To date, GPS tracking data for minibus taxis has only been captured at a sampling frequency of once per minute. This is the first GPS tracking data captured on a per-second (1 Hz) basis. Minibus taxi paratransit vehicles in South Africa are notorious for their aggressive driving behaviour characterised by rapid acceleration/deceleration events, which can have a large effect on vehicle energy consumption. Infrequent sampling cannot capture these micro-mobility patterns, thus missing out on their effect on vehicle energy consumption (kWh/km). We hypothesised that to construct high fidelity estimates of vehicle energy consumption, higher resolution data that captures several samples per movement would be needed. Estimating the energy consumption of an electric equivalent (EV) to an internal combustion engine (ICE) vehicle is requisite for stakeholders to plan an effective transition to an EV fleet. Energy consumption was calculated following the kinetic model outline in "The bumpy ride to electrification: High fidelity energy consumption estimates for minibus taxi paratransit vehicles in South Africa".

Six tracking devices were used to record GPS data to an SD card at a frequency of 1Hz. The six recording devices are based on the Arduino platform and powered from alkaline battery packs. The device can therefore operate independently of any other device during tests. The acquired data is separately processed after the completion of data recording. Data captured is initiated with the press of a button, and terminated once the vehicle reached the destination. Each recorded trip creates an isolated file. This allows for different routes to be separately investigated and compared to other recordings made on the same route.

There are 62 raw trip files, all found in the attached 'raw data' folder under the corresponding route and time of day in which they were captured. The raw data includes date, time, velocity, elevation, latitude, longitude, heading, number of satellites connected, and signal quality. Data was recorded on three routes, in both directions, for a total of six distinct routes. Each route had trips recorded in the morning (before 11:30AM) , afternoon (11:30AM-4PM) and evening (after 4PM).

The processed data is available in the 'Processed Data' folder. In addition to the raw data, these processed data files include the displacement between observations, calculated using Geopy's geodesic package, and the estimated energy provided by the vehicle's battery for propulsion, braking, and offload work. The python code for the kinetic model can be found in the attached GitHub link https://github.com/ChullEPG/Bumpy-Ride.

Future research can use this data to develop standard driving cycles for paratransit vehicles, and to improve the validity of micro-traffic simulators that are used to simulate per-second paratransit vehicle drive cycles between minutely waypoints.

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

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.

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).

Irys specializes in collecting and curating high-quality GPS signals from millions of connected devices worldwide. Our Geospatial 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, Mobile Location Data, Mobility Data, and IP Address Data.

