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.

Geotweet Archive v2.0 The Harvard Center for Geographic Analysis (CGA) maintains the Geotweet Archive, a global record of tweets spanning time, geography, and language. The primary purpose of the Archive is to make a comprehensive collection of geo-located tweets available to the academic research community. The Archive extends from 2010 to the present and is updated daily. The number of tweets in the collection totals approximately 10 billion, and it is stored on Harvard University’s High Performance Computing (HPC) cluster. The Harvard HPC supports many applications for working with big spatio-temporal datasets, including two geospatial tools recently deployed by the CGA: OmniSci Immerse, and PostGIS. The Geotweet Archive consists of tweets which carry two types of geospatial signature: 1) GPS-based longitude/latitude generated by the originating device 2) Place-name-centroid-based longitude/latitude from the bounding box provided by Twitter, based on the user-define place designation (typically a town name). Any tweet which carries one or both of these signatures is included in the Archive. Approximately 1-2% of all tweets contain such geographic coordinates, (this percentage needs verification and may vary over time). The current version of the Archive is Version 2.0. The original Version 1.0 archive began in 2012 as part of a project with Ben Lewis of CGA and then Harvard graduate student Todd Mostak, to develop a GPU-powered spatial database called GEOPS. GEOPS formed the basis for technology startup MapD Technologies, which is now OmniSci. OmniSci Immerse software now runs on Harvard’s High Performance Computing (HPC) environment to support interactive exploration and analytics with the Geotweet Archive and any other large datasets. Version 2.0 of the archive represents the results of a merge between the CGA archive, and an archive developed by the Department of Geoinformatics at the University of Salzburg in Austria, as well as several other archives. Clemens Havas and Bernd Resch at University of Salzburg, and Devika Kakkar of Harvard CGA collaborated to deploy Version 2.0. ======================================================== Schema of Geotweet Archive v2.0 Field name_TYPE_Description message_id----BIGINT----Tweet ID tweet_date----TIMESTAMP----Date and time of tweet from Twitter (utc) tweet_text----TEXT ENCODING----Text content of tweet tags----TEXT ENCODING DICT----Tweet hashtags tweet_lang----TEXT ENCODING DICT----Language that the tweet is in source ----TEXT ENCODING DICT----Operating system or application type used to create the tweet place*----TEXT ENCODING NONE----The geographic place as defined by the user, usually a town name. A bounding box determined by Twitter based on this field, from which centroids (see longitude and latitude fields) and the spatial_error field are derived, and used when not overridden by a GPS coordinate. See Twitter tweet object for place. retweets ----SMALLINT----Number of retweets as of last time it was checked tweet_favorites----SMALLINT----Now known as ‘likes’ photo_url----TEXT ENCODING DICT----URL of any image referenced quoted_status_id ----BIGINT----ID number for quote status user_id ----BIGINT----User ID number user_name----TEXT ENCODING NONE----User name user_location*----TEXT ENCODING NONE----User defined location, usually a city or town. See Twitter user object. followers ----SMALLINT----Followers as of the last time checked friends ----SMALLINT----Number of users followed by this user user_favorites----INT----Number of topics the user is interested in status----INT----Code for what user is doing as of last time it was checked user_lang----TEXT ENCODING DICT----User defined language latitude----FLOAT----Latitude from GPS or bounding box based on Place field longitude----FLOAT----Longitude from GPS or bounding box based on Place field data_source*----TEXT ENCODING DICT----The source crawler or dataset for the tweet gps----TEXT ENCODING DICT----Flag for whether lon/lat is from GPS or town name bounding box (SRID – 4326). When both are present, the GPS coordinate takes priority. spatialerror----FLOAT----Estimate in meters horizontal error for lon/lat coordinate. 10m for GPS coordinates, error for bounding boxes calculated as radius of circle with area of bounding box. ===================================================== *data_source_Code U. Salzburg REST API crawler----1 Harvard CGA streaming crawler----2 U. Salzburg streaming API crawler----3 Ryan Qi Wang and Harvard Medical School datasets----4 U. Heidelberg dataset----5 Archive.org dataset----6 ---------------------------------------------------------------------------------------------- Note: Before April of 2015 the default for GPS coordinate capture was turned on for Twitter users. After this date users have had to opt-in to share their precise location. This is one reason for the large decrease in volume of geotweets after this date. A number of automated...

