Global Positioning System Points Research Articles

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Scavengers use natural and anthropogenic resources connecting protected areas with surrounding lands

Anthropogenic food subsidies can alter ecosystem processes, such as competition, predation, or nutrient transfer, and may strongly affect protected areas. Increasing recreation and ecotourism often create food subsidies, especially in the surrounding lands through fast-growing gateway communities. How the effects of these subsidies extend into protected areas when animals move across boundaries remains largely unexplored. We addressed this question by studying the movement and foraging of common ravens (Corvus corax), opportunistic scavengers that are well known to feed on predator kills and on anthropogenic food subsidies. We analyzed spatiotemporal data from 57 global positioning system (GPS)-tagged ravens, trapped within or close to Yellowstone National Park, to study their seasonal use of natural and anthropogenic food sources. Although Yellowstone National Park contains a full suite of native predators and ravens can be observed at virtually every kill site, we show that anthropogenic subsidies were utilized to a much greater extent than other natural resources, especially during winter. Important subsidies included gut piles of harvested game animals left by hunters, fat present on wastewater settling ponds, roadkill, and waste from agricultural and urban activities. These subsidies were distributed over vast areas beyond Yellowstone National Park. During fall/winter, ravens traveled longer distances (21.9 vs. 13 km) and spent more time outside Yellowstone National Park (73% of GPS points outside), mostly feeding on anthropogenic subsidies, than in spring/summer (42% of GPS points outside). This difference between seasons was more pronounced for individuals holding territories within the protected area than for those without territories. The large area over which ravens used anthropogenic food subsidies (within more than 100 km of Yellowstone National Park) affects the park’s raven population and the scavenging services they provide. We suggest that the scale of resource use must be considered by managers seeking to control expanding raven populations elsewhere.

Systemwide Identification of Signal Retiming Opportunities with Connected Vehicle Data to Reduce Split Failures

Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the U.S.A. It is important for agencies to be able to systematically monitor traffic signal performance and have procedures to identify locations as proactively as possible where the redistribution of green time has the greatest potential of reducing congestion. Currently, Automated Traffic Signal Performance Measures (ATSPMs) require extensive communication networks and detectors on all phases at each intersection to effectively monitor the traffic signal performance for every traffic movement. This study utilizes commercially available connected vehicle (CV) trajectory data to identify overcapacity movements where there is availability of unused green time and no downstream blockage by an adjacent signal. Over 100 intersections, 400,000 trajectories, and 6,800,000 Global Positioning System points are evaluated using this method to prioritize signal retiming opportunities in a proposed Relative Performance Diagram (RPD) by assessing split failures and downstream blockage of all traffic movements. Out of the intersections identified as having opportunities for operational improvements, three were selected for retiming. A post-retiming review of the traffic signal performance measures showed up to 30% reductions in split failures, 53 s/veh reductions in control delay, and 21% increase in arrivals on green at the benefited movements. The techniques easily scale to any intersection where CV data is available without the need for vehicle detection or communication technology.

Sensor Based Algorithm for Self-Navigating Robot Using Internet of Things (IoT)

Abstract— The purpose of this paper is to show the design technique and programming of an autonomous robot prototype that can go to a predetermined location and return to its starting place utilizing the Global Positioning System (GPS). An HD camera along with an ultrasonic sensor and two IR sensors is used to provide necessary data from the real world to the robot to avoid obstructions, by following GPS Waypoints. A smart algorithm was introduced for path planning and re-planning. The robot can modify its direction using a digital compass reading and input from sensors by computing the heading angle from the current GPS point. This self-navigation mobile robot's accuracy was evaluated in several locations and is quantified in terms of heading angle and path length from the starting point to the goal point.
  

