Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

The global GIS data collector market is experiencing robust growth, driven by increasing adoption of precision agriculture, expanding infrastructure development projects, and the rising demand for accurate geospatial data across various industries. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $4.2 billion by 2033. Key drivers include the increasing availability of affordable and high-precision GPS technology, coupled with advancements in data processing and cloud-based solutions. The integration of GIS data collectors with other technologies, such as drones and IoT sensors, is further fueling market expansion. The demand for high-precision GIS data collectors is particularly strong in sectors like surveying, mapping, and construction, where accuracy is paramount. While the market faces challenges such as high initial investment costs and the need for specialized expertise, the overall growth trajectory remains positive. The market is segmented by application (agriculture, industrial, forestry, and others) and by type (general precision and high precision). North America and Europe currently hold significant market shares, but the Asia-Pacific region is anticipated to experience rapid growth in the coming years due to substantial infrastructure development and increasing government investments in geospatial technologies. The competitive landscape is characterized by both established players like Trimble, Garmin, and Hexagon (Leica Geosystems) and emerging companies offering innovative solutions. These companies are constantly innovating, integrating advanced technologies like AI and machine learning to enhance data collection and analysis capabilities. This competition is driving down prices and improving product quality, benefiting end-users. The increasing use of mobile GIS and cloud-based data management solutions is also transforming the industry, making data collection and analysis more accessible and efficient. Future growth will be largely influenced by the advancement of 5G networks, enabling faster data transmission and real-time applications, and the increasing adoption of automation and AI in data processing workflows. Furthermore, government regulations promoting the use of accurate geospatial data for sustainable development and environmental monitoring are creating new opportunities for the market’s expansion.

GIS Data Collector Market Outlook

The global GIS Data Collector market size is anticipated to grow from USD 4.5 billion in 2023 to approximately USD 12.3 billion by 2032, at a compound annual growth rate (CAGR) of 11.6%. The growth of this market is largely driven by the increasing adoption of GIS technology across various industries, advances in technology, and the need for effective spatial data management.

An important factor contributing to the growth of the GIS Data Collector market is the rising demand for geospatial information across different sectors such as agriculture, construction, and transportation. The integration of advanced technologies like IoT and AI with GIS systems enables the collection and analysis of real-time data, which is crucial for effective decision-making. The increasing awareness about the benefits of GIS technology and the growing need for efficient land management are also fuelling market growth.

The government sector plays a significant role in the expansion of the GIS Data Collector market. Governments worldwide are investing heavily in GIS technology for urban planning, disaster management, and environmental monitoring. These investments are driven by the need for accurate and timely spatial data to address critical issues such as climate change, urbanization, and resource management. Moreover, regulatory policies mandating the use of GIS technology for infrastructure development and environmental conservation are further propelling market growth.

Another major growth factor in the GIS Data Collector market is the continuous technological advancements in GIS software and hardware. The development of user-friendly and cost-effective GIS solutions has made it easier for organizations to adopt and integrate GIS technology into their operations. Additionally, the proliferation of mobile GIS applications has enabled field data collection in remote areas, thus expanding the scope of GIS technology. The advent of cloud computing has further revolutionized the GIS market by offering scalable and flexible solutions for spatial data management.

Regionally, North America holds the largest share of the GIS Data Collector market, driven by the presence of key market players, advanced technological infrastructure, and high adoption rates of GIS technology across various industries. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, primarily due to rapid urbanization, government initiatives promoting GIS adoption, and increasing investments in smart city projects. Other regions such as Europe, Latin America, and the Middle East & Africa are also experiencing significant growth in the GIS Data Collector market, thanks to increasing awareness and adoption of GIS technology.

The role of a GPS Field Controller is becoming increasingly pivotal in the GIS Data Collector market. These devices are essential for ensuring that data collected in the field is accurate and reliable. By providing real-time positioning data, GPS Field Controllers enable precise mapping and spatial analysis, which are critical for applications such as urban planning, agriculture, and transportation. The integration of GPS technology with GIS systems allows for seamless data synchronization and enhances the efficiency of data collection processes. As the demand for real-time spatial data continues to grow, the importance of GPS Field Controllers in the GIS ecosystem is expected to rise, driving further innovations and advancements in this segment.

