Estimates of Vertical Velocity Errors for IGS ITRF2014 Stations by Applying the Improved Singular Spectrum Analysis Method and Environmental Loading Models

Evaluating traffic networks is crucial for administration of roadway systems, to better address congestion, safety, and air quality issues around the globe. However, challenges in implementation abound, including major investment costs, large and dynamic data streams and a need for real-time response. Recently developed Global Positioning System (GPS) data loggers are a promising tool for traffic monitoring, thanks to their low cost, ready availability on smartphones, and ability to simultaneously track many travelers and vehicles, relative to expensive, built-in traffic GPS. GPS data from many travelers provides real-time details of traffic conditions and can improve active traffic management using various big-data analytics. We demonstrate how to couple such GPS data to estimate relative roadway speeds in order to improve system management. By analyzing real-time traffic surveillance software with high data coupling and concurrent processing, a new coupling method for real-time traffic evaluation is proposed. Experimental results show efficient coupling of all available GPS data with road condition can improve traffic state estimation accuracy. This new method may increase matching accuracy by more than 1 m in vehicle position. Over 98% of GPS data can be successfully matched to service routes when the low-cost GPS devices are used to detect real-time traffic conditions. The results of traffic network evaluation could well serve as a driving assistant for connected and autonomous vehicles (C/AVs) and other traffic operations.

The accuracy of global positioning system (GPS) locations obtained from study animals tagged with GPS monitoring devices has been a concern as to the degree it influences assessments of movement patterns, space use, and resource selection estimates. Many methods have been proposed for screening data to retain the most accurate positions for analysis, based on dilution of precision (DOP) measures, and whether the position is a two dimensional or three dimensional fix. Here we further explore the utility of these measures, by testing a Telonics GEN3 GPS collar's positional accuracy across a wide range of environmental conditions. We found the relationship between location error and fix dimension and DOP metrics extremely weak (r 2 adj ∼ 0.01) in our study area. Environmental factors such as topographic exposure, canopy cover, and vegetation height explained more of the variance (r 2 adj = 15.08%). Our field testing covered sites where sky‐view was so limited it affected GPS performance to the degree fix attempts failed frequently (fix success rates ranged 0.00–100.00% over 67 sites). Screening data using PDOP did not effectively reduce the location error in the remaining dataset. Removing two dimensional fixes reduced the mean location error by 10.95 meters, but also resulted in a 54.50% data reduction. Therefore screening data under the range of conditions sampled here would reduce information on animal movement with minor improvements in accuracy and potentially introduce bias towards more open terrain and vegetation.

The 2002 Denali fault, Alaska, earthquake ( M w 7.9) caused one or more components of most broadband seismometers in western Canada to clip, yet did not trigger strong-motion instruments, thus leaving a substantial gap in the seismic record of this event. However, the large-amplitude surface waves generated by this event were well recorded by the Global Positioning System (GPS) in the same region, out to epicentral distances of more than 3000 km. In this article, we explore the capabilities of GPS seismology, specifically how the relative strengths of GPS and seismic data sets can be exploited in order to more effectively study earthquake source characteristics and wave-propagation effects. High-rate (1-Hz) GPS data from 23 stations throughout western North America have been analyzed to derive displacement waveforms for this event, and the impact of instrumentation (GPS receiver model) and error-reduction strategy (modified sidereal and spatial filtering) on the noise characteristics of displacement time series at each GPS site was assessed. After applying error-reduction methods to GPS displacements, the final average noise floors of 0.5 cm in the horizontal and 1.5 cm in the vertical indicate that large dynamic displacements are observable by GPS. We validate the GPS displacements by comparing broadband seismic recordings (integrated to displacement) with GPS recordings for four effectively colocated sets of instruments. We show excellent agreement between the unclipped seismic and GPS recordings of the surface waves from the Denali earthquake over the period range of 10–50 sec and for ground displacements exceeding about 1.0 cm. Thus, a large GPS displacement data set is now available for western North America, an area where records of this event were previously missing or incomplete. The final GPS seismograms are archived at Incorporated Research Institutions for Seismology (IRIS) for public use in future studies of the 2002 Denali earthquake.

