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

McGookin, David; Gkatzia, Dimitra; Hastie, Helen

Authors

David McGookin

Helen Hastie



Abstract

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.

Presentation Conference Type Conference Paper (Published)
Conference Name 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
Start Date Aug 24, 2015
End Date Aug 27, 2015
Online Publication Date Aug 24, 2015
Publication Date 2015
Deposit Date Aug 1, 2016
Publisher Association for Computing Machinery (ACM)
Pages 357-361
Series Title Mobile HCI '15
Book Title Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
ISBN 978-1-4503-3652-9
DOI https://doi.org/10.1145/2785830.2785879
Keywords Exploratory Navigation; Running; Machine Learning; Regression Analysis; Foursquare; Pedestrian Navigation; OpenStreetMap
Public URL http://researchrepository.napier.ac.uk/Output/321783
Publisher URL http://doi.acm.org/10.1145/2785830.2785879