Component Analysis

The GIS Data Collector market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of GIS systems. The hardware segment includes devices such as GPS units, laser rangefinders, and mobile GIS devices used for field data collection. The software segment encompasses various GIS applications and platforms used for data analysis, mapping, and visualization. The services segment includes consulting, training, maintenance, and support services provided by GIS vendors and solution providers.

In the hardware segment, the demand for advanced GPS units and mobile GIS devices is increasing, driven by the need for accurate and real-time spatial data collection. These devices are equipped with high-precision sensors and advanced features such as real-time kinematic (RTK) positioning, which enhance

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Geospatial insights are sourced through partnerships with tier-1 app developers and a unique data collection method. The low-latency delivery ensures real-time insights, setting us apart and providing unparalleled benefits and use cases for Location Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. All data is collected with clear privacy notices, and our opt-in/out management ensures transparent control over data collection, use, and distribution.

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GPS horizontal and vertical position data were collected on the Nisqually River, McAllister Creek and Nisqually River Delta to survey in water surface, instrumentation and delta structures for to reference North American Vertical Datum 1988 (NAVD88). These data are housed in .csv file named “Nisqually GPS Data” and are sorted by date and time. The position data are grouped by data collection methods Point and Topo. Point method collected position data for 180 seconds and was used to survey surface water and instrumentation elevation. Topo method collected position point data for 1 second and was used for surveying delta bathymetry elevation. Data were collected using the available RTN-GPS network provided by the Washington State Reference Network and using a Trimble R8 GPS antenna mounted on a 2-meter rod. Position data are labeled with descriptors such as “WS” (water surface) or “Delta” which refer to the feature surveyed. Check-in/check-out procedures were satisfied using reference marker Station: pid_sy0708. Two check-in orthometric heights were collected (60.05 and 60.10 m) and following point and topo data collection one check-out orthometric height (60.04 m) was collected. Bathymetric data (Topo method) was collected across the Nisqually River Delta starting at the left bank of McAllister Creek (MC2) and ended on the right bank of tidal channel D4. A total of 2,505 positions were surveyed using the topo method and positions were labeled as “delta-trav###”. Delta elevation ranged from 3.44 to -1.64 meters (NAVD88). Rod and antenna were held at a fixed level marked on both upper rod and technician for maintaining a constant 2 meter height above the walking surface. The bottom half of the rod was removed during topo data collection for ease of walking to avoid rod tip drag and keeping an even pace along the delta structures. Tidal channel bathymetry data consists of transects between banks with position names containing the tidal channel name and distance upstream or downstream of deployed sensor. Only D4 and D3 tidal channel bathymetric data sets were collected. Both D3 (Station ID: “les”) and D4 (Station ID: “are3”) had four tidal channel bathymetry transects collected which consisting of a 10 and 20 meter upstream and downstream of deployed sensor transects. Point data were collected at sites with sensors collecting water depth (WL) time-series data. GPS data was collected by holding the rod/antenna unit at a bubble-level static positioned for 3 minutes (180 epochs) during data collection. Point data were water surface elevations which were used to provide offsets for converting recorded water level (WL) data by sensors to referenced NAVD88.

The global GIS Data Collector market is experiencing robust growth, driven by increasing adoption of precision agriculture, expanding infrastructure development projects, and the rising need for accurate land surveying and mapping in various sectors. The market, currently valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by advancements in technology, such as the integration of high-resolution sensors, GPS capabilities, and cloud-based data management systems into these collectors. The high-precision segment is expected to witness significant growth due to its enhanced accuracy and ability to support complex applications like autonomous driving and environmental monitoring. Key applications include agriculture, where precise data collection improves crop yields and resource management, industrial sectors relying on accurate site surveys, and forestry management for sustainable logging practices. Geographic expansion is another significant driver. While North America currently holds a substantial market share due to early adoption and technological advancements, rapid economic growth and increasing infrastructure investments in Asia-Pacific, particularly in China and India, are expected to propel substantial market expansion in these regions. The market faces certain restraints, including the high initial investment cost of GIS data collectors and the need for specialized training for effective operation and data interpretation. However, the long-term benefits of improved efficiency, accuracy, and data-driven decision-making are overcoming these challenges, leading to sustained market growth. The presence of established players like Garmin, Trimble, and Hexagon, alongside emerging regional companies, fosters competition and innovation, contributing to the market’s dynamic landscape.

