Global Positioning System Data Collection Research Articles

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.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data...

A NOVEL MULTIFREQUENCY GPS APP FOR STUDYING OLDER ADULTS: IMPROVING ACCURACY AND RELIABILITY OF DATA

Abstract 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.

3 Smartphone Digital Phenotyping for Unobtrusive and Continuous Assessment of Everyday Cognition and Movement Trajectories in Older Adults

Objective:To evaluate the feasibility, usability, and preliminary validity of a digital phenotyping protocol to capture everyday cognition and activities in vivo among older adults.Participants and Methods:Eight participants (M age=69.1 + 2.6; M education=18.0 + 1.4; 50% female; 88% non-Hispanic White) with normal cognition or mild cognitive impairment used an open-source smartphone application (mindLAMP) to passively and continuously capture sensor data including global positioning system (GPS) trajectories for a 4-week study period. Baseline neuropsychological tests and measures of depression, self-reported cognitive decline and mobility patterns were collected as external validators for digital data. Participants downloaded mindLAMP onto their smartphones and resumed their daily routines for 4 weeks before removing mindLAMP and completing a debriefing questionnaire. A cognitive composite was derived by averaging T-scores across domains of attention, executive functioning, processing speed, memory, and language. GPS raw data were processed to generate monthly average and standard deviation mobility metrics for each participant, including time spent at home, distance travelled, radius of gyration, flight length, and circadian routine. Feasibility and usability findings are presented along with correlation coefficients >.4 between GPS metrics and external validators.Results:100% of enrolled participants completed the 4-week study without requesting to withdraw. Usability ratings ranged from poor to excellent. 75% of participants agreed that mindLAMP was easy to use, whereas only 1 participant enjoyed using mindLAMP. 100% of participants were satisfied with the study team’s explanation of procedures, privacy safeguards, data encryption methods and risks/benefits, reflected in an average score of 98.8% on the comprehension of consent quiz. No participants reported feeling uncomfortable, suspicious, or paranoid due to the study application running on their smartphone. No participants endorsed new problems using their smartphone, though 75% reported charging it more frequently during the study period. On average each day, participants spent 1121 + 227 minutes at home, travelled 38727 + 36210 geodesic units, and had 201 + 149 minutes of missing GPS data. Overall, greater amounts of activity (monthly average) and higher variability (monthly standard deviation) in GPS metrics were associated with better outcomes. Specifically, less time spent at home, greater distance travelled, larger radius of gyration, greater flight length, and greater variability in home time, distance travelled, radius of gyration and flight length were associated with less depression, less self-reported cognitive decline, better cognition, and greater self-reported mobility (.40< |r| <.69). On the other hand, greater circadian routine was associated with more self-reported cognitive decline (r=.66) and less self-reported mobility (r=-.43).Conclusions:Smartphone digital phenotyping is a feasible and acceptable method to capture everyday activities in older adults. Continuous collection of data from personal devices warrants caution; however, participants denied privacy concerns and expressed an overall positive experience. High frequency GPS data collection impacts battery life and should be considered among relative risks and confounds to naturalistic assessment. Patterns of behavior from passive smartphone data show promise as an unobtrusive method to identify cognitive risk and resilience in older adults. Subsequent analyses will evaluate additional sensor metrics across a larger and more heterogeneous cohort.

Effects of the CarFreeMe Traumatic Injuries, a Community Mobility Group Intervention, to Increase Community Participation for People With Traumatic Injuries: A Randomized Controlled Trial With Crossover.