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

Background There is interest in identifying the most reliable method for detecting early mobility limitations. Accelerometry and Global Positioning System (GPS) could provide insight into declines in mobility, but few studies have used this multi-sensor approach to monitor mobility in older adults. Methods Thirty-two volunteers (66.2±6.3 years) agreed to participate in our validation study. We conducted two experiments to determine the validity of the TicWatch S2 and Pro 3 Ultra GPS models against the Qstarz receiver in measuring life-space mobility, trip frequency, duration, and mode. We also assessed the accuracy of the TicWatch in measuring step count and agreement with the ActiGraph wGT3X-BT for activity counts and sedentary behavior. Participants wore devices simultaneously for three consecutive days and recorded activity and trip information. Results The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under supervised conditions, the TicWatch Pro 3 Ultra GPS measured step count consistently with the gold standard observer, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%). Conclusion Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models are valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-world mobility in older adults and advancing our knowledge of early mobility decline. Methods We conducted two experiments to validate the TicWatch for collecting movement and navigation data: 1) we compared the TicWatch S2 against the Qstarz BT-Q1000X GPS Data Logger to measure life-space mobility, trip frequency, and duration. We also assessed the agreement of the TicWatch S2 in measuring step count against an observer, activity counts per minute (CPM), and sedentary and non-sedentary activity using ActiGraph's proprietary algorithms; 2) we compared the TicWatch Pro 3 Ultra GPS against a stand-alone Qstarz BT-Q1000X GPS Data Logger to measure life-space mobility, trip frequency and duration, and mode of transportation. We evaluated the level of agreement between the TicWatch Pro 3 Ultra GPS in measuring steps compared to direct measures of step count reported by the participants. Additionally, we tested two different GPS configurations of the TicWatch Pro 3 Ultra GPS in a free-living study setting to observe battery life performance: a) periodic fix collection every 10 seconds (i.e., the GPS receiver turned off for 10 seconds before searching for a new location point), and b) stay connected fix collection every 5 seconds (i.e., the GPS receiver never turns off, allowing for more continuous data collection). Finally, for assessing body posture, we evaluated the agreement of the thigh-worn algorithms of the ActiGraph wGT3X-BT with an observer in identifying lying and sitting from standing. The study results not only inform the use of the TicWatch for assessing and monitoring early changes in mobility in the MacM3 cohort study but also offer valuable information on the validity of wearable devices for researchers considering collecting movement and navigation data in their research protocols. Protocol In Experiment 1, participants were provided with three devices, the TicWatch S2, Qstarz GPS Data Logger, and the ActiGraph. They were instructed to wear the TicWatch S2 and the ActiGraph on the non-dominant wrist simultaneously and to carry the GPS data logger with them whenever they travelled outside their homes. They were instructed to charge the TicWatch S2 and Qstarz every night using the chargers provided. In Experiment 2, participants were provided with three devices, the TicWatch Pro 3 Ultra GPS, Qstarz GPS Data Logger, and the ActiGraph. They were instructed to wear the TicWatch Pro 3 Ultra GPS on the non-dominant wrist, to carry the GPS whenever they travelled outside the home, and to record the time they put the watch on and off. They were also instructed to attach the ActiGraph to the anterior aspect of the left or right thigh just above the kneecap using the adhesive patches provided to perform the body posture tasks. Data Reduction The GPS data collected by the Qstarz and TicWatch models were first cleaned by excluding any points with speeds above 160 km/h, as the fastest roadways in our study area have a maximum speed limit of 110 km/h. We then processed each participant’s data to ensure the time periods compared between devices were identical, such that discrepancies due to battery life or participant error were excluded from the analysis. Measures related to the life-space area, such as maximum distance from home, minimum convex hull (MCH), and standard deviational ellipse (SDE), were calculated using ArcGIS® Pro, a desktop Geographic Information System application developed by Esri®. Each participant’s trip frequency and trip duration were determined using GPS data collected by both Qstarz and TicWatch. To accomplish this, we adapted the stop and trip detection algorithm of Montoliu et al.1 We chose to use algorithm settings proposed by Fillekes et al2 for trip detection and used them to derive trip frequency and duration independently for each device. Using the Qstarz data, we manually verified the algorithm results and adjusted the values for accuracy. We then compared the algorithm-derived measures from the TicWatch against these results. Additionally, we used the method proposed for segmenting GPS segments into active (non-motorized) and passive (motorized) trips, which is adapted from the work of Carlson et al.3 and Vanwolleghem et al.4 Specifically, trips with 90th percentile speed ≥ 25 km/h were classified as passive, whereas trips below that threshold were classified as active. Accelerometer data were collected at different frequencies for the ActiGraph and TicWatch devices. TicWatch data was adjusted to match ActiGraph's frequency for comparison. In Experiment 1, the accelerometer data from the ActiGraph and TicWatch S2 were screened for the period of wear times using the method described by Choi et al.5 We first determined the activity counts per minute (CPM) using a Python script that generates the ActiGraph physical activity counts.6 We applied the script on both the S2 and ActiGraph devices. Based on the activity counts and using an epoch length of 60 seconds, non-wear time was defined as 90 consecutive minutes of zero counts, with an allowance of 2 minutes of nonzero counts, provided there were 30-minute consecutive zero counts before and after that allowance5. Wear times in Experiment 2 were obtained using the on-body sensor of the TicWatch Pro 3 Ultra GPS. To evaluate PA intensity, we computed the vector magnitude (VM) by taking the square root of the summed squared counts per minute for each axis on both devices. The VM counts were then calculated per 60-second epoch, and we applied the cut-off scores developed by Montoye et al.7 specifically for wrist-worn devices. We classified activities as "sedentary" if the VM counts were below 2,860 and collapsed light and moderate/vigorous activity categories into "non-sedentary" which included VM counts of 2,860 or higher.7 Following this, we determined the time, in minutes, spent on sedentary and non-sedentary behaviour. We also calculated the mean activity counts per epoch length of 60-second for various activities, including exercising, sitting, lying, and walking, as reported by the participants, for both ActiGraph and TicWatch S2. To ensure an accurate comparison of accelerometer data, we restricted our analysis to the periods when participants reported wearing both the TicWatch and ActiGraph devices simultaneously. Step count was obtained directly using the step count and step detector sensors from the TicWatch models. In Experiment 1, we selected the step counter that keeps track of the total number of steps taken over time. In Experiment 2, we used the step detector that detects when a step is taken and generates an event each time it detects a step, but it does not keep track of the total number of steps taken. Body posture classification in Experiment 2 was obtained using the thigh-worn algorithm from the ActiGraph that relies on movement and the thigh angle to accurately classify lying and sitting vs. standing positions.8 REFERENCES

