65 datasets found
  1. A

    ‘Pay Stations’ analyzed by Analyst-2

    • analyst-2.ai
    Updated May 16, 2019
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2019). ‘Pay Stations’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-pay-stations-bc82/latest
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    Dataset updated
    May 16, 2019
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘Pay Stations’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8f7302e2-13fd-42eb-a03e-3d95e3f1da53 on 27 January 2022.

    --- Dataset description provided by original source is as follows ---

    Displays the locations of Paid Parking Kiosks that distribute a receipt that is displayed in the vehicle.

    | Attribute Information: https://www.seattle.gov/Documents/Departments/SDOT/GIS/Pay_Station_OD.pdf

    | Data Update Cycle: Weekly (Due to issues with nightly update)
    | Contact Email: DOT_IT_GIS@seattle.gov

    --- Original source retains full ownership of the source dataset ---

  2. Prepare for Search and Rescue Incidents

    • hub.arcgis.com
    Updated Mar 19, 2019
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    Esri Tutorials (2019). Prepare for Search and Rescue Incidents [Dataset]. https://hub.arcgis.com/documents/243905284dab4c468aab61181e8b2fae
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    Dataset updated
    Mar 19, 2019
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri Tutorials
    Description

    During a search and rescue (SAR) operation, officials don't have time to wait until a GIS specialist is on scene. They need maps immediately. Preconfigured and ready-to-use GIS tools must be available to SAR teams before an incident occurs.

    In this lesson, you'll create a web map to prepare data for search operations. Your map will contain static base data showing regional boundaries and key features, as well as editable layers that can be changed as an incident develops. Then, you'll use the map to create a web app that even non-GIS professionals can use. Finally, you'll use the app to track a fictional SAR mission.

    In this lesson you will build skills in the these areas:

    • Mapping base and incident data
    • Configure widgets in a web app
    • Adding Situational Awareness and Smart Editor widgets
    • Plotting Initial Planning Point and adding trail blocks
    • Creating search assignments
    • Mapping incident data

    Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

  3. A

    ‘2018 CT Data Catalog (GIS)’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 26, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘2018 CT Data Catalog (GIS)’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-2018-ct-data-catalog-gis-8148/4aa04a6c/?iid=001-843&v=presentation
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    Dataset updated
    Jan 26, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Connecticut
    Description

    Analysis of ‘2018 CT Data Catalog (GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5a93e011-4ea8-40b1-a888-0f573e6b785d on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management. This catalog contains information on high value GIS data only. A catalog of high value non-GIS data may be found at the following link: https://data.ct.gov/Government/CT-Data-Catalog-Non-GIS-/ghmx-93jn

    As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

    High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

    This dataset was last updated 1/2/2019 and will continue to be updated as high value data inventories are submitted to OPM.

    --- Original source retains full ownership of the source dataset ---

  4. BLM OR Areas of Critical Environmental Concern Line

    • data.doi.gov
    Updated Mar 17, 2021
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    Bureau of Land Management (2021). BLM OR Areas of Critical Environmental Concern Line [Dataset]. https://data.doi.gov/dataset/blm-or-areas-of-critical-environmental-concern-line
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    Dataset updated
    Mar 17, 2021
    Dataset provided by
    Bureau of Land Managementhttp://www.blm.gov/
    Description

    ACEC_ARC: This data set shows the boundary lines for Areas of Critical Environmental Concern under BLM management in Oregon and Washington. The district Data Steward will define the ACEC boundary and work with the GIS specialist to ensure that the appropriate GIS coordinate sources are used and that only federal land is included.

  5. g

    Explore California Historical Wildland Fire Perimeters App

    • gimi9.com
    • data.cnra.ca.gov
    • +4more
    Updated Jun 6, 2024
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    (2024). Explore California Historical Wildland Fire Perimeters App [Dataset]. https://gimi9.com/dataset/california_explore-california-historical-wildland-fire-perimeters-app
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    Dataset updated
    Jun 6, 2024
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    California
    Description

    CAL FIRE's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, the National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data. This app contains three pages of maps and documentation of the historical fire perimeter metadata: Historical Fire Perimeters: The landing page highlights the recent large fires (≥5,000 acres) on a backdrop of all of the dataset's documented fire perimeters dating back to 1878. This map includes perimeters symbolized by decade, county boundaries, California Vegetation, and NAIP imagery back to 2005. This page provides users the ability to add their own data or filter the fire perimeter data. It cleanly lists fire perimeters shown on the map with their name, year, and GIS calculated acreage. The user can navigate to the CAL FIRE current incident webpage or provide comments to the dataset's steward. Times Burned: The second page provides a map showing an analysis performed annually on the fire perimeter dataset to show case burn frequency from 1950 to present for fires greater than one acre. Fire Across Time: This third page provides a time enabled layer of the fire perimeter dataset, featuring a time slider to allow users to view the perimeter dataset across time. The final page provides the user with the dataset's metadata, including its most current data dictionary. For any questions, please contact the data steward: Kim Wallin, GIS Specialist CAL FIRE, Fire & Resource Assessment Program (FRAP) kimberly.wallin@fire.ca.gov

  6. a

    Centerline Features

    • data-cosm.hub.arcgis.com
    Updated Jul 19, 2024
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    City of San Marcos (2024). Centerline Features [Dataset]. https://data-cosm.hub.arcgis.com/datasets/centerline-features-1
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    Dataset updated
    Jul 19, 2024
    Dataset authored and provided by
    City of San Marcos
    Area covered
    Description