IntroductionAfter traumatic injuries community participation is a common goal, promoting wellbeing and independence. Community mobility and transportation influence an individual's independence in community participation. With the ability to drive safely often compromised after traumatic injuries, the adverse consequences of driving cessation include a loss of identity and reduced participation in chosen activities. In rehabilitation, individualized community mobility intervention is not routinely provided. The primary aim of this trial was to evaluate whether a group-based intervention, the CarFreeMe TI program was more effective than standard intervention, an information sheet of alternative transport, in improving community mobility for people following traumatic injuries. The secondary aim of this study was to evaluate the effect: types of transport used, transport satisfaction, community mobility self-efficacy, quality of life, goal satisfaction and performance, for people following traumatic injuries; and to undertake a preliminary assessment of the potential resource use associated with the intervention, and lessons for implementation.DesignProspective, pilot, randomized, blind observer, controlled trial with crossover.ParticipantsTwenty individuals with traumatic injuries.InterventionSix-week group-based support and education program, the CarFreeMe TI delivered in community settings (intervention) and standard information related to transport options available (control).Primary Outcome MeasuresCommunity participation using a Global Positioning System device to record the location and number of outings from home.Secondary Outcome MeasuresCarFreeMe TI Transport Questionnaire, Community Mobility Self-efficacy Scale, quality of life measures, Modified Canadian Occupational Performance Measure for goals (importance and satisfaction), participant satisfaction survey results and researcher logs.ResultsThose who received the intervention were more likely to use public transport and transport services and had an improved quality of life, when compared to the control group. The intervention group also reported high levels of improvement in goal performance and satisfaction. Global Positioning System data collection was incomplete, with geolocation data unusable. There was no significant change in number/type of visits away from home.ConclusionsA group-based community mobility education program promoted modes of active independent transport but did not impact on outings from home. Future research could include passive collection methods using a smartphone to record community participation.Clinical Trial Registrationhttps://www.anzctr.org.au/, identifier: ACTRN12616001254482.

Airborne Validation of ICESat-2 ATLAS Data over Crevassed Surfaces and Other Complex Glacial Environments: Results from Experiments of Laser Altimeter and Kinematic GPS Data Collection from a Helicopter over a Surging Arctic Glacier (Negribreen, Svalbard)

The topic of this paper is the airborne evaluation of ICESat-2 Advanced Topographic Laser Altimeter System (ATLAS) measurement capabilities and surface-height-determination over crevassed glacial terrain, with a focus on the geodetical accuracy of geophysical data collected from a helicopter. To obtain surface heights over crevassed and otherwise complex ice surface, ICESat-2 data are analyzed using the density-dimension algorithm for ice surfaces (DDA-ice), which yields surface heights at the nominal 0.7 m along-track spacing of ATLAS data. As the result of an ongoing surge, Negribreen, Svalbard, provided an ideal situation for the validation objectives in 2018 and 2019, because many different crevasse types and morphologically complex ice surfaces existed in close proximity. Airborne geophysical data, including laser altimeter data (profilometer data at 905 nm frequency), differential Global Positioning System (GPS), Inertial Measurement Unit (IMU) data, on-board-time-lapse imagery and photographs, were collected during two campaigns in summers of 2018 and 2019. Airborne experiment setup, geodetical correction and data processing steps are described here. To date, there is relatively little knowledge of the geodetical accuracy that can be obtained from kinematic data collection from a helicopter. Our study finds that (1) Kinematic GPS data collection with correction in post-processing yields higher accuracies than Real-Time-Kinematic (RTK) data collection. (2) Processing of only the rover data using the Natural Resources Canada Spatial Reference System Precise Point Positioning (CSRS-PPP) software is sufficiently accurate for the sub-satellite validation purpose. (3) Distances between ICESat-2 ground tracks and airborne ground tracks were generally better than 25 m, while distance between predicted and actual ICESat-2 ground track was on the order of 9 m, which allows direct comparison of ice-surface heights and spatial statistical characteristics of crevasses from the satellite and airborne measurements. (4) The Lasertech Universal Laser System (ULS), operated at up to 300 m above ground level, yields full return frequency (400 Hz) and 0.06–0.08 m on-ice along-track spacing of height measurements. (5) Cross-over differences of airborne laser altimeter data are −0.172 ± 2.564 m along straight paths, which implies a precision of approximately 2.6 m for ICESat-2 validation experiments in crevassed terrain. (6) In summary, the comparatively light-weight experiment setup of a suite of small survey equipment mounted on a Eurocopter (Helicopter AS-350) and kinematic GPS data analyzed in post-processing using CSRS-PPP leads to high accuracy repeats of the ICESat-2 tracks. The technical results (1)–(6) indicate that direct comparison of ice-surface heights and crevasse depths from the ICESat-2 and airborne laser altimeter data is warranted. Numerical evaluation of height comparisons utilizes spatial surface roughness measures. The final result of the validation is that ICESat-2 ATLAS data, analyzed with the DDA-ice, facilitate surface-height determination over crevassed terrain, in good agreement with airborne data, including spatial characteristics, such as surface roughness, crevasse spacing and depth, which are key informants on the deformation and dynamics of a glacier during surge.