Impacts of perceived safety and beauty of park environments on time spent in parks: Examining the potential of street view imagery and phone-based GPS data

Much research shows that urban parks can benefit human health. Research has shown that perceptions of park environments are an important determinant of park usage. Most perception-based research collects data through costly and time-consuming survey approaches, which limits data collection on a large scale. Google Street View (GSV) imagery presents a cost-effective source for deriving perceptions of the park environment: for large parks, GSV images are available on both peripheral roads and internal roads; for small parks, majorly covered by grassland, GSV images on peripheral roads can capture their general built environment. Additionally, the available Global Positioning System (GPS) incorporated into cellular phones enables researchers to measure how long people stay in parks conveniently by checking the time of the first and last GPS points in a park. Taking Chicago as the case study, this research introduces GSV images and the SafeGraph phone-based GPS dataset to study the association between perceptions of park environments and time spent in parks, which is rarely explored by previous studies. We derive both perceived safety and beauty of park environments using GSV images in 2018 and machine-learning Support Vector Machine models trained by a crowdsourcing dataset on human perception of environments. Time spent in each park is obtained from SafeGraph data in 2018. We build negative binomial regression models to explore the relationship between perception variables and time spent in parks. Results show that higher levels of both perceived safety and beauty are positively associated with increased time, and adding perception variables can improve the model performance. It benefits urban planners in designing a better park environment in support of park usage.

Fan-Shaped Model for Generating the Anisotropic Catchment Area of Subway Stations Based on Feeder Taxi Trips

The catchment areas of subway stations have always been considered as a circular shape in previous research. Although some studies show the catchment area may be affected by road conditions, public transportation, land use, and other factors, few studies have discussed the shape of the catchment area. This study focuses on analyzing the anisotropy of catchment areas and developing a sound methodology to generate them. Based on taxi global positioning system (GPS) data, this paper first proposes a data mining method to identify feeder taxi trips around subway stations. Then, a fan-shaped model is proposed and applied to Xi’an Metro Line 1 to generate catchment areas. The number and angle of fan areas are determined according to the spatial distribution characteristics of GPS points. Results show that the acceptable distance of the catchment area has significant differences in different directions. The average maximum acceptable distance of one station is 2.31 times the minimum. Furthermore, for feeder taxis, the overlap ratio of the catchment area is very high. Travelers in several places could choose several different stations during the travel. A multiple linear regression model was introduced to find the influencing factors, and the result shows the anisotropy of the catchment area is affected not only by neighboring subway stations, but also by the road network, distance from the city center, and so on.

Unsupervised Ethograms of a Vulnerable Bird Species: The Red-Footed Falcon in Northern Italy

Behavioral and movement ecology have quickly advanced as a result of the development of biotelemetry devices and analytical techniques. Global positioning system (GPS) transmitters assist scientists in gathering location and movement data at detailed spatial and temporal resolutions. Machine-learning methods can then be applied to GPS data to provide insights into the ecological mechanisms of animal behavior and movements. By means of accurate GPS data-loggers, in 2019, 2020, and 2021, we tracked 8 red-footed falcons at the two largest colonies in Italy. We collected 13,484 GPS points and used recently introduced machine-learning methodology Unsupervised Animal Behaviour Examiner (UABE) to deduce the regular, nested, and hourly ethograms of the tracked individuals. We found clear and significant patterns of the red-footed falcons’ behaviors on monthly, daily, and hourly bases. Our study is a step forward in advancing the knowledge of this threatened species, and provides a baseline assessment of the current behavioral patterns of this red-footed falcon population, with which results of future studies can be compared to detect potential behavioral changes that act as early warnings of increased human disturbance.

MULTI-BRANCH DEEP LEARNING BASED TRANSPORT MODE DETECTION USING WEAKLY SUPERVISED LABELS

Abstract. Mobility data, based on global positioning system (GPS) tracking, have been widely used in many areas. These include, but not limited to: user direction guidance, analyzing travel patterns, and evaluating travel impacts. Transport Mode Detection (TMD) is an essential factor in understanding mobility within the transport system. A TMD model assigns a GPS point or a GPS trajectory to a particular transport mode based on the user’s current activity. However, the complexity of the prediction procedure increases with the number of modes that need to be predicted given the increasing overlaps in feature values between multiple transportation modes. Hence, this study proposes a two-branch deep learning-based TMD model that predicts multi-class transport modes to improve prediction accuracy. In addition, it proposed a weakly supervised labelling model using snorkel to improve the volume of labelled data and resulting TMD model prediction accuracy. We considered publicly available road networks, railway networks, bus routes, etc., for creating road, bus, train labels by overlaying GPS points on these transportation networks. We introduced a boolean (true/false) based soft-labelling function, where the same GPS point overlaid on road or railway network. The raw GPS data were used to generate point-level features such as speed, speed difference, acceleration, acceleration difference, initial bearing and bearing difference, all used as derived features for the TMD model. To construct the model we opted to use two branches where raw GPS latitude and longitude values were used in one and the derived mobility features are used in the other.

