Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

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).

This "Best of" Color image service (web mercator projection) allows you to stream Vermont's best available imagery into your GIS or web mapping application. HOW TO USE: The service is available by plugging in the following REST endpoint into your browser, web mapping application, or GIS software.https://maps.vcgi.vermont.gov/arcgis/rest/services/EGC_services/IMG_VCGI_CLR_WM_CACHE/ImageServer NOTE: Clicking the "Download" button to the right will not actually give you the data OR access to the service. Ignore the "Download" button and use the URL above instead. HELP: Refer to the following video describing how you can use VCGI's services in ArcGIS or QGIS.

These data provide an accurate high-resolution shoreline compiled from imagery of Great Bay, NH . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source...

The Digital Geologic-GIS Map of Great Basin National Park and Vicinity, Nevada is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (grba_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 3.X map file (.mapx) file (grba_geology.mapx) and individual Pro 3.X layer (.lyrx) 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 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 readme file (grba_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (grba_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 (grba_geology_metadata_faq.pdf). Please read the grba_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: 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: Stanford University and the Stanford 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 (grba_geology_metadata.txt or grba_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 Pro, 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).

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

Essential configurations for highly scalable ArcGIS Online web apps (ArcGIS Blog).Learn best practices for configuring web applications that receive a high amount of web traffic, use a quick checklist focus on critical settings._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Background: Between 2011 and 2018, the NASA Dawn spacecraft visited asteroid (4) Vesta and dwarf planet (1) Ceres to investigate the surfaces of both protoplanets through optical and hyperspectral imaging and their composition through gamma-ray and neutron spectroscopy from orbit.
For both Vesta and Ceres, a geologic mapping investigation was realized based on optical and hyperspectral data as well as a photogrammetrically derived digital terrain model. For the global mapping investigation, mappers employed Geographic Information System (GIS) software to map 15 quadrangles. The results were published as individual map sheets alongside research papers discussing the geologic evolution. The style of collaborative mapping to produce a consistent global view represented by individual quadrangle maps is comparably new despite abundantly available mapping experiences. Ongoing data acquisition during mapping created considerable challenges for the coordination and homogenization of mapping results.

To handle this issue simultaniously to the active mission phase as best as possible a GIS-based environment was needed in order to conduct one homogenous dataset (w.r.t. geometrical and visual character) that represents one geologically-consistent map at the end. Therefore, the mapping team was supported by an predefined mapping template which was generated in the proprietary ArcGIS environment. The template contains different layers (called feature classes) for the different object/geomoetry types and contains predefined attribute values as well as cartographic symbols. The cartographic symbols follow international standards as far as possible. The colours for the geological units refering to established colour values used in geologic maps, e.g., standardized planetary maps generated by USGS, but considering individual needs and requests within the mapping team, too.

The data product pubished here based on the mentioned GIS-based template and represents the merged global GIS-dataset of the 15 individually conducted geological maps of Ceres within the Dawn Mission. The detailed descriptions of all those scientific interpretions are published in the papers listed within the reference section. Based on team-internal decisions the dataset is provided within the properitary format of ESRIs ArcGIS environment. However, in order to use the data product also outside this software environment, single shapefiles with additional information about the symbology are also included. All available data are available within the compressed folder and the readme-file gives some informative remarks for the useage of the data

Additional remark: The data set provided here does not represent a holistic (in term of topological and scientifical) unification of the 15 individual mapping data as primarily geometric and content-related inconsistencies at quadrangle boundaries prohibited a unified compilation. On the one side, this is due to the fact that the the aim of the mapping project was not to produce a uniform global map, but rather to gain a first impression of the geology of Ceres and publish associated scientific papers. On the other side, that the geological mapping project ran parallel to the regular mission phase, and a finalizing review process for creating a global geological dataset wasn´t scheduled in the mission planning. This deficiency cannot be remedied simply by merging topological missmatches or changing the visualisation. Rather it will require ongoing and detailed scientific discussion of the interpretation results, which could be solved within an updating version of the global map.