Montoliu, R., Blom, J. & Gatica-Perez, D. Discovering places of interest in everyday life from smartphone data. in Multimedia Tools and Applications vol. 62 179–207 (2013). Fillekes, M. P., Giannouli, E., Kim, E. K., Zijlstra, W. & Weibel, R. Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research. Int J Health Geogr 18, 17 (2019). Carlson, J. A. et al. Association between neighbourhood walkability and GPS-measured walking, bicycling and vehicle time in adolescents. Health Place 32, 1 (2015). Vanwolleghem, G. et al. Children’s GPS-determined versus self-reported transport in leisure time and associations with parental perceptions of the neighborhood environment. Int J Health Geogr 15, (2016). Choi, L., Liu, Z., Matthews, C. E. & Buchowski, M. S. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc 43, 357–364 (2011). BrØnd, J. C., Andersen, L. B. & Arvidsson, D. Generating ActiGraph Counts from Raw Acceleration

The GNSS (Global Navigation Satellite System), or satellite positioning system, includes all satellite navigation systems. It allows you to know your location, anywhere in the country. Theoretical GNSS specifications estimate the accuracy of the position obtained from a receiver to be approximately 15 meters in planimetry and 25 meters in altimetry. By combining the data with that of another receiver placed on a known geodesic point, the accuracy of the obtained position can vary from a few centimeters to a few meters, depending on the type of receiver used. In order to increase accuracy, the Government of Quebec records data continuously through a network of 18 GNSS stations. These stations are located on geodetic points that are free of any obstacles and capture data from the GPS and GLONASS constellations. Some of these stations receive signals from the Galileo constellation. This data is available in the standard exchange format*Receiver Independent Exchange Format* (RINEX), version 2.11. This format is recognized by the majority of GNSS data processing software. The data is accessible on the _ ftp server_) of the MRNF or using the _ Interactive Map_) of the geodetic network. It should be noted that only data from the last 366 days is kept. The structure of the directories and files on the _ ftp server_) as well as the coordinates of the stations are presented in the document _ GNSS sensor stations_. # #État of GNSS stations## You can consult the status of the stations in the document _ Status of GNSS stations_. You will be notified if a station is in service, out of service, or if equipment maintenance is planned. # #GNSS in real time by cellular telephony The government also offers GNSS data by cellular telephony that allows centimeter positioning work to be carried out in real time. Users of georeferenced data can thus, with a single multi-frequency GNSS receiver equipped with a modem by cellular telephone, identify or implement any physical detail with an accuracy of a few centimeters in the NAD 83 reference system (SCRS) (period 1997.0). The signal that contains this data is available to everyone. The range depends on telephone coverage, ionospheric conditions and especially on the instruments used. For more information on using GNSS in real time, see document _ Guidelines for GNSS RTK/RTN Surveys in Canada_. # #Détails techniques The transmission of GNSS data as well as the station's NAD 83 (SCRS) coordinates (era 1997.0) is transmitted by cellular telephony from an IP address on the Internet. Each station transmits its data in one of the following two formats: CMR+ or RTCM V3.2. The document _ GNSS capture stations_) gives for each city the IP address of the CMR+ or RTCM V3.2 formats as well as the antenna model. It should be noted that the data is not broadcast according to the*Networked Transport of RTCM protocol via Internet Protocol* (NTRIP).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