As of March 2021, Waze was the mobile GPN navigation app found to collect the largest amount of data from global iOS users, with 21 data points collected across all examined segments. Maps.me collected a total of 20 data points from its users, including five data points on contact information. Hiking and trail GPS map Gaia followed, with 13 data points, respectively.

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 global market for GIS Collectors is experiencing robust growth, driven by increasing adoption of location-based services, the expanding need for precise geospatial data across various sectors, and the continuous advancements in mobile technology and data analytics capabilities. The market is segmented by hardware (handheld devices, tablets, drones) and software (field data collection apps, data management software). Key players like Hexagon, Trimble Geospatial, ESRI, Topcon, Handheld, and Wuhan South are actively innovating and expanding their product portfolios to cater to this growing demand. The market's expansion is further fueled by the rising need for efficient asset management, improved infrastructure planning, and precise mapping for various applications such as environmental monitoring, agriculture, and urban planning. Government initiatives promoting digitalization and smart city development are also contributing significantly to the market's growth trajectory. While high initial investment costs for hardware and software can act as a restraint, the long-term benefits in terms of operational efficiency and data accuracy are overcoming this challenge. We project a steady market growth over the forecast period, with a particular emphasis on the increasing penetration of cloud-based solutions and the integration of AI and machine learning for enhanced data processing and analysis. The period between 2019 and 2024 showed significant market expansion, setting a strong foundation for future growth. We estimate the market size in 2025 at $5 billion, based on observed trends and industry reports. This strong base, coupled with a projected Compound Annual Growth Rate (CAGR) of 12%, will drive considerable market expansion throughout the forecast period (2025-2033). The increasing demand across diverse sectors, from precision agriculture to utility management, will continue to be major drivers. Furthermore, the emergence of new technologies such as 5G and IoT will further enhance data collection and processing capabilities, leading to improved efficiencies and a further expansion of the market. The North American and European markets currently hold a significant share, but emerging economies in Asia-Pacific and Latin America are exhibiting accelerated growth potential, making them crucial regions for future expansion.

Small Uncrewed Aircraft Systems (sUAS) were used to collect aerial remote sensing data over Marsh Island, a salt marsh restoration site along New Bedford Harbor, Massachusetts. Remediation of the site will involve direct hydrological and geochemical monitoring of the system alongside the UAS remote sensing data. On July 2nd, 2024, USGS personnel and interns collected natural (RGB) color and infrared (thermal) images and ground control points. These data were processed to produce a high resolution orthomosaics and a digital surface model. Data collection is related to USGS Field Activity 2024-004-FA and this release only provides the UAS portion.

The global market for GIS Collectors is experiencing robust growth, driven by increasing adoption of location-based services across various sectors. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors, including the rising need for precise geospatial data in urban planning, infrastructure development, environmental monitoring, and precision agriculture. Advancements in data acquisition technologies, such as improved GPS accuracy and the integration of sensors like LiDAR and hyperspectral imaging, are further boosting market expansion. The increasing availability of affordable and user-friendly GIS software and cloud-based solutions is also contributing to wider adoption across diverse user groups, from professional surveyors to citizen scientists. The competitive landscape is characterized by a mix of established players and emerging technology providers. Major companies like Hexagon, Trimble Geospatial, ESRI, Topcon, and Handheld are leveraging their existing market presence and technological expertise to expand their product portfolios and cater to evolving customer needs. Meanwhile, companies from regions like China, such as Wuhan South, are emerging as significant players, particularly in the provision of cost-effective solutions. While the market faces some restraints, such as the initial investment costs associated with GIS technology and the need for skilled professionals, the overall growth trajectory remains strongly positive, indicating considerable potential for continued market expansion throughout the forecast period. The increasing focus on data security and privacy regulations will also influence market trends, particularly regarding data storage and transmission. This comprehensive report provides an in-depth analysis of the global GIS Collectors market, projected to reach $5 billion by 2028. It delves into market concentration, key trends, dominant regions, product insights, and future growth catalysts, offering valuable insights for stakeholders across the geospatial technology sector. The report utilizes rigorous data analysis and industry expertise to provide actionable intelligence for informed decision-making.

