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Publicly available, outdoor webcams continuously view the world and share images. These cameras include traffic cams, campus cams, ski-resort cams, etc. The Archive of Many Outdoor Scenes (AMOS) is a project aiming to geolocate, annotate, archive, and visualize these cameras and images to serve as a resource for a wide variety of scientific applications. The AMOS dataset has archived over 750 million images of outdoor environments from 27,000 webcams since 2006. Our goal is to utilize the AMOS image dataset and crowdsourcing to develop reliable and valid tools to improve physical activity assessment via online, outdoor webcam capture of global physical activity patterns and urban built environment characteristics.

Results and figures of the article by Adlakha et al. (2014) contained minor errors, which we hereby rectify. Results show that a large majority of running and walking routes were through or tangential to a park or green space. A total of 1,722.01 miles from 287 running routes and 236.84 miles from 71 walking routes appear in Figure ​Figure11 and Table ​Table1.1. The average lengths of a run and walk in this sample were 6.00 and 3.33 miles, respectively. Of all the parks in the study area, 70% were located in low-income neighborhoods. Of the 287 running routes, 80.80% traversed a park at some point during the run and 6.97% of these runs took place in parks located in low-SES neighborhoods. Of the 71 walking routes, 70.40% traversed a park at some point during the walk and 15.50% of walking routes occurred in parks located in low-SES neighborhoods. Figure ​Figure22 illustrates the availability of many parks across St. Louis, but shows fewer mapped running or walking routes in the northern half of the region that features more low-SES neighborhoods. Figure 1 Running routes, walking routes, and poverty rate in St. Louis, MO, USA. Table 1 Use of parks in St. Louis, MO for physical activity in 2012a. Figure 2 Running and walking routes in parks and poverty rate in St. Louis, MO, USA. The odds of reported running and walking routes traversing low-SES neighborhoods were significantly lower than the odds of running and walking routes reported in higher-SES neighborhoods (runs: OR = 0.36, CI = 0.21–0.62; walks: OR = 0.41, CI = 0.23–0.73) (Table ​(Table2).2). The odds of running in a park in a low-SES neighborhood were 52% lower than running in a park in a higher-SES neighborhood (OR = 0.48, CI = 0.29–0.79). The odds of walking reported in a park in a low-SES neighborhood were 64% lower than walking in a park in a higher-SES neighborhood (OR = 0.36, CI = 0.16–0.82). Table 2 Logistic regression: odds of running and walking in a low-SES neighborhood (N = 238) and park (N = 511), compared to higher-SES neighborhoods. Revised results indicate decreased odds of reported running and walking in low-SES St. Louis neighborhoods compared to higher-SES St. Louis neighborhoods (Table ​(Table1).1). This finding is consistent with the disparate rates of PA in low versus higher-SES areas (1, 2). Overall, the lower odds of reported running and walking in low-SES neighborhoods and parks located in these low-SES neighborhoods compared to higher-SES neighborhoods and parks corroborates several health and environmental disparities between north and south St. Louis.

The Archive of Many Outdoor Scenes has captured 400 million images. Many of these cameras and images are of street intersections, a subset of which has experienced built environment improvements during the past seven years. We identified six cameras in Washington, DC, and uploaded 120 images from each before a built environment change (2007) and after (2010) to the crowdsourcing website Amazon Mechanical Turk (n=1,440). Five unique MTurk workers annotated each image, counting the number of pedestrians, cyclists, and vehicles. Two trained Research Assistants completed the same tasks. Reliability and validity statistics of MTurk workers revealed substantial agreement in annotating captured images of pedestrians and vehicles. Using the mean annotation of four MTurk workers proved most parsimonious for valid results. Crowdsourcing was shown to be a reliable and valid workforce for annotating images of outdoor human behavior.