GPS Data Logger Market Outlook

The global GPS data logger market size is projected to grow from USD 1.2 billion in 2023 to USD 2.5 billion by 2032, reflecting a robust CAGR of 8.2% during the forecast period. The growth of this market can be attributed to the increasing demand for precise and efficient tracking systems across various industries, including transportation and logistics, automotive, and aerospace and defense.

One major growth factor driving the GPS data logger market is the ever-increasing need for fleet management solutions. As companies strive to improve operational efficiency and reduce costs, the demand for accurate and reliable tracking systems has surged. GPS data loggers provide real-time data on vehicle locations, driving behavior, and route optimization, which helps organizations better manage their fleets. Additionally, advancements in GPS technology, coupled with decreasing hardware costs, have made these devices more accessible to businesses of all sizes.

Another significant factor contributing to market growth is the rising adoption of these devices in asset tracking applications. Companies are increasingly recognizing the importance of safeguarding their high-value assets, such as machinery, equipment, and cargo. GPS data loggers offer an effective solution for tracking and monitoring the location and status of these assets, thus reducing the risk of theft and loss. The integration of GPS data loggers with IoT platforms further enhances their functionality, providing real-time alerts and actionable insights for asset management.

The personal use segment also plays a vital role in the expansion of the GPS data logger market. As consumers become more health and fitness conscious, there is a growing demand for devices that can track physical activities and outdoor adventures. GPS data loggers are increasingly being utilized by enthusiasts for hiking, biking, and other outdoor activities to record routes, distances, and speed. The technological advancements in wearable devices and smartphones have further fueled this demand, making GPS data logging more accessible to the general public.

Regionally, North America holds a significant share of the GPS data logger market, primarily due to the presence of major market players and the early adoption of advanced technologies in the region. The region's well-established transportation and logistics sector also drives the demand for GPS data loggers. Europe and Asia Pacific are also expected to witness substantial growth over the forecast period, driven by the increasing use of these devices in automotive and industrial applications. The rising emphasis on improving supply chain efficiency and enhancing vehicle safety standards in these regions further propels market growth.

Product Type Analysis

Within the GPS data logger market, product types can mainly be categorized into standalone GPS data loggers and integrated GPS data loggers. Standalone GPS data loggers are self-contained devices that record GPS data independently. They are widely used in applications where continuous data logging is essential without the need for constant connectivity. These devices are particularly favored in areas with limited network coverage or where ruggedness and durability are critical. Their simple design and lower cost contribute to their popularity among individual users and small businesses.

Integrated GPS data loggers, on the other hand, combine GPS functionality with other features or systems such as mobile devices, fleet management software, or asset tracking solutions. These integrated systems offer enhanced capabilities, including real-time data transmission, cloud storage, and advanced analytics. They are predominantly used in large-scale commercial applications where comprehensive data analysis and remote monitoring are essential. The integration of GPS data loggers with broader IoT ecosystems further enhances their utility, allowing for seamless data sharing and improved decision-making processes.

The demand for integrated GPS data loggers is expected to grow significantly over the forecast period, driven by the increasing adoption of connected devices and smart systems. Businesses are increasingly seeking solutions that provide end-to-end visibility and control over their operations. Integrated GPS data loggers fit well into this paradigm, offering a holistic approach to data management and operational efficiency. The advancements in wireless communication technologies, such as 5G, further bolster the growth prospects of integrated GPS data loggers by enabl

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.

