
The widespread adoption of mobile phone and other location-tracking devices, and the enormous amounts of data they produce, has provided municipalities with the opportunity to automate previously time-consuming and labour-intensive data collection processes. Municipal planners, in particular, have begun to integrate the aggregated data sets of private urban technology platforms into active transportation and broader infrastructure planning initiatives. To date, however, there has been limited research on the implications of this integration for municipal decision-making and governance processes. Using the Strava Metro data stream and its free-access model as a case study, this paper explores both the motivations behind municipal adoption of the Strava platform and the benefits that accrue from its usage. Through the application of a mixed methods approach, including the building of a use case database via a search of internet and academic literature sources and qualitative interviews with municipal planning staff, our research examines how Strava data is used to support the work of municipal planners and evaluates the strengths and weaknesses of that use. Our study finds that Strava Metro data aided municipal staff in the planning of cycling and pedestrian infrastructure, complementing available in-house data sets; helped spur new active transportation initiatives; and enabled innovation and professional curiosity on the part of planners. The paper concludes by exploring the ramifications of Strava data for community wellness and broader public realm improvements, as well as extending a discussion with respect to the platform’s sociodemographic representativeness and related limitations.
Data, Données, Strava, évolution de la technologie, platformization, plateformisation, evolving technology
Data, Données, Strava, évolution de la technologie, platformization, plateformisation, evolving technology
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| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