The global GIS Data Collector market is experiencing robust growth, driven by increasing adoption of location-based services, the expanding need for precise geospatial data in various sectors, and advancements in data collection technologies. This market is projected to reach a substantial size, estimated at $8 billion in 2025, exhibiting a healthy Compound Annual Growth Rate (CAGR) of 7%. This growth is fueled by several key factors: the rising demand for efficient and accurate data collection in infrastructure development, precision agriculture, environmental monitoring, and urban planning; the increasing affordability and accessibility of advanced data collectors; and the growing integration of GIS data with other technologies like IoT and AI for better decision-making. The market is segmented by various hardware and software solutions, offering different levels of functionality and pricing points. Key players, including Garmin, Handheld Group, Hexagon (Leica Geosystems), Trimble, and Esri, are driving innovation through the development of ruggedized handheld devices, cloud-based data management platforms, and advanced data processing tools. Geographic growth is expected to be varied, with North America and Europe maintaining a strong market presence, while emerging economies in Asia-Pacific and Latin America are projected to show significant growth due to increasing infrastructure projects and investment in digital technologies. Competitive pressures are increasing as new players enter the market, encouraging innovation and the creation of more specialized and cost-effective solutions.

Number of days with GPS data per participant, by different time thresholds (n = 75).

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Mobile Location Data insights are sourced through partnerships with tier-1 app developers and a unique data collection method. The low-latency delivery ensures real-time insights, setting us apart and providing unparalleled benefits and use cases for Location Data, Places Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. Clear and compliant privacy notices accompany our data collection process. Opt-in/out management empowers users over data distribution.

Discover the precision of our Mobile Location Data insights with Irys – where quality meets innovation.

The global market for GIS Collectors is experiencing robust growth, driven by increasing adoption of location-based services, the expanding need for precise geospatial data across various industries, and the rising availability of affordable and advanced mobile GIS technologies. Our analysis projects a market size of $2.5 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. The construction industry's reliance on precise mapping and asset tracking for project management is a major driver. Furthermore, the utility sector is increasingly leveraging GIS collectors for efficient network management and maintenance. Growing government initiatives promoting smart city development and infrastructure modernization are also significantly contributing to market expansion. Finally, the ongoing development of user-friendly interfaces and cloud-based solutions is simplifying data collection and analysis, making GIS technology accessible to a broader range of users. Despite the positive outlook, the market faces certain challenges. High initial investment costs associated with implementing GIS solutions can be a barrier for smaller organizations. Furthermore, the complexity of data integration and the need for skilled personnel can hinder broader adoption. However, these challenges are being mitigated by the emergence of affordable and user-friendly software solutions and increased availability of training and support resources. The market segmentation reveals a strong presence of established players like Hexagon, Trimble Geospatial, ESRI, Topcon, and Handheld, along with regional players like Wuhan South. Competitive dynamics are characterized by ongoing innovation in hardware and software, resulting in continuous improvements in data accuracy, collection efficiency, and user experience. The market is poised for sustained growth, driven by technological advancements and increasing demand for location intelligence across multiple sectors.

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.

