Global Positioning System Data Research Articles

Investigation of the Effect of Integrated Offset, GPS, and InSAR Data in the Stochastic Source Modeling of the 2002 Denali Earthquake

This study investigates the effect of geological field measurement (offset), global positioning system (GPS), and interferometric synthetic aperture radar (InSAR) data on the estimation of the co-seismic earthquake displacements of the 2002 Denali earthquake. The analysis is conducted using stochastic source modeling. Uncertainties associated with each dataset limit their effectiveness in source model selection and raise questions about the adequate number of datasets and their type for reliable source estimation. To address these questions, stochastic source models with heterogeneous earthquake slip distributions are synthesized using the von Kármán wavenumber spectrum and statistical scaling relationships. The surface displacements of the generated stochastic sources are obtained using the Okada method. The surface displacements are compared with the available datasets (i.e., offset, GPS, and InSAR) individually and in an integrated form. The results indicate that the performance of stochastic source generation can be significantly improved in the case of using GPS data and in the integrated case. Overall, based on the case study of the 2002 Denali earthquake, the combined use of all available datasets increases the robustness of the stochastic source modeling method in characterizing surface displacement. However, GPS data contribute more than InSAR and offset data in producing reliable source models.

The source characteristics and ground motion parameters measure with structural damages of Mw6.4, 2018 Hualien earthquake Taiwan from GPS data

On February 6, 2018, a moderate earthquake with a magnitude of ML6.2, known as the 0206 earthquake, struck Hualien in eastern Taiwan, resulting in significant destruction, 17 fatalities, and over 300 injuries. This research investigates the kinematic source model of the earthquake by inverting coseismic Global Positioning System (GPS) displacement recordings from the area surrounding the epicenter. The inverted source model indicates that most asperities above the hypocenter are minor, with large slips occurring at depths shallower than 10 km, which contributed to the severe damage in Hualien City. Additionally, we calculated the Coulomb stress changes at depths ranging from 6 km to 14 km, revealing that most aftershocks within a month occurred within a Coulomb stress range of >0.1 bar, extending from shallow to deep and from southwest to northeast of the epicenter. Notably, despite high Coulomb stress, few aftershocks were generated in the western part of the epicenter, suggesting a seismic gap that may lead to a more significant earthquake in the future. Two years later, two moderate earthquakes (ML=5) occurred at depths of 17.53 and 12.1 km in the region where Coulomb stress had increased, validating our predictions. Furthermore, by applying the derived seismic source model and a stochastic semi-empirical technique to assess five parameters related to building damage, we found that all damaged buildings were within the danger alert level, with some located along the Meilun Fault, indicating that the destruction was due to the combined effects of the 0206 earthquake and the fault's induced movement.

Development of a Large-Scale Horizontal Curve Inventory for Safety Analysis Using Open GIS Data

Abstract Horizontal curves are crucial for the safety and mobility of roadways. To enhance the understanding of these curves, transportation agencies are investigating methods to accurately determine their spatial locations, geometric attributes, and conditions within their extensive road networks. Leveraging the widespread availability of Global Positioning System (GPS) and Geographic Information System (GIS) data, significant advancements have been made in developing automated algorithms that identify horizontal curves from GPS trajectories and GIS basemaps. This study introduces a novel dataset—the Horizontal Curve Inventory—that encapsulates detailed features of horizontal curves. We employed a modified automated cycle regression algorithm designed for large-scale road network analysis to extract horizontal curve data. This method was applied nationally to process an expansive network utilizing open-source GIS basemaps, encompassing over 300,000 miles across interstate, U.S. highway, and state highway systems. The inventory includes comprehensive data on curve radius, rotation angle, start and end point coordinates, and the curve’s center points. These data are essential for supporting safety research pertaining to road alignment. Our approach demonstrated robust performance across various open GIS data sources and provides the first accurate, efficient, and comprehensive inventory of horizontal curves for all major highway systems in the United States. The dataset is available at http://aichengbo.com/research/national-horizontal-curve-inventory/.

