Muscle functional MRI identifies changes in metabolic activity in each muscle and provides a quantitative index of muscle activation and damage. No previous studies have analyzed the hamstrings activation over a football match. This study aimed at detecting different patterns of hamstring muscles activation after a football game, and to examine inter- and intramuscular differences (proximal-middle-distal) in hamstring muscles activation using transverse relaxation time (T2)–weighted magnetic resonance images. Eleven healthy football players were recruited for this study. T2 relaxation time mapping-MRI was performed before (2 hours) and immediately after a match (on average 13 min). The T2 values of each hamstring muscle at the distal, middle, and proximal portions were measured. The primary outcome measure was the increase in T2 relaxation time value after a match. Linear mixed models were used to detect differences pre and postmatch. MRI examination showed that there was no obvious abnormality in the shape and the conventional T2 weighted signal of the hamstring muscles after a match. On the other hand, muscle functional MRI T2 analysis revealed that T2 relaxation time significantly increased at distal and middle portions of the semitendinosus muscle (p = 0.0003 in both cases). By employing T2 relaxation time mapping, we have identified alterations within the hamstring muscles being the semitendinosus as the most engaged muscle, particularly within its middle and distal thirds. This investigation underscores the utility of T2 relaxation time mapping in evaluating muscle activation patterns during football matches, facilitating the detection of anomalous activation patterns that may warrant injury reduction interventions.

Discover the booming GIS Data Collector market! This comprehensive analysis reveals a $2.5B market in 2025, projected to reach $4.2B by 2033, fueled by precision agriculture, infrastructure development, and technological advancements. Explore key trends, drivers, restraints, and leading companies shaping this dynamic sector.

Summary: How to configure Esri Collector for ArcGIS with a Bad Elf GPS Receiver for High-Accuracy Field Data Collection Storymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) 1: Standard 1-LS3-1 - Heredity: Inheritance and Variation of Traits - Make observations to construct an evidence-based account that young plants and animals are like, but not exactly like, their parentsGrade level(s) 4: Standard 4-ESS2-2 - Earth’s Systems - Analyze and interpret data from maps to describe patterns of Earth’s featuresGrade level(s) 5: Standard 5-ESS1-2 - Earth’s Place in the Universe - Represent data in graphical displays to reveal patterns of daily changes in length and direction of shadows, day and night, and the seasonal appearance of some stars in the night skyGrade level(s) 6-8: Standard MS-LS4-5 - Biological Evolution: Unity and Diversity - Gather and synthesize information about technologies that have changed the way humans influence the inheritance of desired traits in organisms.Grade level(s) 6-8: Standard MS-LS4-6 - Biological Evolution: Unity and Diversity - Use mathematical representations to support explanations of how natural selection may lead to increases and decreases of specific traits in populations over timeGrade level(s) 6-8: Standard MS-ESS1-3 - Earth’s Place in the Universe - Analyze and interpret data to determine scale properties of objects in the solar systemGrade level(s) 6-8: Standard MS-ESS2-2 - Earth’s Systems - Construct an explanation based on evidence for how geoscience processes have changed Earth’s surface at varying time and spatial scalesGrade level(s) 9-12: Standard HS-LS4-4 - Biological Evolution: Unity and Diversity - Construct an explanation based on evidence for how natural selection leads to adaptation of populationsGrade level(s) 9-12: Standard HS-ESS2-1 - Earth’s Systems - Develop a model to illustrate how Earth’s internal and surface processes operate at different spatial and temporal scales to form continental and ocean-floor features.Most frequently used words:featurebadelfselectgpsApproximate Flesch-Kincaid reading grade level: 9.9. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