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
Unsplash Images are distributed under a unique Unsplash License.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

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 2.3 2020 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 12/15/2020These 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.

Summary This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Records from FMIS (Fire Management Information System) were reviewed and compared to refuge records. Polygon data in FMIS only occurs from 2012 to current and many acreage estimates did not match. This dataset includes ALL fires no matter the size. This feature class documents the fire history on CMR from 1964 - present. This is 1 of 2 feature classes, a polygon and a point. This data has a variety of different origins which leads to differing quality of data. Within the polygon feature class, this contains perimeters that were mapped using a GPS, hand digitized, on-screen digitized, and buffered circles to the estimated acreage. These 2 files should be kept together. Within the point feature class, fires with only a location of latitude/longitude, UTM coordinate, TRS and no estimated acreage were mapped using a point location. GPS started being used in 1992 when the technology became available. Data origins include: Data origins include: 1) GPS Polygon-data (Best), 2) GPS Lat/Long or UTM, 3)TRS QS, 4)TRS Point, 6)Hand digitized from topo map, 7) Circle buffer, 8)Screen digitized, 9) FMIS Lat/Long. Started compiling fire history of CMR in 2007. This has been a 10 year process.FMIS doesn't include fires polygons that are less than 10 acres. This dataset has been sent to FMIS for FMIS records to be updated with correct information. The spreadsheet contains 10-15 records without spatial information and weren't included in either feature class. Fire information from 1964 - 1980 came from records Larry Eichhorn, BLM, provided to CMR staff. Mike Granger, CMR Fire Management Officer, tracked fires on an 11x17 legal pad and all this information was brought into Excel and ArcGIS. Frequently, other information about the fires were missing which made it difficult to back track and fill in missing data. Time was spent verifiying locations that were occasionally recorded incorrectly (DMS vs DD) and converting TRS into Lat/Long and/or UTM. CMR is divided into 2 different UTM zones, zone 12 and zone 13. This occasionally caused errors in projecting. Naming conventions caused confusion. Fires are frequently names by location and there are several "Soda Creek", "Rock Creek", etc fires. Fire numbers were occasionally missing or incorrect. Fires on BLM were included if they were "Assists". Also, fires on satellite refuges and the district were also included. Acreages from GIS were compared to FMIS acres. Please see documentation in ServCat (URL) to see how these were handled.

DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

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 (Erwi... Visit https://dataone.org/datasets/https%3A%2F%2Fpasta.lternet.edu%2Fpackage%2Fmetadata%2Feml%2Fknb-lter-vcr%2F156%2F20 for complete metadata about this dataset.

In this work, we present a dataset containing a collection of pictures taken during the fieldwork of a farmland abandonment study. Data was taken in 2010 with a compact camera which incorporates GPS and a digital compass sensor. The photographs are taken as a part of a GIS database. Using their Exif metadata we created a layer of Geographic Fields Of View (GeoFOVs) that can be used to perform very specific spatial queries. The dataset contains 2,235 pictures and GIS layers of GeoFOVs contextualizing the agricultural plots being photographed.

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 LTER annual crops (corn, soy and wheat), treatments 1-4, are harvested annually using a combine equipped with a GPS and precision agriculture software to allow detailed yield measurements with coincident GPS latitude and longitude data.. original data source http://lter.kbs.msu.edu/datasets/40 Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-kbs&identifier=37 Webpage with information and links to data files for download

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on August 17-21, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

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. In order to avoid two repetitive ground field efforts, the sampling plan was devised from a combination of both vegetation maps. Using OR logic, overlays were created using both maps as input for each class, and random samples were developed for each class in excess of 30 polygons. Where there were less than 30 polygons sample sites were selected non-randomly from each polygon (i.e. a 100% sample). A total of 512 ground sampling sites were developed from a total of 21 vegetation and land cover classes which are represented on both vegetation maps. Using GIS tools, an ASCII file was generated with ground coordinates representing each of these sites. The 512 sets of coordinates were appropriately re-formatted and directly downloaded as waypoints in three North American Rockwell PLGR GPS receivers. During the week of August 4, 1997 three field crews of two persons each worked together at the monument in a coordinated effort to identify vegetation/cover types at each of the sites. The field crews had a paper map showing the location of the plots and the polygon boundaries (but not attributes) overlaid on topographic data. One team member operated the GPS receiver to navigate to the site, and the other identified the vegetation/cover type and provided a general physical description of the site environs. Sites were considered to be circular with a radius of 50 m. from the coordinate point. Where 2 or more vegetation/cover types occurred, or there was a mosaic of types, all were described within the 50 m. radius of the site coordinate.