Daily space-time activities, multiple environmental exposures, and anxiety symptoms: A cross-sectional mobile phone-based sensing study

BackgroundFew mobility-based studies have investigated the associations between multiple environmental exposures, including social exposures, and mental health. ObjectiveTo assess how exposure to green space, blue space, noise, air pollution, and crowdedness along people's daily mobility paths are associated with anxiety symptoms. Methods358 participants were cross-sectionally tracked with Global Positioning System (GPS)-enabled mobile phones. Anxiety symptoms were measured at baseline using the Generalized Anxiety Disorder-7 (GAD-7) questionnaire. Green space, blue space, noise, and air pollution were assessed based on concentric buffers of 50 m and 100 m around each GPS point. Crowdedness was measured by the number of nearby Bluetooth-enabled devices detected along the mobility paths. Multiple linear regressions with full covariate adjustment were fitted to examine anxiety-environmental exposures associations. Random forest models were applied to explore possible nonlinear associations and exposure interactions. ResultsRegression results showed null linear associations between GAD-7 scores and environmental exposures. Random forest models indicated that GAD-7-environment associations varied nonlinearly with exposure levels. We found a negative association between green space and GAD-7 scores only for participants with moderate green space exposure. We observed a positive association between GAD-7 scores and noise levels above 60 dB and air pollution concentrations above 17.2 μg m−3. Crowdedness was positively associated with GAD-7 scores, but exposure-response functions flattened out with pronounced crowdedness of >7.5. Blue space tended to be positively associated with GAD-7 scores. Random forest models ranked environmental exposures as more important to explain GAD-7 scores than linear models. ConclusionsOur findings indicate possible nonlinear associations between mobility-based environmental exposures and anxiety symptoms. More studies are needed to obtain an in-depth understanding of underlying anxiety-environment mechanisms during daily life.

Map-Matching on Low Sampling Rate Trajectories through Frequent Pattern Mining

Map-matching, an important preprocessing task in many location-based services (LBS), projects each point of the global positioning system (GPS) within a trajectory dataset onto a digital map. The state-of-the-art map-matching algorithms typically employ Hidden Markov model (HMM) via shortest path computation. But the computation of the shortest path might not work well on low-sampling-rate trajectory data (e.g., one GPS point every 1–5 min), leading to low matching precision and high running time. To solve the problem, this paper firstly identifies frequent patterns (FPs) in historical trajectories to capture meaningful mobility behaviors, and then extracts mobile behavior criterion (MBC) of mobile users. Such a criterion generally represents the route choice of mobile users on road networks. Moreover, the temporal information within trajectory data was employed to estimate the speed of mobile users on road segments. The identified FPs, coupled with MBC and moving speed, help to improve the map-matching precision of low-sampling-rate trajectories. In addition, an FP-forest structure was proposed to index the identified FPs. The structure could greatly speed up the lookup of frequent paths for shorter running time. Furthermore, the FP-forest structure was pruned to reduce redundancy with smaller space cost. Finally, experiments were carried out on real-world datasets. The results confirm that our FP-matching method outperforms state-of-the-art in terms of effectiveness and efficiency.

