Webcams, Crowdsourcing, and Enhanced Crosswalks: Developing a Novel Method to Analyze Active Transportation

Demographic and clinical profile of an inception cohort of road trauma survivors

BACKGROUND

Road trauma is a major public health concern, often resulting in reduced health-related quality of life and prolonged absenteeism from work even after so-called 'minor' injuries that do not result in hospitalization. This manuscript compares pre-injury health, sociodemographic characteristics and injury details between age, sex, and road user categories in a cohort of 1,480 road trauma survivors.

METHODS

This was a prospective observational inception cohort study of road trauma survivors recruited between July 2018 and March 2020 from three trauma centres in British Columbia, Canada. Participants were aged ≥ 16 years and arrived in a participating emergency department within 24 h of involvement in a motor vehicle collision. Data were collected from structured interviews and review of medical records.

RESULTS

The cohort of 1,480 road trauma survivors included 280 pedestrians, 174 cyclists, 118 motorcyclists, 683 motor vehicle drivers, and 225 passengers. Median age was 40 (IQR = [27, 57]) years; 680 (46%) were female. Males and younger patients were significantly more likely to report better pre-injury physical health. Motorcyclists and cyclists tended to report better physical health and less severe somatic symptoms, whereas pedestrians and motor vehicle drivers reported better mental health. Injury severity and hospital admission rates were higher in pedestrians and motorcyclists and lower in motorists. Upper and lower extremity injuries were most common in pedestrians, cyclists and motorcyclists, whereas neck injuries were most common in motor vehicle drivers and passengers.

CONCLUSIONS

In a large cohort of road trauma survivors, overall injury severity was low. Motorcyclists and pedestrians, but not cyclists, had more severe injuries than motorists. Extremity injuries were more common in vulnerable road users. Future research will investigate one-year recovery outcomes and identify risk factors for poor recovery.

Implementing Geospatial Science and Technology to Get to Zero New HIV Infections

PURPOSE OF REVIEW

Tremendous advancements have been made in HIV treatment and prevention during the last 40 years that zero new HIV cases has become an attainable goal declared by international agencies. However, new cases of HIV infection persist.

RECENT FINDINGS

The emerging field of geospatial science is positioned to play key role in the reduction of continued HIV incidence through technology-driven interventions and innovative research that gives insights into at-risk populations. As these methods become more utilized, findings consistently show the important role of location and environment plays in HIV incidence and treatment adherence. This includes distance to HIV provider, locations of where HIV transmissions occurs compared to where people with HIV reside, and how geospatial technology has been leveraged to identify unique insights among varying groups of those at increased risk for HIV, among others. Given these insights, leveraging geospatial technology would play a prominent role in achieving zero new cases of HIV infections.

Physical Activity Surveillance in the United States for Work and Commuting: Understanding the Impact on Population Health and Well-being

OBJECTIVE

To summarize and describe the current US surveillance systems that assess physical activity (PA) for work and commuting.

METHODS

An expert group conducted an environmental scan, generating a list (n = 18) which was ultimately reduced to 12, based on the inclusion of PA and/or sedentary behavior data.

RESULTS

The 12 surveys or surveillance systems summarized provide nationally representative data on occupational-level PA or individual-level PA at work, data on active commuting, some are scorecards that summarize workplace health best practices and allow benchmarking, and one is a comprehensive nationally representative survey of employers assessing programs and practices in different worksites.

CONCLUSIONS

The various surveillance systems and surveys/scorecards are disparate and need to be better analyzed and summarized to understand the impact of occupational-level PA and commuting on population health and well-being, life expectancy, and workforce productivity.

Field Test of a Passive Infrared Camera for Measuring Trail-Based Physical Activity

Introduction: Trails are ubiquitous and far-reaching, but research on the impact trails have on physical activity is limited by the lack of resource-efficient, accurate, and practical systematic observation tools. Commonly used infrared trail sensors count trail use and may broadly differentiate activity (i.e., bicyclist vs. pedestrian), but cannot detect nuances needed for outcomes research such as frequency, intensity, time, and type of activity. Motion-activated passive infrared cameras (PICs), used in ecological research and visitor management in wildlife areas, have potential applicability as a systematic observation data collection tool.