Validation of water vapor retrieval from Moderate Resolution Imaging Spectro-radiometer (MODIS) in near infrared channels using GPS data over IAO-Hanle, in the trans-Himalayan region

Precise Orbit Determination (POD) is an integral part for analyzing measurements from space geodetic techniques such as Satellite Laser Ranging (SLR) and Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS). In the last two decades, POD based on GPS data has furthermore been established as one of the standard techniques to derive trajectories of satellites in the low Earth orbit (LEO) with highest accuracy. Since the launch of dedicated gravity missions, GPS sensors are not only used as a key tracking system for LEO POD, but also for extracting the long wavelength part of the Earth’s gravity field (together with SLR to spherical satellites). This chapter introduces SLR and GNSS measurements collected by the terrestrial networks of the International Laser Ranging Service (ILRS) and the International GNSS Service (IGS) as the observational basis for the realization of a terrestrial reference frame from satellite data. On this foundation, the basic equations and mathematical methods of orbit determination are introduced and extensively discussed. Pseudo-stochastic orbit modeling techniques are eventually presented as a general and efficient concept to determine satellite trajectories of highest quality even in presence of deficient force models, covering the full range between dynamic and purely kinematic solutions. Selected results from the application of the discussed orbit determination techniques are highlighted for GPS LEO data. Special emphasis is also put to present orbit determination in the context of more general orbit determination problems, where satellite trajectories are simultaneously determined with other parameters encompassing (at maximum) all pillars of geodesy, i.e., the shape, rotation, and gravity field of the Earth.

SUMMARY The elastic deformation of Earth’s surface caused by internal mass distribution varies significantly across loading models, especially in high-precision applications. Although several studies have applied loading corrections to Global Navigation Satellite System (GNSS) time-series in mainland China, discrepancies between models, particularly those involving Gravity Recovery and Climate Experiment (GRACE) data and its downscaled derivatives, remain insufficiently explored. Moreover, previous research has not comprehensively assessed vertical crustal deformation after applying different environmental loading corrections. This study systematically evaluates the correction effects of various environmental loading models on GNSS vertical displacements across mainland China, generating vertical velocity maps along with their associated uncertainties. The results show that hydrological loading (HYDL) has the most significant impact on GNSS vertical displacements, whereas non-tidal oceanic loading (NTOL) has the least effect. Substantial differences exist between various HYDL models, while discrepancies between non-tidal atmospheric loading and NTOL models are relatively minor. A comparison of correction effects between the HYDL model and GRACE data reveals that the HYDL model offers more accurate corrections, whereas downscaled GRACE data demonstrates improved performance, underscoring its potential advantages. After applying loading corrections and filtering common mode error, the uncertainty in GNSS vertical velocity is notably reduced, although velocity variation remains small. This effect is also evident in seasonal variations. Furthermore, a comparison of vertical land motion (VLM) constrained by different HYDL models and downscaled GRACE data with VLM constrained by an independent land–water fusion model reveals higher consistency between the downscaled GRACE data and the independent model in the North China and northwestern Tianshan regions, suggesting that VLM derived from downscaled GRACE data may be more reliable. We also quantify the combined impact of geocentre motion and glacial isostatic adjustment on the VLM trend across mainland China, estimated it at approximately 0.13 mm yr−1. While the spatial characteristics of the VLM trend show minimal changes after correction, its intensity is significantly affected. This study provides crucial insights into the correcting of environmental loading effects in GNSS vertical displacements and contributes the latest observational results on vertical crustal deformation in mainland China.

Numerous map-matching techniques have been developed to improve positioning, using Global Positioning System (GPS) data and other sensors. However, most existing map-matching algorithms process GPS data with high sampling rates, to achieve a higher correct rate and strong universality. This paper introduces a novel map-matching algorithm based on a hidden Markov model (HMM) for GPS positioning and mobile phone positioning with a low sampling rate. The HMM is a statistical model well known for providing solutions to temporal recognition applications such as text and speech recognition. In this work, the hidden Markov chain model was built to establish a map-matching process, using the geometric data, the topologies matrix of road links in road network and refined quad-tree data structure. HMM-based map-matching exploits the Viterbi algorithm to find the optimized road link sequence. The sequence consists of hidden states in the HMM model. The HMM-based map-matching algorithm is validated on a vehicle trajectory using GPS and mobile phone data. The results show a significant improvement in mobile phone positioning and high and low sampling of GPS data.

