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.

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.

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.

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.

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.

CDFW BIOS GIS Dataset, Contact: Erin Zulliger, Description: Migration corridor, stopover, and winter range locations for Rocky Mountain elk (Cervus canadensis nelsoni) from the East Shasta Valley herd, Siskiyou County, California, and Klamath County, Oregon. Corridors, stopovers, and winter ranges were developed in Migration Mapper with Brownian Bridge Movement Models using GPS locations from collared elk. Migration corridors represent movement routes used by elk between winter and summer range habitats.

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.

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.

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 October 19-23, 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.

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...

Some of the finest mountain scenery in the Southwest is found in the 1.6-million-acre Santa Fe National Forest. Here, you can find the headwaters of Pecos, Jemez, and Gallinas Rivers; mountain streams; lakes; and trout fishing. Travel into Pecos, San Pedro Parks, Chama, and Dome Wildernesses via wilderness pack trips, saddle, or on 1,000 miles of hiking trails. Try whitewater rafting on the Rio Chama or Rio Grande from May to September. Consider turkey, elk, deer, and bear hunting, or visit one of many nearby Indian pueblos, Spanish missions, and Indian ruins. Golden aspen grace the high country from September to October and snow blankets Santa Fe Ski Basin in winter. The Santa Fe National Forest GIS data available for download includes Santa Fe National Forest Geospatial (GIS) Datasets, Motor Vehicle Use Map (MVUM) Travel Aids - digital maps and data of the SFNF to upload to GPS units or Smart Phones, 7.5 Minute Topographic Maps (PDF and GeoTIFF) - US Forest Service topo maps only, USFS Geospatial Clearinghouse - includes GIS data of vegetation treatments, administrative boundaries, inventoried roadless areas, FSTopo datasets, USGS Map Locator and Downloader - download current and historic topo maps, Hardcopy Maps with information on how to purchase hard copy visitor, wilderness, or topographic maps. Resources in this dataset:Resource Title: Santa Fe National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/main/santafe/landmanagement/gis

NMFWRI represents the state’s only dedicated capability for supporting the spatial data analysis needs of external stakeholders in the natural resources sector, as well as the GIS/GPS capacity for Highlands University and for most of northern New Mexico. NMFWRI’s GIS work also provides help with maps and other geographic information to New Mexico groups engaged in forest restoration and land management, but who are too small to maintain their own GIS capability. These groups include soil and water conservation districts, municipalities, private groups and individuals, and tribal organizations.

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.

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.

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.

The Australian Antarctic Data Centre's Mawson Station GIS data were originally mapped from March 1996 aerial photography. Refer to the metadata record 'Mawson Station GIS Dataset'. Since then various features have been added to this data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, other features have been 'eyed in' as more accurate data were not available. The eyeing in has been done based on advice from Australian Antarctic Division staff and using as a guide sources such as an aerial photograph, an Engineering plan, a map or a sketch. GPS data or measurements using a measuring tape may also have been used.

The data are included in the data available for download from a Related URL below. The data conform to the SCAR Feature Catalogue which includes data quality information. See a Related URL below. Data described by this metadata record has Dataset_id = 119. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

Dataset Abstract 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

These data represent 1) Telemetry from Florida manatee from Crystal River, FL 2006-2018 2) Observations of manatees, swimmers and paddlecraft from Three Sisters Springs, Crystal River, FL 2014-2017 3) GIS polygons of aquatic landscape features from Crystal River, FL.

City of Champaign GPS control network.

The project lead for the collection of this data was Erin Zulliger. Elk (11 adult females, 3 adult males) were captured and equipped with GPS collars (Litetrack/Pinpoint Iridium collars, Lotek Wireless Inc., Newmarket, Ontario, Canada or Vectronic Aerospace) transmitting data from 2019-2023. The West Goose Lake herd migrates between traditional summer and winter seasonal ranges, and migration corridors, migration stopovers, and winter ranges were modeled separately for this herd, but were not a part of this analysis. Annual home ranges were modeled using year-round data to demarcate high use areas. GPS locations were fixed at 1-6 hour intervals in the dataset. To improve the quality of the data set, the GPS data locations fixed in 2D space and 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 annual range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 14 elk, including 44 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. Home range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution. Annual home range designations for this herd may expand with a larger sample.