Correlates of the Built Environment and Active Travel: Evidence from 20 US Metropolitan Areas

Le, Huyen; Buehler, Ralph; Hankey, Steve

doi:10.1289/ehp3389

Cited by 41 publications

(17 citation statements)

References 48 publications

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“…For example, continental-scale studies of environmental hazards are often limited by inconsistent data of varying quality to describe land use and urban form 35 – 38 . Similarly, research that assesses how the built environment impacts travel behavior (e.g., alternative transportation) 39 – 41 , physical activity 42 , 43 , and mental health 44 is often limited to a single urban area or suffers from crude or inconsistent measures across large geographies. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact these important human and environmental outcomes.…”

Section: Background and Summarymentioning

confidence: 99%

Combining expert and crowd-sourced training data to map urban form and functions for the continental US

et al. 2020

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Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.

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Section: Background and Summarymentioning

confidence: 99%

Combining expert and crowd-sourced training data to map urban form and functions for the continental US

et al. 2020

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“…Models support planners' and researchers' ability to understand transportation trends and scenarios based on limited data and to analyze policy and infrastructure changes for immediate and future contexts. Bicycle transportation models support analysis of the likelihood of cycling in a variety of conditions (25), including built environment factors (26), seasonal and weather factors (27), and temporal variation (28). Researchers' continuing model improvements may nonetheless be difficult to replicate or integrate into planning practice.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data

Dadashova

Griffin

Das

et al. 2020

Transportation Research Record

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Traffic volumes are fundamental for evaluating transportation systems, regardless of travel mode. A lack of counts for non-motorized modes poses a challenge for practitioners developing and managing multimodal transportation facilities, whether they want to evaluate transportation safety or the potential need for infrastructure changes, or to answer other questions about how and where people bicycle and walk. In recent years, researchers and practitioners alike have been using crowdsourced data to supplement the non-motorized counts. As such, several methods and tools have been developed. The objective of this paper is to take advantage of new data sources that provide a limited and biased sample of trips and combine them with traditional counts to develop a practical tool for estimating annual average daily bicycle (AADB) counts. This study developed a direct-demand model for estimating AADB in Texas. Data from 100 stations, installed in 12 cities across the state, was used together with the crowdsourced Strava, roadway inventory, and American Community Survey data to develop the count model for estimating AADB. The results indicate that crowdsourced Strava data is an acceptable predictor of bicycle counts, and when used with the roadway functional class and number of high-income households in a block group, can provide quite an accurate AADB estimate (29% prediction error).

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“…Few previous studies have examined greenspace with objective adult physical activity measures of both walking and cycling. Le et al [31] quantified the built environment surrounding bicycle and pedestrian counters in 20 US cities and found a greater positive effect on cycling than walking (though greenspace and bluespace were combined in their analysis).…”

Section: Path-based Greenspacementioning

confidence: 99%

Neighbourhood and path-based greenspace in three European countries: associations with objective physical activity

et al. 2021

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Background Greenspace has been associated with health benefits in many contexts. An important pathway may be through outdoor physical activity. We use a novel approach to examine the link between greenspace microenvironments and outdoor physical activity levels in the HEALS study conducted in Edinburgh (UK), the Netherlands, and Athens and Thessaloniki (Greece). Methods Using physical activity tracker recordings, 118 HEALS participants with young children were classified with regard to daily minutes of moderate to vigorous physical activity (MVPA); 60 were classified with regard to the metabolic equivalent task (MET)-minutes for each of the 1014 active trips they made. Greenspace indicators were generated for Normalised Difference Vegetation Index (NDVI), tree cover density (TCD), and green land use (GLU). We employed linear mixed-effects models to analyse (1) daily MVPA in relation to greenspace within 300 m and 1000 m of residential addresses and (2) trip MET-minutes in relation to average greenspace within a 50 m buffer of walking/cycling routes. Models were adjusted for activity, walkability, bluespace, age, sex, car ownership, dog ownership, season, weekday/weekend day, and local meteorology. Results There was no clear association between MVPA-minutes and any residential greenspace measure. For example, in fully adjusted models, a 10 percentage point increase in NDVI within 300 m of home was associated with a daily increase of 1.14 (95% CI − 0.41 to 2.70) minutes of MVPA. However, we did find evidence to indicate greenspace markers were positively linked to intensity and duration of activity: in fully adjusted models, 10 percentage point increases in trip NDVI, TCD, and GLU were associated with increases of 10.4 (95% CI: 4.43 to 16.4), 10.6 (95% CI: 4.96 to 16.3), and 3.36 (95% CI: 0.00 to 6.72) MET-minutes, respectively. The magnitude of associations with greenspace tended to be greater for cycling. Conclusions More strenuous or longer walking and cycling trips occurred in environments with more greenspace, but levels of residential greenspace did not have a clear link with outdoor MVPA. To build on our research, we suggest future work examine larger, more diverse populations and investigate the influence of greenspace for trip purpose and route preference.

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Correlates of the Built Environment and Active Travel: Evidence from 20 US Metropolitan Areas

Cited by 41 publications

References 48 publications

Combining expert and crowd-sourced training data to map urban form and functions for the continental US

Combining expert and crowd-sourced training data to map urban form and functions for the continental US

Estimation of Average Annual Daily Bicycle Counts using Crowdsourced Strava Data

Neighbourhood and path-based greenspace in three European countries: associations with objective physical activity

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