Between 2020 and 2025, the global GPS tracking device market size is forecast to grow steadily. In 2022, the size of this market amounted to roughly **** billion U.S. dollars, with this number expected to peak to over *** billion dollars at the end of the given period.

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.

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).

This statistic illustrates the share of people who used their cell phone for maps/GPS navigation (in the last four weeks) in the United States as of 2023. The results were sorted by age. As of December 2023, some 37 percent of respondents aged 18 to 29 years stated they used their cell phone for maps/GPS navigation. The survey was conducted in 2023, among 9,587 respondents. Access millions of exclusive survey results with Statista Consumer Insights.

This dataset was recorded for evaluating the accuracies of a GPS- and a camera-based object tracking. For this purpose, one person was equipped with several (equal) GPS loggers. Further, a camera was used to observe this person following a predefined course. Thus, this dataset contains the GPS trajectories provided by the loggers, the corresponding video and some meta information describing the setup (time synchronization, field coordinates, waypoint coordinates, camera location and the homography matrix).

Three Global Positioning System (GPS) datasets collected during the MPD-NetDemo field campaign. The data were collected between 16 April 2019 and 22 July 2019. The GFZ German Research Centre for Geosciences in Potsdam, Germany processed the data to provide precipitable water vapor (PWV) fields.

The dataset provided here is an output of the Track & Know project, shared with the scientific community. It is an anonymized dataset of private vehicles. The dataset, containing anonymous GPS traces of private vehicles, was made accessible by the data owner to the partners of the Track & Know project, for activities relevant to the project. The proprietary dataset is not accessible to the public. It includes vehicle engine status.

Comma-separated values (.csv) file containing data (and derived data) from GPS tracking of free-flying bald eagles (Haliaeetus leucocephalus), Iowa, USA.

The Out of Eden Walk is a 24,000-mile journalistic project that aims to create a global record of human life at the dawn of a new millennium, as told by people who rarely make the news—villagers, nomads, traders, farmers, soldiers, and artists. Sponsored by the National Geographic Society, the project is led by Pulitzer Prize-winning writer Paul Salopek, who is retracing the path of human migration around the world. Along his journey, Paul documents his experiences through writing, photographs, videos, and audio. To date, his walking route has logged 18.8 million GPS locations from Ethiopia to China using a Garmin eTrex GPS receiver. These GPS points are enriched with various data using ArcGIS Pro and KNIME Analytics software, including travel mode, country, nearest milestone, collection mode, date, time, longitude, latitude, slope, elevation, population density, land cover, and nearest village.

In 2023, GPS Tracking Devices Market reached a value of USD 3.2 billion, and it is projected to surge to USD 7.4 billion by 2030.

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

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

Discover the precision of our Geospatial insights with Irys – where quality meets innovation.

Dataset used for the evaluation described in "A Systematic Evaluation of an RTK-GPS device for Wearable Augmented Reality".

The Dataset is divided into sheets:

• PT_LOCATION_TIME:
  • PT: Positional Test
  • LOCATION: LOC_A or LOC_B
  • TIME: 60s or 300s
• ST_AT_DSM_LOCATION_SMARTPHONE_TIME:
  • ST: Static condition
  • AT: Accuracy Test
  • DSM: Data Saving Modality
  • SMARTPHONE: SMA0 or SMA1
  • LOCATION: LOC_A or LOC_B
  • TIME: 60s or 300s
• DY_AT_DSM_LOCATION_SMARTPHONE_TIME:
  • DY: Dynamic condition
  • AT: Accuracy Test
  • DSM: Data Saving Modality
  • SMARTPHONE: SMA0 or SMA1
  • LOCATION: LOC_A or LOC_B
  • TIME: 60s or 300s
• ST_AT_RTM_LOCATION_TIME:
  • ST: Static condition
  • AT: Accuracy Test
  • RTM: Real-time Modality
  • LOCATION: LOC_A or LOC_B
  • TIME: 60s or 300s
• DY_AT_RTM_LOCATION_TIME:
  • DY: Dynamic condition
  • AT: Accuracy Test
  • RTM: Real-time Modality
  • LOCATION: LOC_A or LOC_B
  • TIME: 60s or 300s

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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

This data set contains a 1:100,000 scale vector digital representation of all interstate highways, all US highways, most of the state highways, and some county roads in New Mexico. The data were collected using Trimble Pathfinder Basic Plus GPS units and differentially corrected with Trimble Pfinder software, version 2.40-07. They were converted to ARC/INFO format using ARC/INFO 7.0.3. The file size is approximately 4.2 Mb, compressed.

17535 Global import shipment records of Gps Tracker with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This data set contains GPS point coordinates corresponding to the clean GPS tracking of persons

Global positioning system (GPS) data were compiled and processed to support models for seismic hazard assessment that will be included in the 2023 USGS National Seismic Hazard Model (NSHM). This data release presents the updated GPS velocity field for the western United States. Data processing centers and field networks, seven in total, supported this work, and solutions include both survey and continuous-mode GPS velocity measurements. Processing procedures were followed according to the UCERF3 (Uniform California Earthquake Rupture Forecast version 3) and the 2014 NSHM deformation modeling project. The final velocity field consists of 4,979 horizontal velocity vectors.

We have produced a global dataset of ~4000 GPS vertical velocities that can be used as observational estimates of glacial isostatic adjustment (GIA) uplift rates. GIA is the response of the solid Earth to past ice loading, primarily, since the Last Glacial Maximum, about 20 K yrs BP. Modelling GIA is challenging because of large uncertainties in ice loading history and also the viscosity of the upper and lower mantle. GPS data contain the signature of GIA in their uplift rates but these also contain other sources of vertical land motion (VLM) such as tectonics, human and natural influences on water storage that can mask the underlying GIA signal. […]

This dataset contains raw GPS data collected by Purdue University. GPS readings were taken from various stations from June 6 to June 12. Included with the data is a readme in .pdf format with start and end times and approximate locations for each observation.

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.