Global Positioning System (GPS) tags are nowadays widely used in wildlife tracking. This geolocation technique can suffer from fix loss biases due to poor satellite GPS geometry, that result in tracking data gaps leading to wrong research conclusions. In addition, new solar-powered GPS tags deployed on birds can suffer from a new “battery drain bias” currently ignored in movement ecology analyses. We use a GPS tracking dataset of bearded vultures (Gypaetus barbatus), tracked for several years with solar GPS tags, to evaluate the causes and triggers of fix and data retrieval loss biases. We compare two models of solar GPS tags using different data retrieval systems (Argos vs GSM-GPRS), and programmed with different duty cycles. Neither of the models was able to accomplish the duty cycle programed initially. Fix and data retrieval loss rates were always greater than expected, and showed non-random gaps in GPS locations. Number of fixes per month of tracking was a bad criterion to identify tags with smaller biases. Fix-loss rates were four times higher due to battery drain than due to poor GPS satellite geometry. Both tag models were biased due to the uneven solar energy available for the recharge of the tag throughout the annual cycle, resulting in greater fix-loss rates in winter compared to summer. In addition, we suggest that the bias found along the diurnal cycle is linked to a complex three-factor interaction of bird flight behavior, topography and fix interval. More fixes were lost when vultures were perching compared to flying, in rugged versus flat topography. But long fix-intervals caused greater loss of fixes in dynamic (flying) versus static situations (perching). To conclude, we emphasize the importance of evaluating fix-loss bias in current tracking projects, and deploying GPS tags that allow remote duty cycle updates so that the most appropriate fix and data retrieval intervals can be selected.

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.

Studies utilizing Global Positioning System (GPS) telemetry rarely result in 100% fix success rates (FSR). Many assessments of wildlife resource use do not account for missing data, either assuming data loss is random or because a lack of practical treatment for systematic data loss. Several studies have explored how the environment, technological features, and animal behavior influence rates of missing data in GPS telemetry, but previous spatially explicit models developed to correct for sampling bias have been specified to small study areas, on a small range of data loss, or to be species-specific, limiting their general utility. Here we explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use. We also evaluate patterns in missing data that relate to potential animal activities that change the orientation of the antennae and characterize home-range probability of GPS detection for 4 focal species; cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Part 1, Positive Openness Raster (raster dataset): Openness is an angular measure of the relationship between surface relief and horizontal distance. For angles less than 90 degrees it is equivalent to the internal angle of a cone with its apex at a DEM _location, and is constrained by neighboring elevations within a specified radial distance. 480 meter search radius was used for this calculation of positive openness. Openness incorporates the terrain line-of-sight or viewshed concept and is calculated from multiple zenith and nadir angles-here along eight azimuths. Positive openness measures openness above the surface, with high values for convex forms and low values for concave forms (Yokoyama et al. 2002). We calculated positive openness using a custom python script, following the methods of Yokoyama et. al (2002) using a USGS National Elevation Dataset as input. Part 2, Northern Arizona GPS Test Collar (csv): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. The model training data are provided here for fix attempts by hour. This table can be linked with the site _location shapefile using the site field. Part 3, Probability Raster (raster dataset): Bias correction in GPS telemetry datasets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix aquistion. We found terrain exposure and tall overstory vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The models predictive ability was evaluated using two independent datasets from stationary test collars of different make/model, fix interval programing, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. We evaluated GPS telemetry datasets by comparing the mean probability of a successful GPS fix across study animals home-ranges, to the actual observed FSR of GPS downloaded deployed collars on cougars (Puma concolor), desert bighorn sheep (Ovis canadensis nelsoni), Rocky Mountain elk (Cervus elaphus ssp. nelsoni) and mule deer (Odocoileus hemionus). Comparing the mean probability of acquisition within study animals home-ranges and observed FSRs of GPS downloaded collars resulted in a approximatly 1:1 linear relationship with an r-sq= 0.68. Part 4, GPS Test Collar Sites (shapefile): Bias correction in GPS telemetry data-sets requires a strong understanding of the mechanisms that result in missing data. We tested wildlife GPS collars in a variety of environmental conditions to derive a predictive model of fix acquisition. We found terrain exposure and tall over-story vegetation are the primary environmental features that affect GPS performance. Model evaluation showed a strong correlation (0.924) between observed and predicted fix success rates (FSR) and showed little bias in predictions. The model's predictive ability was evaluated using two independent data-sets from stationary test collars of different make/model, fix interval programming, and placed at different study sites. No statistically significant differences (95% CI) between predicted and observed FSRs, suggest changes in technological factors have minor influence on the models ability to predict FSR in new study areas in the southwestern US. Part 5, Cougar Home Ranges (shapefile): Cougar home-ranges were calculated to compare the mean probability of a GPS fix acquisition across the home-range to the actual fix success rate (FSR) of the collar as a means for evaluating if characteristics of an animal’s home-range have an effect on observed FSR. We estimated home-ranges using the Local Convex Hull (LoCoH) method using the 90th isopleth. Data obtained from GPS download of retrieved units were only used. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose as additional 10% of data. Comparisons with home-range mean probability of fix were also used as a reference for assessing if the frequency animals use areas of low GPS acquisition rates may play a role in observed FSRs. Part 6, Cougar Fix Success Rate by Hour (csv): Cougar GPS collar fix success varied by hour-of-day suggesting circadian rhythms with bouts of rest during daylight hours may change the orientation of the GPS receiver affecting the ability to acquire fixes. Raw data of overall fix success rates (FSR) and FSR by hour were used to predict relative reductions in FSR. Data only includes direct GPS download datasets. Satellite delivered data was omitted from the analysis for animals where the collar was lost or damaged because satellite delivery tends to lose approximately an additional 10% of data. Part 7, Openness Python Script version 2.0: This python script was used to calculate positive openness using a 30 meter digital elevation model for a large geographic area in Arizona, California, Nevada and Utah. A scientific research project used the script to explore environmental effects on GPS fix acquisition rates across a wide range of environmental conditions and detection rates for bias correction of terrestrial GPS-derived, large mammal habitat use.