    Road segments representing centerlines of all roadways or carriageways in a local government. Typically, this information is compiled from orthoimagery or other aerial photography sources. This representation of the road centerlines support address geocoding and mapping. It also serves as a source for public works and other agencies that are responsible for the active management of the road network. (From ESRI Local Government Model "RoadCenterline" Feature)**This dataset was significantly revised in August of 2014 to correct for street segments that were not properly split at intersections. There may be issues with using data based off of the original centerline file. ** The column Speed Limit was updated in November 2014 by the Transportation Intern and is believed to be accurate** The column One Way was updated in November of 2014 by core GIS and is believed to be accurate.[MAXIMOID] A unique id field used in a work order management software called Maximo by IBM. Maximo uses GIS CL data to assign locations to work orders using this field. This field is maintained by the Transportation GIS specialists and is auto incremented when new streets are digitized. For example, if the latest digitized street segment MAXIMOID = 999, the next digitized line will receive MAXIMOID = 1000, and so on. STREET NAMING IS BROKEN INTO THREE FIELDS FOR GEOCODING:PREFIX This field is attributed if a street name has a prefix such as W, N, E, or S.NAME Domain with all street names. The name of the street without prefix or suffix.ROAD_TYPE (Text,4) Describes the type of road aka suffix, if applicable. CAPCOG Addressing Guidelines Sec 504 U. states, “Every road shall have corresponding standard street suffix…” standard street suffix abbreviations comply with USPS Pub 28 Appendix C Street Abbreviations. Examples include, but are not limited to, Rd, Dr, St, Trl, Ln, Gln, Lp, CT. LEFT_LOW The minimum numeric address on the left side of the CL segment. Left side of CL is defined as the left side of the line segment in the From-To direction. For example, if a line has addresses starting at 101 and ending at 201 on its left side, this column will be attributed 101.LEFT_HIGH The largest numeric address on the left side of the CL segment. Left side of CL is defined as the left side of the line segment in the From-To direction. For example, if a line has addresses starting at 101 and ending at 201 on its left side, this column will be attributed 201.LOW The minimum numeric address on the RIGHT side of the CL segment. Right side of CL is defined as the right side of the line segment in the From-To direction. For example, if a line has addresses starting at 100 and ending at 200 on its right side, this column will be attributed 100.HIGHThe maximum numeric address on the RIGHT side of the CL segment. Right side of CL is defined as the right side of the line segment in the From-To direction. For example, if a line has addresses starting at 100 and ending at 200 on its right side, this column will be attributed 200.ALIAS Alternative names for roads if known. This field is useful for geocode re-matching. CLASSThe functional classification of the centerline. For example, Minor (Minor Arterial), Major (Major Arterial). THIS FIELD IS NOT CONSISTENTLY FILLED OUT, NEEDS AN AUDIT. FULLSTREET The full name of the street concatenating the [PREFIX], [NAME], and [SUFFIX] fields. For example, "W San Antonio St."ROWWIDTH Width of right-of-way along the CL segment. Data entry from Plat by Planning GIS Or from Engineering PICPs/ CIPs.NUMLANES Number of striped vehicular driving lanes, including turn lanes if present along majority of segment. Does not inlcude bicycle lanes. LANEMILES Describes the total length of lanes for that segment in miles. It is manually field calculated as follows (( [ShapeLength] / 5280) * [NUMLANES]) and maintained by Transportation GIS.SPEEDLIMIT Speed limit of CL segment if known. If not, assume 30 mph for local and minor arterial streets. If speed limit changes are enacted by city council they will be recorded in the Traffic Register dataset, and this field will be updating accordingly. Initial data entry made by CIP/Planning GIS and maintained by Transportation GIS.[YRBUILT] replaced by [DateBuilt] See below. Will be deleted. 4/21/2017LASTYRRECON (Text,10) Is the last four-digit year a major reconstruction occurred. Most streets have not been reconstructed since orignal construction, and will have values. The Transportation GIS Specialist will update this field. OWNER Describes the governing body or private entity that owns/maintains the CL. It is possible that some streets are owned by other entities but maintained by CoSM. Possible attributes include, CoSM, Hays Owned/City Maintained, TxDOT Owned/City Maintained, TxDOT, one of four counties (Hays, Caldwell, Guadalupe, and Comal), TxState, and Private.ST_FROM Centerline segments are split at their intersections with other CL segments. This field names the nearest cross-street in the From- direction. Should be edited when new CL segments that cause splits are added. ST_TO Centerline segments are split at their intersections with other CL segments. This field names the nearest cross-street in the To- direction. Should be edited when new CL segments that cause splits are added. PAV_WID Pavement width of street in feet from back-of-curb to back-of-curb. This data is entered from as-built by CIP GIS. In January 2017 Transportation Dept. field staff surveyed all streets and measured width from face-of-curb to face-of-curb where curb was present, and edge of pavement to edge of pavement where it was not. This data was used to field calculate pavement width where we had values. A value of 1 foot was added to the field calculation if curb and gutter or stand up curb were present (the face-of-curb to back-of-curb is 6 in, multiple that by 2 to find 1 foot). If no curb was present, the value enter in by the field staff was directly copied over. If values were already present, and entered from asbuilt, they were left alone. ONEWAY Field describes direction of travel along CL in relation to digitized direction. If a street allows bi-directional travel it is attributed "B", a street that is one-way in the From_To direction is attributed "F", a street that is one-way in the To_From direction is attributed "T", and a street that does not allow travel in any direction is attibuted "N". ROADLEVEL Field will be aliased to [MINUTES] and be used to calculate travel time along CL segments in minutes using shape length and [SPEEDLIMIT]. Field calculate using the following expression: [MINUTES] = ( ([SHAPE_LENGTH] / 5280) / ( [SPEEDLIMIT] / 60 ))ROWSTATUS Values include "Open" or "Closed". Describes whether a right-of-way is open or closed. If a street is constructed within ROW it is "Open". If a street has not yet been constructed, and there is ROW, it is "Cosed". UPDATE: This feature class only has CL geometries for "Open" rights-of-way. This field should be deleted or re-purposed. ASBUILT field used to hyper link as-built documents detailing construction of the CL. Field was added in Dec. 2016. DateBuilt Date field used to record month and year a road was constructed from Asbuilt. Data was collected previously without month information. Data without a known month is entered as "1/1/YYYY". When month and year are known enter as "M/1/YYYY". Month and Year from asbuilt. Added by Engineering/CIP. ACCEPTED Date field used to record the month, day, and year that a roadway was officially accepted by the City of San Marcos. Engineering signs off on acceptance letters and stores these documents. This field was added in May of 2018. Due to a lack of data, the date built field was copied into this field for older roadways. Going forward, all new roadways will have this date. . This field will typically be populated well after a road has been drawn into GIS. Entered by Engineering/CIP. ****In an effort to make summarizing the data more efficient in Operations Dashboard, a generic date of "1/1/1900" was assigned to all COSM owned or maintained roads that had NULL values. These were roads that either have not been accepted yet, or roads that were expcepted a long time ago and their accepted date is not known. WARRANTY_EXP Date field used to record the expiration date of a newly accepted roadway. Typically this is one year from acceptance date, but can be greater. This field was added in May of 2018, so only roadways that have been excepted since and older roadways with valid warranty dates within this time frame have been populated.

  7. A

    ‘500 Cities: Census Tract-level Data (GIS Friendly Format), 2017 release’...

    • analyst-2.ai
    Updated Feb 12, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘500 Cities: Census Tract-level Data (GIS Friendly Format), 2017 release’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-500-cities-census-tract-level-data-gis-friendly-format-2017-release-3c93/latest
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    Dataset updated
    Feb 12, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘500 Cities: Census Tract-level Data (GIS Friendly Format), 2017 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a5ba8426-6de9-48f9-bb7a-b9ff00296c47 on 12 February 2022.

    --- Dataset description provided by original source is as follows ---

    2015, 2014. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. 500 cities project census tract-level data in GIS-friendly format can be joined with census tract spatial data (https://chronicdata.cdc.gov/500-Cities/500-Cities-Census-Tract-Boundaries/x7zy-2xmx) in a geographic information system (GIS) to produce maps of 27 measures at the census tract level. Because some questions are only asked every other year in the BRFSS, there are 7 measures in this 2017 release from the 2014 BRFSS that were the same as the 2016 release.