Validation of airborne and satellite altimetry data by Arctic Truck citizen science

The elevation of ice sheets changes due to climate change, and satellite altimetry is the preferred tool for measuring ice sheet-wide height changes. In situ validation is needed to ensure the quality of the observed elevation changes, but the cost often limits the amount of in situ data which can be collected. As more tourists are accessing the ice sheets, citizen science might provide in situ data in an environmentally friendly and cost-efficient way. Here, we investigate the opportunistic kinematic global positioning system (GPS) profiles across the Greenland ice sheet, collected by the American-Icelandic expedition on the Greenlandic icecap 2018. The collected GPS data are in good agreement with the widely used NASA’s Operation IceBridge Airborne LiDAR data measured within ± 10 days, with an average difference of 10.7 cm ± 11.7 cm. The main difference is attributed to changes in the compaction of the snow while driving and changes in the tires’ pressure. The kinematic GPS data are then compared with data from the European Space Agency’s CryoSat-2 mission. Here, an average bias of 92.3 cm ± 65.7 cm in the two records is observed between the spring CryoSat-2 and the truck GPS data obtained largely in the dry-snow zone. This suggests that the surface penetration of Ku-band radar on the Greenland ice sheet and the observed magnitude are consistent with the literature. Finally, we compared the 2018 GPS data to a profile obtained in 2005 near Kangerlussuaq, West Greenland. Here, the records show an average ice-elevation decrease of 9 m, with peaks at 26 m. These results show that the citizen science kinematic GPS data can provide high-resolution data necessary for the validation of satellite altimetry, with the added benefit of potential direct sampling properties of the surface and firn. Linking up with citizen-science expeditions is a beneficial way of providing cost-effective satellite validations and may also have a societal impact by involving more people in the climate monitoring of ice sheets.

A GPS-Based Framework for Understanding Outdoor Mobility Patterns of Older Adults with Dementia: An Exploratory Study

Introduction: An active lifestyle may protect older adults from cognitive decline. Yet, due to the complex nature of outdoor environments, many people living with dementia experience decreased access to outdoor activities. In this context, conceptualizing and measuring outdoor mobility is of great significance. Using the global positioning system (GPS) provides an avenue for capturing the multi-dimensional nature of outdoor mobility. The objective of this study is to develop a comprehensive framework for comparing outdoor mobility patterns of cognitively intact older adults and older adults with dementia using passively collected GPS data. Methods: A total of 7 people with dementia (PwD) and 8 cognitively intact controls (CTLs), aged 65 years or older, carried a GPS device when travelling outside their homes for 4 weeks. We applied a framework incorporating 12 GPS-based indicators to capture spatial, temporal, and semantic dimensions of outdoor mobility. Results: Despite a small sample size, the application of our mobility framework identified several significant differences between the 2 groups. We found that PwD participated in more medical-related (Cliff’s Delta = 0.71, 95% CI: 0.34–1) and fewer sport-related (Cliff’s Delta = −0.78, 95% CI: −1 to −0.32) activities compared to the cognitively intact CTLs. Our results also suggested that longer duration of daily walking time (Cliff’s Delta = 0.71, 95% CI: 0.148–1) and longer outdoor activities at night, after 8 p.m. (Hedges’ g = 1.42, 95% CI: 0.85–1.09), are associated with cognitively intact individuals. Conclusion: Based on the proposed framework incorporating 12 GPS-based indicators, we were able to identify several differences in outdoor mobility in PwD compared with cognitively intact CTLs.

Outdoor life in dementia: How predictable are people with dementia in their mobility?