Advances in bio-logging technology for wildlife monitoring have expanded our ability to study space use and behavior of many animal species at increasingly detailed scales. However, such data can be challenging to analyze due to autocorrelation of GPS positions. As a case study, we investigated spatiotemporal movements and habitat selection in the little owl (Athene noctua), a bird species that is declining in central Europe and verges on extinction in Denmark. We equipped 6 Danish food-supplemented little owls and 6 non-supplemented owls in the Czech Republic with high-resolution GPS loggers that recorded one position per minute. Nightly home ranges, measured as 95% kernel density estimates, of Danish male owls were on average 62 ha (± 64 SD, larger than any found in previous studies) compared to 2 ha (± 1) in females, and to 3 ± 1 ha (males) versus 3 ± 5 ha (females) in the Czech Republic. Foraging Danish male owls moved on average 4-fold further from their nest and at almost double the distance per hour than Czech males. To create availability data for the habitat selection analysis, we accounted for high spatiotemporal autocorrelation of the GPS data by simulating correlated random walks with the same autocorrelation structure as the actual little owl movement trajectories. We found that habitat selection was similar between Danish and Czech owls, with individuals selecting for short vegetation and areas with high structural diversity. Our limited sample size did not allow us to infer patterns on a population level, but nevertheless demonstrates how high-resolution GPS data can help to identify critical habitat requirements to better formulate conservation actions on a local scale.

Methods We captured 6 adult owls from both the Danish (June 2019) and Czech populations (June 2020) using mist-nets or traps baited with dead chickens (Denmark) or live mice (Czech Republic). Captured owls were sexed (based on brood patch and body mass) and fitted with a GPS backpack (Technosmart Europe srl., Rome; total mass including Teflon backpack harness of ca. 3.2 g; constituting <2.5% of the owls’ body mass). GPS units were programmed to record one position per minute, and to start recording positions the night after capture (avoiding potential capture effects).

Lots of brief GNSS signal snapshots to develop and test snapshot GNSS satellite acquisition and positioning algorithms.

Overview

This repository contains two new dynamic GNSS satellite signal snapshot datasets. These were created by carrying SnapperGPS receivers (https://snappergps.info/) while walking or cycling in Oxfordshire, UK.