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. To produce the digital map, a combination of 1:12,000-scale color infrared digital ortho-imagery acquired in 2003, 1:12,000-scale true color ortho-rectified imagery acquired in 2005, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. All imagery was acquired from the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office and the National Agriculture Imagery Program. In the end, 27 map units (14 vegetated and 13 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 thirty three accuracy assessment (AA) data points were collected in 2006 and used to determine the map’s accuracy. GIS Database 2002-2005: Project Size = 4,600 acres Lyndon B. Johnson National Historical Park = 674 acres Lyndon B. Johnson State Park and Historic Site = 418 acres Base Imagery acquired from the USDA FSA Aerial Photography Field Office acquired through the National Agriculture Imagery Program: 2005 - 1:12,000-scale true color ortho-rectified imagery, compressed county mosaic,2 meter pixel resolution 2003 - 1:12,000-scale color infrared digital ortho-imagery, compressed county mosaic,1 meter pixel resolution 27 Map Classes 14 Vegetated 13 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at LYJO to ¼ acre. Total Size = 1,080 Polygons Average Polygon Size = 4.3 acres Overall Thematic Accuracy = 92%

This map was digitized using 2010 true-color 1-meter National Agriculture Imagery Program (NAIP) aerial imagery. The minimum mapping unit is 0.25 acre with a 10-foot minimum width. The map classification is the same as used for the Delta Vegetation and Land Use map that was based on 2002 imagery and finalized in 2007. The map [ds292] can be obtained here: https://wildlife.ca.gov/Data/BIOS. The report, which provides the classification detail can be obtained here: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=18211. Because Liberty Island was mapped as part of the Delta mapping effort, changes over the eight year period between imagery dates can be seen, most obviously land accretion and erosion and invasion of the non-native water primrose, Ludwigia. Mappers used reconnaissance-level data to refine and verify the map. The data were collected at 48 points during two field missions: 1) August 9, 2012 by land covering the levee on the western boundary of the area, and 2) August 28 - 29, 2012 by boat. All vegetation samples were marked with GPS units and can serve as long-term monitoring points. Photos taken at these GPS points can be used for future monitoring. A database of field data and the digital photos are archived at the office of the Vegetation Classification and Mapping Program (VegCAMP) of DFW. This map has not been assessed for accuracy, but over 25% of the polygons were visited during field reconnaissance. Lead staff of the program cross-checked and edited the map attributes and delineations.

Maryland Roadway Centerline data consists of linear geometric features which represent the street centerline for all public roadways in the State of Maryland. The centerline represents the geographic location on the roadway between both shoulders (physical center), which often but not always coincides with the center painted line dividing bi-directional travel lanes. Roadway Centerlines data plays an important role in transportation management and planning, while also being the basis for all other roadway related data products. Maryland Roadway Centerline data is the end product of a statewide data sharing process between the Federal Highway Administration (FHWA), Maryland Department of Transportation (MDOT), Maryland Department of Transportation State Highway Administration (MDOT SHA), county governments and local municipal governments. Using a common centerline allows for better exchange of information related to the roadway system and provides opportunities for more efficient collection of information about roadway assets. Some centerlines were created in-house using imagery, GPS data, and MDOT SHA's Highway Performance Monitoring System (HPMS) database and others were received from county governments and updated in house using imagery, GPS data and MDOT SHA's HPMS database. The Centerline data includes annual HPMS updates / improvements submitted to the Federal Highway Administration (FHWA). Maryland Roadway Centerline data is needed for emergency response and management, routing buses and other vehicles, planning for land use and transportation needs, continuity of roadway data and display at county boundaries leading to the same "look and feel" across jurisdictions, tracking assets on and along the roadway network, producing maps at various scales, and numerous other applications. There are opportunities to make these processes more efficient, and this program addresses a shared foundation to solve some of these issues. This data is also used by various business units throughout MDOT, as well as many other Federal, State and local government agencies. Maryland Roadway Centerline data is updated and published on an annual basis for the prior year. This data is for the year 2017. For additional information, contact MDOT SHA Geospatial Technologies Email: GIS@mdot.state.md.us For additional information related to the Maryland Department of Transportation (MDOT) Website: https://www.mdot.maryland.gov/ For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA): Website: https://roads.maryland.gov/Home.aspx MDOT SHA Geospatial Data Legal Disclaimer: The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

