Quantifying Distance Overestimation From Global Positioning System in Urban Spaces

Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition

As a result of the development of wireless indoor positioning techniques such as WiFi, Bluetooth, and Ultra-wideband (UWB), the positioning traces of moving people or objects in indoor environments can be tracked and recorded, and the distances moved can be estimated from these data traces. These estimates are very useful in many applications such as workload statistics and optimized job allocation in the field of logistics. However, due to the uncertainties of the wireless signal and corresponding positioning errors, accurately estimating movement distance still faces challenges. To address this issue, this paper proposes a movement status recognition-based distance estimating method to improve the accuracy. We divide the positioning traces into segments and use an encoder–decoder deep learning-based model to determine the motion status of each segment. Then, the distances of these segments are calculated by different distance estimating methods based on their movement statuses. The experiments on the real positioning traces demonstrate the proposed method can precisely identify the movement status and significantly improve the distance estimating accuracy.

Locations of Adolescent Physical Activity in an Urban Environment and Their Associations with Air Pollution and Lung Function

Rationale: Physical activity while being exposed to high concentrations of air pollution may lead to greater inhalation of pollutant particles and gases. Thus, owing to features of the built city environment, specific locations where physical activity take place may put individuals at increased risk for harmful inhaled exposures leading to decrements in lung function.Objectives: The objectives were to determine locations throughout an urban landscape where children engage in moderate to vigorous activity (MVA). We hypothesized that outdoor activity would be associated with increased exposure to air pollution and reduced lung function.Methods: Children aged 9-14 years living in New York City (NYC) (n = 151) wore global positioning system devices and wrist accelerometers for two 24-hour periods. Time-stamped global positioning system points and accelerometer data were aggregated and mapped using ArcGIS to determine locations where children engaged in MVA. Location-specific particulate matter <2.5 microns and nitrogen dioxide (NO₂) was determined based on land use regression models of street-level pollution. Temporal air pollution exposure was determined based on daily concentrations collected at one central site in NYC. Forced expiratory volume in 1 second (FEV₁), forced vital capacity (FVC), and forced expiratory flow, midexpiratory phase (FEF_25-75) were collected following each 24-hour period. Data were analyzed using multivariable linear regression models to examine associations between MVA time and both lung function and air pollution in separate models. Additionally, a multiplicative interaction term (MVA time × season) was included to test whether the association between MVA time and lung function outcomes varied by warmer versus colder months.Results: On average, children spent less MVA time outdoors (38.2 ± 39.6 min/d) compared with indoors (71.9 ± 74.7 min/d, P < 0.01), regardless of season. The majority of outdoor MVA occurred along sidewalks and roadbeds (30.2 ± 33.3 min/d, 76.9% of outdoor) where annual average concentrations of NO₂ were relatively high. Interquartile range (IQR) increase in outdoor MVA time (44 min) was associated with higher levels of annual average NO₂ (P < 0.01) but not particulate matter <2.5 microns. In warmer months, for IQR increase in outdoor MVA time, children had 1.41% lower FEV₁/FVC (95% confidence interval [95% CI], -2.46 to -0.36) and 4.40% lower percent predicted FEF_25-75 (95% CI, -8.02 to -0.78). These results persisted even after adjustment for location-specific annual average concentrations of NO₂. No association was observed between MVA time and lung function in colder months (P > 0.05), and a formal test for interaction (MVA time × season) was significant (P value for interaction = 0.01 and 0.03 for FEV₁/FVC and FEF_25-75, respectively).Conclusions: Children in NYC spent less time active outdoors compared with indoors. Outdoor activity was greatest near traffic sources and associated with higher annual average concentrations of NO₂. In warmer months, outdoor activity was associated with lower lung function, but this association did not appear to be mediated by higher exposure to outdoor pollution during exercise.

Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles

Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates.

