Exploratory navigation for runners through geographic area classification with crowd-sourced data

David McGookin, Dimitra Gkatzia, Helen Hastie

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
Place of PublicationNew York
PublisherACM
Pages357-361
Number of pages5
ISBN (Electronic)978-1-4503-3652-9
ISBN (Print)978-1-4503-3652-9
DOIs
Publication statusPublished - 24 Aug 2015
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Human-Computer Interaction with Mobile Devices and Services - Copenhagen, Denmark
Duration: 24 Aug 201527 Aug 2015
Conference number: 17

Conference

ConferenceInternational Conference on Human-Computer Interaction with Mobile Devices and Services
Abbreviated titleMobileHCI
CountryDenmark
CityCopenhagen
Period24/08/201527/08/2015

Keywords

  • Exploratory navigation
  • Foursquare
  • Machine learning
  • Open street map
  • Pedestrian navigation
  • Regression analysis
  • Running

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