• Walking: 15 individual recordings/tracks with 26,423 datapoints in total captured in one-second intervals totalling 7:20 hours. Motion during these recordings was slower on average, but acceleration, deceleration, and directional changes were more frequent and usually sudden.
• Cycling: Twelve individual recordings/tracks with 27,237 datapoints in total captured in one-second intervals totalling 7:34 hours. Motion during these recordings was on average faster, but acceleration, deceleration, and directional changes were less frequent and usually smooth.

SnapperGPS V1.0.0 boards (https://github.com/SnapperGPS/snappergps-pcb) were used for 18 recordings and SnapperGPS V2.0.0 boards (https://github.com/SnapperGPS/snappergps-pcb-2) for nine recordings. The more expensive Siretta Echo 27 active patch antenna that was already used for a previous data collection (https://doi.org/10.5287/bodleian:eXrp1xydM) was only used for six of these recordings. In addition, either the low-cost Abracon APAM2764YK0175 active patch antenna (twelve recordings) or low-cost Taoglas GP.1575.25.2.A.02 (five recordings) or GP.1575.25.4.A.02 (four recordings) passive patch antennas were used.

The core component of a low-cost SnapperGPS receiver is an SE4150L (https://www.skyworksinc.com/Products/Amplifiers/SE4150L) integrated GPS receiver circuit. Like most civilian low-cost GPS receivers, SnapperGPS operates in the L1 band with a centre frequency of 1.57542 GHz. However, Galileo's E1 signal, BeiDou's B1C signal, GPS' novel L1C signal, and SBAS' L1 signal have the identical centre frequency. So, a SnapperGPS receiver captures those signals, too. A SnapperGPS receiver down-mixes the incoming signal to a nominal intermediate frequency of 4.092 MHz, samples the resulting near-baseband signal at 4.092 MHz and digitises it with an amplitude resolution of one bit per sample. It considers only the in-phase component and discards the quadrature component.

The datasets are expected to be more challenging than the previous data collection (https://doi.org/10.5287/bodleian:eXrp1xydM). Firstly, due to the use of lower-cost antennas and, secondly, due to more track sections in forests and along tree-lined avenues. The latter is likely to reduce the number of visible satellites and the signal strengths of visible ones.

Ground truth was collected with real-time kinematic (RTK) receivers (u-blox ZED-F9P) with persistent cellular connections to a base station to receive assistance data and perform differential GNSS for improved accuracy.

Data

The data is provided in JSON format.

• /snapshots: Raw twelve-millisecond GNSS signal snapshots from the SnapperGPS receivers stored in the same data format that is used for data transfers from SnapperGPS boards to the SnapperGPS app/website. The field snapshots is an array each of which elements has the fields timestamp, temperature, and data. data is a Base64 encoded byte array. One byte of the decoded array holds the amplitude values of eight signal samples, i.e., the first byte holds the first eight samples. A zero bit represents a signal amplitude of +1 and a one bit a signal amplitude of -1. The order of the bits is 'little', i.e., reversed. For example, the byte 0b01100000 corresponds to the signal chunk [1 1 1 1 1 -1 -1 1].
• /fixes: Fixes calculated with the SnapperGPS app/website (without smoothing) stored in the same data format that is used for data downloads from the SnapperGPS app/website. The data comes in array each of which elements has the fields datetime, latitude, longitude, confidence, temperature, and id, the first four of which are estimated. datetime is in UTC, latitude and longitude are in decimal degrees, and confidence is a confidence radius (50%) in metres.
• /ground-truth: Ground truth fixes from an RTK receiver stored in array each of which elements has the fields time, latitude, and longitude. time is in UTC, latitude and longitude are in decimal degrees.
• /smoothed: Plots of non-smoothed and smoothed tracks (see below). Non-smoothed fixes (pink), 2D RTS smoothing (dark gray), 2D GPR (purple), 2D loosely-coupled FGO (light gray), and tightly-coupled FGO (white).

Table 1: Overview of recordings.

