Research Output

Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data

  Navigation when running is exploratory, characterised by both starting and ending in the same location, and iteratively foraging the environment to find areas with the most suitable running conditions. Runners do not wish to be explicitly directed, or refer to navigation aids that cause them to stop running, such as maps. Such undirected navigation is also common in other 'on-foot' scenarios, but how to support it is under-investigated. We contribute a novel method that uses crowd-sourced venue databases to rate a geographical area on its suitability to run in using linear regression. Our regression model is able to accurately predict the suitability of an area to run in (Pearson r=0.74) with a low mean error (RMSE=1.0). We outline how our method can support runners, and can be applied to other undirected navigation scenarios.

  • Date:

    24 August 2015

  • Publication Status:

    Published

  • Publisher

    ACM

  • DOI:

    10.1145/2785830.2785879

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

Citation

McGookin, D., Gkatzia, D., & Hastie, H. (2015). Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, 357-361. https://doi.org/10.1145/2785830.2785879

Authors

Keywords

Exploratory Navigation; Running; Machine Learning; Regression Analysis; Foursquare; Pedestrian Navigation; OpenStreetMap

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