.mdb, using the data file from GAL project

                      team (b) Created draft BA-LEB-GAL-140-ReceptorRegister-v20160104.xlsx (c) Added table 

                       tbl_Receptors in GAL_asset_database_20160104.mdb and GM_GAL_ReceptorList_pt (created by ERIN 

                       using the location data from GAL project team) in GAL_asset_database_20160104_GISONLY.gdb,; (d)

                       Add SQL query "Find_used_Receptor_a" and "Find_used_Receptor_b" for extracting all used receptor for 

                        the register.

                     (2)(a)Updated M2 test for GAL from GAL_Species_TEC_decisions_reveiw_23112015.(b) Created draftBA-

                        LEB-GAL-130-WaterDependentAssetRegister-AssetList-v20160104.xlsx"

Dataset Citation

Bioregional Assessment Programme (2013) Asset database for the Galilee subregion on 04 January 2016. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/12ff5782-a3d9-40e8-987c-520d5fa366dd.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases

• Derived From Matters of State environmental significance (version 4.1), Queensland

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

• Derived From South Australia SA - Regional - NRM Board - Water Asset Information Tool - WAIT - databases

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From National Groundwater Information System (NGIS) v1.1

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

• Derived From Queensland QLD Regional CMA Water Asset Information WAIT tool databases RESTRICTED Includes ALL Reports

• Derived From [Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA

Abstract

The public version of this Asset database can be accessed via the following dataset:

Asset database for the Cooper subregion on 27 August 2015 Public (526707e0-9d32-47de-a198-9c8f35761a7e)

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.

The asset database for Cooper subregion (v3) supersedes previous version (v2) of the Cooper Asset database (Asset database for the Cooper subregion on 14 August 2015, 5c3697e6-8077-4de7-b674-e0dfc33b570c). The M2_Reason in the Assetlist table and DecisionBrief in the AssetDecisions table have been updated with short descriptions (<255 characters) provided by project team 21/8, and the draft "water-dependent asset register and asset list" (BA-LEB-COO-130-WaterDependentAssetRegister-AssetList-V20150827) also updated accordingly. This change was made to avoid truncation in the brief reasons fields of the database and asset register. There have been no changes to assets or asset numbers.

This dataset contains a combination of spatial and non-spatial (attribute) components of the Cooper subregion Asset List - an mdb file (readable as an MS Access database or as an ESRI personal geodatabase) holds the non-spatial tabular attribute data, and an ESRI file geodatabase contains the spatial data layers, which are attributed only with unique identifiers ("AID" for assets, and "ElementID" for elements). The dataset also contains an update of the draft "Water-dependent asset register and asset list" spreadsheet (BA-NIC-COO-130-WaterDependentAssetRegister-AssetList-V20150827.xlsx).

The tabular attribute data can be joined in a GIS to the "Assetlist" table in the mdb database using the "AID" field to view asset attributes (BA attribution). To view the more detailed attribution at the element-level, the intermediate table "Element_to_asset" can be joined to the assets spatial datasets using AID, and then joining the individual attribute tables from the Access database using the common "ElementID" fields. Alternatively, the spatial feature layers representing elements can be linked directly to the individual attribute tables in the Access database using "ElementID", but this arrangement will not provide the asset-level groupings.

Further information is provided in the accompanying document, "COO_asset_database_doc20150827.doc" located within this dataset.

Dataset History

Version ID Date Notes

1.0 27/03/2015 Initial database

2.0 14/08/2015 "(1) Updated the database for M2 test results provided from COO assessment team and created the draft BA-LEB-COO-130-WaterDependentAssetRegister-AssetList-V20150814.xlsx

(2) updated the group, subgroup, class and depth for (up to) 2 NRM WAIT assets to cooperate the feedback to OWS from relevant SA NRM regional office (whose staff missed the asset workshop). The AIDs and names of those assets are listed in table LUT_changed_asset_class_20150814 in COO_asset_database_20150814.mdb

(3) As a result of (2), added one new asset separated from one existing asset. This asset and its parent are listed in table LUT_ADD_1_asste_20150814 in COO_asset_database_20150814.mdb. The M2 test result for this asset is inherited from its parent in this version

(5) Added Appendix C in COO_asset_database_doc_201500814.doc is about total elements/assets in current Group and subgroup

(6)Added Four SQL queries (Find_All_Used_Assets, Find_All_WD_Assets, Find_Amount_Asset_in_Class and Find_Amount_Elements_in_Class) in COO_asset_database_20150814.mdb.mdb for total assets and total numbers

(7)The databases, especially spatial database (COO_asset_database_20150814Only.gdb), were changed such as duplicated attribute fields in spatial data were removed and only ID field is kept. The user needs to join the Table Assetlist or Elementlist to the relevant spatial data"

3.0 27/08/2015 M2_Reason in the Assetlist table and DecisionBrief in the AssetDecisions table have been updated with short descriptions (<255 characters) provided by project team 21/8, and the draft "water-dependent asset register and asset list" (BA-LEB-COO-130-WaterDependentAssetRegister-AssetList-V20150827) also updated accordingly. No changes to asset numbers.

Dataset Citation

Bioregional Assessment Programme (2014) Asset database for the Cooper subregion on 27 August 2015. Bioregional Assessment Derived Dataset. Viewed 27 November 2017, http://data.bioregionalassessments.gov.au/dataset/0b122b2b-e5fe-4166-93d1-3b94fc440c82.

Dataset Ancestors

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements 20131204

• Derived From Queensland QLD - Regional - NRM - Water Asset Information Tool - WAIT - databases

• Derived From Matters of State environmental significance (version 4.1), Queensland

• Derived From Geofabric Surface Network - V2.1

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

• Derived From South Australia SA - Regional - NRM Board - Water Asset Information Tool - WAIT - databases

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From National Groundwater Information System (NGIS) v1.1

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

• Derived From Queensland QLD Regional CMA Water Asset Information WAIT tool databases RESTRICTED Includes ALL Reports

• Derived From Queensland wetland data version 3 - wetland areas.

• Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Water Management Areas 141007

• Derived From South Australian Wetlands - Groundwater Dependent Ecosystems (GDE) Classification

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

• Derived From Asset database for the Cooper subregion on 14 August 2015

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores v3 03122014

• 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 Permanent and Semi-Permanent Waterbodies of the Lake Eyre Basin (Queensland and South Australia) (DRAFT)

• Derived From SA EconomicElements v1 20141201

• Derived From QLD Dept of Natural Resources and Mines, Groundwater Entitlements linked to bores and NGIS v4 28072014

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

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

• Derived From SA Department of Environment, Water and Natural Resources (DEWNR) Groundwater Licences 141007

• Derived From Lake Eyre Basin (LEB) Aquatic Ecosystems Mapping and Classification

• Derived From Australia - Species of National Environmental Significance Database

• Derived From Asset database for the Cooper subregion on 27 March 2015

• Derived From Australia, Register of the National Estate (RNE) - Spatial Database (RNESDB) Internal

• Derived From Directory of Important Wetlands in Australia (DIWA) Spatial Database (Public)

• Derived From Collaborative Australian Protected Areas Database (CAPAD) 2010 (Not current release)