Smoothing

Table 2: Median horizontal localisation errors of different smoothing algorithms considering different sampling intervals and travel modes. (RTS smoother: Rauch-Tung-Striebel smoother. GPR: Gaussian process regression. FGO: factor graph optimisation.) Two-dimensional (2D) smoothers and tight FGO use a constant-position-random-velocity model, except for GPR, which uses adaptive models for the two spatial dimensions. The 4D smoother uses a constant-velocity-random-acceleration model. All estimations are loosely-coupled (snapshot positioning followed by smoothing) except for tight FGO. Localisation errors without smoothing are provided for comparison.

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 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.

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GPS Market Size 2024-2028

The GPS market size is forecast to increase by USD 111.6 million, at a CAGR of 22.1% between 2023 and 2028.

The Global Positioning System (GPS) market is experiencing significant growth, driven by increasing investment in satellite deployment and the rising demand for advanced GPS devices. These trends reflect the market's potential for innovation and expansion. However, connectivity issues with GPS pose a notable challenge. As satellite coverage can be disrupted by various factors, ensuring uninterrupted GPS service remains a critical concern. Companies must invest in robust technologies to mitigate these disruptions and maintain reliable connectivity. To capitalize on market opportunities and navigate challenges effectively, businesses should focus on developing advanced GPS solutions that address connectivity concerns while offering enhanced features and functionality.
By doing so, they can cater to the evolving needs of consumers and industries, positioning themselves as leaders in the dynamic the market. Despite this,the market is expected to continue its expansion, driven by technological advancements and growing applications across various industries, including automotive technologies.

What will be the Size of the GPS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report.
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The Global Positioning System (GPS) market continues to evolve, with dynamic applications across various sectors. Weather alerts integrated with GPS navigation systems provide real-time information, enhancing safety and convenience for travelers. Head-up displays merge GPS navigation with vehicle data, projecting essential information onto the windshield for easy viewing. Aviation navigation relies on GPS for precise flight tracking and route planning, while autonomous vehicles leverage GPS for positioning and navigation. Automotive navigation systems offer turn-by-turn directions, real-time traffic updates, and subscription models. GNSS receivers provide positioning accuracy for asset tracking in industries like logistics and construction. Smart cities utilize GPS for efficient traffic management, emergency response, and field data collection.

Outdoor navigation systems cater to hikers and adventurers, while security protocols ensure location tracking and positioning accuracy for personal safety. Mapping technologies and navigation services are essential for marine navigation, precision agriculture, and geospatial data collection. Navigation software upgrades, antenna design improvements, and signal strength enhancements continue to drive market innovation. Positioning algorithms and lane guidance systems offer more accurate and efficient navigation solutions. Voice guidance and subscription models cater to diverse user preferences. Road closures and speed limit warnings help optimize travel routes, while satellite positioning and cloud-based services enable remote sensing and real-time data processing.

The ongoing development of GPS technologies and their integration into various industries ensure a continuously evolving market landscape.

How is this GPS Industry segmented?

The GPS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Logistics and transportation
  Construction and mining
  Others


Type

  Handheld GPS Devices
  Vehicle GPS Devices
  Personal GPS Devices
  Asset Tracking Devices
  Smartphone GPS


End-use Industry

  Automotive
  Transportation & Logistics
  Consumer Electronics
  Aerospace & Defense
  Agriculture
  Mining
  Construction
  Healthcare
  Retail & E-commerce


Technology

  GNSS
  A-GPS
  DR-GPS


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa

    Egypt
    KSA
    Oman
    UAE


  APAC

    China
    India
    Japan


  South America

    Argentina
    Brazil


  Rest of World (ROW)

By Application Insights

The logistics and transportation segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth as businesses increasingly prioritize the optimization of their supply chains. Weather alerts and real-time traffic updates ensure the timely delivery of perishable goods, such as food, maintaining their market value. In the e-commerce sector, GPS navigation systems and voice guidance facilitate on-time delivery, enhancing customer satisfaction. For industries dealing with valuable assets, such as jewelry or electronics, security protocols and location tracking through GPS technology safeguard against the

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.