IntroductionPeople with dementia (PWD) often become disoriented, which increases their risk of getting lost. This article explores the extent to which we can predict future whereabouts of PWD by learning from their past mobility patterns using Global Positioning System (GPS) tracking devices.MethodsSeven older adults with dementia and eight healthy older adults completed 8 weeks of GPS data collection. We computed the probability that an appropriate algorithm can correctly predict the participant's future destinations using spatial and temporal patterns in each participant's GPS trajectories.ResultsRelying on both spatial and temporal patterns, our results suggest that a 4‐week record of mobility patterns displays 95% potential predictability across the dementia group, which is significantly higher than 92% potential predictability among the controls, t(13) = –3.39, P < .01, d = –1.75. That is, we can hope to be able to predict destinations of PWD about 95% of the time and destinations of controls about 92% of the time.DiscussionsOur findings on predictability of mobility patterns among PWD offer new perspectives on predictive mobility models that can be used to locate missing persons with dementia.

Adaptive multi-phase approach for solving the realistic vehicle routing problems in logistics with innovative comparison method for evaluation based on real GPS data

ABSTRACT Transportation management, as a part of the supply chain management, is a complex process that consists of planning and delivering goods to customers. The paper presents a complete multi-phase intelligent and adaptive transportation management system, which includes data collection, parameter tuning, and the heuristic algorithm based on the Tabu search for vehicle routing. The paper describes the procedure for collecting Global Positioning System (GPS) data and analyzing the compliance with the proposed routes based on the data collected. The described routing algorithm is powerful and supports many real-world limitations. An algorithm for the anomaly detection in the GPS data is presented as well as the usage of collected GPS data to improve the future results of the algorithm. The concept was implemented and tested on real data in some of the largest distribution companies in Bosnia and Herzegovina. The proposed approach resulted with more than satisfactory results in real-world application.

Acceptability of smartphone applications for global positioning system (GPS) and ecological momentary assessment (EMA) research among sexual minority men.

BackgroundEmerging research is using global positioning system (GPS) and ecological momentary assessment (EMA) methods among sexual minority men (SMM), a population that experiences multiple health disparities. However, we are not aware of any research that has combined these approaches among SMM, highlighting the need for acceptability and feasibility research. The purpose of this study was to examine the acceptability of implementing GPS and EMA research protocols using smartphone applications among SMM as well as related socio-demographic correlates.MethodsData come from a sample of SMM on a popular geosocial-networking app in Paris, France (n = 580). We assessed the acceptability of implementing GPS and EMA research protocols on smartphone apps as well as socio-demographic characteristics (i.e., age, sexual orientation, country of origin, employment status, and relationship status). We examined the anticipated acceptability of GPS and EMA data collection methods as well as socio-demographic correlates of acceptability of GPS and EMA methods.ResultsWe found that over half (54.1%) of the sample was willing to download a smartphone app for GPS-based research and we found that almost 60% of the participants were willing to download a smartphone app for EMA-based research. In total, 44.0% reported that they were willing to download both GPS and EMA apps. In addition, we found that older participants were less willing to download a smartphone app for EMA research than younger participants aged 18–24 (40–49 years: aPR = 0.40; 95% CI = 0.20, 0.78) and students were more willing to download smartphone apps for both GPS and EMA research (aPR = 1.41; 95% CI = 1.02, 1.95).ConclusionResults from this study suggest that using smartphone apps to implement GPS and EMA methods among some SMM are acceptable. However, care should be taken as segments of SMM are less likely to be willing to engage in this type of research.

Vehicle swept path analysis based on GPS data

Vehicle swept path analysis presents an essential step while working on at-grade intersection and roundabout designs. Following the intensive development of computer-aided design (CAD) software in the past two decades, numerous CAD-based computer programs for vehicle movement simulation have been developed and commercially distributed. The accuracy of these simulation programs is usually verified by conducting experimental field tests in which real movement trajectories of design vehicles, equipped with global positioning system (GPS) receivers, are recorded. This paper proposes an improved methodology for retrieving vehicle movement trajectories from collected GPS data. The proposed methodology reduces the trajectory inaccuracy resulting from pavement grading characteristics and the inability to accurately install GPS receivers in relation to streamlined vehicle body. Results of field experiments show that the reduction of positioning errors in the horizontal projection is not smaller than 50.0 mm compared with previous studies.

