Using crowdsourced data to monitor change in spatial patterns of bicycle ridership
| dc.contributor.author | Boss, Darren | |
| dc.contributor.author | Nelson, Trisalyn | |
| dc.contributor.author | Winters, Meghan | |
| dc.contributor.author | Ferster, Colin J. | |
| dc.date.accessioned | 2018-10-29T23:50:04Z | |
| dc.date.available | 2018-10-29T23:50:04Z | |
| dc.date.copyright | 2018 | en_US |
| dc.date.issued | 2018 | |
| dc.description.abstract | Cycling is a sustainable mode of transportation with numerous health, environmental and social benefits. Investments in cycling specific infrastructure are being made with the goal of increasing ridership and population health benefits. New infrastructure has the potential to impact the upgraded corridor as well as nearby street segments and cycling patterns across the city. Evaluation of the impact of new infrastructure is often limited to manual or automated counts of cyclists before and after construction, or to aggregate statistics for a large region. Due to methodological limitations and a lack of data, few spatially explicit approaches have been applied to evaluate how patterns of ridership change following investment in cycling infrastructure. Our goal is to demonstrate spatial analysis methods that can be applied to emerging sources of crowdsourced cycling data to monitor changes in the spatial-temporal distribution of cyclists across a city. Specifically, we use crowdsourced ridership data from Strava to examine changes in the spatial-temporal distribution of cyclists in Ottawa-Gatineau, Canada, using local indicators of spatial autocorrelation. Strava samples of bicyclists were correlated with automated counts at 11 locations and correlations ranged for 0.76 to 0.96. Using a local indicator of spatial autocorrelation, implemented on a network, we applied a threshold of change to separate noise from patterns of change that are unexpected given a null hypothesis that processes are random. Our results indicate that the installation or temporary closure of cycling infrastructure can be detected in patterns of Strava sample bicyclists and changes in one location impact flow and relative volume of cyclists at multiple locations in the city. City planners, public health professionals, and researchers can use spatial patterns of Strava sampled bicyclists to monitor city-wide changes in ridership patterns following investment in cycling infrastructure or other transportation network change. | en_US |
| dc.description.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Faculty | en_US |
| dc.description.sponsorship | This research was supported by the Natural Sciences and Engineering Research Council of Canada. We would like to thank the City of Ottawa for their assistance in data collection and support throughout the project. | en_US |
| dc.identifier.citation | Boss, D., Nelson, T. Winters, M. & Ferster, C.J. (2018). Using crowdsourced data to monitor change in spatial patterns of bicycle ridership. Journal of Transport & Health, 9, 226-233. https://doi.org/10.1016/j.jth.2018.02.008 | en_US |
| dc.identifier.uri | https://doi.org/10.1016/j.jth.2018.02.008 | |
| dc.identifier.uri | http://hdl.handle.net/1828/10199 | |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Transport & Health | en_US |
| dc.subject | Cycling | |
| dc.subject | Crowdsource | |
| dc.subject | Spatial analysis | |
| dc.subject | Networks | |
| dc.subject | Infrastructure | |
| dc.subject.department | Department of Geography | |
| dc.title | Using crowdsourced data to monitor change in spatial patterns of bicycle ridership | en_US |
| dc.type | Article | en_US |