Development of a Combined Orchard Harvesting Robot Navigation System

Our research concerned the development of an autonomous robotic navigation system for orchard harvesting with a dual master-slave mode, the autonomous navigation tractor orchard transport robot being the master followed by a navigation orchard picking robot as the slave. This addresses the problem that in single master-slave navigation mode, agricultural combined harvesting equipment cannot stop repeatedly between rows of apple trees and drive continuously when turning. According to distances obtained from a global positioning system (GNSS), ground points were used to switch the navigation mode of the transport and picking robot. A cloth simulation filter (CSF) and random sample consensus (RANSAC) algorithm was used to obtain inter-row waypoints. The GNSS point was manually selected as the turn waypoint of the master and a kinematic model was used to compute the turn waypoints of the slave. Finally, we used a pure pursuit algorithm to track these waypoints sequentially to achieve master-slave navigation and ground head master-slave command navigation. The experimental results show that the data packet loss rate was less than 1.2% when the robot communicated in the orchard row within 50 m which meets the robot orchard communication requirements. The master-slave robot can achieve repeated stops in the row using follow navigation, which meets the demands of joint orchard harvesting. The maximum, minimum, mean and standard deviation of position deviation of the master robot were 5.3 cm, 0.8 cm, 2.4 cm, and 0.9 cm, respectively. The position deviations of the slave robot were larger than those of the master robot, with maximum, minimum, mean and standard deviation of 39.7 cm, 1.1 cm, 4.1 cm, and 5.6 cm, respectively. The maximum, minimum, mean and standard deviation of the following error between the master-slave robot were 4.4 cm, 0 cm, 1.3 cm, and 1 cm respectively. Concerning the ground head turn, the command navigation method allowed continuous turning, but the lateral deviation between robots was more than 0.3 m and less than 1 m, and the heading deviation was more than 10° and less than 90°.

PyTrack: A Map-Matching-Based Python Toolbox for Vehicle Trajectory Reconstruction

The exponential growth of IoT devices, smartphones, smartwatches, and vehicles equipped with positioning technology, such as Global Positioning System (GPS) modules, has boosted the development of location-based services for several applications in Intelligent Transportation Systems. However, the inherent error of location-based technologies makes it necessary to align the positioning trajectories to the actual underlying road network, a process known as map-matching. To the best of our knowledge, there are no comprehensive tools that allow us to model street networks, conduct topological and spatial analyses of the underlying street graph, perform map-matching processes on GPS point trajectories, and deeply analyse and elaborate these reconstructed trajectories. To address this issue, we present PyTrack, an open-source map-matching-based Python toolbox designed for academics, researchers and practitioners that integrate the recorded GPS coordinates with data provided by the OpenStreetMap, an open-source geographic information system. This manuscript overviews the architecture of the library offering a detailed description of its capabilities and modules. Besides, we provide an introductory guide to getting started with PyTrack covering the most fundamental steps of our framework. For more information on PyTrack, users are encouraged to visit the official repository at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cosbidev/PyTrack</uri> or the official documentation at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://pytrack-lib.readthedocs.io</uri> .

Assessment of GPS positioning performance using different signals in the context of ionospheric scintillation: a month-long case study on São José dos Campos, Brazil

The ionospheric scintillation associated to small-scale irregularities in the ionospheric layer can lead to performance degradation of Global Navigation Satellite Systems (GNSS) signals, and the reduction of positioning accuracy. The influence of the ionospheric layer on the GNSS systems is expected to be different for each signal since it is transmitted on different carrier frequencies. This paper presents the results of a quantitative analysis of the scintillation amplitude of GPS (Global Positioning System) signals at L1, L2 and L5 frequencies, aiming to evaluate the impact of the ionospheric scintillation effects on the GPS frequencies. As the ionospheric scintillation may impact positioning accuracy, we also present an assessment of GPS point positioning using those frequencies. The GPS sample data were collected for 30 days between November and December 2014 at SJCE station located in São José dos Campos (SP), Brazil. Such a region is subjected to the equatorial anomaly effects being characterized by the occurrence of strong ionosphere scintillation. Considering the quantitative analysis, during the different levels of ionospheric scintillation presented a similar behavior, the magnitude of scintillations is small for the L1 signal and larger for L5. In general, the results confirmed that lower frequencies (L2 and L5) suffer more impact from intense scintillation than L1. Regarding the positioning assessment, the multi-frequency positioning was more accurate than single frequency. Considering dual-frequency positioning, results with L1-L2 were more accurate than those with L1-L5 signals. With single-frequency positioning, the L1 signal was more accurate compared to the L2 frequency.