Integrating Drone Technology with GPS Data Collection to Enhance Forestry Students Interactive Hands-On Field Experiences

Undergraduate students pursuing a Bachelor of Science in Forestry (BSF) at Stephen F. Austin State University (SFA) within the Arthur Temple College of Forestry and Agriculture (ATCOFA) attend an intensive 6-week hands-on instruction in applied field methods. The second week of field station is focused on land measurement activities to introduce students to practical, hands-on, and technology based ways to survey forest boundaries. On Monday of the second week students are introduced to the concepts of how to use a handheld compass to navigate from point to point, use a consumer-grade handheld Global Positioning System (GPS) unit for collecting the geographic coordinates of given locations, use a GPS unit to calculate the area of a forest opening, use a GPS unit to walk and record a forest hiking trail, and evaluate the accuracy of their GPS derived locations via a Root Mean Square Error (RMSE) analysis. RMSE analysis between a students collected geographic coordinates and the instructors collected geographic coordinates indicated that the students were sufficient in correctly recording the geographic coordinates of point, line, and polygon features identified in the field. Grades on the student submitted reports summarizing Monday’s activities resulted in 33 of 56 students (59.0%) receiving a high A, 14 of 56 students (25.0%) receiving a low A, and 9 of 56 students (16.0%) receiving a high B indicating that the interactive hands-on nature of ATCOFA’s field station is effective at providing students with real-world applications whereby they will be ready to make a difference the day after graduation. Interactive drone imagery and video integrated into the daily activities in the field to enhance a student’s understanding of their specific objectives provided the students in the field with a bird’s eye perspective of the landscape to aid their understanding and planning of the field tasks assigned. In conclusion, employers can have confidence that when hiring recent BSF graduates from ATCOFA that the students have been introduced to geospatial technologies within a proven one-on-one instruction methodology designed to increase cognitive retention and can traverse from location to location accurately and record the geographic coordinates of earth surface features correctly.

The Unanticipated Challenges Associated With Implementing an Observational Study Protocol in a Large-Scale Physical Activity and Global Positioning System Data Collection.

BackgroundLarge-scale primary data collections are complex, costly, and time-consuming. Study protocols for trial-based research are now commonplace, with a growing number of similar pieces of work being published on observational research. However, useful additions to the literature base are publications that describe the issues and challenges faced while conducting observational studies. These can provide researchers with insightful knowledge that can inform funding proposals or project development work.ObjectivesIn this study, we identify and reflectively discuss the unforeseen or often unpublished issues associated with organizing and implementing a large-scale objectively measured physical activity and global positioning system (GPS) data collection.MethodsThe SPACES (Studying Physical Activity in Children’s Environments across Scotland) study was designed to collect objectively measured physical activity and GPS data from 10- to 11-year-old children across Scotland, using a postal delivery method. The 3 main phases of the project (recruitment, delivery of project materials, and data collection and processing) are described within a 2-stage framework: (1) intended design and (2) implementation of the intended design.ResultsUnanticipated challenges arose, which influenced the data collection process; these encompass four main impact categories: (1) cost, budget, and funding; (2) project timeline; (3) participation and engagement; and (4) data challenges. The main unforeseen issues that impacted our timeline included the informed consent process for children under the age of 18 years; the use of, and coordination with, the postal service to deliver study information and equipment; and the variability associated with when participants began data collection and the time taken to send devices and consent forms back (1-12 months). Unanticipated budgetary issues included the identification of some study materials (AC power adapter) not fitting through letterboxes, as well as the employment of fieldworkers to increase recruitment and the return of consent forms. Finally, we encountered data issues when processing physical activity and GPS data that had been initiated across daylight saving time.ConclusionsWe present learning points and recommendations that may benefit future studies of similar methodology in their early stages of development.

