GPS Dataset Research Articles

Active tectonics in Quito, Ecuador, assessed by geomorphological studies, GPS data, and crustal seismicity

AbstractThe Quito Fault System (QFS) extends over 60 km along the Interandean Depression in northern Ecuador. Multidisciplinary studies support an interpretation in which two major contemporaneous fault systems affect Quaternary volcanoclastic deposits. Hanging paleovalleys and disruption of drainage networks attest to ongoing crustal deformation and uplift in this region, further confirmed by 15 years of GPS measurements and seismicity. The resulting new kinematic model emphasizes the role of the N‐S segmented, en echelon eastward migrating Quito Fault System (QFS). Northeast of this major tectonic feature, the strike‐slip Guayllabamba Fault System (GFS) aids the eastward transfer of the regional strain toward Colombia. These two tectonic fault systems are active, and the local focal mechanisms are consistent with the direction of relative GPS velocities and the regional stress tensor. Among active features, inherited N‐S direction sutures appear to play a role in confining the active deformation in the Interandean Depression. The most frontal of the Quito faults formed at the tip of a blind thrust, dipping 40°W, is most probably connected at depth to inactive suture to the west. A new GPS data set indicates active shortening rates for Quito blind thrust of up to 4 mm/yr, which decreases northward along the fold system as it connects to the strike‐slip Guayllabamba Fault System. The proximity of these structures to the densely populated Quito region highlights the need for additional tectonic studies in these regions of Ecuador to generate further hazard assessments.

Consistently Estimating Link Speed Using Sparse GPS Data with Measured Errors

Data sources using new technology such as the Geographical Positioning System are increasingly available. In many different applications, it is important to predict the average speed on all the links in a network. The purpose of this study is to estimate the link speed in a network using sparse GPS data set. Average speed is consistently estimated using Indirect Inference approach. In the end, the Monte Carlo evidence is provided to show that the results are consistent with parameter estimates.

Disorientation detection by mining GPS trajectories for cognitively-impaired elders

The aging population is a major concern of human society in the 21st century. Elders who suffer physical or cognitive impairments often have difficulties in navigational tasks and remembering landmarks. As such, disorientation (i.e., getting lost) becomes common for people with dementia in unfamiliar or even in familiar environments. In order to provide appropriate real-time assistive services to these elders, such as reminders and alerts, we propose a disorientation detection method that detects outliers in one’s GPS trajectories. In particular, we first model an individual’s movement trajectories as a graph based on her historical GPS traces. We then identify outlying trajectories that have a defined wandering or deviating pattern as potential instances of disorientation. We develop a method called iBDD that is able to detect two categories of outlying trajectories in a uniform framework in real-time. Using ten individuals’ real-world GPS datasets, we demonstrate that iBDD can achieve 95% detection rate of disorientation with less than 3% of false positives, based on properly chosen parameters.

Highway versus Urban Roads

In this study, the differences in travel time variability patterns between urban roads and highways were analyzed. For urban roads, a GPS data set that included all taxi trips in New York City was used. For highways, automatic vehicle location data from the New Jersey Turnpike were used. The turnpike was divided into two sections, urban and suburban highway, according to urbanization level, time-of-day demands, and physical roadway features. The analysis not only compared travel time patterns for highways and urban roads but also investigated travel time characteristics along the same highway facility. First, the temporal variations of travel times on both facility types were calculated and compared. Second, the travel time distributions were extracted for various time periods and compared visually to determine the distributional patterns. Finally, the relationship between the average travel time and variability was investigated. Not only did travel time patterns differ between urban roads and highways, but major differences in travel time characteristics could be observed along the same highway. Higher travel times corresponded to lower reliability on highways yet corresponded to higher reliability on urban roads. Overall, the findings suggest that attributing differences in travel time variability patterns to facility type may be an oversimplification of the phenomenon.

Using trajectories for collaborative filtering-based POI recommendation

Current mobile guides often suffer from the following problems: a long knowledge acquisition process of recommending relevant points of interest (POIs), the lack of social navigation support, and the challenge of making implicit user-generated content (e.g., trajectories) useful. Collaborative filtering (CF) is a promising solution for these problems. This article employs CF to mine GPS trajectories for providing Amazon-like POI recommendations. Three CF methods are designed: simple_CF, freq_CF (considering visit frequencies of POIs), and freq_seq_CF (considering both user’s preferences and spatio-temporal behaviour). With these, services like “after visiting …, people similar to you often went to …” can be provided. The methods are evaluated with two GPS datasets. The results show that the CF methods can provide more accurate predictions than simple location-based methods. Also considering visit frequencies (popularity) of POIs and spatio-temporal motion behaviour (mainly the ways in which POIs are visited) in CF can improve the predictive performance.