Estimation of the ocean tide loading displacements by GPS and GLONASS kinematic Precise Point Positioning

Ocean Tide Loading Displacement (OTLD) parameters, derived by Global Positioning System (GPS) approach, need more time to converge to a stable state and show poor precision of K1 and K2 constituents. Combining multi-system Global Navigation Satellite System (GNSS) data can greatly improve the positioning accuracy and significantly reduce the convergence time compared to single GPS PPP, combined GPS+GLONASS with ambiguity resolved in kinematic Precise Point Positioning (PPP) is improved on the basis of the algorithm of GPS kinematic PPP. We collected eight years of continuous observations of 11 worldwide sites from International GNSS Service to generate three dimensional OTLD parameters of eight constituents using GPS with ambiguity fixed, GLONASS with ambiguity free and combined GPS+GLONASS with ambiguity fixed kinematic PPP approach. The results show the eight constituents derived by GPS_FX and GPS+GLONASS_FX solution need four years and three years to converge to stable values, respectively. GLONASS can significantly improve the convergence rate of K1 and K2 constituents which needs only two years to converge to stable values, and S2 and P1 constituents which need three years to stable values. The results also show the GPS+GLONASS/reference misfits are the smallest for the most constituents in three components (less than 2mm) except for K2 and K1 in vertical and north directions. GLONASS can improve more than 40% in vertical and north components over GPS and GPS+GLONASS for K2 and K1 constituents and show very close agreement with GPS_FX solution in vertical and north component for S2 and P1 constituents. Ambiguity resolved in OTLD estimation can significantly accelerate the convergence and improve the accuracy for eight constituents, especially for combined GPS+GLONASS kinematic PPP mode, but no single constellation mode solution for all eight major constituents exists.

Three-Dimensional Surface Deformation Characteristics Based on Time Series InSAR and GPS Technologies in Beijing, China

Excessive exploitation of the groundwater has resulted in obvious three-dimensional (3D) deformation features on the surface of the Beijing Plain. This paper, by combining Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) technologies, has obtained time-series information of the 3D surface deformation in the Beijing Plain, analyzing its spatial distribution characteristics. On this basis, the relationship between different controlling factors with the 3D deformation of the surface has been analyzed as well. The following results are obtained: (1) From 2013 to 2018, the land subsidence, which generally showed the trend of slowing down, was mainly concentrated in the eastern, northern, and southern regions of Beijing Plain, with multiple subsidence centers. (2) Under the International Terrestrial Reference Frame 2005 (ITRF2005), the horizontal direction of all GPS points in the plain is basically the same, with the dominant movement direction being NE112.5°~NE113.8°. Under the Eurasian reference frame, the horizontal movement rate of GPS points significantly decreases. The movement rate and direction of each point are not characteristic of overall trend activity. (3) The distribution and extent of the 3D surface deformation in the Beijing Plain are controlled by the basement structure. Part of the subsided area corresponds to a Quaternary depression formed at the junction of active faults disrupting the area. Similarly, the distribution of horizontal deformation in the E-W and N-S directions of the plain is controlled by the regional basement structure comprising major faults bounding horizontal deformation. (4) Groundwater exploitation is the main cause of the 3D surface deformation in the Beijing Plain. The groundwater funnels of the second and third confined aquifer are in suitable agreement with the land subsidence. The horizontal movement in the Beijing Plain is either directed toward the center of the groundwater or the land subsidence funnel, and the deformation is directed from areas with higher to areas with lower groundwater levels.

