Smartphone GPS accuracy study in an urban environment.
Ontology highlight
ABSTRACT:
SUBMITTER: Merry K
PROVIDER: S-EPMC6638960 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundPhysical activity (PA) is closely linked with child and youth health, and active travel may be a solution to enhancing PA levels. Activity spaces depict the geographic coverage of one's travel. Little is known about activity spaces and PA in adolescents.ObjectiveTo explore the relation between adolescent travel (using a spatial measure of activity space size) and daily moderate-to-vigorous PA (MVPA), with a focus on school days.MethodsIn Fall 2012, we used Global Positioning Systems to manually identify trips and generate activity spaces for each person-day; quantified by area for 39 students (13.8±0.6 years, 38% female) attending high school in urban Downtown Vancouver, Canada. We assessed the association between activity space area and MVPA using multi-level regression. We calculated total, school-day and trip-based MVPA for each valid person-day (accelerometry; ≥ 600 min wear time).ResultsOn school days, students accrued 68.2 min/day (95% CI 60.4-76.0) of MVPA. Daily activity spaces averaged 2.2 km2 (95% CI 1.3-3.0). There was no association between activity space size and school-day MVPA. Students accrued 21.8 min/day (95% CI 19.2-24.4) of MVPA during school hours, 19.4 min/day (95% CI 15.1-23.7) during travel, and 28.3 min/day (95% CI 22.3-34.3) elsewhere.ConclusionSchool and school travel are important sources of PA in Vancouver adolescents, irrespective of activity space area covered.
| S-EPMC4718608 | biostudies-literature
Project description:GPS is taken as the most prevalent positioning system in practice. However, in urban areas, as the GPS satellite signal could be blocked by buildings, the GPS positioning is not accurate due to multi-path errors. Estimating the negative impact of urban environments on GPS accuracy, that is the GPS environment friendliness (GEF) in this paper, will help to predict the GPS errors in different road segments. It enhances user experiences of location-based services and helps to determine where to deploy auxiliary assistant positioning devices. In this paper, we propose a method of processing and analysing massive historical bus GPS trajectory data to estimate the urban road GEF integrated with the contextual information of roads. First, our approach takes full advantage of the particular feature that bus routes are fixed to improve the performance of map matching. In order to estimate the GEF of all roads fairly and reasonably, the method estimates the GPS positioning error of each bus on the roads that are not covered by its route, by taking POIinformation, tag information of roads, and building layout information into account. Finally, we utilize a weighted estimation strategy to calculate the GEF of each road based on the GPS positioning performance of all buses. Based on one month of GPS trajectory data of 4835 buses within the second ring road in Chengdu, China, we estimate the GEF of 8831 different road segments and verify the rationality of the results by satellite maps, street views, and field tests.
| S-EPMC7146484 | biostudies-literature
Project description:BackgroundHuman movement is likely an important risk factor for environmentally-transmitted pathogens. While epidemiologic studies have traditionally focused on household risk factors, individual movement data could provide critical additional information about risk of exposure to such pathogens. We conducted global positioning system (GPS) tracking of urban slum residents to quantify their fine-scale movement patterns and evaluate their exposures to environmental sources of leptospirosis transmission.Methodology/principal findingsWe recruited participants from an ongoing cohort study in an urban slum in Brazil and tracked them for 24 hours at 30-second intervals. Among 172 subjects asked to participate in this cross-sectional study, 130 agreed to participate and 109 had good quality data and were included in analyses. The majority of recorded locations were near participant residences (87.7% within 50 meters of the house), regardless of age or gender. Similarly, exposure to environmental sources of leptospirosis transmission did not vary by age or gender. However, males, who have higher infection rates, visited a significantly larger area during the 24-hour period than did females (34,549m2 versus 22,733m2, p = 0.005). Four male participants had serologic evidence of Leptospira infection during the study period. These individuals had significantly larger activity spaces than uninfected males (61,310m2 vs 31,575m2, p = 0.006) and elevated exposure to rodent activity (p = 0.046) and trash deposits (p = 0.031).Conclusions/significanceGPS tracking was an effective tool for quantifying individual mobility in the complex urban slum environment and identifying risk exposures associated with that movement. This study suggests that in addition to source reduction, barrier interventions that reduce contact with transmission sources as slum residents move within their communities may be a useful prevention strategy for leptospirosis.
| S-EPMC6143277 | biostudies-literature
Project description:Empiric quantification of human mobility patterns is paramount for better urban planning, understanding social network structure and responding to infectious disease threats, especially in light of rapid growth in urbanization and globalization. This need is of particular relevance for developing countries, since they host the majority of the global urban population and are disproportionally affected by the burden of disease. We used Global Positioning System (GPS) data-loggers to track the fine-scale (within city) mobility patterns of 582 residents from two neighborhoods from the city of Iquitos, Peru. We used ?2.3 million GPS data-points to quantify age-specific mobility parameters and dynamic co-location networks among all tracked individuals. Geographic space significantly affected human mobility, giving rise to highly local mobility kernels. Most (?80%) movements occurred within 1 km of an individual's home. Potential hourly contacts among individuals were highly irregular and temporally unstructured. Only up to 38% of the tracked participants showed a regular and predictable mobility routine, a sharp contrast to the situation in the developed world. As a case study, we quantified the impact of spatially and temporally unstructured routines on the dynamics of transmission of an influenza-like pathogen within an Iquitos neighborhood. Temporally unstructured daily routines (e.g., not dominated by a single location, such as a workplace, where an individual repeatedly spent significant amount of time) increased an epidemic's final size and effective reproduction number by 20% in comparison to scenarios modeling temporally structured contacts. Our findings provide a mechanistic description of the basic rules that shape human mobility within a resource-poor urban center, and contribute to the understanding of the role of fine-scale patterns of individual movement and co-location in infectious disease dynamics. More generally, this study emphasizes the need for careful consideration of human social interactions when designing infectious disease mitigation strategies, particularly within resource-poor urban environments.
| S-EPMC3620113 | biostudies-literature
Project description:Cities are growing in density and coverage globally, increasing the value of green spaces for human health and well-being. Understanding the interactions between people and green spaces is also critical for biological conservation and sustainable development. However, quantifying green space use is particularly challenging. We used an activity index of anonymized GPS data from smart devices provided by Mapbox (www.mapbox.com) to characterize human activity in green spaces in the Greater Toronto Area, Canada. The goals of our study were to describe i) a methodological example of how anonymized GPS data could be used for human-nature research and ii) associations between park features and human activity. We describe some of the challenges and solutions with using this activity index, especially in the context of green spaces and biodiversity monitoring. We found the activity index was strongly correlated with visitation records (i.e., park reservations) and that these data are useful to identify high or low-usage areas within green spaces. Parks with a more extensive trail network typically experienced higher visitation rates and a substantial proportion of activity remained on trails. We identified certain land covers that were more frequently associated with human presence, such as rock formations, and find a relationship between human activity and tree composition. Our study demonstrates that anonymized GPS data from smart devices are a powerful tool for spatially quantifying human activity in green spaces. These could help to minimize trade-offs in the management of green spaces for human use and biological conservation will continue to be a significant challenge over the coming decades because of accelerating urbanization coupled with population growth. Importantly, we include a series of recommendations when using activity indexes for managing green spaces that can assist with biomonitoring and supporting sustainable human use.
| S-EPMC9754188 | biostudies-literature
Project description:Urban greenspace is a valuable component of the urban form that has the potential to improve the health and well-being of residents. Most quantitative studies of relationships between health and greenspace to date have investigated associations only with what greenspace exists in the local environment (i.e. provision of greenspace), rather than to what extent it is used. This is due to the difficulty of obtaining usage data in large amounts. In recent years, GPS functionality integrated into mobile phones has provided a potential solution to this problem by making it possible to track which parts of the environment people experience in their day-to-day lives. In this paper, we demonstrate a method to derive cleaned, trip-level information from raw GPS data collected by a mobile phone app, then use this data to investigate the characteristics of trips to urban greenspace by residents of the city of Sheffield, UK. We find that local users of the app spend an average of an hour per week visiting greenspaces, including around seven trips per week and covering a total distance of just over 2.5 km. This may be enough to provide health benefits, but is insufficient to provide maximal benefits. Trip characteristics vary with user demographics: ethnic minority users and users from more socioeconomically deprived areas tend to make shorter trips than White users and those from less deprived areas, while users aged 34 years and over make longer trips than younger users. Women, on average, make more frequent trips than men, as do those who spent more time outside as a child. Our results suggest that most day-to-day greenspace visits are incidental, i.e. travelling through rather than to greenspace, and highlight the importance of including social and cultural factors when investigating who uses and who benefits from urban greenspace.
| S-EPMC8262795 | biostudies-literature
Project description:Studies often rely on home locations to access built environment (BE) influences on physical activity (PA). We use GPS and accelerometer data collected for 288 individuals over a two-week period to examine eight GPS-derived BE characteristics and moderate-to-vigorous PA (MVPA) and light-to-moderate-vigorous PA (LMVPA). NDVI, parks, blue space, pedestrian-orientated intersections, and population density were associated with increased odds of LMVPA and MVPA, while traffic air pollution and noise were associated with decreased odds of LMVPA and MVPA. Associations varied by population density and when accounting for multiple BE measures. These findings provide further information on where individuals choose to be physically active.
| S-EPMC8328940 | biostudies-literature
Project description:BackgroundAlthough alcohol breath testing devices that pair with smartphones are promoted for the prevention of alcohol-impaired driving, their accuracy has not been established.MethodsIn a within-subjects laboratory study, we administered weight-based doses of ethanol to two groups of 10 healthy, moderate drinkers aiming to achieve a target peak blood alcohol concentration (BAC) of 0.10%. We obtained a peak phlebotomy BAC and measured breath alcohol concentration (BrAC) with a police-grade device (Intoxilyzer 240) and two randomly ordered series of 3 consumer smartphone-paired devices (6 total devices) with measurements every 20 min until the BrAC reached <0.02% on the police device. Ten participants tested the first 3 devices, and the other 10 participants tested the other 3 devices. We measured mean paired differences in BrAC with 95% confidence intervals between the police-grade device and consumer devices.ResultsThe enrolled sample (N = 20) included 11 females; 15 white, 3 Asian, and 2 Black participants; with a mean age of 27 and mean BMI of 24.6. Peak BACs ranged from 0.06-0.14%. All 7 devices underestimated BAC by >0.01%, though the BACtrack Mobile Pro and police-grade device were consistently more accurate than the Drinkmate and Evoc. Compared with the police-grade device measurements, the BACtrack Mobile Pro readings were consistently higher, the BACtrack Vio and Alcohoot measurements similar, and the Floome, Drinkmake, and Evoc consistently lower. The BACtrack Mobile Pro and Alcohoot were most sensitive in detecting BAC driving limit thresholds, while the Drinkmate and Evoc devices failed to detect BAC limit thresholds more than 50% of the time relative to the police-grade device.ConclusionsThe accuracy of smartphone-paired devices varied widely in this laboratory study of healthy participants. Although some devices are suitable for clinical and research purposes, others underestimated BAC, creating the potential to mislead intoxicated users into thinking that they are fit to drive.
| S-EPMC9359437 | biostudies-literature
Project description:BackgroundARGOS satellite telemetry is one of the most widely used methods to track the movements of free-ranging marine and terrestrial animals and is fundamental to studies of foraging ecology, migratory behavior and habitat-use. ARGOS location estimates do not include complete error estimations, and for many marine organisms, the most commonly acquired locations (Location Class 0, A, B, or Z) are provided with no declared error estimate.Methodology/principal findingsWe compared the accuracy of ARGOS Locations to those obtained using Fastloc GPS from the same electronic tags on five species of pinnipeds: 9 California sea lions (Zalophus californianus), 4 Galapagos sea lions (Zalophus wollebaeki), 6 Cape fur seals (Arctocephalus pusillus pusillus), 3 Australian fur seals (A. p. doriferus) and 5 northern elephant seals (Mirounga angustirostris). These species encompass a range of marine habitats (highly pelagic vs coastal), diving behaviors (mean dive durations 2-21 min) and range of latitudes (equator to temperate). A total of 7,318 ARGOS positions and 27,046 GPS positions were collected. Of these, 1,105 ARGOS positions were obtained within five minutes of a GPS position and were used for comparison. The 68(th) percentile ARGOS location errors as measured in this study were LC-3 0.49 km, LC-2 1.01 km, LC-1 1.20 km, LC-0 4.18 km, LC-A 6.19 km, LC-B 10.28 km.Conclusions/significanceThe ARGOS errors measured here are greater than those provided by ARGOS, but within the range of other studies. The error was non-normally distributed with each LC highly right-skewed. Locations of species that make short duration dives and spend extended periods on the surface (sea lions and fur seals) had less error than species like elephant seals that spend more time underwater and have shorter surface intervals. Supplemental data (S1) are provided allowing the creation of density distributions that can be used in a variety of filtering algorithms to improve the quality of ARGOS tracking data.
| S-EPMC2806907 | biostudies-literature
Project description:BackgroundMental health problems are one of the leading causes of disease burden worldwide, and are mainly diagnosed and treated in general practice. It is unclear however, how general practitioners (GPs) identify mental health problems in their patients. The aim of this study was to explore how patients' self-reported levels of mental distress correspond with psychological diagnoses made by their GPs, and associations with sex, age, number of consultations, and somatic symptom diagnoses.MethodsA questionnaire study coupled with retrospective and prospective cohort data from 553 patients aged 16-65 years in six GP offices in Oslo, Norway during 21 months in 2014-2016.ResultsWe found that 73.3% of patients with self-reported high levels of mental distress versus only 13.3% of the patients with low levels of mental distress had received a psychological diagnosis (p < 0.01). We found an increase in number of consultations for the group with high levels of mental distress regardless of having received a psychological diagnosis (p < 0.01). There was also an increase in number of somatic symptoms (p = 0.04) and higher number of females (0.04) in this group. 35% of patients had received one or more psychological diagnosis by their GP. Mean CORE-10 score, being female and a high number of consultations was associated with having received a psychological diagnosis. In the adjusted analyses high CORE-10 score and a high number of consultations still predicted a psychological diagnosis.ConclusionsWe found a clear association between self-reported mental distress and having received a psychological diagnosis amongst the participants, and the probability for being identified increased with increasing levels of mental distress, and increasing number of visits to their doctor. This suggests that GPs can identify patients with high levels of mental distress in general practice in an adequate way, even though this can sometimes be a complex issue.Trial registrationTrial registration The main study was retrospectively registered in ClinicalTrials.gov on August 10 2019 with identification number NCT03624829.
| S-EPMC9549632 | biostudies-literature