It is about updating to GIS information database, Decision Support Tool (DST) in collaboration with IWMI. With the support of the Fish for Livelihoods field team and IPs (MFF, BRAC Myanmar, PACT Myanmar, and KMSS) staff, collection of Global Positioning System GPS location data for year-1 (2019-20) 1,167 SSA farmer ponds, and year-2 (2020-21) 1,485 SSA farmer ponds were completed with different GPS mobile applications: My GPS Coordinates, GPS Status & Toolbox, GPS Essentials, Smart GPS Coordinates Locator and GPS Coordinates. The Soil and Water Assessment Tool (SWAT) model that integrates climate change analysis with water availability will provide an important tool informing decisions on scaling pond adoption. It can also contribute to a Decision Support Tool to better target pond scaling. GIS Data also contribute to identify the location point of the F4L SSA farmers ponds on the Myanmar Map by fiscal year from 1 to 5.

This GIS dataset shows the locations of fire hydrants at Davis Station. The data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.

This GIS dataset shows the locations of fire hydrants at Casey Station. The data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.

Very little information is known about the distribution and abundance of snow petrels at the regional scale. This dataset contains locations of bird nests, mostly snow petrels, mapped in the Windmill Islands during the 2002-2003 season. Location of nests were recorded with handheld GPS receivers connected to a pocket PC and stored as a shapefile using Arcpad (ESRI software). Descriptive information relating to each bird nest was recorded and a detailed description of data fields is provided in the detailed description of the shapefiles.

Two observers conducted the surveys using distinct methodologies, Frederique Olivier (FO) and Drew Lee (DL). Three separate nest location files (ArcView point shapefiles) were produced and correspond to each of the survey methodologies used. Methodology 1 was the use of 200*200 m grid squares in which exhaustive searches were conducted (FO). Methodology 2 was the use of 2 transects within each the 200*200 m grid squares; methodology 3 was the use of 4 small quadrats (ca 25 m) located within the 200*200m grid squares (DL). Nests mapped in a non-systematic manner (not following a specific methodology) are clearly identified within each dataset. Datasets were kept separate due to the uncertainties caused by GPS errors (the same nest may have different locations due to GPS error).

Three separate shapefiles describe survey methodologies:

• one polygon shapefile locates the 200*200 grid sites searched systematically (FO)

• one polygon shapefile locates the small quadrats (DL)

• one line shapefile locates line transects (DL)

Spatial characteristics, date of survey, search effort, number of nests found and other parameters are recorded for the grid sites, transect and quadrats.

See the word document in the file download for more information.

This work has been completed as part of ASAC project 1219 (ASAC_1219).

The fields in this dataset are:

Species

Activity

Type

Entrances

Slope

Remnants

Latitude

Longitude

Date

Snow

Eggchick

Cavitysize

Cavitydepth

Distnn

Substrate

Comments

SitedotID

Aspect

Firstfred

Systematic/Edge/Incidental

RecordCode

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.The Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Content

NYSNet is a spatial reference network of continuously operating Global Positioning System (GPS) reference stations (CORS) throughout New York State that can be used for differential GPS applications. Depending on equipment and procedures, this network can provide users the ability to achieve centimeter positioning for surveying applications or sub-meter positioning for GIS mapping applications. Position information from this reference network can be utilized by using static data in post processing or by using the real time network (RTN).