Modeling Taxi Trip Demand by Time of Day in New York City

Identifying the factors that influence taxi demand is very important for understanding where and when people use taxis. A large set of GPS data from New York City taxis is used along with demographic, socioeconomic, and employment data to identify the factors that drive taxi demand. A technique was developed to measure and map transit accessibility on the basis of transit access time (TAT) to understand the relationship between taxi use and transit service. The taxi data were categorized by pickups and drop-offs at different times of day. A multiple linear regression model was estimated for each hour of the day to model pickups and another to model drop-offs. Six important explanatory variables that influence taxi trips were identified: population, education, age, income, TAT, and employment. The influence of these factors on taxi pickups and drop-offs changed at different times of the day. The number of jobs in each industry sector was an indication of the types of economic activities occurring at a location, and in some sectors the number of jobs were strongly associated with taxi use. This study demonstrates the temporal and spatial variation of taxi demand and shows how transit accessibility and other factors affect it.

Climatological study of ionospheric irregularities over the European mid-latitude sector with GPS

High-frequency variability of the ionosphere, or irregularities, constitutes the main threat for real-time precise positioning techniques based on Global Navigation Satellite Systems (GNSS) measurements. Indeed, during periods of enhanced ionospheric variability, GNSS users in the field—who cannot verify the integrity of their measurements—will experience positioning errors that can reach several decimeters, while the nominal accuracy of the technique is cm-level. In the frame of this paper, a climatological analysis of irregularities over the European mid-latitude region is presented. Based on a 10 years GPS dataset over Belgium, the work analyzes the occurrence rate (as a function of the solar cycle, season and local time) as well as the amplitude of ionospheric irregularities observed at a single GPS station. The study covers irregularities either due to space weather events (solar origin) or of terrestrial origin. If space weather irregularities are responsible for the largest effects in terms of ionospheric error, their occurrence rate highly depends on solar activity. Indeed, the occurrence rate of ionospheric irregularities is about 9 % during solar maximum, whereas it drops to about 0 % during medium or low solar activity periods. Medium-scale ionospheric disturbances (MSTIDs) occurring during daytime in autumn/winter are the most recurrent pattern of the time series, with yearly proportions slightly varying with the solar cycle and an amplitude of about 10 % of the TEC background. Another recurrent irregularity type, though less frequent than MSTIDs, is the noise-like variability in TEC observed during summer nighttime, under quiet geomagnetic conditions. These summer nighttime irregularities exhibit amplitudes ranging between 8 and 15 % of the TEC background.

Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China

Using the notion of commuting flexibility, this paper investigates the intra-personal day-to-day variability and flexibility of commuting behavior using a 7-day GPS dataset collected in Beijing, China. Four dimensions of commuting variability are evaluated: space, time, travel mode, and travel route. The results indicate that the commute trip is flexible and complex in a variety of ways. Through 3D geovisualizations we were able to identify seven distinctive commuting patterns based on different combinations of the four dimensions of commuting flexibility. The results call into question the common presupposition that the commute trip is stable and fixed in many respects. Among the four dimensions of commuting flexibility, we found that variation or flexibility in time is more common than variation in the other three dimensions of commuting flexibility. This means that temporal adjustment for coping with commute problems is likely to be the most feasible option for suburban residents in Beijing.

Home range plus: a space-time characterization of movement over real landscapes.

BackgroundAdvances in GPS technology have created both opportunities in ecology as well as a need for analytical tools that can deal with the growing volume of data and ancillary variables associated with each location.ResultsWe present T-LoCoH, a home range construction algorithm that incorporates time into the construction and aggregation of local kernels. Time is integrated with Euclidean space using an adaptive scaling of the individual's characteristic velocity, enabling the construction of utilization distributions that capture temporal partitions of space as well as contours that differentiate internal space based on movement phase and time-use metrics. We test T-LoCoH against a simulated dataset and provide illustrative examples from a GPS dataset from springbok in Namibia.ConclusionsThe incorporation of time into home range construction expands the concept of utilization distributions beyond the traditional density gradient to spatial models of movement and time, opening the door to new applications in movement ecology.Electronic supplementary materialThe online version of this article (doi:10.1186/2051-3933-1-2) contains supplementary material, which is available to authorized users.