    --- Original source retains full ownership of the source dataset ---

  8. BLM OR Northern Spotted Owl Sites Publication Point Hub

    • catalog.data.gov
    • s.cnmilf.com
    • +2more
    Updated Nov 20, 2024
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    Bureau of Land Management (2024). BLM OR Northern Spotted Owl Sites Publication Point Hub [Dataset]. https://catalog.data.gov/dataset/blm-or-northern-spotted-owl-sites-publication-point-hub
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    Dataset updated
    Nov 20, 2024
    Dataset provided by
    Bureau of Land Managementhttp://www.blm.gov/
    Description

    NSO_SITE_PUB_PT: The Northern Spotted Owl (NSO) data standard documents how spatial location and information about inventory and monitoring activities for Northern Spotted Owls is stored. This dataset is a replacement of the former Northern Spotted Owl database. BLM wildlife biologists and GIS specialists enter and query data that was collected by district staff or contractors. The dataset includes four spatial feature classes and four non-spatial tables to support the following data collection: This data is only updated annually after the data entry has been completed for the previous years' field season.

  9. b

    BLM Western U.S. GRSG Biologically Significant Units October 2017 Update

    • navigator.blm.gov
    Updated Oct 15, 2017
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    (2017). BLM Western U.S. GRSG Biologically Significant Units October 2017 Update [Dataset]. https://navigator.blm.gov/data/SQLUQJUW_9887/blm-western-u-s-grsg-biologically-significant-units-october-2017-update
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    Dataset updated
    Oct 15, 2017
    Area covered
    United States, Western United States
    Description

    The Sheeprocks (UT) was revised to resync with the UT habitat change as reflected in the Oct 2017 habitat data, creating the most up-to-date version of this dataset. Data submitted by Wyoming in February 2018 and by Montana and Oregon in May 2016 were used to update earlier versions of this feature class. The biologically significant unit (BSU) is a geographicalspatial area within Greater Sage-Grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. This BSU unit, or subset of this unit is used in the calculation of the anthropogenic disturbance threshold and in the adaptive management habitat trigger. BSU feature classes were submitted by individual statesEISs and consolidated by the Wildlife Spatial Analysis Lab. They are sometimes referred to as core areascore habitat areas in the explanations below, which were consolidated from metadata submitted with BSU feature classes. These data provide a biological tool for planning in the event of human development in sage-grouse habitats. The intended use of all data in the BLMs GIS library is to support diverse activities including planning, management, maintenance, research, and interpretation. While the BSU defines the geographic extent and scale of these two measures, how they are calculated differs based on the specific measures to reflect appropriate assessment and evaluation as supported by scientific literature.

        There are 10 BSUs for the Idaho and Southwestern Montana GRSG EIS sub-region. For the Idaho and Southwestern Montana Greater Sage-Grouse Plan Amendment FEIS the biologically significant unit is defined as: a geographicalspatial area within greater sage-grouse habitat that contains relevant and important habitats which is used as the basis for comparative calculations to support evaluation of changes to habitat. Idaho: BSUs include all of the Idaho Fish and Game modeled nesting and delineated winter habitat, based on 2011 inventories within Priority andor Important Habitat Management Area (Alternative G) within a Conservation Area. There are eight BSUs for Idaho identified by Conservation Area and Habitat Management Area: Idaho Desert Conservation Area - Priority, Idaho Desert Conservation Area - Important, Idaho Mountain Valleys Conservation Area - Priority, Idaho Mountain Valleys Conservation Area - Important, Idaho Southern Conservation Area - Priority, Idaho Southern Conservation Area - Important, Idaho West Owyhee Conservation Area - Priority, and Idaho West Owyhee Conservation Area - Important. Raft River : Utah portion of the Sawtooth National Forest, 1 BSU. All of this areas was defined as Priority habitat in Alternative G. Raft River - Priority.
    
        Montana: All of the Priority Habitat Management Area. 1 BSU. SW Montana Conservation Area - Priority. Montana BSUs were revised in May 2016 by the MT State Office. They are grouped together and named by the Population in which they are located: Northern Montana, Powder River Basin, Wyoming Basin, and Yellowstone Watershed. North and South Dakota BSUs have been grouped together also.
    
        California and Nevadas BSUs were developed by Nevada Department of Wildlifes Greater Sage-Grouse Wildlife Staff Specialist and Sagebrush Ecosystem Technical Team Representative in January 2015. Nevadas Biologically Significant Units (BSUs) were delineated by merging associated PMUs to provide a broader scale management option that reflects sage grouse populations at a higher scale. PMU boundarys were then modified to incorporate Core Management Areas (August 2014; Coates et al. 2014) for management purposes. (Does not include Bi-State DPS.) 
    
        Within Colorado, a Greater Sage-Grouse GIS data set identifying Preliminary Priority Habitat (PPH) and Preliminary General Habitat (PGH) was developed by Colorado Parks and Wildlife. This data is a combination of mapped grouse occupied range, production areas, and modeled habitat (summer, winter, and breeding). PPH is defined as areas of high probability of use (summer or winter, or breeding models) within a 4 mile buffer around leks that have been active within the last 10 years. Isolated areas with low activity were designated as general habitat. PGH is defined as Greater sage-grouse Occupied Range outside of PPH. Datasets used to create PPH and PGH: Summer, winter, and breeding habitat models. Rice, M. B., T. D. Apa, B. L. Walker, M. L. Phillips, J. H. Gammonly, B. Petch, and K. Eichhoff. 2012. Analysis of regional species distribution models based on combined radio-telemetry datasets from multiple small-scale studies. Journal of Applied Ecology in review. Production Areas are defined as 4 mile buffers around leks which have been active within the last 10 years (leks active between 2002-2011). Occupied range was created by mapping efforts of the Colorado Division of Wildlife (now Colorado Parks and Wildlife –CPW) biologists and district officers during the spring of 2004, and further refined in early 2012. Occupied Habitat is defined as areas of suitable habitat known to be used by sage-grouse within the last 10 years from the date of mapping. Areas of suitable habitat contiguous with areas of known use, which do not have effective barriers to sage-grouse movement from known use areas, are mapped as occupied habitat unless specific information exists that documents the lack of sage-grouse use. Mapped from any combination of telemetry locations, sightings of sage grouse or sage grouse sign, local biological expertise, GIS analysis, or other data sources. This information was derived from field personnel. A variety of data capture techniques were used including the SmartBoard Interactive Whiteboard using stand-up, real-time digitizing atvarious scales (Cowardin, M., M. Flenner. March 2003. Maximizing Mapping Resources. GeoWorld 16(3):32-35). Update August 2012: This dataset was modified by the Bureau of Land Management as requested by CPW GIS Specialist, Karin Eichhoff. Eichhoff requested that this dataset, along with the GrSG managment zones (population range zones) dataset, be snapped to county boundaries along the UT-CO border and WY-CO border. The county boundaries dataset was provided by Karin Eichhoff. In addition, a few minor topology errors were corrected where PPH and PGH were overlapping. Update October 10, 2012: NHD water bodies greater than 100 acres were removed from GrSG habitat, as requested by Jim Cagney, BLM CO Northwest District Manager. 6 water bodies in total were removed (Hog Lake, South Delaney, Williams Fork Reservoir, North Delaney, Wolford Mountain Reservoir (2 polygons)). There were two “SwampMarsh†polygons that resulted when selecting polygons greater than 100 acres; these polygons were not included. Only polygons with the attribute “LakePond†were removed from GrSG habitat. Colorado Greater Sage Grouse managment zones based on CDOW GrSG_PopRangeZones20120609.shp. Modified and renumbered by BLM 06092012. The zones were modified again by the BLM in August 2012. The BLM discovered areas where PPH and PGH were not included within the zones. Several discrepancies between the zones and PPH and PGH dataset were discovered, and were corrected by the BLM. Zones 18-21 are linkages added as zones by the BLM. In addition to these changes, the zones were adjusted along the UT-CO boundary and WY-CO boundary to be coincident with the county boundaries dataset. This was requested by Karin Eichhoff, GIS Specialist at the CPW. She provided the county boundaries dataset to the BLM. Greater sage grouse GIS data set identifying occupied, potential and vacantunknown habitats in Colorado. The data set was created by mapping efforts of the Colorado Division of Wildlife biologist and district officers during the spring of 2004, and further refined in the winter of 2005. Occupied Habitat: Areas of suitable habitat known to be used by sage-grouse within the last 10 years from the
    
