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, socio-economic 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.

This study explores the potential influence of the urban built environment on pedestrian flow in an intermediate Latin American city, Cuenca-Ecuador. Data from samples of 48 street segments were analyzed to explore how physical and spatial features of streets and sidewalks, as well as land use and occupation conditions influence the number of pedestrians. A quantitative approach was used to model the individual and combined influences of several variables on pedestrian counts using multiple regression models. Results from statistical modeling indicate that variables related to the physical features of the streets, such as sidewalk width, are positively correlated to pedestrian counts, whereas parking space and front setback are negatively correlated. We conclude that streets in our study area are largely inadequate for pedestrians due to their poor design, scarce walking infrastructure, and the prevalence of all sorts of obstacles on the sidewalks. Pedestrians, however, seem to cope with this adverse environment by adopting different strategies. We expect that this study will help city planners design better environments for non-motorized travel in intermediate cities.