The global GIS Data Collector market is experiencing robust growth, driven by increasing adoption of precision agriculture techniques, expanding infrastructure development projects, and the rising need for accurate geospatial data across various industries. The market's Compound Annual Growth Rate (CAGR) is estimated to be around 8% for the forecast period of 2025-2033, projecting significant market expansion. This growth is fueled by technological advancements in GPS technology, improved data processing capabilities, and the increasing affordability of GIS data collection devices. Key segments driving market expansion include high-precision data collection systems and their application in agriculture, where farmers are increasingly leveraging real-time data for optimized resource management and increased yields. The industrial sector also contributes significantly to market growth, with applications ranging from construction and surveying to utility management and environmental monitoring. While the market faces certain restraints, such as the need for skilled professionals to operate the sophisticated equipment and the potential for data security concerns, these are outweighed by the overwhelming benefits of improved efficiency, accuracy, and cost savings provided by GIS data collectors. The market's regional landscape shows significant participation from North America and Europe, owing to established technological infrastructure and early adoption of advanced GIS technologies. However, rapid growth is expected in the Asia-Pacific region, especially in countries like China and India, fueled by infrastructure development and expanding agricultural activities. Leading players like Garmin, Trimble, and Hexagon are driving innovation and competition, while a growing number of regional players offer more cost-effective solutions. The competitive landscape is characterized by a mix of established global players and regional manufacturers. The established players leverage their technological expertise and extensive distribution networks to maintain market leadership. However, the increasing affordability and accessibility of GIS data collection technologies are attracting new entrants, creating a more dynamic market. Future growth will likely be shaped by the integration of artificial intelligence and machine learning into GIS data collection systems, further enhancing data processing capabilities and automation. The continued development of robust and user-friendly software applications will also contribute to market expansion. Furthermore, the adoption of cloud-based GIS platforms is expected to increase, facilitating greater data sharing and collaboration. This convergence of hardware and software innovations will drive market growth and broaden the applications of GIS data collectors across diverse sectors.

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).

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 Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

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.

Discover the booming GIS Data Collector market, projected to reach $4.7 billion by 2033 with an 8% CAGR. This comprehensive analysis explores market drivers, trends, restraints, key players (Garmin, Trimble, Hexagon), and regional growth opportunities in agriculture, forestry, and industrial applications. Get insights into high-precision vs. general precision segments.

These files provide the data points (~20k) collected during the project with two types of GPS trackers, i.e., TTGO T-Beam and Adeunis Field Test Device (collection period: 01.01.2019-current).

GIS Data Collector Market Outlook

According to our latest research, the global GIS Data Collector market size reached USD 6.8 billion in 2024, reflecting robust demand across multiple industries. The market is projected to grow at a healthy CAGR of 11.2% from 2025 to 2033, reaching an anticipated value of USD 19.7 billion by 2033. This significant expansion is driven by increasing adoption of geospatial technologies in urban planning, environmental monitoring, and the digital transformation strategies of enterprises worldwide. As per our findings, the surge in smart city initiatives and the proliferation of IoT-based mapping solutions are key contributors to the accelerating growth of the GIS Data Collector market globally.

The primary growth driver for the GIS Data Collector market is the escalating need for precise and real-time geospatial data across diverse sectors. Urbanization and the rapid expansion of metropolitan regions have intensified the demand for advanced mapping and surveying tools, enabling city planners and government agencies to make informed decisions. The integration of GIS data collectors with cutting-edge technologies such as artificial intelligence, machine learning, and cloud computing has further enhanced data accuracy and accessibility. As organizations seek to optimize resource allocation and improve operational efficiency, the utilization of GIS data collectors has become indispensable in applications ranging from infrastructure management to disaster response and land administration.

Another crucial factor propelling the market is the growing use of GIS data collectors in environmental monitoring and natural resource management. With the increasing frequency of climate-related events and the global emphasis on sustainability, accurate geospatial data is vital for tracking environmental changes, managing agricultural lands, and monitoring deforestation or water resources. Advanced GIS data collectors equipped with remote sensing and mobile mapping capabilities are enabling stakeholders to gather high-resolution data, analyze spatial patterns, and implement effective conservation strategies. The synergy between GIS and remote sensing technologies is empowering organizations to address environmental challenges more proactively and efficiently.

Technological advancements in data collection methods have also played a pivotal role in shaping the GIS Data Collector market landscape. The advent of unmanned aerial vehicles (UAVs), mobile mapping systems, and real-time kinematic (RTK) GPS has revolutionized the way geospatial data is captured and processed. These innovations have not only improved the accuracy and speed of data collection but have also reduced operational costs and enhanced safety in field surveys. The integration of GIS data collectors with cloud-based platforms allows seamless data sharing and collaboration, fostering a more connected and agile ecosystem for geospatial data management. As industries continue to digitize their operations, the demand for sophisticated and user-friendly GIS data collection solutions is expected to witness sustained growth.