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Kelsey Knight on Unsplash

This dataset is a compendium of GPS Data collected by Randy Carlson and collaborators on the Virginia Coast Reserve (primarily), Plum Island and North Inlet. A master data table was extracted by Charles L. Carlson during 2013 that includes all the individual point locations recovered from individual surveys. In addition to the data table, the data is also shared as a .zip file containing a static web page with links to particular projects and the underlying data. To use the data, unzip it and use your web browser to open the index.html file. Web page contents include: American Oyster Catchers on the Virginia Coast Reserve - 2003 Lynette Winters - Salicornia - MSL elevation project Dynamic Evolution of Barrier Island Morphology and Ecology from 1996-2002 Documented Using High -Resolution GPS-GIS Topographic Mapping Surveys, Virginia Coast Reserve (for GSA, Denver, CO Oct 27-30, 2002 Broadwater Tower Overwash Fan Photos - Feburary 15, 2002 Hog Island Bay DGPS Drifter Study 2001 Ray Dueser/Nancy Moncrief Small Mammal GPS/GIS A Topographical History of North Myrtle Island, 1974 to 2001 Ray Dueser/Nancy Moncrief - Highest Elevations on VCR Barrier Islands Myrtle Island Planimetric area, Surface area & Volumetric Calculations 1996-2001 Myrtle, Ship Shoal GIS/GPS UTM Shape Files and Grids Myrtle, Ship Shoal, ESNWR, Shirley, Steelman's Landing Text Files Complete List of All Small Mammal Trap locations 1995 - 2001 Ship Shoal Island Small Mammal Traps 1997 - 2000 LTER Cross-Site GPS Surveys Hobcaw Barony / Baruch Institue SET/GPS Survey, South Carolina, December 2000 PIE/LTER - Plum Island Sound GPS Network, July 1998 Montandon Marsh at Bucknell University, Lewisburg, Pennsylvania 1997 Bathymetric Survey Procedures, Schematic Diagrams and Instructions The following instructions and procedures are used with reference to the Trimble 4000 SE Global Positioning System receiver, the Trimble NavBeacon XL, the Innerspace Digital Fathometer (Model 448) and the Innerspace DataLog with Guidance Software. GPS-referenced digital bathymetry Schematic Diagram of DGPS/Digital Fathometer connections for bathymetry Instructions for DataLog w/Guidance Software (Innerspace Digital Fathometer) Instructions for Trimble 4000 SE GPS Receiver and Trimble Navbeacon XL Innerspace Digital Fathometer - Model 448 - Field Protocol for Bathymetric Surveys Archived Bathymetric Projects Hog Island Bay DGPS Bathymetric Survey, 1999/2000 Phillip's Creek DGPS Bathymetric Survey 1999/2000 Oyster Harbor Bathymetric Survey (February 2000) Smith Point, Chesapeake Bay, Maryland DGPS Bathymetric Survey, Sept. 2001 Fishermans/Smith/Mockhorn Bay Bathymetric Survey 2000 to 2001 Post-processed Kinematic GPS data: Is It Precise? (1998) Small Mammal GPS/GIS Applications Hog Island Small Mammal Traps on T1, T2, T4, T5 Fowling Point 1996, 1997 Geomorphology Applications Parramore Island, Virginia Parramore Pimple Overwash Fans 1996 Parramore Pimple Overwash Fans 1997 Parramore Island Overwash Fans June 1998 Parramore Island Plugs - August 1998 Parramore Island Overwash Fan 1999 Hog Island, Virginia Broadwater Tower Overwash Fan June 1998 Photos of Broadwater Tower Overwash Fan - March 13, 1999 Broadwater Tower Overwash Fan 1999 Myrtle Island, Virginia A Topographical History of Myrtle Island, 1996 to 2001 Cobb and Fisherman's Islands, Virginia Cobb Island Overwash Fan July 1998 Fisherman's Island - ESNWR and ODU September /1998 Brownsville Farm GPS/GIS Project Long-Term Inundation Project, Christian/Thomas Brinson/Christian/Blum Project Eileen Appolone (ECU) Lisa Ricker's Static GPS Points in Northampton County Eileen Applone (East Carolina University) Static Survey d99124 Brownsville Farm GPS/GIS Project, Christian/Blum/Brinson VCR/LTER Tide Gauges and Water Level Recorders Red Bank Tide Gauge (part of Fowling Pt. survey) Hog Island WLR's 1996 (Brinson) Hog Island Tide Gauge 12/96 High tide surveys at PIE/LTER with Chuck Hopkinson Jim Morris, USC, at Debidue Island, South Carolina Benchmark BRNV in Brownsville, VCR/LTER Miscellaneous Static Sub-Networks Frank Day/Don Young - North Hog 2/99 (Excel File) or a TEXT file Frank Day 120 YR Old Dune Survey (Excel File) or a TEXT file Kindra Loomis GPS Kinematic/Topographic Survey 12/97 Clubhouse Creek at Parramore Island 1997 Phragmites on Southern Hog Island - (dataset only) (9/98) Oyster Harbor 1997 (Hayden & Porter) Southern Hog 1996 (Zieman) VCR/LTER Sediment Elevation Tables - Mockhorn/Wachapreague, August 2001 Aaron Mills Benchmarks - Research Field in Oyster, October 2001 Birds Nests on the Virginia Coast Reserve VCR Birds 1997 (Erwin) VCR Birds 1995 and 1996 (Erwin) Mike Erwin/ Rachel Rounds/ Shellpile Points, August 2001 Miscellaneous Post-processed GPS data: Is It Accurate? (1998) Miscellaneous GPS Points (pre-1992) 1992 and 1994 GPS Work by VCR/LTER UTM's OF RESEARCH SITES VCR/LTER GPS NETWORK (gif image)

This point GIS dataset shows the locations of the fire hydrants and fire hose reels at Mawson station, Antarctica. The data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.

This GIS dataset consists of the locations of fire hydrants at Macquarie Island Station. The data are formatted according to the SCAR Feature Catalogue (see Related URL below). Enter the Qinfo number of any feature at the 'Search datasets and quality' tab to search for data quality information about the feature: for example, the source of the data.

The Australian Antarctic Data Centre's topographic GIS data for the Windmill Islands, Antarctica were originally mapped mainly from aerial photography: refer to the metadata record 'Windmill Islands 1:50000 Topographic GIS Dataset'. Since then features from various sources have been added to this data.

The data are available for download as part of the Windmill Islands GIS dataset from a Related URL.