Materials and Methods: We conducted a 7-month field test of a PIC as a systematic observation data collection tool on a hiking trail, using photos to identify each trail user's physical activity type, age, sex, and other characteristics. We also tallied hourly trail use counts from the photos, using Bland–Altman plots, paired t-tests, Concordance Correlation Coefficient, Kendall's Tau-b, and a novel inter-counter reliability measure to test concordance against concurrent hourly counts from an infrared sensor.

Results: The field test proved informative, providing photos of 2,447 human users of the trail over 4,974 h of data collection. Nearly all of the users were walkers (94.0%) and most were male (69.2%). More of the males used the trail alone (44.8%) than did females (29.8%). Concordance was strong between instruments (p < 0.01), though biased (p < 0.01). Inter-counter reliability was 91.1% during the field study, but only 36.2% when excluding the hours with no detectable trail use on either device. Bland–Altman plots highlighted the tendency for the infrared sensor to provide higher counts, especially for the subsample of hours that had counts >0 on either device (14.0%; 694 h).

Discussion: The study's findings highlight the benefits of using PICs to track trail user characteristics despite the needs to further refine best practices for image coding, camera location, and settings. More widespread field use is limited by the extensive amount of time required to code photos and the need to validate the PICs as a trail use counter. The future potential of PICs as a trail-specific PA research and management tool is discussed.

Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate pedestrians in the captured images is a promising technique for analyzing pedestrian activity. However, it is challenging to efficiently transform the time series of pedestrian locations in the images to information suitable for geospatial analytics, as well as visualize data in a meaningful way to inform urban design or decision making. In this study, we propose to use a space-time cube (STC) representation of pedestrian data to analyze the spatio-temporal patterns of pedestrians in public spaces. We take advantage of AMOS (The Archive of Many Outdoor Scenes), a large database of images captured by thousands of publicly available, outdoor webcams. We developed a method to obtain georeferenced spatio-temporal data from webcams and to transform them into high-resolution continuous representation of pedestrian densities by combining bivariate kernel density estimation with trivariate, spatio-temporal spline interpolation. We demonstrate our method on two case studies analyzing pedestrian activity of two city plazas. The first case study explores daily and weekly spatio-temporal patterns of pedestrian activity while the second one highlights the differences in pattern before and after plaza’s redevelopment. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time series of pedestrian occurrences captured by webcam images.

Using street imagery and crowdsourcing internet marketplaces to measure motorcycle helmet use in Bangkok, Thailand

INTRODUCTION

The majority of Thailand's road traffic deaths occur on motorised two-wheeled or three-wheeled vehicles. Accurately measuring helmet use is important for the evaluation of new legislation and enforcement. Current methods for estimating helmet use involve roadside observation or surveillance of police and hospital records, both of which are time-consuming and costly. Our objective was to develop a novel method of estimating motorcycle helmet use.

METHODS

Using Google Maps, 3000 intersections in Bangkok were selected at random. At each intersection, hyperlinks of four images 90° apart were extracted. These 12 000 images were processed in Amazon Mechanical Turk using crowdsourcing to identify images containing motorcycles. The remaining images were sorted manually to determine helmet use.

RESULTS

After processing, 462 unique motorcycle drivers were analysed. The overall helmet wearing rate was 66.7 % (95% CI 62.6 % to 71.0 %). Taxi drivers had higher helmet use, 88.4% (95% CI 78.4% to 94.9%), compared with non-taxi drivers, 62.8% (95% CI 57.9% to 67.6%). Helmet use on non-residential roads, 85.2% (95% CI 78.1 % to 90.7%), was higher compared with residential roads, 58.5% (95% CI 52.8% to 64.1%). Using logistic regression, the odds of a taxi driver wearing a helmet compared with a non-taxi driver was significantly increased 1.490 (p<0.01). The odds of helmet use on non-residential roads as compared with residential roads was also increased at 1.389 (p<0.01).