The large aftershocks triggered by the 2011 Mw 9.0 Tohoku-Oki earthquake, Japan

The Mw 9.0 Tohoku-Oki earthquake that occurred off the Pacific coast of Japan on March 11, 2011, was followed by thousands of aftershocks, both near the plate interface and in the crust of inland eastern Japan. In this paper, we report on two large, shallow crustal earthquakes that occurred near the Ibaraki-Fukushima prefecture border, where the background seismicity was low prior to the 2011 Tohoku-Oki earthquake. Using densely spaced geodetic observations (GPS and InSAR datasets), we found that two large aftershocks in the Iwaki and Kita-Ibarake regions (hereafter referred to as the Iwaki earthquake and the Kita-Ibarake earthquake) produced 2.1m and 0.44m of motion in the line-of-sight (LOS), respectively. The azimuth-offset method was used to obtain the preliminary location of the fault traces. The InSAR-based maximum offset and trace of the faults that produced the Iwaki earthquake are consistent with field observations. The fault location and geometry of these two earthquakes are constrained by a rectangular dislocation model in a multilayered elastic half-space, which indicates that the maximum slips for the two earthquakes are 3.28m and 0.98m, respectively. The Coulomb stress changes were calculated for the faults following the 2011 Mw 9.0 Tohoku-Oki earthquake based on the modeled slip along the fault planes. The resulting Coulomb stress changes indicate that the stresses on the faults increased by up to 1.1MPa and 0.7MPa in the Iwaki and Kita-Ibarake regions, respectively, suggesting that the Tohoku-Oki earthquake triggered the two aftershocks, supporting the results of seismic tomography.

A grid-based clustering algorithm for wild bird distribution

Advanced satellite tracking technologies provide biologists with long-term location sequence data to understand movement of wild birds then to find explicit correlation between dynamics of migratory birds and the spread of avian influenza. In this paper, we propose a hierarchical clustering algorithm based on a recursive grid partition and kernel density estimation (KDE) to hierarchically identify wild bird habitats with different densities. We hierarchically cluster the GPS data by taking into account the following observations: 1) the habitat variation on a variety of geospatial scales; 2) the spatial variation of the activity patterns of birds in different stages of the migration cycle. In addition, we measure the site fidelity of wild birds based on clustering. To assess effectiveness, we have evaluated our system using a large-scale GPS dataset collected from 59 birds over three years. As a result, our approach can identify the hierarchical habitats and distribution of wild birds more efficiently than several commonly used algorithms such as DBSCAN and DENCLUE.

Deviance from truth: Telemetry location errors erode both precision and accuracy of habitat-selection models

Radiocollars are an increasingly important tool in wildlife research. Yet, as with all remotely compiled data, measurement error is inherent in the technology. We directly compare radiocollars with low measurement error (Global Positioning System [GPS]) with radiocollars with high measurement error (Argos satellite). Specifically, we compare how differences in precision between GPS and Argos satellite technologies affect the estimation of resource selection functions (RSFs). We estimated RSF models from GPS and Argos satellite radiocollar data collected in December 2008 through April 2009 from wolves within the same pack in southwestern Alberta, Canada, and used Akaike's Information Criterion (AIC) to identify the most parsimonious models. In general, β coefficients were closer to zero and coefficients of variation were higher for models estimated using Argos data. But even more serious, AIC identified different top models between the Argos and GPS data sets because measurement error alone can induce attenuation bias, which leads to erroneous conclusions on selection of habitats. GPS radiocollar data were more precise and more accurate, resulting in RSF models that were a better representation of true habitat selection by each wolf pack. © 2013 The Wildlife Society.