Using Qualitative Geospatial Methods to Explore Physical Activity in Children with Developmental Disabilities: A Feasibility Study

Children with developmental disabilities (DDs) experience barriers to physical activity (PA) participation. Greater contextual information regarding their PA behaviors is needed for effective PA promotion. We investigated the feasibility of using activity trackers and Global Positioning Systems (GPS) devices with follow-up interviews to explore PA behaviors in children with DDs. Fifteen children with DDs (aged 10 ± 2 years) wore an activity tracker and GPS device for 7 days. Data were time-aligned to measure PA and identify PA locations. Maps were created to guide follow-up semi-structured interviews with the children and their parents/guardians to understand PA contexts and perceptions of daily PA. The children took 8680 ± 4267 steps/day across 6 ± 1 days. The children provided preferences for PA locations and the parents/guardians gave context by expressing how DDs affect PA and identifying environmental factors in PA locations. The children with DDs who lived near parks, participated in PA that leveraged the strengths of their individual skillsets, and had parents/guardians who provided social support had more positive PA experiences. Combining activity tracking and GPS data with follow-up map-based interviews is feasible to explore PA behaviors and the experiences of children with DDs. This methodology may provide novel insight into daily PA in children with DDs, which can inform how future interventions can support them to be more active and have positive experiences while being active.

Validity of GPS data in driving cycles

AbstractThere is continuous research into driving cycles (DCs) as researchers across the globe seek to define driving characteristics, energy consumption, and emissions in a local context. For decades, data collection for the development of DCs has been conducted in three ways: chase car, instrumented vehicle, or a combination of both. Many studies have moved on to cheap and easily available global positioning system (GPS) technology, while others record vehicle data directly through the on‐board diagnostics (OBD) port. However, there are major limitations to GPS data collection such as frequent inaccuracies and loss of coverage in urban environments. For this reason, both OBD and GPS vehicle speed data have been collected. Then, the recorded data has been analysed to capture any differences in sampling rates and dropping data. Finally, basic DCs were created from smoothed GPS and OBD data and compared. DCs were developed with a microtrip‐based method, and a relative error term was used to compare candidate DCs to the collected data. DCs were compared based on kinematic characteristic parameters that are most used in the field. The results of this study could be used to assess the validity of GPS‐based DCs compared to OBD cycles using low‐cost devices.

Identifying Truck Activity from Global Positioning System Data Based on Satellite Imagery

Road freight transportation plays a crucial role in fostering economic growth and enhancing logistics efficiency. Existing research primarily focuses on analyzing freight truck activity characteristics based on global positioning system (GPS) data of freight truck trajectories. Those studies proposed a variety of identification methods from different perspectives, but they mainly rely on rule-based methods and subjectively defined key threshold parameters, ignoring the complexity of truck freight activities and relying on long sequences of trajectory data. To bridge this gap, we propose the satellite image augmented truck activity recognition method, a structured framework that utilizes satellite imagery to identify truck activities from GPS data. Firstly, we categorize truck activities into three types: loading/unloading, resting, and driving. We extract geographic entities from the area of interest data to create the satellite image dataset of truck activity. Secondly, we evaluate the recognition accuracy of benchmark image classification models on our dataset, and ResNet50 achieves 98.04% accuracy on the test set. Thirdly, we apply the image classification model to identify truck activity from GPS trajectory data and use the recognition results of neighboring points to improve the identification results. The framework delineated in this paper entails the extraction of truck activities from GPS data at the satellite image level. Our approach holds promise in facilitating logistics operation fleets and traffic management authorities to maintain near real-time surveillance of the truck status, thereby fostering enhancements in both the efficiency and safety of freight transport.

A novel system architecture of intelligent adaptive cruise control for safety aspects

Following industrial and safety standards for autonomous vehicles, Adaptive Cruise Control (ACC) is a widely employed Advanced Driving Assistance System (ADAS) feature in modern vehicles. ACC currently facilitates speed control based on the driver's desired speed value. This study introduces a significant advancement: the Intelligent Adaptive Cruise Control (IACC) feature, accompanied by the development of a control system architecture poised to make noteworthy contributions in scientific, economic, and social dimensions through its integration into autonomous vehicles. The design incorporates crucial elements such as Traffic Sign and Limit Recognition (TSLR), ADAS features, and Global Positioning System (GPS) data, primarily enhancing driver safety through these supportive features. The main focus revolves around designing a system architecture that accommodates these new features to ensure safe driving. The creation of the IACC system architecture is approached using Model-Based System Engineering (MBSE). Through this MBSE methodology, system-level diagrams were crafted, and security considerations were systematically addressed. Several scenarios were devised to evaluate the contributions and were subsequently tested and analyzed. The architecture places particular emphasis on the security aspects of IACC. Leveraging the TSLR feature, the system interprets traffic signs and acquires speed limit data from external sources, preventing the vehicle's speed from exceeding the specified limit. The comparison between the set speed value and the speed limit ensures adherence to safety parameters. In such scenarios, the system enhances driver support on winding roads by utilizing GPS data to recognize the vehicle in front. This approach significantly elevates the reliability of the IACC feature, particularly in terms of safety sensitivity, when compared to other adaptive cruise control concepts.

