Riding through the pandemic: Using Strava data to monitor the impacts of COVID-19 on spatial patterns of bicycling.

COVID-19 prompted a bike boom and cities around the world responded to increased demand for space to ride with street reallocations. Evaluating these interventions has been limited by a lack of spatially-temporally continuous ridership data. Our paper aims to address this gap using crowdsourced data on bicycle ridership. We evaluate changes in spatial patterns of bicycling during the first wave of the COVID-19 pandemic (Apr - Oct 2020) in Vancouver, Canada using Strava data and a local indicator of spatial autocorrelation. We map statistically significant change in ridership and reference clusters of change to a high-resolution base map. Amongst streets where bicycling increased, we measured the proportion of increase occurring on pre-existing bicycle facilities or street reallocations compared to streets without. In all our analyses, we evaluate patterns across subsets of Strava data representing recreation, commuting, and ridership generated by women and older adults (55 + ). We found consistent and unique patterns by trip purpose and demographics: samples generated by women and older adults showed increases near green and blue spaces and on street reallocations that increased access to parks, and these patterns were also mirrored in the recreation sample. Commute ridership highlighted distinct patterns of increase around the hospital district. Across all subsets most increases occurred on bicycle facilities (pre-existing or provisional), with a strong preference for high-comfort facilities. We demonstrate that changes in spatial patterns of bicycle ridership can be monitored using Strava data, and that nuanced patterns can be identified using trip and demographic labels in the data.

Crowdsourced Data for Physical Activity-Built Environment Research: Applying Strava Data in Chengdu, China.

The lack of physical activity has become a rigorous challenge for many countries, and the relationship between physical activity and the built environment has become a hot research topic in recent decades. This study uses the Strava Heatmap (novel crowdsourced data) to extract the distribution of cycling and running tracks in central Chengdu in December 2021 (during the COVID-19 pandemic) and develops spatial regression models for numerous 500 × 500 m grids (N = 2,788) to assess the impacts of the built environment on the cycling and running intensity indices. The findings are summarized as follows. First, land-use mix has insignificant effects on the physical activity of residents, which largely contrasts with the evidence gathered from previous studies. Second, road density, water area, green space area, number of stadiums, and number of enterprises significantly facilitate cycling and running. Third, river line length and the light index have positive associations with running but not with cycling. Fourth, housing price is positively correlated with cycling and running. Fifth, schools seem to discourage these two types of physical activities during the COVID-19 pandemic. This study provides practical implications (e.g., green space planning and public space management) for urban planners, practitioners, and policymakers.