Global Positioning System-Determined and Stopwatch-Determined Running Speeds of Horses Differ

Global positioning systems (GPS) have become very popular tools to determine the running speed of horses. However, information on the accuracy of these measurements is scarce. The objective of this study was to examine the accuracy of speed determinations using GPS. For this purpose, the running speeds determined using the GPS of the Polar M400 Equine heart rate meter (G-speed) and a stopwatch (W-speed; manual division of the measured time over the distance run) were compared. The hypothesis was that the speeds determined by both methods would be the same. Eleven horses ran a standardized exercise test (SET) with 130 m laps, and 8 horses ran a SET with 250 m laps in two different sandy riding arenas (one was indoor). The length of the laps was determined with a distance measuring wheel and marked with red traffic cones for the riders to maintain an accurate course. The SETs consisted of five intervals at increasing speeds each. The duration of the intervals was between 3 and 6 minutes. Horses ran a defined number of laps in each interval to reach the prescribed durations. Data were analyzed using a paired Student's t-test; P < .05 denoted significance. W-speeds differed from G-speeds for all intervals in both riding arenas (P between .01 and .001). G-speed was lower for each interval. With increasing speed, the difference between the two methods augmented. The hypothesis was rejected, therefore questioning the accuracy of the Polar M400 in determining speed under the conditions of this study.

A Local View of Informal Urban Environments: a Mobile Phone-Based Neighborhood Audit of Street-Level Factors in a Brazilian Informal Community

Street-level environment characteristics influence the health behaviors and safety of urban residents, and may particularly threaten health within informal communities. However, available data on how such characteristics vary within and among informal communities is limited. We sought to adapt street audit strategies designed to characterize the physical environment for use in a large informal community, Rio das Pedras (RdP) located in Rio de Janeiro, Brazil. A smartphone-based systematic observation protocol was used to gather street-level information for a high-density convenience sample of street segments (N = 630, estimated as 86% of all street segments in the community). We adapted items related to physical disorder and physical deterioration. Measures selected to illustrate the approach include the presence of the following: (1) low-hanging or tangled wires, (2) litter, (3) structural evidence of sinking, and (4) an unpleasant odor. Intercept-only spatial generalized additive models (GAM) were used to evaluate and visualize spatial variation within the RdP community. We also examined how our estimates and conclusions about spatial variation might have been affected by lower-density sampling from random subsets street observations. Random subsets were selected to determine the robustness of study results in scenarios with sparser street sampling. Selected characteristics were estimated to be present for between 18% (unpleasant odor) to 59% (low-hanging or tangled wires) of the street segments in RdP; estimates remain similar (± 6%) when relying on a random subset created to simulate lower-density spatial sampling. Spatial patterns of variation based on predicted probabilities across RdP differed by indicator. Structural sinking and low-hanging or tangled wires demonstrated relatively consistent spatial distribution patterns across full and random subset sample sizes. Smartphone-based systematic observations represent an efficient and potentially feasible approach to systematically studying neighborhood environments within informal communities. Future deployment of such tools will benefit from incorporating data collection across multiple time points to explore reliability and quantify neighborhood change. These tools can prove useful means to assess street-level exposures that can be modifiable health determinants across a wide range of informal urban settings. Findings can contribute to improved urban planning and provide useful information for identifying potential locations for neighborhood-scaled interventions that can improve living conditions for residents in Rio das Pedras.

Assessing Individuals' Exposure to Environmental Conditions Using Residence-based Measures, Activity Location-based Measures, and Activity Path-based Measures

BACKGROUND

Many approaches are available to researchers who wish to measure individuals' exposure to environmental conditions. Different approaches may yield different estimates of associations with health outcomes. Taking adolescents' exposure to alcohol outlets as an example, we aimed to (1) compare exposure measures and (2) assess whether exposure measures were differentially associated with alcohol consumption.

METHODS

We tracked 231 adolescents 14-16 years of age from the San Francisco Bay Area for 4 weeks in 2015/2016 using global positioning systems (GPS). Participants were texted ecologic momentary assessment surveys six times per week, including assessment of alcohol consumption. We used GPS data to calculate exposure to alcohol outlets using three approach types: residence-based (e.g., within the home census tract), activity location-based (e.g., within buffer distances of frequently attended places), and activity path-based (e.g., average outlets per hour within buffer distances of GPS route lines). Spearman correlations compared exposure measures, and separate Tobit models assessed associations with the proportion of ecologic momentary assessment responses positive for alcohol consumption.

RESULTS

Measures were mostly strongly correlated within approach types (ρ ≥ 0.7), but weakly (ρ < 0.3) to moderately (0.3 ≤ ρ < 0.7) correlated between approach types. Associations with alcohol consumption were mostly inconsistent within and between approach types. Some of the residence-based measures (e.g., census tract: β = 8.3, 95% CI = 2.8, 13.8), none of the activity location-based approaches, and most of the activity path-based approaches (e.g., outlet-hours per hour, 100 m buffer: β = 8.3, 95% CI = 3.3, 13.3) were associated with alcohol consumption.

