The Experience of Running: Recommending Routes Using Sensory Mapping in Urban Environments

TL;DR

The paper tackles the challenge of tailoring urban running routes to the subjective experience of runners. It employs an exploratory sequential design, starting with 7 qualitative interviews and a large online survey (n=387) to identify three Experience of Running dimensions ($ERS$): $\text{performance & achievement}$, $\text{environment}$, and $\text{mind & social connectedness}$. It then derives two path preferences, scenic and urban, and builds a routing engine that weights seven environmental dimensions with path-type-specific coefficients, informed by sensory-mapping data (Flickr-based smell and sound, LIWC-like mood cues) and OpenStreetMap/crime data, implemented on GraphHopper. The ERS is distilled into a compact six-item short form for pre- and post-run assessment, and a two-route framework (scenic vs urban) is validated through clustering and qualitative feedback. Practically, the work provides a data-driven approach to generate experience-aligned running routes and offers guidelines and open-source tooling for integrating this into mobile and wearable running apps to enhance safety, enjoyment, and adherence to activity.

Abstract

Depending on the route, runners may experience frustration, freedom, or fulfilment. However, finding routes that are conducive to the psychological experience of running remains an unresolved task in the literature. In a mixed-method study, we interviewed 7 runners to identify themes contributing to running experience, and quantitatively examined these themes in an online survey with 387 runners. Using Principal Component Analysis on the survey responses, we developed a short experience sampling questionnaire that captures the three most important dimensions of running experience: \emph{performance \& achievement}, \emph{environment}, and \emph{mind \& social connectedness}. Using path preferences obtained from the online survey, we clustered them into two types of routes: \emph{scenic} (associated with nature and greenery) and \emph{urban} (characterized by the presence of people); and developed a routing engine for path recommendations. We discuss challenges faced in developing the routing engine, and provide guidelines to integrate it into mobile and wearable running apps.

Figures (6)

• Figure 1: Environmental preferences of scenic and urban running paths. The plots show the preference ratings for each item (in the range of 'totally dislike' (-2) to 'totally like' (2)). Results of the Holm-Bonferroni corrected Mann-Whitney U comparison test are shown above each criterion (uncorrected, original $\alpha$ was '*' - $p < 0.05$, '**' - $p<0.01$, '***' - $p<0.001$.)
• Figure 2: Example showing scenic (blue, eastwards route) and urban (red, westwards route) paths of 5km length in central London.
• Figure 3: (a) Coverage of evaluated routes, and (b) the area used to characterize those routes using Flickr images.
• Figure 4: The relative importance of computer vision tags along the routes, with tags positioned higher on the vertical axis appearing more frequently in scenic routes compared to urban ones. Tags with a relative importance close to 1 are equally represented in both route types. Circle size corresponds to the number of occurrences of each tag.
• Figure 5: Overview of the main themes and sub-themes from the interviews.