Associations between street connectivity and active transportation

Abstract

BackgroundPast studies of associations between measures of the built environment, particularly street connectivity, and active transportation (AT) or leisure walking/bicycling have largely failed to account for spatial autocorrelation of connectivity variables and have seldom examined both the propensity for AT and its duration in a coherent fashion. Such efforts could improve our understanding of the spatial and behavioral aspects of AT. We analyzed spatially identified data from Los Angeles and San Diego Counties collected as part of the 2001 California Health Interview Survey.ResultsPrincipal components analysis indicated that ~85% of the variance in nine measures of street connectivity are accounted for by two components representing buffers with short blocks and dense nodes (PRIN1) or buffers with longer blocks that still maintain a grid like structure (PRIN2). PRIN1 and PRIN2 were positively associated with active transportation (AT) after adjustment for diverse demographic and health related variables. Propensity and duration of AT were correlated in both Los Angeles (r = 0.14) and San Diego (r = 0.49) at the zip code level. Multivariate analysis could account for the correlation between the two outcomes.After controlling for demography, measures of the built environment and other factors, no spatial autocorrelation remained for propensity to report AT (i.e., report of AT appeared to be independent among neighborhood residents). However, very localized correlation was evident in duration of AT, particularly in San Diego, where the variance of duration, after accounting for spatial autocorrelation, was 5% smaller within small neighborhoods (~0.01 square latitude/longitude degrees = 0.6 mile diameter) compared to within larger zip code areas. Thus a finer spatial scale of analysis seems to be more appropriate for explaining variation in connectivity and AT.ConclusionsJoint analysis of the propensity and duration of AT behavior and an explicitly geographic approach can strengthen studies of the built environment and physical activity (PA), specifically AT. More rigorous analytical work on cross-sectional data, such as in the present study, continues to support the need for experimental and longitudinal study designs including the analysis of natural experiments to evaluate the utility of environmental interventions aimed at increasing PA.

Examining ways to enhance active transportation and the impact on commuters' health, wellbeing and sustainable development

“…Some studies showed that street connectivity is statistically significantly correlated with active transportation (Berrigan et al, 2010). More specifically, an explicitly geographic approach can strengthen the built environment and physical activity and active transportation.…”

Walkability and Street Intersections in Rural-Urban Fringes: A Decision Aiding Evaluation Procedure

“…The geometric, functional and operational characteristics of street intersections usually receive little mention, except for the rough typological features such as the number of streets converging at each node. This, for instance, is the case of the so called spatial connectivity indicators included in the most widely used methods for urban walkability evaluation [16,17], expressed as the number and type of nodes (commonly number of legs joining in each node) within large spatial units (square km, block, neighbourhood). As a result, the incorporation of spatial connectivity indicators into the composite indicators of walkability can lead to unreliable aggregate results, given that they combine detailed data on street segments, with rougher and less detailed description of intersections.…”

Valuing Public Investments to Support Bicycling

“…The bicycle mode choice/share is influenced by the presence of bicycle lanes/ paths; the lane width, the volume and speed of motorized traffic; competition for space between drivers and cyclists; the number of stops, traffic lights or other obstacles; the number of intersections and their characteristics, accident risk; and qualitative aspects about bicycle lanes such as continuity and connectivity or the presence of on-street parking (Berrigan, Pickle and Dill, 2010;Broach, Dill and Gliebe, 2012). Menghini et al (2010) conclude that trip length is the dominant factor for route choice, which is consistent with high time costs and/ or costs of physical exertion.…”