  10. NPS Fire Management Zones (Public View)

    • anrgeodata.vermont.gov
    • visionzero.geohub.lacity.org
    • +2more
    Updated Nov 23, 2022
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    National Interagency Fire Center (2022). NPS Fire Management Zones (Public View) [Dataset]. https://anrgeodata.vermont.gov/datasets/4a93b69a105d43a881d62d0170b46d05
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    Dataset updated
    Nov 23, 2022
    Dataset authored and provided by
    National Interagency Fire Centerhttps://www.nifc.gov/
    Area covered
    Pacific Ocean, North Pacific Ocean
    Description

    Some regions call them Fire Management Areas, others call them Fire Management Clusters, still others call them Fire Management Zones. Regardless of what their called, Fire Management Zones (FMZ) are administrative boundaries established to facilitate communication and information sharing amongst National Park Service (NPS) units. Each FMZ has a lead park; that provides fire management support to satellite parks that do not typically have fire staff.Update Cycle:The boundaries of FMZs rarely change. The attributes, especially the Zone FMO and contact information change on an infrequent basis. If edits to the attributes are needed contact your Regional Fire GIS Specialist.

  11. Data from: Vrba was right: Historical climatic fragmentation, and not...

    • zenodo.org
    • portalcientifico.uvigo.gal
    • +3more
    bin, csv, zip
    Updated May 13, 2024
    + more versions
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    Sara Gamboa; Sara Gamboa; Sofía Galván; Sara Varela; Sofía Galván; Sara Varela (2024). Data from: Vrba was right: Historical climatic fragmentation, and not current climate, explains mammal biogeography [Dataset]. http://doi.org/10.5061/dryad.x69p8czsn
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    bin, zip, csvAvailable download formats
    Dataset updated
    May 13, 2024
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Sara Gamboa; Sara Gamboa; Sofía Galván; Sara Varela; Sofía Galván; Sara Varela
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Measurement technique
    <p><strong><em>Climate Data and </em></strong><strong><em>Classification</em></strong></p> <p>In this study, we employed the Köppen-Geiger climate classification to categorize climate zones. This system relies on climatic parameters, specifically monthly mean temperature (ºC) and total precipitation (mm), to define climate types (Beck et al., 2018; Köppen, 1884). Given the close correlation between climate and vegetation, these climate zones tend to align closely with global biome patterns (Belda et al., 2014), providing a proxy for examining how climate shapes biome distributions (Mucina, 2019). The Köppen-Geiger climate classification recognises 23 distinct climate regimes, grouped into five major zones: Tropical, Arid, Temperate, Cold, and Polar (Figure 1A). These zones served as the basis for our analysis of the impact of climate change on environmental fragmentation.</p> <p>Climate data for the last 5 million years were obtained from the high-resolution paleoclimate emulator, PALEO-PGEM (Holden et al., 2019). This dataset offers monthly climate information at a spatial resolution of 0.5º and temporal resolution of 1,000 years, beginning from the pre-industrial era (ca. 1760). We reclassified the climate data into the five major climate zones (tropical, arid, temperate, cold, and polar) for each 1,000-year interval following the methodology outlined by Beck et al. (2018). To facilitate computational operations, we introduced a "-99" value for missing data and made specific adjustments to the function 'KoppenGeiger.m' (Beck et al., 2018), as communicated by H. Beck (personal communication, December 18, 2021), to align with defined precipitation thresholds: "Pthreshold = 2×MAT if >70% of precipitation falls in winter, Pthreshold = 2×MAT+28 if >70% of precipitation falls in summer, otherwise Pthreshold = 2×MAT+14 (Galván et al., 2023). This change was made to rectify a previous code typo that prevented the accurate assignment of some pixels to their climate zone. "Pthreshold" refers to the precipitation threshold for determining the aridity of a climate zone. Meanwhile, "MAT" corresponds to Mean Annual Temperature.</p> <p><strong><em>Geographical Framework</em></strong></p> <p>This study was conducted on a global scale, to assess whether similar climate zones behaved consistently across different continents. To facilitate these comparisons, we divided the world into three distinct landmasses, hereafter referred to as Americas, Africa, and Eurasia+Oceania (EurOc). The rationale behind this division was to partition our planet into distinct landmasses, each of which would encompass a tropical zone. Upon delimiting the three main landmasses, the different islands were assigned to the nearest landmass in a straight line. This, in turn, corresponds with other biogeographical criteria based on the similarity of flora and fauna. Thus, the three studied landmasses were established as follows:</p> <p>· Americas: This category encompasses continental North, Central, and South America, as well as the Caribbean Islands. In the North (Bering Strait), we have included Aleutian St. Matthew, St. Paul, St. George, and Nunivak Islands. St. Lawrence Island is excluded due to its proximity to Europe. In the West, we encompass the Islands off the Mexican West Coast, the Galápagos Islands, and Easter Island. To the South, the Malvinas Islands are included. In the East, we consider Fernando de Noronha, Atol das Rocas Biological Reserve, and Boi Islands. Greenland is part of this category, while Iceland is excluded.</p> <p>· Africa: This category covers continental Africa and Madagascar. In the West, it includes the Canary, Madeira, and Savage Islands, the Cabo Verde archipelago, St. Helena, Tristan da Cunha, and Ascension Islands. In the East, Socotra, Seychelles, and the Mayotte archipelago, Comoros, and Mascarene Islands are encompassed.</p> <p>· Eurasia + Oceania: This category comprises continental Eurasia, the Arabian Peninsula, Iceland, St. Lawrence Island, Japan, Philippines, Indonesia Australia, New Zealand, and Papua-New Guinea Islands. All the islands of the Pacific Ocean, including the Hawaii Archipelago, are also included. In the Indian Ocean, we consider the Laccadive, Maldives, and Chagos Islands in the West and Ceylon and the Andaman Islands in the East.</p> <p>The French Austral and Antarctic Lands islands that are closer to the Antarctic region were excluded from the study.