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

David  McGookin; Dimitra Gkatzia; Helen Hastie

doi:10.1145/2785830.2785879

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 proceeding › Conference contribution

3 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 language	English
Title of host publication	MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services
Publisher	Association for Computing Machinery
Pages	357-361
Number of pages	5
ISBN (Print)	9781450336529
DOIs	https://doi.org/10.1145/2785830.2785879
Publication status	Published - 24 Aug 2015
Event	17th International Conference on Human-Computer Interaction with Mobile Devices and Services 2015 - Copenhagen, Denmark Duration: 24 Aug 2015 → 27 Aug 2015

Conference

Conference	17th International Conference on Human-Computer Interaction with Mobile Devices and Services 2015
Abbreviated title	MobileHCI 2015
Country/Territory	Denmark
City	Copenhagen
Period	24/08/15 → 27/08/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

ASJC Scopus subject areas

Information Systems
Software
Computer Networks and Communications
Human-Computer Interaction

Access to Document

10.1145/2785830.2785879

http://mobilehci.acm.org/2015/accepted_papers.html

Cite this

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

@inproceedings{5403d61901e84bb9a94b034a93d5a3bc,

title = "Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data",

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.",

keywords = "Exploratory navigation, Foursquare, Machine learning, Open street map, Pedestrian navigation, Regression analysis, Running",

author = "David McGookin and Dimitra Gkatzia and Helen Hastie",

year = "2015",

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doi = "10.1145/2785830.2785879",

language = "English",

isbn = "9781450336529",

pages = "357--361",

booktitle = "MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services",

publisher = "Association for Computing Machinery",

address = "United States",

note = "17th International Conference on Human-Computer Interaction with Mobile Devices and Services 2015, MobileHCI 2015 ; Conference date: 24-08-2015 Through 27-08-2015",

}

McGookin, D, Gkatzia, D & Hastie, H 2015, Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data. in MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. Association for Computing Machinery, pp. 357-361, 17th International Conference on Human-Computer Interaction with Mobile Devices and Services 2015, Copenhagen, Denmark, 24/08/15. https://doi.org/10.1145/2785830.2785879

Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data. / McGookin, David; Gkatzia, Dimitra; Hastie, Helen.
MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. Association for Computing Machinery, 2015. p. 357-361.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Exploratory Navigation for Runners Through Geographic Area Classification with Crowd-Sourced Data

AU - McGookin, David

AU - Gkatzia, Dimitra

AU - Hastie, Helen

PY - 2015/8/24

Y1 - 2015/8/24

N2 - 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.

AB - 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.

KW - Exploratory navigation

KW - Foursquare

KW - Machine learning

KW - Open street map

KW - Pedestrian navigation

KW - Regression analysis

KW - Running

UR - https://www.scopus.com/pages/publications/84959420388

U2 - 10.1145/2785830.2785879

DO - 10.1145/2785830.2785879

M3 - Conference contribution

SN - 9781450336529

SP - 357

EP - 361

BT - MobileHCI 2015 - Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services

PB - Association for Computing Machinery

T2 - 17th International Conference on Human-Computer Interaction with Mobile Devices and Services 2015

Y2 - 24 August 2015 through 27 August 2015

ER -

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

Abstract

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this