Loading of the Earth’s crust due to variations in global atmospheric pressure can displace the position of geodetic stations. However, the station displacements induced by the diurnal and semidiurnal atmospheric tides (S1-S2) are often neglected during Global Positioning System (GPS) processing. We first studied the magnitudes of S1-S2 deformation in the Earth’s center of mass (CM) frame and compared the global S1-S2 grid models provided by the Global Geophysical Fluid Center (GGFC) and the Vienna Mapping Function (VMF) data server. The magnitude of S1-S2 tidal displacement can reach 1.5 mm in the Up component at low latitudes, approximately three times that of the horizontal components. The most significant difference between the GGFC and VMF grid models lies in the phase of S2 in the horizontal components, with phase discrepancies of up to 180° observed at some stations. To investigate the effects of S1-S2 corrections on GPS coordinates, we then processed GPS data from 108 International GNSS Service (IGS) stations using the precise point positioning (PPP) method in two processing strategies, with and without the S1-S2 correction. We observed that the effects of S1-S2 on daily GPS coordinates are generally at the sub-millimeter level, with maximum root mean square (RMS) coordinate differences of 0.18, 0.08, and 0.51 mm in the East, North, and Up components, respectively. We confirmed that part of the GPS draconitic periodic signals was induced by unmodeled S1-S2 loading deformation, with the amplitudes of the first two draconitic harmonics induced by atmospheric tide loading reaching 0.2 mm in the Up component. Moreover, we recommend using the GGFC grid model for S1-S2 corrections in GPS data processing, as it reduced the weighted RMS of coordinate residuals for 45.37%, 46.30%, and 53.70% of stations in the East, North, and Up components, respectively, compared with 39.81%, 44.44%, and 50.00% for the VMF grid model. The effects of S1-S2 on linear velocities are very limited and remain within the Global Geodetic Observing System (GGOS) requirements for the future terrestrial reference frame at millimeter level.

The aim of this study was to examine the spatial mobility of homeless people in urban areas, exploring homeless mobility, its drivers, limits and links to personal attributes, and whether there is an association between the extent of spatial activity and an individual’s housing situation. To our knowledge, there has been no prior exhaustive attempt to explore the spatial mobility of homeless people using Global Positioning System (GPS) location devices. The theoretical background of the research was based on time-geography approaches. The research used a mixed method approach involving participatory GPS mapping. Spatial mobility was measured by GPS location devices. GPS tracking made it possible to capture the precise location of a person in time and space, and subsequently to identify the daily and weekly mobility rhythms of such people. The GPS data were further contextualised by conducting interviews with homeless people and asking about their daily mobility. The groundwork for the interviews resulted in printed maps of the participants’ daily spatial mobility (n = 598). The combination of time-location data and ethnographic methods presented several technical and organisational difficulties, but the pilot study provided valuable knowledge about the everyday-life mobility of homeless people in cities. A novel understanding of the links between homeless mobilities, urban commons and the life conditions of homeless people can inform current welfare policies relating to the poor.

Acquiring global positioning system (GPS) data can help researchers understand the relationship between older adult’s location/activity and their health, but existing GPS data collection modes suffer from issues with precision, consistency, and acceptability. Some limitations of current GPS mobile apps are satellite service disruption, power saving mode interference, miscalibration of device’s compass, and user-error during data collection which result in missing or inaccurate data. We present a novel multilingual GPS mobile app and live administrative dashboard to address these limitations. The app performs triangulation between GPS satellite data, nearest cellular tower, and WiFi router location data which are processed through a unique GPS algorithm with a mean error of only 1.5 meters. Since GPS data results in coordinates commonly reverse geo-coded to place-based datasets, the accuracy is critical in determining place type or other characteristics. The app is additionally designed with redundancy and fault tolerance in mind to run continuously in the background of a user’s smartphone, so there is no data loss from accidentally closing the application or restarting the phone. Moreover, this new application has a flexible sampling rate ranging from 1 second to 20 minutes. Sampling frequency flexibility can adjust the battery life and reduce the need to charge as frequently. Last, application programming interface (API) integrations allow for live data stream, which can improve monitoring and troubleshooting in the field. For research with older adult populations, these features can not only improve the accuracy of collected data but also increase the acceptability of collecting GPS data.