</p> <p><strong><em>Measuring Fragmentation</em></strong></p> <p>To assess climate zone fragmentation, we used the R package landscapemetrics v1.5.4<u> (</u>Hesselbarth et al., 2019), employing the equal-area Mollweide projection. We applied the "lsm_p_area" function to calculate the number of fragments within each climate zone for each time interval, classifying them based on their area into four size categories:</p> <p>- Small fragments (S): Those with an area of up to 3,000 km2, approximately equivalent to the area of a single pixel under our 0.5º resolution.</p> <p>- Medium fragments (M): Those with an area between 3,000 and 30,000 km2.</p> <p>- Large fragments (L): Those with an area between 30,000 and 600,000 km2.</p> <p>- Extra-large fragments (XL): Those with an area exceeding 600,000 km2.</p> <p>Upon confirming that the number of fragments in the different climate zones followed a normal distribution but did not meet the assumption of variance homogeneity, we conducted the corresponding Welch One-Way ANOVA tests to determine the significance of the results. Given that we were comparing five climate zones, we applied Bonferroni correction to post-hoc results significance. Statistical analyses were conducted using the R library <em>jmv </em>(v2.3.4;<em>53</em>)</p> <p>In addition to quantifying the number of fragments within climate zones at each time in our series, we computed several additional measures to assess fragmentation:</p> <p>-Fragmentation Events: The count of instances when the number of fragments increased compared to the previous point in time.</p> <p>-Fragmentation Strength: The median number of fragments generated in each fragmentation event.</p> <p>-Maximum Fragmentation: The highest number of fragments produced in a single fragmentation event.</p> <p>Higher levels of climatic fragmentation are operationally defined as a prevalence of small (S) and medium (M) fragments, while lower levels of fragmentation are characterised by a greater abundance of larger patches (L and XL).</p> <p><strong><em>Fragmentation vs. Richness</em></strong></p> <p>To explore the relationship between climate fragmentation and specialist mammal richness we sourced mammal range maps from IUCN polygons (IUCN, 2022). Terrestrial mammal data was downloaded on 24th January 2022, while freshwater mammal data was obtained on 21th September 2022. We imported these range maps in shapefile format into R using the 'rgdal' package version 1.5-28 (Bivand et al., 2021). We excluded polygons associated with certain families such as Delphinidae, Iniidae, Phocidae, Phocoenidae, Platanistidae, Trichechidae, and the possibly extinct Lipotidae, due to their predominantly aquatic habits. We further excluded species range polygons with presence values of 3 ("possibly extant") and 6 ("presence uncertain"), as well as range values of 3 ("introduced") and 4 ("vagrant") to retain only reliable natural range data (Miraldo et al., 2016).</p> <p>Range data for each species were converted into a 0.5º raster using the 'terra' R package version 1.5-21 (Hijmans, 2022). Mammal species were classified according to their range into specialists, those species that are restricted to a single climate zone, and generalists, which are found in more than one climate zone. To this end, we considered the current distribution of terrestrial mammal species as a reliable representation of their climatic specificities. We then quantified the richness of specialist and generalist mammal species within each climate zone on every continent. We considered various factors of climate fragmentation, including the total number of fragments categorized by size (S, M, L, and XL), the frequency of fragmentation events, as well as the fragmentation strength and maximum fragmentation within each fragment size, climate zone, and continent. In addition, we calculated the mean annual temperature and mean annual precipitation for each climate zone on each continent.</p> <p>To explore the relationship between these variables and specialist mammal richness, we employed a generalized linear model (GLM). To refine our model and identify the most influential predictors, we employed a bidirectional stepwise regression. This method systematically evaluates interaction terms, ensuring the final model contains only strong predictors or those involved in substantial interactions (Gelman & Hill, 2006). The stepwise regression process continues until no further terms can enhance the model. The selected variables were subsequently evaluated through significance tests, residual analysis, and sensitivity assessments.</p>
    Description

    Climate plays a crucial role in shaping species distribution and evolution over time. Dr. Elisabeth Vrba's Resource-Use hypothesis posited that zones at the extremes of temperature and precipitation conditions should host a greater number of climate specialist species than other zones because of higher historical fragmentation. Here, we tested this hypothesis by examining climate-induced fragmentation over the past 5 million years. Our findings revealed that, as stated by Vrba, the number of climate specialist species increases with historical regional climate fragmentation, whereas climate generalist species richness decreases. This relationship is approximately 40% stronger than the correlation between current climate and species richness for climate specialist species and 77% stronger for generalist species. These evidences suggest that the effect of climate historical fragmentation is more significant than that of current climate conditions in explaining mammal biogeography. These results provide empirical support for the role of historical climate fragmentation and physiography in shaping the distribution and evolution of life on Earth.

  12. ArcGIS Hub Fundamentals

    • coronavirus-resources.esri.com
    • coronavirus-disasterresponse.hub.arcgis.com
    Updated Apr 3, 2020
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    Esri’s Disaster Response Program (2020). ArcGIS Hub Fundamentals [Dataset]. https://coronavirus-resources.esri.com/documents/443d382065a24cf2a02a070736d34d3d
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    Dataset updated
    Apr 3, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri’s Disaster Response Program
    Description

    Become an ArcGIS Hub Specialist.ArcGIS Hub is a cloud-based engagement platform that helps organizations work more effectively with their communities. Learn how to use ArcGIS Hub capabilities and related technology to coordinate and engage with external agencies, community partners, volunteers, and citizens to tackle the projects that matter most in your community._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...