The data are formatted according to the SCAR Feature Catalogue (see Related URL). Data that are part of this dataset have Dataset_id = 164 in the SCAR Feature Catalogue format. Dataset_id is an attribute in the attribute table. Data quality information for any feature can be searched for at the Related URL by entering the Qinfo number of the feature at the 'Search datasets and quality' tab. Qinfo is an attribute in the attribute table.

This data represents the measured distance between the The Utah Reference Network Global Positioning System (TURN GPS) base stations. It models the current coverage of the network and in some areas extends past the boundary of Utah. Baselines with a distance greater than 70 km will start to decrease your precision.

An ArcGIS shapefile layer showing the extent of all extant and relic Adelie penguin (Pygoscelis adeliae) colonies at Whitney Point, Windmill Islands, February 2006. The field 'Status' describes each polygon as extant, relic or maximum. Extant refers to the area used by breeding birds in the summer 2005/06. Maximum refers to the historic maximal extent of the colony. Relic refers to any colony which was not occupied by any breeding pairs during 2005/06.

Positional accuracy is approx. 1-2 m, after accounting for dGPS errors and errors in identification of the boundaries of colonies. Mapping was conducted after the end of the breeding season, so boundaries were identified as the extent of nest pebbles/fresh faeces, and it was considered that they could be reliably identified to within 0.5m.

Data were acquired using a Trimble Pro XH differential GPS. This work was completed as part of ASAC project 1219 (ASAC_1219).

Also for this project, three aerial photographs of Whitney point showing the adelie penguin colonies and taken on 17 December 1990 were georeferenced. These aerial photographs are film ANTC1219 run 54 frames 21 to 23.

Work on this project also utilised a Digital Elevation Model (DEM) created for Shirley Island. See the metadata record, 'A digital elevation model (DEM) and orthophoto of the Whitney Point area of the Windmill Islands, Antarctica' for more information (linked below).

Since the 2005/06 summer was a low-ice year the opportunity was also taken to survey with differential GPS a section of coastline about 230 metres long east of Whitney Point on Clark Peninsula. This section of coastline was ice free and accessible. The data was collected with differential GPS on 10 February 2006.

This web map features a vector basemap of OpenStreetMap (OSM) data created and hosted by Esri. Esri produced this vector tile basemap in ArcGIS Pro from a live replica of OSM data, hosted by Esri, and rendered using a creative cartographic style emulating a blueprint technical drawing. The vector tiles are updated every few weeks with the latest OSM data. This vector basemap is freely available for any user or developer to build into their web map or web mapping apps.OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new vector basemap available available to the OSM, GIS, and Developer communities.

Dataset Abstract The KBS LTER has a large collection of vector and raster GIS files. A selection of GIS resources is available here and requests for additional layers can be made by contacting the Principal Contact listed below. original data source http://lter.kbs.msu.edu/datasets/74

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

This data depicts the Public Land Survey System (PLSS) for the state of Utah and are based on Geographic Coordinate Database (GCDB) coordinate data. This dataset was created to provide continuous cadastre data for the state of Utah.This data is Version 2.1 2019 of the Utah PLSS Fabric. This data set represents the GIS Version of the Public Land Survey System. Updates are expected annually as horizontal control positions from published sources and global positioning system (GPS) observations are added. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. This data was originally published on 1/3/2017. Updated 1/18/2019These are the corner points of the PLSS. This data set contains summary information about the coordinate location and reliability of corner coordinate information. The information in the corner feature has been collected by the identified data steward. For more information about corner locations, credits and use limitations the identified data steward in the corner feature should be contacted.

The project leads for the collection of this data were Josh Bush and Tom Batter. Elk (8 adult females, 9 adult males) from the East Park Reservoir herd were captured and equipped with Lotek GPS collars (LifeCycle 800 GlobalStar, Lotek Wireless, Newmarket, Ontario, Canada), transmitting data from 2017-2021. The study area was within the East Park Reservoir and Bear Valley Elk Management Units, encompassing the Indian Valley and located to the east of Mendocino National Forest. The East Park Reservoir herd contains short distance, elevation-based movements likely due to seasonal habitat conditions, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, the herd displays a residential pattern, slowly moving up or down elevational gradients. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 13-hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual elk is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this analysis allowed for the mapping of the herd''s annual range based on a small sample. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 15 elk in total, including 35 year-long sequences, location, date, time, and average location error as inputs in Migration Mapper to assess annual range. Annual range BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Population-level annual range designations for this herd may expand with a larger sample, filling in some of the gaps between high-use annual range polygons in the map. Annual range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution.

The project lead for the collection of this data was Carrington Hilson. Elk (9 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2023-2024. The Potter-Redwood Valley herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 6.5 hour intervals in the dataset. To improve the quality of the data set, all points with DOP values greater than 5 and those points visually assessed as a bad fix by the analyst were removed. The methodology used for this migration analysis allowed for the mapping of the herd's home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 8 elk, including 15 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.

This data represents The Utah Reference Network Global Positioning System (TURN GPS) base station locations. It models the current base station locations on the network. In some areas we extends past the boundary of Utah when we have been invited by those communities.