Investigation on Comprehensive Multi - point GPS - based Traffic Information Treatment

GPS data sets of several vehicles on the road are obtained via wireless network, from which specific sections of data are picked up by filtering algorithm. Then, a variety of fusion algorithms are applied to the several sets of specific sections of data to remove the error data, to guarantee the veracity of the fused result. On the basis of valid data, traffic conditions of this section can be acquired through the smart identification algorithm, which will provide the real-time traffic information. The method presented in this paper is simple and reliable. With good performance on the experimental data, it opens a new door for information collection of urban road.

Inferring social ties between users with human location history

The location-based social networks have been becoming flourishing in recent years. In this paper, we aim to estimate the similarity between users according to their physical location histories (represented by GPS trajectories). This similarity can be regarded as a potential social tie between users, thereby enabling friend and location recommendations. Different from previous work using social structures or directly matching users’ physical locations, this approach model a user’s GPS trajectories with a semantic location history (SLH), e.g., shopping malls → restaurants → cinemas. Then, we measure the similarity between different users’ SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user’s interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. When matching SLHs, we consider the sequential property, the granularity and the popularity of semantic locations. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. The results show that SLH outperforms a physical-location-based approach and MTM is more effective than several widely used sequence matching approaches given this application scenario.

Development of the Kinematic Global Positioning System Precise Point Positioning Method Using 3-Pass Filter

Kinematic global positioning system precise point positioning (GPS PPP) technology is widely used to the several area such as monitoring of crustal movement and precise orbit determination (POD) using the dual-frequency GPS observations. In this study we developed a kinematic PPP technology and applied 3-pass (forward/backward/forward) filter for the stabilization of the initial state of the parameters to be estimated. For verification of results, we obtained GPS data sets from six international GPS reference stations (ALGO, AMC2, BJFS, GRAZ, IENG and TSKB) and processed in daily basis by using the developed software. As a result, the mean position errors by kinematic PPP showed 0.51 cm in the east-west direction, 0.31 cm in the north-south direction and 1.02 cm in the up-down direction. The root mean square values produced from them were 1.59 cm for the east-west component, 1.26 cm for the south-west component and 2.95 cm for the up-down component.

Towards mobile intelligence: Learning from GPS history data for collaborative recommendation

With the increasing popularity of location-based services, we have accumulated a lot of location data on the Web. In this paper, we are interested in answering two popular location-related queries in our daily life: (1) if we want to do something such as sightseeing or dining in a large city like Beijing, where should we go? (2) If we want to visit a place such as the Birdʼs Nest in Beijing Olympic park, what can we do there? We develop a mobile recommendation system to answer these queries. In our system, we first model the usersʼ location and activity histories as a user–location–activity rating tensor.11A “tensor” is a multi-dimensional array (Symeonidis et al., 2008 [1]; Cichocki et al., 2009 [2]) Because each user has limited data, the resulting rating tensor is essentially very sparse. This makes our recommendation task difficult. In order to address this data sparsity problem, we propose three algorithms22This work is an extension to our previous work (Zheng et al., 2010 [3,4]). We propose a new model in Section 5.3 and completely re-conduct the experiments for all our three algorithms. based on collaborative filtering. The first algorithm merges all the usersʼ data together, and uses a collective matrix factorization model to provide general recommendation (Zheng et al., 2010 [3]). The second algorithm treats each user differently and uses a collective tensor and matrix factorization model to provide personalized recommendation (Zheng et al., 2010 [4]). The third algorithm is a new algorithm which further improves our previous two algorithms by using a ranking-based collective tensor and matrix factorization model. Instead of trying to predict the missing entry values as accurately as possible, it focuses on directly optimizing the ranking loss w.r.t. user preferences on the locations and activities. Therefore, it is more consistent with our ultimate goal of ranking locations/activities for recommendations. For these three algorithms, we also exploit some additional information, such as user–user similarities, location features, activity–activity correlations and user–location preferences, to help the CF tasks. We extensively evaluate our algorithms using a real-world GPS dataset collected by 119 users over 2.5 years. We show that all our three algorithms can consistently outperform the competing baselines, and our newly proposed third algorithm can also outperform our other two previous algorithms.