This Dynamic Image Service provides access to a 2-Foot Digital Elevation Model (DEM) with a Shaded Relief function applied for Kentucky. The service is in Kentucky Single Coordinate System (3089) and is best used in desktop GIS software.

The Unpublished Digital Geologic-GIS Map of Great Sand Dunes National Park, Colorado is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (grsa_geology.gdb), a 10.1 ArcMap (.mxd) map document (grsa_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (grsa_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (grsa_geology_gis_readme.pdf). Please read the grsa_geology_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O'Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 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. Google Earth software is available for free at: http://www.google.com/earth/index.html. 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). 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 (grsa_geology_metadata.txt or grsa_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 1:35,000 and United States National Map Accuracy Standards features are within (horizontally) 17.8 meters or 58.3 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 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: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 13N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Great Sand Dunes National Park and Preserve.

Kentucky's statewide Phase2 DEM rendered using a shaded relief function accessible as a dynamic image service. The service is in Kentucky Single Coordinate System (3089) and is best used in desktop GIS software.

Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

These maps show for the first time an accurate georeferenced mosaic of the Marshall Islands, the Federated States of Micronesia, the Republic of Palau and their respective corresponding shallow water areas. Shallow-water (generally, less than 30 meters) bank and land areas in these areas were identified through analysis of Landsat 7 ETM+ satellite imagery. The mosaics are laid over ETOPO2 Bathymetric Data to provide an enhanced understanding of how the Atolls and Islands fit together. In addition selected islands and atolls are shown next to the mosaic. This project was conducted in support of the U.S. Coral Reef Task Force.

Data in this accession are best used with appropriate Geographic Information System (GIS) software.

Best Management Practices (BMPs) are structural controls used to manage stormwater runoff. Examples include green roofs, rain gardens, and cisterns. BMPs reduce the effects of stormwater pollution and help restore the District’s waterbodies. The District’s stormwater regulations require that large construction or renovation projects install BMPs to manage stormwater runoff once construction is complete. The District also provides financial incentives for properties that install BMPs voluntarily. This dataset includes BMPs that were installed to comply with the District’s stormwater regulations, to participate in the Stormwater Retention Credit (SRC) trading program, to participate in the RiverSmart Homes program, to participate in the Green Roof Rebate program, or to participate in the RiverSmart Rewards stormwater fee discount program. These BMPs have been reviewed by the Department of Energy and Environment (DOEE) as part of these programs. This dataset is updated weekly with data from the District’s Stormwater Database.

These data provide an accurate high-resolution shoreline compiled from imagery of Back Bays, Great Egg Harbor Bay to Reed Bay, NJ . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Abel Tasman National Park is located northwest of Nelson and is home to a richness of invertebrates. It is also one of the few places in New Zealand with continuous forest from the mountains to the sea. However, an exotic wasp, accidentally introduced to NZ in the 1940s, has impacted the ecosystem. With mild winters and no natural predators, these wasps thrive in Abel Tasman National Park and achieve some of the highest densities of anywhere in the world. The Department of Conservation has a limited budget for wasp control measures so they want you to locate the best place to introduce these measures. The Abel Tasman National Park is divided into two zones. The Northern Zone is serviced by the Department of Conservation out of Takaha by land and the Southern Zone is serviced by sea from Motueka.

It is your job to use GIS software to prepare a report and layout that:

Shows and explains possible treatment areas to deploy wasp traps.Recommends the best management zone for treatment. Original idea for this lesson was presented at a GIS Champions meeting by the Department of ConservationAchievement Standard 91247.This lesson has not been moderated. If you have any feedback on the lesson please email gisinschools@eagle.co.nz

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

These data provide an accurate high-resolution shoreline compiled from imagery of BARNEGAT INLET TO GREAT EGG HARBOR INLET, NJ . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Carto...

These data provide an accurate high-resolution shoreline compiled from lidar and imagery of GREAT EGG HARBOR INLET, NJ . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic...