Global Positioning System Readings Research Articles

Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability

BackgroundPassive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection platform was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal.MethodsMothers (15–25 years old) with infants (< 12 months old) were recruited in person from vaccination clinics in rural Nepal. They were provided with an Android smartphone and a Bluetooth beacon to collect data in four domains: the mother’s location using the Global Positioning System (GPS), physical activity using the phone’s accelerometer, auditory environment using episodic audio recording on the phone, and mother-infant proximity measured with the Bluetooth beacon attached to the infant’s clothing. Feasibility and acceptability were evaluated based on the amount of passive sensing data collected compared to the total amount that could be collected in a 2-week period. Endline qualitative interviews were conducted to understand mothers’ experiences and perceptions of passive data collection.ResultsOf the 782 women approached, 320 met eligibility criteria and 38 mothers (11 depressed, 27 non-depressed) were enrolled. 38 mothers (11 depressed, 27 non-depressed) were enrolled. Across all participants, 5,579 of the hour-long data collection windows had at least one audio recording [mean (M) = 57.4% of the total possible hour-long recording windows per participant; median (Mdn) = 62.6%], 5,001 activity readings (M = 50.6%; Mdn = 63.2%), 4,168 proximity readings (M = 41.1%; Mdn = 47.6%), and 3,482 GPS readings (M = 35.4%; Mdn = 39.2%). Feasibility challenges were phone battery charging, data usage exceeding prepaid limits, and burden of carrying mobile phones. Acceptability challenges were privacy concerns and lack of family involvement. Overall, families’ understanding of passive sensing and families’ awareness of potential benefits to mothers and infants were the major modifiable factors increasing acceptability and reducing gaps in data collection.ConclusionPer sensor type, approximately half of the hour-long collection windows had at least one reading. Feasibility challenges for passive sensing on mobile devices can be addressed by providing alternative phone charging options, reverse billing for the app, and replacing mobile phones with smartwatches. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing for psychological interventions and other health services.Registration International Registered Report Identifier (IRRID): DERR1-10.2196/14734

A latent-class adaptive routing choice model in stochastic time-dependent networks

Transportation networks are inherently uncertain due to random disruptions; meanwhile, real-time information potentially helps travelers adapt to realized traffic conditions and make better route choices under such disruptions. Modeling adaptive route choice behavior is essential in evaluating real-time traveler information systems and related policies. This research contributes to the state of the art by developing a latent-class routing policy choice model in a stochastic time-dependent network with revealed preference data. A routing policy is defined as a decision rule applied at each link that maps possible realized traffic conditions to decisions on the link to take next. It represents a traveler’s ability to look ahead in order to incorporate real-time information not yet available at the time of decision.A case study is conducted in Stockholm, Sweden and data for the stochastic time-dependent network are generated from hired taxi Global Positioning System (GPS) readings. A latent-class Policy Size Logit model is specified, with routing policy users who follow routing policies and path users who follow fixed paths. Two additional layers of latency in the measurement equation are accounted for: 1) the choice of a routing policy is latent and only its realized path on a given day can be observed; and 2) when GPS readings have relatively long gaps, the realized path cannot be uniquely identified, and the likelihood of observing vehicle traces with non-consecutive links is instead maximized.Routing policy choice set generation is based on the generalization of path choice set generation methods. The generated choice sets achieve 95% coverage for 100% overlap threshold after correcting GPS mistakes and breaking up trips with intermediate stops, and further achieve 100% coverage for 90% overlap threshold.Estimation results show that the routing policy user class probability increases with trip length, and the latent-class routing policy choice model fits the data better than a single-class path choice or routing policy choice model. This suggests that travelers are heterogeneous in terms of their ability and/or willingness to plan ahead and utilize real-time information, and an appropriate route choice model for uncertain networks should take into account the underlying stochastic travel times and structured traveler heterogeneity in terms of real-time information utilization.

Showcasing the Tourism Potentials of Lokoja, Kogi State, Nigeria

Kogi State is one of the 36 states of the Federal Republic of Nigeria with Lokoja as the capital. Objective of the Study: To quantify the tourism potentials (abiotic and biotic) of some tourism sites in Lokoja, because information is needed for tourism development, marketing and promotion. Instruments Used: Surveying tape, Global Positioning system (GPS), Prismatic compass clinometers, appropriate books for vegetation identification, writing materials for noting field observations and a digital camera. Data collection involved stock assessment, direct measurements (quantitative) of the tourism resources on sites including their qualitative assessment. Results obtained are GPS readings of Latitudes and Longitudes in degrees and minutes, Elevations in metres, Bearing in degrees, Length and Breadth, Height of buildings and wall thickness in metres and vegetation identification. Tourism resources at the sites were also recorded. These tourists’ attractions (heritage, relics) are unique and are not available anywhere else in the world.

Improving Low-Cost Solutions for Path Mapping in Autonomous Vehicle

One of the main challenge in designing autonomous vehicle is developing the algorithms necessary for simultaneous localization and mapping (SLAM). While this process heavily depends on an expensive hardware called Light Detection and Ranging (LiDAR), there are cheaper alternatives that can be implemented in earlier stage of autonomous vehicle development. Inertial Measurement Unit (IMU), vehicle odometry, and Global Positioning System (GPS) can be used as a solution. The main problem is these cheaper alternatives relies heavily on the precision of the hardware used. In this project, we outline the mapping process using these cheaper solutions which can also be used to complement LiDAR-based SLAM. We show that using simple approaches such as static bias drift removal, high-pass filtering, signal downsampling and linear interpolation, we can increase the robustness and improve the accuracy and precision of the IMU and GPS used respectively. We manage to increase the precision of the GPS readings and reduce the drift of IMU on average from -17.105 deg/min to -0.1177 deg/min. We show the improvement achieved by our proposed method by mapping the road around Engine Square, Jalan Ilmu 1/1, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.

Comparison of industrial emissions and carpet dust concentrations of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans in a multi-center U.S. study.

Proximity to facilities emitting polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) has been associated with increased risk of non-Hodgkin lymphoma (NHL). There is limited information about whether proximity to industrial sources leads to indoor PCDD/F contamination of homes. We measured carpet dust concentrations (pg/g) of 17 toxic PCDD/F congeners and calculated their toxic equivalence (TEQ) in 100 homes in a population-based case-control study of NHL in Detroit, Los Angeles, Seattle, and Iowa (1998-2000). We took global positioning system readings at residences and obtained coordinates and PCDD/F emissions (ng TEQ/yr) from an Environmental Protection Agency database for 6 facility types: coal-fired electricity generating plants, cement kilns burning non-hazardous waste, hazardous waste incinerators, medical waste incinerators, municipal solid waste incinerators, and sewage sludge incinerators. For each residence, we computed an inverse distance-squared weighted average emission index (AEI [pg TEQ/km2/yr]) for all facilities within 5km from 1983 to 2000. We also computed AEIs for each of the 6 facility types. We evaluated relationships between PCDD/F dust concentrations and the all-facility AEI or categories of facility-type AEIs using multivariable linear regression, adjusting for study center, demographics, and home characteristics. A doubling of the all-facility AEI was associated with a 4-8% increase in PCDD/F dust concentrations of 7 of 17 PCDD/F congeners and the TEQ (p-value<0.1). We also observed positive associations between PCDD/F dust concentrations and facility-type AEIs (highest vs. lowest exposure category) for municipal solid waste incinerators (9 PCDD/F, TEQ), and medical waste incinerators (7 PCDD/F, TEQ) (p<0.1). Our results from diverse geographical areas suggest that industrial PCDD/F emission sources contribute to residential PCDD/F dust concentrations. Our emissions index could be improved by incorporating local meteorological data and terrain characteristics. Future research is needed to better understand the links between nearby emission sources, human exposure pathways, and health risks.

Travel time estimation for ambulances using Bayesian data augmentation

We introduce a Bayesian model for estimating the distribution of ambulance travel times on each road segment in a city, using Global Positioning System (GPS) data. Due to sparseness and error in the GPS data, the exact ambulance paths and travel times on each road segment are unknown. We simultaneously estimate the paths, travel times, and parameters of each road segment travel time distribution using Bayesian data augmentation. To draw ambulance path samples, we use a novel reversible jump Metropolis-Hastings step. We also introduce two simpler estimation methods based on GPS speed data. We compare these methods to a recently published travel time estimation method, using simulated data and data from Toronto EMS. In both cases, out-of-sample point and interval estimates of ambulance trip times from the Bayesian method outperform estimates from the alternative methods. We also construct probability-of-coverage maps for ambulances. The Bayesian method gives more realistic maps than the recently published method. Finally, path estimates from the Bayesian method interpolate well between sparsely recorded GPS readings and are robust to GPS location errors.

Patterns and determinants of communal latrine usage in urban poverty pockets in Bhopal, India

To explore and explain patterns of use of communal latrine facilities in urban poverty pockets. Six poverty pockets with communal latrine facilities representing two management models (Sulabh and municipal) were selected. Sampling was random and stratified by poverty pocket population size. A seventh, community-managed facility was also included. Data were collected by exit interviews with facility users and by interviews with residents from a randomly selected representative sample of poverty pocket households, on social, economic and demographic characteristics of households, latrine ownership, defecation practices, costs of using the facility and distance from the house to the facility. A tally of facility users was kept for 1 day at each facility. Data were analysed using logistic regression modelling to identify determinants of communal latrine usage. Communal latrines differed in their facilities, conditions, management and operating characteristics, and rates of usage. Reported usage rates among non-latrine-owning households ranged from 15% to 100%. There was significant variation in wealth, occupation and household structure across the poverty pockets as well as in household latrine ownership. Households in pockets with municipal communal latrine facilities appeared poorer. Households in pockets with Sulabh-managed communal facilities were significantly more likely to own a household latrine. Determinants of communal facility usage among households without a latrine were access and convenience (distance and opening hours), facility age, cleanliness/upkeep and cost. The ratio of male to female users was 2:1 across all facilities for both adults and children. Provision of communal facilities reduces but does not end the problem of open defecation in poverty pockets. Women appear to be relatively poorly served by communal facilities and, cost is a barrier to use by poorer households. Results suggest improving facility convenience and access and modifying fee structures could lead to increased rates of usage. Attention to possible barriers to usage at household level associated particularly with having school-age children and with pre-school childcare needs may also be warranted.

Physical Activity Parameters for Walking Golf Participation

Moderate-intensity walking may effectively offer protection against a host of hypokinetic disorders including cardiovascular disease and associated risk factors, as well as to provide a threshold-level of physical stimulus to promote positive health and well-being. An efficient method of facilitating this type of activity engagement, and subsequent adherence, is to encourage participation in popular leisure activities, such as golf. PURPOSE: In order to ultimately prescribe such a given activity it is necessary to first analyze the dosage attainable through participation in that activity. METHODS: Study subjects were fifty male volunteers (mean age: 24.1 years) of various golf abilities. In order to fulfill the requirements of inclusion criteria, subjects had to complete a single 18-hole round of walking golf. MTI accelerometers, pedometer step counters, heart rate monitors, and Global Positioning System (GPS) bluetooth loggers were worn by all participants in this study to monitor activity intensity, number of steps taken, heart rate response, and total distance walked throughout the round, respectively. Descriptive statistics were analyzed as means and standard deviations. Bivariate correlation analysis was used to assess the relationships between step counts and distance traveled. RESULTS: Volume of physical activity is presented as step counts (12197 ± 1082 total steps) and total distance traveled (9.01 ± 0.93 km). GPS readings were combined with aerial photography vector maps to enable movement calculations for each subject to within 1 meter of accuracy (Differential GPS). Correlation analysis revealed minimal covariation between the two volume measurements (r2 = .14). Relative intensities were calculated for each golfer and stratified based on intuitive heart rate zones. In general, the group spent 8.5%, 25%, 43%, 20%, and 3% of total time at the following heart rate zones, respectively: (1) 60–80 bpm, (2) 81–100 bpm, (3) 101–120 bpm, (4) 121–140 bpm, and (5) 141–160 bpm. Objective intensity of walking, as measured by accelerometry, was classified into sedentary (84.2 ± 32.5 min), light (46.3 ± 10.7 min), moderate (93.2 ± 16.9 min), vigorous (12.2 ± 15.1 min), and moderate + vigorous (mvpa) physical activity (105.4 ± 9.5 min). CONCLUSIONS: The findings of this study clearly demonstrate that walking golf participation involves physical activity of adequate stimulus (as recommended by the ACSM) to promote improved health and fitness. Furthermore, the use of combined technology to monitor ambulatory movement may provide a superior assessment of true volume and intensity of movement during leisure time activity.

Application of merged 1‐m and 4‐m resolution satellite data to research and management in tropical forests

Summary Until very recently there have been no digital data from satellites for studying events that occur at scales of 10–1000 m2 over large areas (100–100 000 ha). Many phenomena of interest to ecologists, such as impacts of selective logging on forest processes, occur over large extents but at local scales. Here we report results from a pilot project to evaluate through visual interpretation the potential of newly available 1‐m panchromatic and 4‐m multi‐spectral data from the IKONOS satellite, for studying forest structure, dynamics and logging impacts in logged and old‐growth tropical moist forest. The study area, the Mil Madeireira Itacoatiara Ltda site of Precious Woods Amazon, near Itacoatiara, Amazonas, Brazil, is managed using reduced‐impact logging practices to minimize environmental impacts, and thus represents a lower bound for logging impacts in tropical rain forests. The IKONOS image was georeferenced using uncorrected global positioning system (GPS) locations for 10 control trees whose crowns were clearly visible in the image. The root mean square error (RMSE) of the geometric transformation was 4 m, while the mean crown diameter of 50 randomly chosen trees in old‐growth forest was 9·4 m. The fact that the RMSE was less than half the average crown diameter implies that it will usually be possible to locate from the ground crowns that are distinct on the image, given sufficiently accurate GPS locations. IKONOS data are well suited for evaluating and monitoring logging impacts. Many impacts of logging were clearly observable in the image, including major and some minor roads, logging patios and larger logging gaps. Smaller extraction roads and logging gaps were not observable. Many individual trees were distinct on the IKONOS image, indicating that it is now feasible to conduct demographic studies of tropical rain forest canopy trees based on repeated satellite observations. Linking these remotely sensed data to ground data will require improved GPS positions, because it is currently difficult to obtain accurate GPS readings in tropical rain forest understoreys. Synthesis and applications. IKONOS 1‐m and 4‐m data were found to be useful for identifying individual trees as well as some logging management features in a tropical moist forest in central Amazonia. These data will have many applications for research and management of intervened and old‐growth tropical forests, including planning and assessment of logging activities, as well as monitoring adherence to certification criteria such as those of the Forest Stewardship Council. Rapid development of these applications will come from building on existing data on forest structure and function, and by fostering collaborations between remote sensing scientists, ecologists and natural resource managers.