Modeling unobserved true position using multiple sources and information semantics

We present a modeling framework that supports semantically informed statistical inference about unobserved true location and positional uncertainty for geographic information spanning multiple sources. We demonstrate the use of a semantic representation of information sources to support construction of a Bayesian belief network that operationalizes the data integration process. Our approach allows for positional data, metadata, and other ancillary and derived information to inform inference regarding unobserved true position. In our application, we use two line feature datasets and a set of GPS data points describing a portion of the street network in Laramie, Wyoming. Using source metadata we inform prior distributions. Additionally, we use feature straightness to illustrate how form and process – gridded streets and the process of the Public Land Survey – can be used to improve inference for true position. The presented modeling framework is suitable for multiple data sources when the best data are not necessarily known and when the information semantics associated with the input data can be described in a systematic way.

Large-Scale Freeway Network Traffic Monitoring: A Map-Matching Algorithm Based on Low-Logging Frequency GPS Probe Data

Low-logging frequency GPS probe data have become a major data source for large-scale freeway network traffic monitoring. A critical step in GPS data processing is map matching. However, traditional map-matching algorithms are developed for in-vehicle navigation with high-logging frequency GPS data, noting that high-logging frequencies can be 1 s, whereas low-logging frequencies can be a few minutes. Such algorithms map a new GPS positioning point instantaneously given its historical points and network topology. Using high-logging frequency data-based map-matching algorithms for low-logging frequency data can cause several problems. First, large mapping errors in previous GPS points can easily propagate to the current points. Second, one-point-a-time processing is not effective and not necessary for traffic monitoring. Multiple GPS points can be processed together to determine routes more effectively. In this article, the authors propose a map-matching framework for low-logging frequency GPS probe data. The proposed framework (a) incorporates curve matching and probabilistic analysis modules of high-logging frequency map-matching algorithms and (b) introduces a new route determination algorithm for multipoint processing on the basis of fuzzy logic and a concurrent version of the N-shortest path algorithm. The authors evaluated the proposed model using field GPS data sets collected in Los Angeles, California. Evaluation methods include not only traditional random mapping case inspection but also a comparison between the GPS-detected speed and the ground truth loop-detector speed to evaluate its effectiveness for traffic monitoring. The evaluation results illustrate the effectiveness and robustness of the proposed framework.

Vertical deformations from homogeneously processed GRACE and global GPS long-term series

Temporal variations in the geographic distribution of surface mass cause surface displacements. Surface displacements derived from GRACE gravity field coefficient time series also should be observed in GPS coordinate time series, if both time series are sufficiently free of systematic errors. A successful validation can be an important contribution to climate change research, as the biggest contributors to mass variability in the system Earth include the movement of oceanic, atmospheric, and continental water and ice. In our analysis, we find that if the signals are larger than their precision, both geodetic sensor systems see common signals for almost all the 115 stations surveyed. Almost 80% of the stations have their signal WRMS decreased, when we subtract monthly GRACE surface displacements from those observed by GPS data. Almost all other stations are on ocean islands or small peninsulas, where the physically expected loading signals are very small. For a fair comparison, the data (79 months from September 2002 to April 2009) had to be treated appropriately: the GPS data were completely reprocessed with state-of-the-art models. We used an objective cluster analysis to identify and eliminate stations, where local effects or technical artifacts dominated the signals. In addition, it was necessary for both sets of results to be expressed in equivalent reference frames, meaning that net translations between the GPS and GRACE data sets had to be treated adequately. These data sets are then compared and statistically analyzed: we determine the stability (precision) of GRACE-derived, monthly vertical deformation data to be ~1.2 mm, using the data from three GRACE processing centers. We statistically analyze the mean annual signals, computed from the GPS and GRACE series. There is a detailed discussion of the results for five overall representative stations, in order to help the reader to link the displayed criteria of similarity to real data. A series of tests were performed with the goal of explaining the remaining GPS–GRACE residuals.

A Query Language for Mobility Data Mining

The technologies of mobile communications and ubiquitous computing pervade society. Wireless networks sense the movement of people and vehicles, generating large volumes of mobility data, such as mobile phone call records and GPS tracks. This data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems, provided that a suitable knowledge discovery process is enacted for mining this mobility data. In this paper, the authors examine a formal framework, and the associated implementation, for a data mining query language for mobility data, created as a result of a European-wide research project called GeoPKDD (Geographic Privacy-Aware Knowledge Discovery and Delivery). The authors discuss how the system provides comprehensive support for the Mobility Knowledge Discovery process and illustrate its analytical power in unveiling the complexity of urban mobility in a large metropolitan area, based on a massive real life GPS dataset.