A Telematics Enabled Analytics Approach for Determining Tractor Usage

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Tractor lease is an attractive proposition for farmers with small land holdings in India as initial investment required for purchasing a tractor is high [&lt;span class="xref"&gt;1&lt;/span&gt;]. The tractor is wet leased on a daily basis with the driver paid by the hour. Thus, there is a natural tendency by the driver to prolong the operation by taking frequent breaks adding to the overall input cost for the marginal farmer. Therefore, there is need to monitor these operations in real-time to ensure maximum utilization of tractors. The advent of connected and data driven technologies have positively disrupted several sectors including agriculture [&lt;span class="xref"&gt;2&lt;/span&gt;]. Vehicular and GPS (Global Positioning System) data from connected tractors powered by telematic devices can be effectively used for monitoring tractor’s health and position in real time using a mobile application. Moving beyond real-time monitoring, data obtained from connected tractors allow the computation of total field area and on-road distance covered during the day. These metrics enable usage-based pricing to pay for both driver’s labour and tractor rental thereby providing significant cost savings to farmers. The computation of field area and on-road distance covered is a challenging problem as it requires classification of geolocation data into field work and on-road travel points without the use of sophisticated sensors or user inputs. This work presents a telematics enabled analytics approach for monitoring tractor usage. The analytics module comprises of an algorithm that uses a clustering technique taking key vehicle parameters along with GPS data for determining the usage pattern. A polygon-based approach for area calculation is adopted that works for fields with irregular shapes. A novel feature to auto detect &amp;amp; deduce area of large untilled area patches enclosed within the field is also developed. The paper also highlights methods developed to reduce misclassification of GPS points and estimate usage during network connectivity issues.&lt;/div&gt;&lt;/div&gt;

Exploring factors affecting individual GPS-based activity space and how researcher-defined food environments represent activity space, exposure and use of food outlets

BackgroundObesity remains one of the most challenging public health issues of our modern time. Despite the face validity of claims for influence, studies on the causes of obesity have reported the influence of the food environment to be inconsistent. This inconsistency has been attributed to the variability of measures used by researchers to represent the food environments—Researcher-Defined Food Environments (RDFE) like circular, street-network buffers, and others. This study (i.) determined an individual’s Activity Space (AS) (ii.) explored the accuracy of the RDFE in representing the AS, (iii.) investigated the accuracy of the RDFE in representing actual exposure, and (iv.) explored whether exposure to food outlet reflects the use of food outlets.MethodsData were collected between June and December 2018. A total of 65 participants collected Global Positioning System (GPS) data, kept receipt of all their food purchases, completed a questionnaire about their personal information and had their weight and height measured. A buffer was created around the GPS points and merged to form an AS (GPS-based AS).ResultsStatistical and geospatial analyses found that the AS size of participants working away from home was positively related to the Euclidean distance from home to workplace; the orientation (shape) of AS was also influenced by the direction of workplace from home and individual characteristics were not predictive of the size of AS. Consistent with some previous studies, all types and sizes of RDFE variably misrepresented individual exposure in the food environments. Importantly, the accuracy of the RDFE was significantly improved by including both the home and workplace domains. The study also found no correlation between exposure and use of food outlets.ConclusionsHome and workplace are key activity nodes in modelling AS or food environments and the relationship between exposure and use is more complex than is currently suggested in both empirical and policy literature.

Geographic Density and Uptake of Pre-exposure Prophylaxis (PrEP) Among Young Gay, Bisexual and Other Sexual Minority Men: A Global Positioning System (GPS) Study

The geographic availability of pre-exposure prophylaxis (PrEP) providers is one important factor that significantly affects PrEP uptake. While most previous studies have employed spatial accessibility in static residential neighborhood definitions or self-reported healthcare accessibility, we examined the associations of the objectively measured geographic density of PrEP services with current PrEP use, using global positioning system (GPS) among sexual minority men (SMM) in New York City. 250 HIV-negative SMM participated in a 2-week GPS monitoring (January 2017–January 2018). Geographic PrEP density was measured as total numbers of PrEP providers in (1) individual activity space defined as daily path area of GPS points, (2) residential street network buffers and (3) census tract and ZIP code of residential locations. Geographic PrEP density within GPS-based activity space was positively associated with current PrEP use (prevalence ratio for 50-m activity space = 1.10, 95% confidence interval: [1.02, 1.18]). PrEP provider counts in residential buffer areas and administrative neighborhoods were not associated with PrEP use. Although it is not generalizable beyond New York City, our finding suggests the importance of daily mobility pattern in HIV prevention and PrEP implementation strategies.