Dead-reckoning facilitates determination of activity and habitat use: a case study with European badgers (Meles meles)

BackgroundStudies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited.MethodsCollars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type.ResultsNightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively).Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances.ConclusionsThis study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

Building a General Algorithm for Seismic Hazard Analysis in the Sunda Arc through Geodynamic Simulations

Abstract Because of its well-documented subduction zone and outer island arc, Sumatra provides a unique setting for studying and forecasting earthquakes within the seismically active Sunda Arc. This study builds on previous research that utilized Global Positioning System data and the Akaike information criterion to analyze probabilistic seismic hazard functions. However, this study replaces surface displacement rate data with a forward model derived from previous fault modeling results to create a more broadly applicable earthquake forecasting algorithm. Although the best-fit model patterns generated by this new algorithm are consistent with past studies, the forward model demonstrates a lower degree of fit compared to models utilizing natural surface displacement data. This discrepancy highlights the need to refine the fault parameter models to estimate surface displacement rates. Despite this limitation, the study makes a valuable contribution by developing a general algorithm applicable to other subduction zones within the Sunda Arc region. With further refinement and incorporation of more accurate fault modeling and data, this algorithm has the potential to formulate the best-fit earthquake spatial forecast models. This approach could be applied to other seismically active areas, particularly those near subduction zones.

Orienteering Is More than Just Running! Acute Effect of Competitive Pressure on Autonomic Cardiac Activity among Elite Orienteering Athletes.

Background and Objectives: Orienteering is a sport characterized by high physical exertion and intense mental demands, which increase susceptibility to errors. Understanding the impact of such errors on psychophysiological responses, particularly on heart rate variability (HRV), is essential. This study aimed to investigate the relationship between psychophysiological indicators and checkpoint errors made by elite orienteers during official competition. Materials and Methods: Fifty-three orienteers participated in this study, and their performance was continuously monitored and recorded by using a global positioning system (GPS) and HRV data. Errors made during the orienteering events were identified and analyzed. HRV data were examined in three temporal segments: before, during, and after the identified and standardized errors. Results: The analyses indicated that errors significantly impacted HRV indices across multiple domains: the time domain, frequency domain, and nonlinear domain. Additionally, a significant effect of sex on the normalized the root mean square of successive differences (r-MSSD) before and after the error was observed. Conclusions: The findings of this study underscore the significant impact of errors made by orienteers on cardiovascular responses, as evidenced by measurable alterations in HRV metrics. Cardiovascular activity, represented by the HRV, can provide useful information for coaches and sport psychologists to adopt effective training programs for athletes.

Early postseismic slip of the 21 November 2022 Mw 5.6 Cianjur, Indonesia, earthquake based on GPS measurements

ABSTRACT Global Positioning System (GPS) data after the 21 November 2022 Mw 5.6 Cianjur earthquake are crucial for understanding the physical processes during the postseismic phase. New continuous GPS stations were installed and recorded data for periods ranging from five to twenty-five days after the mainshock. The daily time series data at GPS stations shows an evolving logarithmic decay over time, as expected due to frictional afterslip. We inverted the postseismic displacement data to solve the postseismic slip distribution, utilising Akaike’s Bayesian information criterion. A cumulative aseismic slip moment of 1.54 × 1016 N·m, equivalent to Mw 4.7, is modelled during the period from five to twenty-five days after the mainshock. This aseismic slip moment is equivalent to approximately 7% of the seismic moment during the mainshock. Our analysis on the newly identified active fault using GPS displacements highlights the imperative need to identify active faults prior to future earthquake occurrences, especially when conducting hazard mitigation in populated regions in Indonesia.

GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.

Impact of Texting-Induced Distraction on Driving Behavior Based on Field Operation Tests

Driver distraction has proven to be among the top contributory factors in road crashes worldwide. The involvement of drivers in secondary activities, such as phone usage, conversation with co-passengers, reaching for objects, eating, and so forth, have been shown to be significant causes of in-vehicle driver distraction. Text messaging using mobile phones while driving can be detrimental to the primary driving task as it invokes manual, visual, and cognitive distractions. Numerous studies in the past have analyzed the effect of texting on driver distraction using driving simulators. This study followed a field operation test–based approach to simulate real road conditions and evaluate the impact of texting on driving behavior. The study was conducted at the connected autonomous vehicle testbed at the Indian Institute of Technology Hyderabad, using an instrumented test vehicle. Data on gaze parameters and vehicle kinematics were recorded using eye-tracker glasses and a high-end global positioning system (GPS) data logger. Thirty-one participants drove on the pre-defined path at the testbed under baseline and texting conditions. The findings demonstrated that the texting task significantly affected fixation count, mean speed, and lateral acceleration. The texting task led to a reduction in mean fixation counts, mean speed, and mean lateral acceleration by 44.37%, 34%, and 46.72%, respectively. The age and driving experience of the participants had a significant effect on their behavior. The results indicate the critical potential of texting-based driver distraction and suggest the enforcement of stringent rules for texting while driving to create a safer road environment.

Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories

AbstractThe potential of passively generated big data sources in transport modelling is well‐recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground‐truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent‐based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation‐based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.

Short-term effects of a park-based group mobility program on increasing outdoor walking in older adults with difficulty walking outdoors: the Getting Older Adults Outdoors (GO-OUT) randomized controlled trial

BackgroundWe estimated the short-term effects of an educational workshop and 10-week outdoor walk group (OWG) compared to the workshop and 10 weekly reminders (WR) on increasing outdoor walking (primary outcome) and walking capacity, health-promoting behavior, and successful aging defined by engagement in meaningful activities and well-being (secondary outcomes) in older adults with difficulty walking outdoors.MethodsIn a 4-site, parallel-group randomized controlled trial, two cohorts of community-living older adults (≥ 65 years) reporting difficulty walking outdoors participated. Following a 1-day workshop, participants were stratified and randomized to a 10-week OWG in parks or 10 telephone WR reinforcing workshop content. Masked evaluations occurred at 0, 3, and 5.5 months. We modeled minutes walked outdoors (derived from accelerometry and global positioning system data) using zero-inflated negative binomial regression with log link function, imputing for missing observations. We modeled non-imputed composite measures of walking capacity, health-promoting behavior, and successful aging using generalized linear models with general estimating equations based on a normal distribution and an unstructured correlation matrix. Analyses were adjusted for site, participation on own or with a partner, and cohort.ResultsWe randomized 190 people to the OWG (n = 98) and WR interventions (n = 92). At 0, 3, and 5.5 months, median outdoor walking minutes was 22.56, 13.04, and 0 in the OWG, and 24.00, 26.07, and 0 in the WR group, respectively. There was no difference between groups in change from baseline in minutes walked outdoors based on incidence rate ratio (IRR) and 95% confidence interval (CI) at 3 months (IRR = 0.74, 95% CI 0.47, 1.14) and 5.5 months (IRR = 0.77, 95% CI 0.44, 1.34). Greater 0 to 3-month change in walking capacity was observed in the OWG compared to the WR group (βz-scored difference = 0.14, 95% CI 0.02, 0.26) driven by significant improvement in walking self-efficacy; other comparisons were not significant.ConclusionsA group, park-based OWG was not superior to WR in increasing outdoor walking activity, health-promoting behavior or successful aging in older adults with difficulty walking outdoors; however, the OWG was superior to telephone WR in improving walking capacity through an increase in walking self-efficacy. Community implementation of the OWG is discussed.Trial registrationClinicalTrials.gov NCT03292510 Date of registration: September 25, 2017.

Advancing container port traffic simulation: A data-driven machine learning approach in sparse data environments

Efficient truck dispatching strategies are paramount in container terminal operations. The quality of these strategies heavily relies on accurate and expedient simulations, which provide a crucial platform for training and evaluating dispatching algorithms. In this study, we introduce data-driven machine learning methods to enhance container port truck dispatching simulation accuracy. These methods effectively surrogate the intersections within the simulation, thereby increasing the accuracy of simulated outcomes without imposing significant computational overhead in sparse data environments. We incorporate three data-driven learning methods: genetic programming (GP), reinforcement learning (RL), and a GP and RL hybrid heuristic (GPRL-H) approach. The GPRL-H method proved the most efficacious through a detailed comparative study, striking an effective balance between simulation accuracy and computational efficiency. It reduced the error rate of simulation from approximately 35% to about 7%, while also halving the simulation time compared to the RL-based method. Our proposed method also does not rely on precise Global Positioning System (GPS) data to simulate truck operations within a port accurately. Demonstrating robustness and adaptability, this approach holds promise for extending beyond port operations to improve the simulation accuracy of vehicle operations in various scenarios characterized by sparse data.