Field Data Collection Software has become an integral component in the realm of GIS data collection, providing users with the capability to efficiently gather, process, and analyze geospatial data in real time. This software facilitates seamless integration with various data collection devices, such as GPS receivers and mobile mapping systems, enabling field operatives to capture high-precision data with ease. The adoption of Field Data Collection Software is particularly beneficial in sectors like urban planning and environmental monitoring, where timely and accurate data is crucial for decision-making. By leveraging cloud-based platforms, this software ensures that data collected in the field is instantly accessible to stakeholders, promoting collaboration and enhancing the overall efficiency of geospatial projects. As the demand for real-time data insights grows, the role of Field Data Collection Software in supporting dynamic and responsive GIS operations continues to expand.

From a regional perspective, North America currently dominates the GIS Data Collector market, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, substantial investments in smart infrastructure, and suppo

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.

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.

Discover the booming GIS Data Collector market! Explore an $8 billion market projected to grow at a 7% CAGR through 2033. This in-depth analysis covers market size, key trends, leading companies (Garmin, Trimble, Esri), and regional insights. Learn how advancements in data collection technologies are transforming industries.

Toll Trip Reconstruction from GPS Data Market Outlook

According to our latest research, the Toll Trip Reconstruction from GPS Data market size was valued at $1.2 billion in 2024 and is projected to reach $4.8 billion by 2033, expanding at a robust CAGR of 16.7% during the forecast period of 2025–2033. The major factor propelling the growth of the global Toll Trip Reconstruction from GPS Data market is the growing demand for intelligent transportation systems that optimize toll collection, enhance traffic management, and improve urban mobility. Increasing adoption of GPS-enabled devices and telematics solutions across government agencies and private fleets is further accelerating market expansion, as stakeholders seek to leverage real-time location data for more efficient tolling, route planning, and infrastructure utilization.

Regional Outlook

North America currently holds the largest share of the global Toll Trip Reconstruction from GPS Data market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region's mature transportation infrastructure, widespread adoption of advanced telematics, and favorable regulatory frameworks promoting electronic toll collection systems. The United States, in particular, has been at the forefront of deploying GPS-based tolling solutions, driven by federal and state-level initiatives to modernize highways and reduce congestion through dynamic pricing models. High penetration of connected vehicles and a robust ecosystem of technology vendors further reinforce North America's leadership in this market.

Asia Pacific is emerging as the fastest-growing region, projected to register a remarkable CAGR of 20.3% from 2025 to 2033. Key drivers include rapid urbanization, increasing vehicle ownership, and government investments in smart city projects. Countries such as China, India, and Japan are investing heavily in intelligent transportation systems, with a focus on integrating GPS data for toll collection, traffic management, and urban planning. The proliferation of smartphone-based navigation apps and affordable telematics solutions is also fueling adoption among both public and private sector stakeholders. Strategic partnerships between local governments and technology providers are expected to further accelerate market growth in this region.

In emerging economies across Latin America, the Middle East, and Africa, adoption of Toll Trip Reconstruction from GPS Data solutions is steadily increasing, albeit at a slower pace compared to developed markets. These regions face unique challenges, including fragmented transportation networks, limited digital infrastructure, and varying regulatory standards. However, localized demand is being driven by the need to address urban congestion, improve toll revenue collection, and enhance road safety. Policy reforms and pilot projects in countries like Brazil, South Africa, and the UAE are laying the groundwork for broader deployment, although scalability and interoperability remain key hurdles that need to be addressed for sustained growth.

Report Scope

The goal of this study is to measure willingness to participate in passive mobile data collection among German smartphone owners. The data come from a two-wave web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2016, 2,623 participants completed the Wave 1 questionnaire on smartphone use and skills, privacy and security concerns, and general attitudes towards survey research and research institutions. In January 2017, all respondents from Wave 1 were invited to participate in a second web survey which included vignettes that varied the levels of several dimensions of a hypothetical study using passive mobile data collection, and respondents were asked to rate their willingness to participate in such a study. A total of 1,957 respondents completed the Wave 2 questionnaire.