Mobility-based exposure assessment of air pollution has been proposed as a potentially more valid approach than home-based assessments. However, methodological uncertainties in operationalizing mobility-based assessment may still increase inaccuracies in estimating exposures. It remains unclear whether using short-term mobility data and yearly average air pollution concentrations is reliable for estimating personal air pollution exposure. This study aimed to assess variability in exposure estimates modeled by short- and long-term global positioning system (GPS) data and air pollution maps with yearly and monthly temporal resolutions. We tracked 428 participants for a short period (14days) with a GPS device and for a long period (several months) with a smartphone application. Exposure estimates of nitrogen dioxide, ozone, and fine particulate matter (PM10 and PM2.5) were computed based on GPS data, air pollution maps, and temporal and indoor/outdoor adjustments. The concordance correlation coefficient (CCC) indicated excellent agreement (0.85-0.99) between exposure estimates based on short- and long-term GPS data from smartphones but ranged from moderate to excellent (0.57-0.99) when comparing exposure estimates based on data from different devices. Agreement between yearly and monthly map-based estimates was poor to moderate without temporal adjustment (CCC: 0-0.63) but excellent after temporal adjustment (CCC: 0.92-1.0). The findings suggest that using short-term (i.e., 7 or 14days) GPS data and yearly average air pollution concentrations in mobility-based assessments can well represent long-term mobility and yearly averages for determining long-term exposures. However, GPS data collected via dedicated devices and smartphones may identify distinct indoor/outdoor patterns, affecting the indoor/outdoor adjustments of exposure estimates. Additionally, careful selection of using yearly or monthly maps is advised for assessing exposures within specific short periods.

Global positioning system (GPS) telemetry units are now small enough to be deployed on terrestrial and semi-aquatic turtles. Many of these GPS units use snapshot technology which collects raw satellite and timestamp data during brief periods of data recording to minimize size. We evaluated locations from snapshot GPS units in stationary tests and on wood turtles (Glyptemys insculpta) in northeastern Minnesota. Stationary GPS units were placed in wood turtle habitat to evaluate location accuracy, fix success rate, and directional bias. The GPS fix success rate and accuracy were reduced in closed canopy conditions and when the stationary GPS unit was placed under a log to simulate wood turtle hiding behavior. We removed GPS location outliers and used a moving average calculation to reduce mean location error in stationary tests from 27 m (SD = 38) to 10 m (SD = 8). We then deployed GPS units and temperature loggers on wood turtles and collected 122,657 GPS locations and 242,781 temperature readings from 26 turtles from May to September 2015 and 2016. Location outliers accounted for 12% of locations when the GPS receiver was on a turtle. We classified each wood turtle location based on the GPS location and by comparing temperature profiles from river, sun, and shaded locations to the temperature logger on the turtle. We estimated that wood turtles were on land 68% (SD = 12) of the time from May to September. The fix success rate for land locations was 38% (SD = 9), indicating that wood turtles often use habitats with obstructed views of the sky. Mean net daily movement was 55 m (SD = 192). Our results demonstrate that snapshot GPS units and temperature loggers provide fine-scale GPS data useful in describing spatial ecology and habitat use of semi-aquatic turtles.

Travel mode imputation using GPS and accelerometer data from a multi-day travel survey

Defining the source region of the Indian Ocean Tsunami from GPS, altimeters, tide gauges and tsunami models

Map-Matching Poor-Quality GPS Data in Urban Environments: The pgMapMatch Package