CONCLUSION

This novel method of estimating helmet use has produced results similar to traditional methods. Applying this technology can reduce time and monetary costs and could be used anywhere street imagery is used. Future directions include automating this process through machine learning.

Are Mechanical Turk worker samples representative of health status and health behaviors in the U.S.?

Introduction

Amazon’s Mechanical Turk (MTurk) is frequently used to administer health-related surveys and experiments at a low cost, but little is known about its representativeness with regards to health status and behaviors.

Methods

A cross-sectional survey comprised of questions from the nationally-representative 2014 Behavioral Risk Factor Surveillance System (BRFSS) and 2014 National Health and Nutrition Examination Survey (NHANES) was administered to 591 MTurk workers and 393 masters in 2016. Health status (asthma, depression, BMI, and general health), health behaviors (influenza vaccination, health insurance, smoking, and physical activity), and demographic characteristics of the two MTurk populations (workers and masters) were compared to each other and, using Poisson regression, to a nationally-representative BRFSS and NHANES samples.

Results

Workers and master demographics were similar. MTurk users were more likely to be aged under 50 years compared to the national sample (86% vs. 55%) and more likely to complete a college degree than the national sample (50% vs. 26%). Adjusting for covariates, MTurk users were less likely to be vaccinated for influenza, to smoke, to have asthma, to self-report being in excellent or very good health, to exercise, and have health insurance but over twice as likely to screen positive for depression relative to a national sample. Results were fairly consistent among different age groups.

Conclusions

MTurk workers are not a generalizable population with regards to health status and behaviors; deviations did not follow a trend. Appropriate health-related uses for MTurk and ways to improve upon the generalizability of MTurk health studies are proposed.

Comparing Amazon's Mechanical Turk Platform to Conventional Data Collection Methods in the Health and Medical Research Literature

BACKGROUND

The goal of this article is to conduct an assessment of the peer-reviewed primary literature with study objectives to analyze Amazon.com 's Mechanical Turk (MTurk) as a research tool in a health services research and medical context.

METHODS

Searches of Google Scholar and PubMed databases were conducted in February 2017. We screened article titles and abstracts to identify relevant articles that compare data from MTurk samples in a health and medical context to another sample, expert opinion, or other gold standard. Full-text manuscript reviews were conducted for the 35 articles that met the study criteria.

RESULTS

The vast majority of the studies supported the use of MTurk for a variety of academic purposes.

DISCUSSION

The literature overwhelmingly concludes that MTurk is an efficient, reliable, cost-effective tool for generating sample responses that are largely comparable to those collected via more conventional means. Caveats include survey responses may not be generalizable to the US population.

Child Pedestrian Street-Crossing Behaviors outside a Primary School: Developing Observational Methodologies and Data from a Case Study in Changsha, China

BACKGROUND

Child pedestrian injury is a significant global public health challenge, and prevention programming requires an understanding of the context children face when crossing the street. Methods to understand children's behavior in real-world pedestrian settings are sparse in low- and middle-income countries.

METHODS

Children in grades 1-6 were videotaped crossing a three-lane street outside their school in Changsha, China. Tapes were coded to collect: (1) extent of adult supervision, (2) whom children crossed the street with, and (3) safe behaviors exhibited by children.

RESULTS

Observational videotape methods yielded data that could be reliably coded to understand Chinese children's behavior crossing the street outside their primary school. In total, we observed 216 child pedestrians crossing the street, including 105 girls, 105 boys, and 6 for whom gender could not be determined. 51% of observations occurred in the morning before school and 49% in the afternoon after school. Children encountered a busy and somewhat-chaotic traffic environment. Adults were always present to help, but children appeared to heed adult advice concerning the crossing only about 70% of the time. Fewer than 1/3 of children looked at oncoming traffic before they entered a lane and over 1/3 entered a lane with moving traffic approaching.

CONCLUSION

Observational methods of coding videotaped behavior proved effective to understand and code children's risk and safety while crossing the street outside their primary school. At the street environment we studied, we found that children's pedestrian behavior involved significant risk.