  13. n

    National IA Frequency Zones (Federal) - Dataset - CKAN

    • nationaldataplatform.org
    Updated Feb 28, 2024
    + more versions
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    (2024). National IA Frequency Zones (Federal) - Dataset - CKAN [Dataset]. https://nationaldataplatform.org/catalog/dataset/national-ia-frequency-zones-federal1
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    Dataset updated
    Feb 28, 2024
    Description

    Initial attack frequency zones are used by pilots and dispatchers for purposes of response to incidents such as wildland fires. Initial attack frequency zones are agreed upon annually by the Communications Duty Officer at the National Interagency Fire Center (NIFC), other frequency managers, and the FAA, and can't be changed during the year without required approval from the CDO at NIFC. Each zone has assigned to it FAA-issued frequencies that are to be used only within the zone boundary. The initial attack frequency zones are delineated to help ensure that frequencies used do not "bleed" over into other incident areas and causing issues for incident communications. The data contains no actual frequencies, but does contain the zones in which they are used. 01/12/2023 - Tabular changes only. Oregon Initial Attack Frequency Zones renumbered per Kim Albracht, Communications Duty Officer, with input from other Northwest personnel. Edits by JKuenzi, USFS. Changes are as follows:OR09 changed to OR02OR02 changed to OR03OR03 changed to OR04OR04 changed to OR05OR05 changed to OR07OR07 changed to OR08OR08 changed to OR09OR01 and OR06 remained unchanged.01/10/2023 - Geospatial and tabular changes made. Two islands on west side of OR05 absorbed into OR03. Change made to both Initial Attack Frequency Zones-Federal and to Dispatch Boundaries per Kaleigh Johnson (Asst Ctr Mgr), Jada Altman (Dispatch Ctr Mgr), and Jerry Messinger (Air Tactical Group Supervisor). Edits by JKuenzi, USFS. 01/09/2023 - Geospatial and tabular changes to align Federal Frequency Zones to Dispatch Area boundaries in Northwest GACC. No alignments made to USWACAC, USWAYAC, or USORWSC. Changes approved by Ted Pierce (NW Deputy Coordination Ctr Mgr), Kaleigh Johnson (Assistant Ctr Mgr), and Kim Albracht (Communications Duty Officer). Edits by JKuenzi, USFS. Specific changes include: WA02 changed to WA04. New WA02 carved out of WA01 and OR01. OR09 carved out of OR01 and OR02. Boundary adjustments between OR07, OR05, and OR03.11/8/2022 - Geospatial and tabular changes. Boundary modified between Big Horn and Rosebud Counties of MT07 and MT08 per KSorenson and KPluhar. Edits by JKuenzi, USFS. 09/06/2022-09/26/2022 - Geospatial and tabular changes in accordance with proposed GACC boundary re-alignments between Southern California and Great Basin in the state of Nevada. Boundary modified between CA03 and NV03, specifically between Queen Valley and Mono Valley. The team making the changes is made up of Southern Calif (JTomaselli) and Great Basin (GDingman) GACCs, with input from Ian Mills and Lance Rosen (BLM). Changes proposed will be put into effect for the 2023 calendar year, and will also impact alignments of GACC boundaries and Dispatch boundaries in the area described. Initial edits provided by Ian Mills and Daniel Yarborough. Final edits by JKuenzi, USFS. A description of the change is as follows: The northwest end of changes start approximately 1 mile west of Mt Olsen and approximately 0.5 mile south of the Virginia Lakes area.Head northwest passing on the northeast side of Red Lake and the south side of Big Virginia Lake to follow HWY 395 North east to CA 270.East through Bodie to the CA/NV state line.Follows the CA/NV State Line south to HWY CA 167/NV 359.East on NV359 to where the HWY intersects the corner of FS/BLM land.Follows the FS/BLM boundary to the east and then south where it ties into the current GACC boundary. 09/07/2022 - 09/08/2022 - Tabular and geospatial changes. Multiple boundaries modified in Northern Rockies GACC to bring Dispatch Boundaries and Initial Attack Frequency Zone lines closer in accordance with State boundaries. Information provided by Don Copple, State Fire Planning & Intelligence Program Manager for Montana Dept of Natural Resources & Conservation (DNRC), Kathy Pipkin, Northern Rockies GACC Center Manager, and Kat Sorenson, R1 Asst Aircraft Coordinator. Edits by JKuenzi, USFS. The following changes were made:Initial Attack Frequency Zone changes made to the following: Dillon Interagency Dispatch Ctr (USMTDDC) (MT03), Helena Interagency Dispatch Ctr (USMTHDC) (MT04), Lewistown Interagency Dispatch Ctr (USMTLEC) (MT06), and Missoula Interagency Dispatch Ctr (USMTMDC) (MT02).Talk was also directed to removing the Initial Attack Frequency Zone line between MT05 and MT07, but that currently remains unchanged until Telecommunications (Kimberly Albracht) can get approval from the Frequency Managers and the FAA.10/15/2021 - Geospatial and tabular changes. Boundary alignments for the Duck Valley Reservation in southern Idaho along the Nevada border. Changes impacting ID02 and NV01. The Duck Valley Reservation remains within NV01. The only change was to the alignment of the physical boundary surrounding the Reservation in accordance with the boundary shown on the 7.5 minute quadrangle maps and data supplied by CClay/JLeguineche/Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. 9/30/2021 - Geospatial and tabular changes. Boundary alignments for Idaho on Hwy 95 NE of Weiser between Boise Dispatch Center and Payette Interagency Dispatch Center - per CClay/JLeguineche/Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. Boundary changes at: Weiser (T11N R5W Sec 32), (T11N, R5W, Sec 3), (T12N R5W, Sec 25), and Midvale.9/21/2021 - Geospatial and tabular changes in accordance with proposed GACC boundary re-alignments between Southwestern and Southern GACCs where a portion of Texas, formerly under Southwestern GACC direction was moved to the Southern GACC. Changes to Federal Initial Attack Frequency Zones by Kim Albracht, Communications Duty Officer (CDO) include the following: State designation TXS06 changed to federal TX06.State designation TXS05 changed to federal TX05.State designation TXS04 changed to federal TX04.State designation TXS03 changed to federal TX03.State designation TXS02 changed to federal TX02.State designation TXS01 changed to federal TX01.The Oklahoma Panhandle, formerly TXS01 changed to OK04.All changes proposed for implementation starting in January 2022. Edits by JKuenzi, USFS. See also data sets for Geographic Area Coordination Centers (GACC), and Dispatch Boundary for related changes.8/17/2021 - Tabular changes only. As part of GACC realignment for 2022, area changed from state designation TXS01 to federal TX01 per Kim Albracht, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC). Edits by JKuenzi, USFS. 2/19/2021 - Geospatial and tabular changes. Boundary changes for Idaho originally submitted in 2016 but never completed in entirety. Changes between Initial Attack Zones ID01 and ID02 and with Dispatch Boundaries - per Chris Clay-BLM Boise, DeniseTolness-DOI/BLM ID State Office GIS Specialist, and Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. Boundary changes at: (T13N R3E Sec 25), (T15N R3E Sec 31), (T16N R3E Sec 18-20, and 30), and (T16N R2E Sec 13) all from ID02 to ID01. (T10N R4E Sec 4-9,17-18, 20) and (T11N R4E Sec15-16, 21-22, 27-29, 34-31) from ID01 to ID02. 11/10/2020 - Michigan split from MI01 only, to MI01(Upper Penninsula) and MI02 in the south, per Kim Albracht, Communications Duty Officer. No change made to Dispatch Zone Boundary. Edits by JKuenzi. 11/4/2020 - Oregon OR07 divided into OR07 and OR08 per Kim Albracht, Communications Duty Officer. Edits by JKuenzi.10/26/2020 - Multiple boundary changes made to Federal Initial Attack Zones, but without any change to Dispatch Zone Boundaries: Raft River District of Sawtooth National Forest changed from UT01 to ID04; land east of Black Pine District of Sawtooth National Forest changed from ID05 to ID04. Direction from Denise Tolness, DOI/BLM GIS Specialist, and Gina Dingman, Great Basin Coordination Center Manager. Parts of Craters of the Moon National Monument changed from ID04 to ID05; Sheep Mountain (Red Rocks) area changed from MT03 to ID05, per Denise Tolness, Gina Dingman, and Kathryn "Kat" Sorenson, R1 Assistant Aircraft Coordinator. Edits for all changes made by JKuenzi.4/2/2020 - State owned land added and a portion of the boundary modified between MT01 and MT02 per Mike J Gibbons, Flathead Dispatch Center Mgr, and Kathryn "Kat" Sorenson, R1 Assistant Aircraft Coordinator. Edits by JKuenzi.2/21/2020 - Existing boundaries are updated, where possible, to a uniform base layer using the August 2019 Census State & County boundaries, along with Geographic Area Command Center boundaries, Dispatch Zone Boundaries, and Initial Attack State Zones. Edits by JKuenzi.2019-2020 - Initial Attack Frequency Zone data was provided by Kim Albracht, Acting and Permanent Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Jill Kuenzi, USFS Fire & Aviation Mgt Geospatial Coordinator, NIFC, Boise, ID. Efforts made to tie changes with the Initial Attack Frequency Zones to other closely related datasets such as Geospatial Area Command Centers (GACCs),and Dispatch Areas, Major work completed to bring all the datasets up to date on consistent base data (8/2019 Census data), into alignment where possible, and to establish a scheduled update cycle for the nation. 2017-2019 - Initial Attack Frequency Zone data was provided by Gary Stewart, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Jill Kuenzi, USFS Fire & Aviation Mgt Geospatial Coordinator, NIFC, Boise, ID.2015-2016 - Initial Attack Frequency Zone data was provided by Gary Stewart, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Dianna Sampson, BLM Geospatial Data Analyst, NIFC, Boise, ID.

