How Are Macro-Scale and Micro-Scale Built Environments Associated with Running Activity? The Application of Strava Data and Deep Learning in Inner London

Abstract

Running can promote public health. However, the association between running and the built environment, especially in terms of micro street-level factors, has rarely been studied. This study explored the influence of built environments at different scales on running in Inner London. The 5Ds framework (density, diversity, design, destination accessibility, and distance to transit) was used to classify the macro-scale features, and computer vision (CV) and deep learning (DL) were used to measure the micro-scale features. We extracted the accumulated GPS running data of 40,290 sample points from Strava. The spatial autoregressive combined (SAC) model revealed the spatial autocorrelation effect. The result showed that, for macro-scale features: (1) running occurs more frequently on trunk, primary, secondary, and tertiary roads, cycleways, and footways, but runners choose tracks, paths, pedestrian streets, and service streets relatively less; (2) safety, larger open space areas, and longer street lengths promote running; (3) streets with higher accessibility might attract runners (according to a spatial syntactic analysis); and (4) higher job density, POI entropy, canopy density, and high levels of PM 2.5 might impede running. For micro-scale features: (1) wider roads (especially sidewalks), more streetlights, trees, higher sky openness, and proximity to mountains and water facilitate running; and (2) more architectural interfaces, fences, and plants with low branching points might hinder running. The results revealed the linkages between built environments (on the macro- and micro-scale) and running in Inner London, which can provide practical suggestions for creating running-friendly cities.

Concatenating Daily Exercise Routes with Public Sports Facilities, Bicycle Lanes, and Green Spaces: A Feasibility Analysis in Nanjing, China

“…Bicycle-oriented spaces should also receive attention. Previous studies have shown that micro-scale built environments, such as wider roads with more streetlights and trees, a higher sky openness, and a proximity to mountains and water, facilitate running [53,54]. Similarly, bicycle-oriented spaces are easily incorporated into the urban fabric through the design of streetscapes, greenways, or greenbelts to promote cycling and improve the travel experience.…”

Exploring the Relationship between Urban Vibrancy and Built Environment Using Multi-Source Data: Case Study in Munich

“…Specifically, density factors include the population density, employment density, and building density; diversity pertains to mixed land use; and design incorporates factors like the intersection density and sidewalk continuity. Subsequent research integrated additional dimensions, including the distance to transit, destination accessibility, demand management, and demographics, enabling more comprehensive and nuanced assessments of built environments [12,19].…”

“…Owing to the accessibility of data, existing studies on the relationship between the built environment and urban vibrancy have primarily focused on Asian countries such as China and Japan, while empirical research in the European context remains relatively limited. The specific concerns include (1) the indicator systems and methods for quantifying and measuring the built environment and urban vibrancy [4,5,12]; (2) the spatial scales and heterogeneity of the built environment that influence urban vibrancy [13,17,22,23]; (3) the relative importance of different built environment dimensions in influencing urban vibrancy [11,17,19,24,25]; (4) and the causal mechanisms and pathways through which built environment factors shape urban vibrancy [9,26,27].…”

An Umbrella Review of the Best and Most Up-to-Date Evidence on the Built Environment and Physical Activity in Older Adults ≥60 Years

“…The built environment (BE), defined as human-created physical aspects of the environment (e.g., buildings, parks, neighbourhoods) [16], is thought to play an important role in heathy aging [17]. Macro-scale BE features include neighbourhood-level structural features (e.g., street connectivity, density, mixed land use) whereas micro-scale BE features include smaller street-level features (e.g., benches, light, sidewalk conditions), and both have differential effects on physical activity [18,19]. Older adults may experience unique challenges to interacting with their physical environment, including physical and psychological barriers such as frailty, fear of falls, and concerns around safety from long distances to destinations, steep sidewalk slopes, high traffic-speed, and crime [20,21].…”