CONCLUSIONS

Methodologic decisions regarding measurement of exposure to environmental conditions may affect study results.

A Time-Based Objective Measure of Exposure to the Food Environment

Exposure to food environments has mainly been limited to counting food outlets near participants’ homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants’ mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and ½ mile. Measures included count of proximal FFRs, time duration in proximity to ≥1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (<100 m) need further research.

Freedom from the Station: Spatial Equity in Access to Dockless Bike Share

BACKGROUND

Bike sharing systems have potential to substantially boost active transportation levels (and consequent physical and mental health) in urban populations. We explored equity of spatial access in a novel 'dockless' bike share system that does not that constrain bike pickup and drop-off locations to docking stations.

METHODS

Starting in July 2017, Seattle, Washington piloted a dockless bike share system that made 10,000 bikes available. We merged data on resident sociodemographic and economic characteristics from the American Community Survey about 93 defined neighborhoods with data about bike locations, bike idle time, and which neighborhoods operators rebalanced bikes to. We used mapping and descriptive statistics to compare access between neighborhoods along sociodemographic and economic lines.

RESULTS

With many bikes available, no neighborhood was consistently excluded from access. However, the average availability ranged from 3 bikes per day to 341 per day. Neighborhoods with more bikes had more college-educated residents (median 75% college-educated vs. 65%) and local community resources (median opportunity index score of 24 vs. 19), and higher incomes (median 83,202 vs. 71,296). Rebalancing destinations were strongly correlated with neighborhood demand (r=0.61).

CONCLUSIONS

The overall scale of the dockless system ensured there was baseline access throughout Seattle. We observed modest inequities in access along sociodemographic lines, similar to prior findings in studies of docked bike share systems. Dockless bike share systems hold promise for offering equitable spatial access to bike sharing.

Assessing the Exposome with External Measures: Commentary on the State of the Science and Research Recommendations

The exposome comprises all environmental exposures that a person experiences from conception throughout the life course. Here we review the state of the science for assessing external exposures within the exposome. This article reviews (a) categories of exposures that can be assessed externally, (b) the current state of the science in external exposure assessment, (c) current tools available for external exposure assessment, and (d) priority research needs. We describe major scientific and technological advances that inform external assessment of the exposome, including geographic information systems; remote sensing; global positioning system and geolocation technologies; portable and personal sensing, including smartphone-based sensors and assessments; and self-reported questionnaire assessments, which increasingly rely on Internet-based platforms. We also discuss priority research needs related to methodological and technological improvement, data analysis and interpretation, data sharing, and other practical considerations, including improved assessment of exposure variability as well as exposure in multiple, critical life stages.

Quantifying spatial misclassification in exposure to noise complaints among low-income housing residents across New York City neighborhoods: a Global Positioning System (GPS) study

PURPOSE

To examine if there was spatial misclassification in exposure to neighborhood noise complaints among a sample of low-income housing residents in New York City, comparing home-based spatial buffers and Global Positioning System (GPS) daily path buffers.

METHODS

Data came from the community-based NYC Low-Income Housing, Neighborhoods and Health Study, where GPS tracking of the sample was conducted for a week (analytic n = 102). We created a GPS daily path buffer (a buffering zone drawn around GPS tracks) of 200 m and 400 m. We also used home-based buffers of 200 m and 400 m. Using these "neighborhoods" (or exposure areas), we calculated neighborhood exposure to noisy events from 311 complaints data (analytic n = 143,967). Friedman tests (to compare overall differences in neighborhood definitions) were applied.

RESULTS

There were differences in neighborhood noise complaints according to the selected neighborhood definitions (P < .05). For example, the mean neighborhood noise complaint count was 1196 per square kilometer for the 400-m home-based and 812 per square kilometer for the 400-m activity space buffer, illustrating how neighborhood definition influences the estimates of exposure to neighborhood noise complaints.

CONCLUSIONS

These analyses suggest that, whenever appropriate, GPS neighborhood definitions can be used in spatial epidemiology research in spatially mobile populations to understand people's lived experience.