Data visibility of cyclists: social justice implications of Strava Metro data in transport planning

Abstract

This article investigates how the ‘datafication’ of cycling risks exacerbating existing mobility injustices associated with the privileging of data visibility over diversity. It focuses on the social justice implications of UK and US transport professionals’ perceptions of Strava Metro data, specifically around sample representativeness and bias. Our findings show how experts simultaneously recognise Strava Metro data as: (a) statistically valid and representative of observable cycling activities, including correlation with complementary cycle counter data. And as (b) demographically biased and unrepresentative of society, as Strava Metro data samples disproportionally capture journeys made by younger male cyclists. All expert interviewees emphasised the importance of considering a variety of data sources to support transport planning decisions, and emphasised Strava Metro data sample biases and limitations. However, our analysis of transport professional’s perceptions reveals how cycling app data, such as Strava Metro, has implications for social justice. These implications include distorting understandings of cycling at the detriment of diversifying cycling participation. Left unchecked, a privileging of hyper-visibility of select cyclists may slow efforts for socially sustainable and diverse cycling. The article closes with a discussion of the research and policy implications for emerging debates of artificial intelligence (AI) in transport.

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