  14. n

    Incident Data Wildland Fire Events Template (NWCG) - NAPSG 2017

    • prep-response-portal.napsgfoundation.org
    • cest-cusec.hub.arcgis.com
    • +1more
    Updated Mar 30, 2018
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    NAPSG Foundation (2018). Incident Data Wildland Fire Events Template (NWCG) - NAPSG 2017 [Dataset]. https://prep-response-portal.napsgfoundation.org/maps/c0172ed1193e4868be707772b9f5fdb5
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    Dataset updated
    Mar 30, 2018
    Dataset authored and provided by
    NAPSG Foundation
    Area covered
    Description

    Purpose - (June 2017) This feature service has been created for the 2017 fire season. The service is based on the National Wildfire Coordination Group's data standard for Wildland Fire Events. These data standards and this feature service are designed to improve exchange of data on wildland fire incidents.These are layer schemas that you must host on your own (or work with a partnering agency to host) and are edtiable. They allow you or others to update the status and other attribute information and are editable. Your ArcGIS Online organizational account allows you to use an existing service to publish an empty feature layer.For more information see the NWCG GIS Specialist Training material.

  15. CPW Facilities

    • geodata.colorado.gov
    • mapping-trout.opendata.arcgis.com
    • +1more
    Updated Nov 9, 2017
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    Colorado Parks & Wildlife (2017). CPW Facilities [Dataset]. https://geodata.colorado.gov/datasets/168fccb0583f42f1afe57de6c9ce846d
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    Dataset updated
    Nov 9, 2017
    Dataset provided by
    Colorado Parks and Wildlifehttps://cpw.state.co.us/
    Authors
    Colorado Parks & Wildlife
    Area covered
    Description

    These data are used to display the public recreational facilities at all Colorado Parks and Wildlife owned properties. The original intent of these data were to show facilities at a park level scale. Over time, this has evolved to be a more comprehensive collection of all recreation facilities - as GIS technology has advanced to allow improved labelling / display of features. Data is being compiled at CPW Area offices in consultation with local Property Technicians and Field Ops staff using high resolution aerial photography (NAIP05, NAIP09) as a reference and at a scale where structures could be visually identified by Chris Johnson. These data were originally combined from individual park shapefiles in 2010 by Bill Gaertner, under the direction of Matt Schulz, Parks GIS Coordinator. Since then, Eric Drummond, temp Trails GIS Specialist, vastly improved upon the ability to use a unique CPW font and the Maplex label engine to have dynamic labelling of standard recreation facility symbols.

  16. FWS Roads - Public View

    • gis.data.alaska.gov
    • datalibrary-lnr.hub.arcgis.com
    • +2more
    Updated Oct 26, 2023
    + more versions
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    U.S. Fish & Wildlife Service (2023). FWS Roads - Public View [Dataset]. https://gis.data.alaska.gov/datasets/fws::fws-roads-public-view
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    Dataset updated
    Oct 26, 2023
    Dataset provided by
    U.S. Fish and Wildlife Servicehttp://www.fws.gov/
    Authors
    U.S. Fish & Wildlife Service
    Area covered
    Pacific Ocean, North Pacific Ocean
    Description

    THIS ITEM IS PUBLIC AND READ ONLYThe FWS_HQ_Roads feature service contains lines representing public and administrative road segments on lands administered by the U.S. Fish and Wildlife Service including National Wildlife Refuges, National Fish Hatcheries, FWS administrative sites, and other conservation areas. This dataset is updated with the Federal Highways Cycle 5 roads. The data collection started in 2018 and was completed in 2023.This is a read-only, public AGOL View of FWS_HQ_Roads. Content can be added/edited by members of the AGOL FWS Regional Transportation Coordinators Roads Editing Group. This public view only shows records that meet the following criteria:Public Display = "Yes"Functional class =Class 1 - Principal Refuge Road”, “Class 2 - Connector Refuge Road”, and “Class 3 – Special Purpose Refuge Road”.Route status = “Existing”, and “Converted”U.S. Fish and Wildlife Service Catalog (ServCat) Record -https://ecos.fws.gov/ServCat/Reference/Profile/161320Data Set Contact: U.S. Fish and Wildlife Service Natural Resource Program Center, GIS Specialist, mery_casady@fws.gov