Financial Reporting and Consumer Behavior

ABSTRACT We show that financial reporting influences consumer behavior by drawing consumer attention to announcing firms. Analyzing global positioning system (GPS) data, we document upticks in foot traffic to firms’ commercial locations immediately following their earnings announcements. This increase is more pronounced for announcements with substantial media attention, fewer concurrent announcements, heightened internet search volume, and extreme stock price jumps and earnings surprises—indicating that announcement coverage impacts consumer behavior by capturing attention. Furthermore, foot traffic increases with positive earnings for firms offering durable goods, suggesting consumers respond to news about firms’ financial prospects. Consumer attention patterns increase revenues and advertising effectiveness, ultimately suggesting that financial reporting serves a marketing function. Data Availability: All other data are available from the public sources cited in the text. JEL Classifications: G10; G11; G12; G14; G40; G41.

Technical note: assessing GPS sensor accuracy using real-time kinematic device for livestock tracking.

The technical aspects of global positioning system (GPS) sensors have been improved substantially over the years, making them valuable and popular tools for livestock tracking. Using GPS sensors allows producers and researchers to locate grazing livestock, monitor their behavior and distribution, and gather referencing information about the health status of the animals and pastures. However, interpreting the behavior of grazing livestock, such as cattle, from GPS data can be difficult due to positioning inaccuracies. Without knowing the positioning accuracy range of GPS sensors, achieving high-level confidence in determining grazing densities and identifying abnormal livestock movement patterns is challenging. In this study, the positioning accuracy of 3 different types of commercial GPS sensors was assessed using a survey-grade real-time kinematics (RTK) device. We outlined the procedures and essential equations and compared the recorded locations from the GPS sensors with reference locations collected from the RTK device. The results demonstrated statistically significant differences (P < 0.05) in positioning accuracy between different types of GPS sensors. The circular error probable (CEP) at 95% probability levels (CEP95) of the 3 sensors ranged from 2.46 to 11.06 m. This accuracy varied among individual sensors within the same brand and across different brands, which underscores the importance of evaluating the positioning accuracy of GPS sensors in livestock tracking. This study provides significant insights for animal scientists, ecologists, and livestock producers engaged in GPS sensor-related research and practices.

Influence of Fix Schedule on the Location Accuracy of a Low-Cost GPS Data Logger on Cattle

ABSTRACT Global positioning system (GPS) data loggers are commonly used to track the movements and distribution of both wild and domestic animals. However, the expense often poses a challenge for researchers. Recently, there has been a rise in the utilization of affordable and user-friendly GPS data loggers for tracking animal movements, albeit with compromised accuracy. We aimed to identify factors influencing the accuracy of a low-cost GPS data logger (I-gotU GT-600) and to enhance its location accuracy. Initial investigations revealed that recording intervals impacted the location error of the GPS data logger. To elucidate the relationship between recording intervals and location accuracy, we conducted stationary and motion tests. Our findings indicated that recording intervals of less than 15 sec substantially enhances the location accuracy of the low-cost GPS data logger. Our results highlight the relationship between the fix schedule and location accuracy for these GPS data loggers. Our study provides information that enhances the quality of data for researchers using low-cost GPS data loggers for short-term studies in various settings, such as zoos and livestock facilities.

Injury rates and mechanisms of starting and replacement players

ABSTRACT To examine injury rates, injury mechanisms and injury inciting tackle characteristics of both starting and replacement players. A prospective, cohort study of four male professional Welsh Rugby Union (WRU) teams. Injury surveillance and Global Positioning System (GPS) data were collected from league matches between the 1st of July 2017 to the 31st of August 2020. Video analysis included league matches from the 1st of July 2017 to the 31st of March 2021. Player-specific GPS exposure was used to calculate injury incidence. Generalised linear mixed models were used to analyse the effect of replacement players on injuries. Tackle injury propensity (injuries/100 tackle events) and descriptive characteristics for the tackle event were reported. A total of 290 injuries were sustained by 105 players. Starting players had a higher injury incidence than replacement players (80.8 vs 57.2 injuries/1000 match hours; rate ratio: 1.41 (95% CI 1.00 – 1.98)). For every replacement used there was a 12% increase in the number of injuries (p = 0.037). However, there was no increase in the number of injuries accounting for the replacement time-in-game (p = 0.099). The injury propensity was highest for starting versus opposition replacement players (RR: 4.08, 95% CI: 2.28 – 7.29). Injury inciting tackles for starting players showed higher body positions, whereas replacement players maintained a lower body position. Match injuries were influenced by the number of replacement players, but this was mitigated when accounting for the replacement player time-in-game. Starting and replacement players have differing injury inciting tackle characteristics, suggesting that injury risk mitigation associated with the tackle should be incorporated at a player-specific level.