Original provider: Florida Fish and Wildlife Conservation Commission - Fish and Wildlife Research Institute

Dataset credits: Florida Fish and Wildlife Conservation Commission - Fish and Wildlife Research Institute

Abstract: This dataset contains data from the geographic information system (GIS) shapefile of recovered Florida manatee (Trichechus manatus latirostris) carcass locations within Florida from April 1974 through to the latest spatially verified data presently available. Locations are based both on coordinates provided by field staff (gathered either by geographic positioning system [GPS] or by using navigation charts to ascertain latitudes and longitudes) and maps provided by the field staff. Fish and Wildlife Research Institute (FWRI) GIS staff in the Marine Mammal subsection verify that the provided coordinates match the intent of the plotted location. Points representing carcass locations were entered into a GIS using a digital shoreline basemap taken largely from NOAA navigation charts (1:40,000) and from USGS quadrangles (1:24,000). The scale is considered to be 1:40,000.

This geodatabase of point, line and polygon features is an effort to consolidate all of the range improvement locations on BLM-managed land in Idaho into one database. Currently, the polygon feature class has some data for all of the BLM field offices except the Coeur d'Alene and Cottonwood field offices. Range improvements are structures intended to enhance rangeland resources, including wildlife, watershed, and livestock management. Examples of range improvements include water troughs, spring headboxes, culverts, fences, water pipelines, gates, wildlife guzzlers, artificial nest structures, reservoirs, developed springs, corrals, exclosures, etc. These structures were first tracked by the Bureau of Land Management (BLM) in the Job Documentation Report (JDR) System in the early 1960s, which was predominately a paper-based tracking system. In 1988 the JDRs were migrated into and replaced by the automated Range Improvement Project System (RIPS), and version 2.0 is currently being used today. It tracks inventory, status, objectives, treatment, maintenance cycle, maintenance inspection, monetary contributions and reporting. Not all range improvements are documented in the RIPS database; there may be some older range improvements that were built before the JDR tracking system was established. There also may be unauthorized projects that are not in RIPS. Official project files of paper maps, reports, NEPA documents, checklists, etc., document the status of each project and are physically kept in the office with management authority for that project area. In addition, project data is entered into the RIPS system to enable managers to access the data to track progress, run reports, analyze the data, etc. Before Geographic Information System technology most offices kept paper atlases or overlay systems that mapped the locations of the range improvements. The objective of this geodatabase is to migrate the location of historic range improvement projects into a GIS for geospatial use with other data and to centralize the range improvement data for the state. This data set is a work in progress and does not have all range improvement projects that are on BLM lands. Some field offices have not migrated their data into this database, and others are partially completed. New projects may have been built but have not been entered into the system. Historic or unauthorized projects may not have case files and are being mapped and documented as they are found. Many field offices are trying to verify the locations and status of range improvements with GPS, and locations may change or projects that have been abandoned or removed on the ground may be deleted. Attributes may be incomplete or inaccurate. This data was created using the standard for range improvements set forth in Idaho IM 2009-044, dated 6/30/2009. However, it does not have all of the fields the standard requires. Fields that are missing from the polygon feature class that are in the standard are: ALLOT_NO, POLY_TYPE, MGMT_AGCY, ADMIN_ST, and ADMIN_OFF. The polygon feature class also does not have a coincident line feature class, so some of the fields from the polygon arc feature class are included in the polygon feature class: COORD_SRC, COORD_SRC2, DEF_FET, DEF_FEAT2, ACCURACY, CREATE_DT, CREATE_BY, MODIFY_DT, MODIFY_BY, GPS_DATE, and DATAFILE. There is no National BLM standard for GIS range improvement data at this time.