Wave 1

Topics: Ownership of smartphone, mobile phone, PC, tablet, and/or e-book reader; type of smartphone; frequency of smartphone use; smartphone activities (browsing, e-mails, taking photos, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, play games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, statistical office, mobile service provider, app companies, credit card companies, online retailer, and social networks); concerns regarding the disclosure of personal data by the aforementioned institutions; general privacy concern; privacy violated by banks/ credit card companies, tax authorities, government agencies, market research companies, social networks, apps, internet browsers); concern regarding data security with smartphone activities for research (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth); number of online surveys in which the respondent has participated in the last 30 days; Panel memberships other than that of mingle; previous participation in a study with downloading a research app to the smartphone (passive mobile data collection).

Wave 2

Topics: Willingness to participate in passive mobile data collection (using eight vignettes with different scenarios that varied the levels of several dimensions of a hypothetical study using passive mobile data collection. The research app collects the following data for research purposes: technical characteristics of the smartphone (e.g. phone brand, screen size), the currently used telephone network (e.g. signal strength), the current location (every 5 minutes), which apps are used and which websites are visited, number of incoming and outgoing calls and SMS messages on the smartphone); reason why the respondent wouldn´t (respectively would) participate in the research study used in the first scenario (open answer); recognition of differences between the eight scenarios; kind of recognized difference (open answer); remembered data the research app collects (recall); previous invitation for research app download; research app download.

Demography: sex; age; federal state; highest level of school education; highest level of vocational qualification.

Additionally coded was: running number; respondent ID; duration (response time in seconds); device type used to fill out the questionnaire; vignette text; vignette intro time; vignette time.

GPS radio collar (Telonics Model #TGW-3790) programmed to record a location every hour from 15 May through 15 November. Collars contained an ultrahigh frequency (UHF) radio transmitter and locations were downloaded using an airplane fitted with a UHF receiver. Collars also collect activity data synchronous with each GPS location with an onboard mercury switch. Activity levels were expressed as the percentage of time being active during the data collection interval. We screened GPS locations for accuracy and removed relocations with a positional dilution of precision (PDOP) greater than 10 [50]. We restricted bear locations to the period of 10 July through the end of August each year to coincide with the annual sockeye salmon run. A sample of 51 female bears.

The Navigation and Mapping Solutions market is experiencing robust growth, driven by the increasing adoption of location-based services (LBS) across various sectors. The market's expansion is fueled by several key factors, including the proliferation of smartphones equipped with advanced GPS technology, the rising demand for real-time traffic updates and navigation assistance, and the increasing integration of mapping solutions into automotive systems. Furthermore, the development of sophisticated mapping technologies, such as 3D mapping and augmented reality (AR) overlays, is enhancing user experience and driving market penetration. The expanding use of these solutions in logistics and transportation, coupled with the growth of e-commerce and the demand for efficient delivery services, contributes significantly to the market's upward trajectory. We estimate the market size in 2025 to be around $15 billion, projecting a Compound Annual Growth Rate (CAGR) of 12% through 2033. Despite the promising outlook, market growth faces certain challenges. High initial investment costs associated with developing and maintaining advanced mapping systems may limit entry for smaller players. Data privacy concerns and regulatory restrictions regarding data collection and usage pose significant hurdles. The accuracy and reliability of mapping data remain critical factors affecting market adoption, particularly in remote or rapidly changing areas. Competition among established players like Google, TomTom, and Garmin is intense, demanding continuous innovation and strategic partnerships to maintain a competitive edge. Despite these restraints, the long-term prospects for the navigation and mapping solutions market remain positive, driven by ongoing technological advancements and expanding applications across diverse industries.

This data set documents locations and location codes used in monk seal data collection as part of the ongoing monk seal population assessment efforts. Information includes atolls, islets within each atoll, sectors valid for each islet, and GPS coordinates. Sectors for the six main breeding locations in the NWHI were established in 1982 at the beginning of concerted data collection by this...

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USE CASES

The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

• Navigation and mapping for digital marketplaces and apps.
• Logistics for online shopping, food delivery, last-mile delivery, and more.
• Improving operational efficiency for rideshare and transportation platforms.
• Demographic and human mobility studies for market consumption and competitive analysis.
• Market assessment, site selection, and business expansion.
• Disaster management and urban mapping for public welfare.
• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.