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

McGookin, David; Gkatzia, Dimitra; Hastie, Helen

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.

Citation

McGookin, D., Gkatzia, D., & Hastie, H. (2015, August). Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data. Presented at 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, Copenhagen, Denmark

• 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