  17. g

    Historic bathymetry maps | gimi9.com

    • gimi9.com
    Updated Oct 9, 2020
    + more versions
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    (2020). Historic bathymetry maps | gimi9.com [Dataset]. https://gimi9.com/dataset/ca_3a277f42-00f4-4b39-8343-364108825661
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    Dataset updated
    Oct 9, 2020
    Description

    Bathymetry is the measurement of water depth in lakes. From the 1940s to the 1990s, the Ministry of Natural Resources and Forestry produced bathymetry maps for over 11,000 lakes across Ontario. The data can be used by the general public and GIS specialists for: * climate change modelling * fish monitoring and other ecological applications * hydrologic cycle modelling * recreational fishing maps * watershed-based water budgeting The maps were created using simple methods to determine lake depths. They were meant for resource management purposes only. Little effort was made to identify shoals and other hazards when creating these bathymetric maps. Since this data was collected, many constructed and naturally occurring events could mean that the depth information is now inaccurate, so these maps should not be used for navigational purposes. In many cases, these maps still represent the only authoritative source of bathymetry data for lakes in Ontario. Technical information These maps are being converted to digital GIS line data which can be found in the Bathymetry Line data class. The Bathymetry Index data class identifies if GIS vector lines have been created and the location of mapped lakes. The historic paper maps have been scanned into digital files. We will add new digital files to this dataset if they become available. The digital files have been grouped and packaged by regions into 13 compressed (zipped) files for download. Note: package 99 contains scanned maps where the location shown on the map could not be determined.

  18. A

    ‘2019 CT Data Catalog (Non GIS)’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Aug 4, 2020
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2020). ‘2019 CT Data Catalog (Non GIS)’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-2019-ct-data-catalog-non-gis-40ea/9e9f6cfc/?iid=001-820&v=presentation
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    Dataset updated
    Aug 4, 2020
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Connecticut
    Description

    Analysis of ‘2019 CT Data Catalog (Non GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/63dbeae9-0f9d-41d7-9ad9-edc2e4fdea74 on 26 January 2022.

    --- Dataset description provided by original source is as follows ---

    Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management, updated in 2019. This catalog does not contain information about high value GIS data, which is compiled in a separate data inventory at the following link: https://data.ct.gov/Government/2019-CT-Data-Catalog-GIS-/kr39-sdfm

    As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

    High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

    This dataset was last updated 2/6/2020 and will continue to be updated as high value data inventories are submitted to OPM.

    The 2018 high value data inventories for Non-GIS and GIS data can be found at the following links: CT Data Catalog (Non GIS): https://data.ct.gov/Government/CT-Data-Catalog-Non-GIS-/ghmx-93jn/ CT Data Catalog (GIS): https://data.ct.gov/Government/CT-Data-Catalog-GIS-/p7we-na27

    --- Original source retains full ownership of the source dataset ---

  19. g

    Soils (Ministry of Natural Resources and Forestry)

    • maps.grey.ca
    • hub.arcgis.com
    Updated Aug 17, 2023
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    Grey County (2023). Soils (Ministry of Natural Resources and Forestry) [Dataset]. https://maps.grey.ca/datasets/soils-ministry-of-natural-resources-and-forestry-1
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    Dataset updated
    Aug 17, 2023
    Dataset authored and provided by
    Grey County
    License

    https://www.ontario.ca/page/open-government-licence-ontariohttps://www.ontario.ca/page/open-government-licence-ontario

    Area covered
    Description

    The soil complex database contains other descriptive information including slope class, Canada Land Inventory (CLI) ranking, stoniness, drainage class, texture etc. The CLI components of the data layer is generally intended to be used as a tool for broad land use planning decision making, and not necessarily for field-level management. The soil complex information can also be applied to source water protection, nutrient management and soil erosion modeling. Note: The soil complex data layer is subject to a continuous improvement strategy - data was last download August 2023.Ontario Ministry of Agriculture, Food, and Rural Affairs and Agriculture and Agri-Food Canada, in cooperation with the Ministry of Natural Resources, have compiled a geo-spatial soils database for Southern Ontario. The database consolidated the existing digital soil data mapped on a county basis into a digitally stitched and standardized product.The soil survey data was mapped by a number of soil surveyors from the 1920s to the 1990s. The Soil Ontario product incorporates soil information from a variety of map scales. The project has brought the individual county or regional municipality surveys together in a digitally stitched database which reveals inconsistencies in soil data across county boundaries. Using GIS and NRVIS (Natural Resource Values Information System) a GIS Specialist matched the soil polygons that crossed boundaries using the best available resources.

  20. FWS HQ National Parking Lots Public

    • gis.data.alaska.gov
    Updated Oct 25, 2023
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    U.S. Fish & Wildlife Service (2023). FWS HQ National Parking Lots Public [Dataset]. https://gis.data.alaska.gov/maps/fws::fws-hq-national-parking-lots-public
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    Dataset updated
    Oct 25, 2023
    Dataset provided by
    U.S. Fish and Wildlife Servicehttp://www.fws.gov/
    Authors
    U.S. Fish & Wildlife Service
    Area covered
    Description

    THIS ITEM IS PUBLIC AND READ ONLYThis feature service contains polygons representing public and administrative parking lots on lands administered by the U.S. Fish and Wildlife Service including National Wildlife Refuges, National Fish Hatcheries, FWS administrative sites, and other conservation areas. This dataset is updated with the Federal Highways Cycle 5 roads. The data collection started in 2018 and was completed in 2023.This is a read-only, public AGOL View of FWS_HQ_Parking. The content can be added/edited by members of theAGOL FWS Regional Transportation Coordinators Roads Editing Group. This public view only shows records that meet the following criteria:Route Number begins with 9 (900 route numbers are for public use).Public Display = "Yes"U.S. Fish and Wildlife Service Catalog (ServCat) Record -https://ecos.fws.gov/ServCat/Reference/Profile/161320Data Set Contact: U.S. Fish and Wildlife Service Natural Resource Program Center, GIS Specialist,mery_casady@fws.gov

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Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2019). ‘Pay Stations’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-pay-stations-bc82/latest

‘Pay Stations’ analyzed by Analyst-2

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Dataset updated
May 16, 2019
Dataset authored and provided by
Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Analysis of ‘Pay Stations’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8f7302e2-13fd-42eb-a03e-3d95e3f1da53 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Displays the locations of Paid Parking Kiosks that distribute a receipt that is displayed in the vehicle.

| Attribute Information: https://www.seattle.gov/Documents/Departments/SDOT/GIS/Pay_Station_OD.pdf

| Data Update Cycle: Weekly (Due to issues with nightly update)
| Contact Email: DOT_IT_GIS@seattle.gov

--- Original source retains full ownership of the source dataset ---

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