Data selection in machine learning for identifying trip purposes and travel modes from longitudinal GPS data collection lasting for seasons

Application of machine learning methods shows a popular attempt to identify the purpose of a trip and mode of travel on Global Positioning System (GPS) trajectory data. Data selection for the training and test sets is important in these methods. However, the feasibility and effects of choosing these data from different periods of the year are still unknown. This detail is particularly important since collecting data via GPS decreases the burden on participants to such an extent that it can last for seasons which may own distinct features. In order to bridge this gap, this paper employs Aslan & Zech’s test (AZ-test) and Random Forests (RF) successively to investigate the influence of data selection from different seasons for training and test sets. The dataset obtained in a city with distinct seasons, Hakodate, Japan, is used for our empirical analysis. The results of AZ-test suggest that explanatory variables of the two data sets from distinct seasons follow different distributions. Furthermore, it concludes that data set from two-seasons and data set from single season also follow different distributions. However, this test achieves some contradictory results in some cases. Due to this, RF is used to check how the accuracy varies in a further detail. RF confirms the findings by AZ-test in most cases. In addition, RF results show that including GIS features as explanatory variables has positive effect on the identification accuracy while including weather features has negative effect on the identification accuracy.

Global Positioning System Data Collection, Processing, and Analysis Conducted by the U.S. Geological Survey Earthquake Hazards Program

ABSTRACT The U.S. Geological Survey Earthquake Science Center collects and processes Global Positioning System (GPS) data throughout the western United States to measure crustal deformation related to earthquakes and tectonic processes as part of a long‐term program of research and monitoring. Here, we outline data collection procedures and present the GPS dataset built through repeated temporary deployments since 1992. This dataset consists of observations at ∼1950 locations. In addition, this article details our data processing and analysis procedures, which consist of the following. We process the raw data collected through temporary deployments, in addition to data from continuously operating western U.S. GPS stations operated by multiple agencies, using the GIPSY software package to obtain position time series. Subsequently, we align the positions to a common reference frame, determine the optimal parameters for a temporally correlated noise model, and apply this noise model when carrying out time‐series analysis to derive deformation measures, including constant interseismic velocities, coseismic offsets, and transient postseismic motion.

Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies

Over the past couple of decades, global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., the need for a large sum of money to buy devices, forgetfulness in the taking of devices to collect data, limiting of the sample size because of the number of devices, etc.), and the fact that the smartphone is becoming one of the necessities of life, there is an opportunity for smartphones to replace dedicated GPS devices. Although several general reviews of GPS travel survey and GPS data-processing methods have been written, a systematic review of smartphone-based GPS data collection and travel mode detection has not be made.. The studies were collected from six databases. The purpose of this review is to critically evaluate the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to a systematic comparison of the different methods of data preprocessing for travel mode detection, this paper details the strengths and weaknesses of the existing methods. Furthermore, it is a very important step towards developing methodologies and applications for GPS raw data collected by smartphones.

Impact of Swarm GPS receiver updates on POD performance

The Swarm satellites are equipped with state-of-the-art Global Positioning System (GPS) receivers, which are used for the precise geolocation of the magnetic and electric field instruments, as well as for the determination of the Earth’s gravity field, the total electron content and low-frequency thermospheric neutral densities. The onboard GPS receivers deliver high-quality data with an almost continuous data rate. However, the receivers show a slightly degraded performance when flying over the geomagnetic poles and the geomagnetic equator, due to ionospheric scintillation. Furthermore, with only eight channels available for dual-frequency tracking, the amount of collected GPS tracking data is relatively low compared with various other missions. Therefore, several modifications have been implemented to the Swarm GPS receivers. To optimise the amount of collected GPS data, the GPS antenna elevation mask has slowly been reduced from 10° to 2°. To improve the robustness against ionospheric scintillation, the bandwidths of the GPS receiver tracking loops have been widened. Because these modifications were first implemented on Swarm-C, their impact can be assessed by a comparison with the close flying Swarm-A satellite. This shows that both modifications have a positive impact on the GPS receiver performance. The reduced elevation mask increases the amount of GPS tracking data by more than 3 %, while the updated tracking loops lead to around 1.3 % more observations and a significant reduction in tracking losses due to severe equatorial scintillation. The additional observations at low elevation angles increase the average noise of the carrier phase observations, but nonetheless slightly improve the resulting reduced-dynamic and kinematic orbit accuracy as shown by independent satellite laser ranging (SLR) validation. The more robust tracking loops significantly reduce the large carrier phase observation errors at the geomagnetic poles and along the geomagnetic equator and do not degrade the observations at midlatitudes. SLR validation indicates that the updated tracking loops also improve the reduced-dynamic and kinematic orbit accuracy. It is expected that the Swarm gravity field recovery will benefit from the improved kinematic orbit quality and potentially also from the expected improvement of the kinematic baseline determination and the anticipated reduction in the systematic gravity field errors along the geomagnetic equator. Finally, other satellites that carry GPS receivers that encounter similar disturbances might also benefit from this analysis.