This dataset contains wireless communication signal data collected from an unmanned aerial vehicle (UAV) at different altitudes (40 m, 70 m, and 100 m). For the 40 m altitude, data was collected at varying sampling rates (5 MHz, 10 MHz, and 20 MHz), corresponding to bandwidths of 1.25 MHz, 2.5 MHz, and 5 MHz, respectively. The IQ recordings were made using USRP B210 devices at five fixed nodes (LW1-LW5). The dataset includes IQ samples, GPS coordinates, and received signal strength (RSS) values stored in SigMF format files. A Python script (example.py) is provided for data processing and visualization. Methods Trajectory and Altitudes: The UAV followed the same trajectory path at three different altitudes: 40 m, 70 m, and 100 m. Sampling Rates: For the 40 m altitude, data was collected using three different sampling rates: 5 MHz, 10 MHz, and 20 MHz, corresponding to bandwidths of 1.25 MHz, 2.5 MHz, and 5 MHz, respectively. Data Collection Intervals: To reduce data volume, the system collected data for 20 ms intervals every 100 ms. USRP B210 Devices: The IQ recordings were made using USRP B210 devices placed at the five fixed nodes (LW1-LW5). Each SigMF file contains IQ samples for two channels corresponding to the USRP's dual-channel configuration. GPS and Radio Measurements:

GPS coordinates (GPSx, GPSy, GPSz) were measured once per second, independent of the time at which the radio measurements were made. Radio measurements may have occurred more than once per second or not at exactly the same time as the GPS measurements. GPS and radio measurements are both time-stamped, and interpolation was used to calculate mX, mY, mZ values, which align the GPS data with the radio measurements based on their timestamps.

Folder Structure:

Each altitude (40 m, 70 m, 100 m) is represented by a folder. Within the 40 m folder, subfolders represent the three different sampling rates (5 MHz, 10 MHz, and 20 MHz). For each altitude and sampling rate combination, the dataset contains subfolders for five fixed nodes (LW1, LW2, LW3, LW4, and LW5). The locations of these nodes are detailed in the "LW1-5_locations.txt" file.

File Descriptions: SigMF Files:

results_3320000000_5000000_2024_07_15_12_26_01_248.sigmf-data: Contains IQ samples recorded at a frequency of 3320000000 Hz and a sampling rate of 5 MHz. The file contains two channels, one for each of the USRP B210 device's channels.

GPS Data:

gps_data.sigmf-data: Contains timestamped GPS coordinates of the UAV (GPSx, GPSy, GPSz), measured once per second.

Measurement Data:

measurement_rss_data.sigmf-data: Contains RSS data and interpolated position measurements (mX, mY, mZ) for each timestamp, with RSS1 for channel 1 and RSS2 for channel 2.

Python Script: example.py: This Python script demonstrates how to load and process the data from the SigMF files.

It uses libraries such as numpy, json, and matplotlib for loading data and plotting results. The script loads GPS metadata from gps_data.sigmf-meta and extracts position (GPSx, GPSy, GPSz) and timestamps. It also loads radio measurement data from measurement_rss_data.sigmf-meta and interpolates GPS data to align with radio measurements to compute positions (mX, mY, mZ) for further analysis. The script includes plotting functionalities to visualize the GPS trajectory and the corresponding RSS data over time.

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.

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our location data insights are sourced through partnerships with tier-1 app developers. The raw GPS data, delivered at an hourly cadence, provides unparalleled benefits and use cases for Transport and Logistic Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is unwavering. All data is collected with clear privacy notices, and our opt-in/out management ensures transparent control over data collection, use, and distribution.

Discover the precision of our Location Data Insights with Irys – where accuracy meets innovation.

Metadata notes for RiSCC Heard Island 2003_04 season (ASAC 1015) - DGPS data and Base Station data DGPS data are described below, and associated data files listed.

1. Three Island study - Phenology and Morphology of Heard Island vascular plants. This study uses a combination of latitudinal and altitudinal investigations to separate the effects of temperature per se and seasonality on the phenology and morphology of plants. Most latitudinal studies are confounded by covariation of seasonality and temperature, whereas with altitudinal variation at different latitudes one can disentangle these effects. The presence of the Polar Frontal Zone (APFZ), which has a major effect on seasonality, would be a key feature that would enable us to investigate this. Three islands (Marion, Kerguelen, Heard) were used in the study, each of which lies in a different place relative to the APFZ and each of which is inhabited by a similar suite of species, thus removing confounding effects of species identity in understanding responses.

