Association between passively collected walking and bicycling data and purposefully collected active commuting survey data-United States, 2019.

Commercially-available location-based services (LBS) data derived primarily from mobile devices may provide an alternative to surveys for monitoring physically-active transportation. Using Spearman correlation, we compared county-level metrics of walking and bicycling from StreetLight with metrics of physically-active commuting among U.S. workers from the American Community Survey. Our strongest pair of metrics ranked counties (n = 298) similarly for walking (rho = 0.53 [95% CI: 0.44-0.61]) and bicycling (rho = 0.61 [0.53-0.67]). Correlations were higher for denser and more urban counties. LBS data may offer public health and transportation professionals timely information on walking and bicycling behavior at finer geographic scales than some existing surveys.

Developing a granular scale environmental burden index (EBI) for diverse land cover types across the contiguous United States.

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.