GPS Height Variations Affected by Ocean Tidal Loading Along the Coast of Taiwan

The Global Positioning System (GPS) uses static relative positioning to mitigate common errors and has the ability to remove the effects of ocean tidal loading (OTL). OTL effects cannot be completely removed by increasing the baseline length during GPS data processing and must be corrected with OTL models, especially in Taiwan, which is surrounded by the ocean. In this paper, we collected GPS data from ten stations that are located along the coast of Taiwan. The periodic effect of OTL is discussed through a wavelet analysis and a harmonic analysis. We also calculated the amplitudes and phases of the semi-diurnal and diurnal tides to understand the effect of OTL on the GPS heights along the coast of Taiwan. The GPS data were divided into short-term (1 month) and long-term (2 years) categories and were calculated using the Bernese 5.0 software. The tidal data and the GPS heights were transferred into a wavelet spectrum for analysis. We found that in the tidal data, the periodic M2 signal was stronger than the K1 signal, which was not the case for the GPS height data. This result indicated that the common errors were not completely removed from the GPS heights and that the corresponding period was approximately 1 day, which might result in a stronger K1 signal. Moreover, five OTL models were chosen and compared in this case. Although the GPS heights with different OTL corrections were very similar, NAO.99b was the best fitting model for the ocean around Taiwan. Finally, we analyzed the tidal data and the M2 amplitudes and phases of the GPS heights using a harmonic analysis and compared the results with the NAO.99b model. The comparison indicated that the best agreement occurred in southwestern Taiwan and that the poorest agreement occurred in southeastern Taiwan with a maximum error of 0.3 cm. The phase results showed that the OTL model overlapped with the tidal data in the open ocean around central Taiwan, and both the GPS heights and the heights from the OTL model were 1.5 to 2 h earlier than the tidal data.

Potential for a Simple GPS‐Based Binary Logit Model to Predict Fishing Effort in a Vertical Hook‐and‐Line Reef Fish Fishery

AbstractAccurate fishing effort information is fundamental to the successful management of fisheries resources. Automated, independent, and reliable methods for quantifying fishing effort are needed. The use of vessel speed from Global Positioning System (GPS) data to identify fishing activity has worked well for trawl fisheries but has been less successful in stationary fisheries. Therefore, five trips on four vessels from a vertical hook‐and‐line reef fish fishery were used to examine the efficacy of GPS (speed and time) and electronic video monitoring (EVM) sensor (drum and video) data to corroborate an observer's account of effort using binary logistic regression classification (logit) models as well as a simple speed and time filter (filter). One minute was the minimum data collection interval examined that documented 100% of fishing events. As no fishing occurred at night, opportunistically defined as the 7 h between 2200 and 0500 hours, these records were excluded from analyses. During the day, vessels spent on average 45.2% of the time fishing. Classification success of the approaches examined ranged from 82.4% to 89.5%. Models that included both GPS and EVM sensor data outperformed the filter and GPS‐only models. In general, the filter and most model results can be used as a proxy for observer effort data, at least for the trips examined here. The GPS‐based speed + time logit model was chosen as the preferred approach because of its discriminatory power compared with the filter and the existing widespread use and lower costs of GPS data collection relative to EVM systems and sensors. The speed + time logit model outlined here may have broad utility in this and similar vertical‐line fisheries, including the offshore marine recreational fishing sector.Received September 9, 2015; accepted December 15, 2015