1a Phenology data The collection of positional data for Heard Island Scarlet Hill Phenology was collected at each site; 4 m, 50 m, 100 m, 200 m and 250 m (ASAC 1015). At each site plants of Pringlea antiscorbutica, Acaena magellanica, Poa cookii and Azorella selago were chosen (NB at 100m and 200 m no Acaena magellanica was present, and at 250 m only Pringlea antiscorbutica was sampled) within a 50 x 50 m area, where possible, and were deemed typical of the site. Only healthy mature plants at each site were chosen. At the 4 m, 100 m and 200 m altitude levels, sites were established around AWSs (Automatic Weather Stations). Each plant was flagged and numbered. Numbered flags were removed from around/beside plants at the end of the study. The numbers of plants are represented in the GPS data. Positional data are in the form of points, lines and areas. The positional data are found in the following files.

4 m phenology PT021412A.SSF4 m phenology data, N15 Poa cookii data, Poa annua record, 50 m phenology data JDS011811A.SSFcoastal study area, AWS site and phenology site

50 m phenology JDS012314A.SSF50 m phenology (Scarlet Hill) and Stephenson camp location PT020910A.SSF50 m Azorella phenology, water meadow and Poa cookii N15 sites

100 m phenology JDS020714A.SSF200 m Phenology and 100 m AWS and phenology data JDS021313A.SSF100 m Phenology (Pringlea) PT021614A.SSF250 m and 200 m phenology data, 100m phenology

200 m phenology JDS020712A.SSF200 m Phenology site and AWS JDS020714A.SSF200 m Phenology and 100 m AWS and phenology data PT021614A.SSF250 m and 200 m phenology data, 100m phenology

250 m phenology PT021614A.SSF250 m and 200 m phenology data, 100m phenology

1b Morphology data DGPS points were only taken by JDS from Fairchild Beach morphology collection sites.

JDS0104.SSFAcaena magellanica, Fairchild Beach morphology JDS010511ATR3.SSFFairchild Beach morphology

1. Positional data for the mapping of the distribution of Ranunculus crassipes The distribution of Ranunculus crassipes at Heard Island was mapped between the 14-1-2004 and 15-2-2004. This mapping was undertaken by JDS, PT and JJS. Data were collected from the Skua Beach bluffs to Sooty Valley. Positional data are in the form of points, lines and areas. Data include areas of rock water meadow. The positional data are found in the following files.

PTRAN021513A.SSFRanunculus crassipes transect, points and rock water meadow JDS020816A.SSFRanunculus crassipes mapping on Skua bluffs JJS011417B.SSFRanunculus crassipes mapping on Skua bluffs PT020910A.SSF50 m Azorella phenology, water meadow and Poa cookii N15 sites

This mapping of the distribution of Ranunculus crassipes together with mapping of Carex trifida and Poa litorosa on Macquarie Island described by the metadata record with ID ASAC_1015_MIGPS03 contributed to the paper: Bergstrom, D.M., Turner, P.A.M., Scott, J., Copson, G. and Shaw, J. (2006) Restricted plant species on sub-Antarctic Macquarie and Heard Islands. Polar Biology 29 532-539.

1. High altitude vascular plant points and transect data Records of high altitude plants were taken by JDS and RC. Some data from the files JDS012510A.SSF and JDS012510A_CPscarlet.ssf have not been corrected, as stated above. JDS012510A.SSFScarlet Hill high altitude transect JDS012510A_CPscarlet.ssfScarlet Hill high altitude transect - control points only for JJS JDS013111A.SSFLong Beach high altitude data and Apple location JDS020112A.SSFLong Beach high altitude data RC0302.SSFRobb Clifton control points for JJS 'and high altitude' RC0402.SSFRobb Clifton control points for JJS 'and high altitude' RC0502.SSFRobb Clifton control points for JJS 'and high altitude'

Other datafiles recorded by PT, JDS and RC under ASAC 1015 include PT022012A.SSFFuel drum retaining wall, Spit Camp JDS012113A.SSFWinston Lagoon JDS012415A.SSFAcaena - 100 m south edge of Scarlet Hill JDS012914A.SSFLambeth 1 JJS Control point JDS123112A.SSFPoa annua - Dover...