A geographically weighted regression model to examine the spatial variation of the socioeconomic and land-use factors associated with Strava bike activity in Austin, Texas. (October 2020) - Munira, Sirajum; Sener, Ipek N.
Abstract: Despite evidence showing the spatial nonstationarity of the determinants of bike activity, very few studies have addressed the phenomena, probably due to the limited sample size of the traditional count data. To address this gap, this study demonstrated the applicability of Strava bike activity data by developing a geographically weighted Poisson regression (GWPR) model that can reveal how the influence of socioeconomic and land-use factors vary across a region. The city of Austin was selected as a case study, and Strava bike volume was gleaned from 1494 intersections. The representativeness of the Strava data was first examined by comparing those data with the video-based actual bicycle volume data from 43 intersections in the study area. Despite the high deviation in several locations, Strava volume exhibited moderate linear relationships with actual volume. The GWPR model developed in this study outperformed the traditional global model and revealed significant spatial variability of nine variables related to age, income, education, transit stops, hub locations, offices, schools, trails, and sidewalk facilities. Notable spatial variations on bike activity were observed across the study area in terms of magnitude, direction, and significance of the impact for all model variables. The analysis and discussion offer guidance to practitioners and policy makers in tailoring policies and programs that consider the spatial context. The study also provides insights for understanding the potential use of crowdsourced data in examining bike activity, especially when resources are limited. Highlights: Explored Strava bike volume using a geographically weighted Poisson regression model. Examined the representativeness of Strava volume by comparing it with the actual data. Reported superior performance of the GWPR model over the traditional global model. Observed spatial variations of the variables in magnitude, direction & significance.