Towards a novel set of GPS-derived metrics to identify the differences between mobility patterns of cognitively intact older adults and older adults with dementia

<h3>Introduction</h3> Maintaining an active lifestyle and participating in social activities are key components of healthy ageing. These components rely on the individuals' ability to remain mobile out of home. The global positioning system (GPS) is increasingly used to assess outdoor mobility of older adults. However, there is a gap in establishing a framework that recognizes the differences between mobility patterns of cognitively intact older adults and older adults with dementia (OAwD). We propose a set of GPS-derived metrics to identify these differences, which can be used to evaluate changes in mobility patterns of OAwD over time. <h3>Methods</h3> We analyzed the mobility profiles of 15 older adults from the greater Toronto area using GPS technology. Participants were aged between 65 and 90 years (M=74.9, SD=7.05) and were either cognitively healthy (n = 8) or diagnosed with dementia (n = 7). First, we created GPS-based features representing different dimensions of outdoor mobility. These features include (1) the typical distance covered by individuals, (2) randomness of the travel patterns, (3) the spatial variation of the GPS locations, (4) the number of distinct stops visited per day, (5) the number of daily trips away from home, (6) the score of three levels of outdoor life-space, and (7) the maximum distance travelled from home. Then, we used Recursive Feature Elimination (RFE) with Random Forest (RF) to select the most important mobility features with respect to cognitive status. <h3>Results</h3> A total of 55,580 GPS points were collected by the 15 unique participants over a period of 4 to 8 weeks. The RFE method indicated that features (1), (2), (3), and (5) had the strongest association with cognitive status. Our results suggested that OAwD displayed more predictability (i.e. smaller randomness) in travel patterns compared to the controls (OAwD: M=4.82, SD=0.57 vs. CTL:M=5.62, SD=0.71). Furthermore, the OAwD made fewer daily out-of-home trips compared to the controls (OAwD: M=1.06, SD=0.79 vs. CTL: M=1.65, SD=0.45), and covered smaller distances compared to the controls (OAwD: M=12.27, SD=11.73 km vs. CTL: M=15.78, SD=24.31 km). <h3>Conclusions</h3> The proposed set of GPS-based features identified the differences between mobility patterns of cognitively intact older adults and OAwD. <h3>Funding</h3> Canadian Consortium on Neurodegeneration in Aging

Towards a novel set of GPS‐derived metrics to identify the differences between mobility patterns of cognitively intact older adults and older adults with dementia

AbstractBackgroundMaintaining an active lifestyle and participating in social activities are key components of healthy ageing. These components rely on the individuals’ ability to remain mobile out of home. The global positioning system (GPS) is increasingly used to assess outdoor mobility of older adults. However, there is a gap in establishing a framework that recognizes the differences between mobility patterns of cognitively intact older adults and older adults with dementia (OAwD). We propose a set of GPS‐derived metrics to identify these differences, which can be used to evaluate changes in mobility patterns of OAwD over time.MethodWe analyzed the mobility profiles of 15 older adults from the greater Toronto area using GPS technology. Participants were aged between 65 and 90 years (M=74.9, SD=7.05) and were either cognitively healthy (n = 8) or diagnosed with dementia (n = 7). First, we created GPS‐based features representing different dimensions of outdoor mobility. These features include (1) the typical distance covered by individuals, (2) randomness of the travel patterns, (3) the spatial variation of the GPS locations, (4) the number of distinct stops visited per day, (5) the number of daily trips away from home, (6) the score of three levels of outdoor life‐space, and (7) the maximum distance travelled from home. Then, we used Recursive Feature Elimination (RFE) with Random Forest (RF) to select the most important mobility features with respect to cognitive status.ResultA total of 55,580 GPS points were collected by the 15 unique participants over a period of 4 to 8 weeks. The RFE method indicated that features (1), (2), (3), and (5) had the strongest association with cognitive status. Our results suggested that OAwD displayed more predictability (i.e. smaller randomness) in travel patterns compared to the controls (OAwD: M=4.82, SD=0.57 vs. CTL: M=5.62, SD=0.71). Furthermore, the OAwD made fewer daily out‐of‐home trips compared to the controls (OAwD: M=1.06, SD=0.79 vs. CTL: M=1.65, SD=0.45), and covered smaller distances compared to the controls (OAwD: M=12.27, SD=11.73 km vs. CTL: M=15.78, SD=24.31 km).ConclusionThe proposed set of GPS‐based features identified the differences between mobility patterns of cognitively intact older adults and OAwD.