Descrição

This study explores the effect of the spatial configuration of street networks on movement
patterns of users of a cycling monitoring app, employing crowdsourced information from
OpenStreetMap and Strava Metro. Choice and Integration measures from Space Syntax were
used to analyse the street network’s configuration for different radiuses. Multiple linear regression
models were fitted to explore the influence of these measures on cycling activity at the
street segment level after controlling other variables such as land use, household density,
socioeconomic status, and cycling infrastructure. The variation of such influence for different time
periods (weekday vs. weekend) and trip purposes (commuting vs. sports) was also analysed. The
results show a positive significant association between normalised angular choice (NACH) and
cycling activity. Although the final regression model explained 5.5% of the log-likelihood of the
intercept model, it represents an important improvement compared with the base (control-only)
model (3.8%). The incidence rate ratio of NACH’s Z scores was 1.63, implying that for an increase
of one standard deviation of NACH, there is an expected increment of about 63% in the total
cyclist counts while keeping all other variables the same. These results are of interest for
researchers, practitioners, and urban planners, since the inclusion of Space Syntax measures
derived from available public data can improve movement behaviour modelling and cycling
infrastructure planning and design.