Welcome Guest [create an account] or log-in:

Chapter 6 Tracking via Volunteered Geographic Information

DOI: 10.23912/9781911635383-4574

ISBN: 9781911635383

Published: Nov 2020

Component type: chapter

Published in: Tracking Tourists

Parent DOI: 10.23912/9781911635383-4277



Over the past 20 years, the use of location-based tracking has become increasingly popular. The introduction of GPS technology into devices such as phones and watches, and its incorporation into tracking apps, has led to widespread use of apps which track activities, particularly those of a sporting nature. There are now over 318,000 health and fitness apps – called mHealth apps (Byambasuren et al., 2018) – and it is estimated that 75% of runners now use them (Janssen et al., 2017). Many of these apps contain the ability for users to track their movement and share it with fellow app users – Strava alone has 42 million accounts with 1 million users each month (Haden, 2019), but others include MapMyFitness, Adidas Running, and Google Fit. Importantly for this book, the data that is produced from mHealth apps is continuous point geo-referenced data that is visualised for the user as a defined route undertaken during a particular activity. This route, and the temporal and spatial aspects of the activity, can be viewed by the user and then released online for their online network to view. Most commonly, it is referred to as volunteered geographic information (VGI). The data that is generated from mHealth apps can be sourced by researchers; this is often referred to as crowd sourcing. Researchers can gather large amounts of data of entire paths taken by individual users, either via gaining consent from individual users to share their routes, or via APIs provided by the app developer which provide access to large amounts of routes and their associated statistics. VGI provides researchers with great potential to facilitate research that assesses tourists’ movement through space and time (Heikinheimo et al., 2017). However, as is the case with single point geo-referenced data (discussed in the previous chapter), research in this space is disparate and tends to focus on one platform at a time, or one context at a time. The rapid increase in VGI is arguably due to three factors: developments in wearable technology; developments in location based technology that has been integrated into smart phone and watch apps; and an increase in usage of urban spaces for walking, running and biking. The latter is largely due to an increased interest in healthy lifestyles and exercise (Santos et al., 2016; Brown et al., 2014) and presents issues for park managers, including those related to environmental impacts due to overuse and conflicts between different types of users, such as walkers and bike riders (Santos et al., 2016; Norman and Pickering, 2017; Pickering et al., 2011; Rossi et al., 2013). This chapter will explore how VGI data can assist researchers and managers in understanding these issues, along with tourists’ mobility.

Sample content

Click here to download PDF


For the source title:

Cite as

Hardy, 2020

Hardy, A. (2020) "Chapter 6 Tracking via Volunteered Geographic Information" In: Hardy, A. (ed) . Oxford: Goodfellow Publishers http://dx.doi.org/10.23912/9781911635383-4574


Bauer, C. (2013) On the (in)accuracy of GPS measures of smartphones: A study of running tracking applications, In Proceedings of 11th Conference on Advances in Mobile Computing & Multimedia (pp. 335-341).


Brown, G., Schebella, M.F. and Weber, D. (2014) Using participatory GIS to measure physical activity and urban park benefits, Landscape and Urban Planning, 121, 34-44.


Byambasuren, O., Sanders, S., Beller, E. and Glasziou, P. (2018) Prescribable mHealth apps identified from an overview of systematic reviews, NPJ Digital Medicine b(1), 1-12.


Campelo, M. B. and Mendes, R. M. N. (2016) Comparing webshare services to assess mountain bike use in protected areas, Journal of Outdoor Recreation and Tourism, 15, 82-88.


Cervero, R. and Duncan, M. (2003) Walking, bicycling, and urban landscapes: Evidence from the San Francisco Bay area, American Journal of Public Health, 93(9), 1478-1483.


Conrow, L., Wentz, E., Nelson, T. and Pettit, C. (2018) Comparing spatial patterns of crowdsourced and conventional bicycling datasets, Applied Geography, 92, 21-30. Foody, G. M., See, L., Fritz, S., Van der Velde, M., Perger, C., Schill, C., Boyd, D.S. and Comber, A. (2015) Accurate attribute mapping from volunteered geographic information: issues of volunteer quantity and quality, The Cartographic Journal, 52 (4), 336-344.


Haden, J. (2019) Strava has 42 million users and adds 1 million more each month. Will it be the next great sports brand?' Inc., Available from: https://www.inc.com/jeff-haden/10-years-in-strava-now-adds-1-million-users-a- month-but-can-it-become-next-great-sports-brand.html [accessed 5th August 2020].

Heesch, K. C., and Langdon, M. (2017) The usefulness of GPS bicycle tracking data for evaluating the impact of infrastructure change on cycling behaviour, Health Promotion Journal of Australia, 27(3), 222-229. Heikinheimo, V., Minin, E. D., Tenkanen, H., Hausmann, A., Erkkonen, J. and


Toivonen, T. (2017) User-generated geographic information for visitor monitoring in a national park: A comparison of social media data and visitor survey. ISPRS International, Journal of Geo-Information, 6(3), 85.


Janssen, M., Scheerder, J., Thibaut, E., Brombacher, A. and Vos, S. (2017) Who uses running apps and sports watches? Determinants and consumer profiles of event runners ' usage of running-related smartphone applications and sports watches, PloS one, 12 (7), e0181167.


Korpilo, S., Virtanen, T., and Lehvävirta, S. (2017) Smartphone GPS tracking - inexpensive and efficient data collection on recreational movement, Landscape and Urban Planning, 157, 608-617.


Newsome, D., Moore, S. and Dowling, R. (2012) Natural Area Tourism: Ecology, impacts and management, Bristol: Chanel View Publications.


Norman, P. and Pickering, C.M. (2017) Using volunteered geographic information to assess park visitation: Comparing three on-line platforms, Applied Geography, 89, 163-172


Norman, P. and Pickering, C.M. (2019) Factors influencing park popularity for mountain bikers, walkers and runners as indicated by social media route data, Journal of Environmental Management, 249.


Norman, P., Pickering, C.M. and Castley, G. (2019) What can volunteered geographic information tell us about the different ways mountain bikers, runners and walkers use urban reserves?, Landscape and Urban Planning, 185, 180-190.


Oksanen, J., Bergman, S., Sainio, J. and Westerholm, J. (2017) Methods for deriving and calibrating privacy-preserving heat maps from mobile sports tracking application data. Journal of Transport Geography, 48, 135-144.


Pickering, C.M., Rossi, S. and Barros, A. (2011) Assessing the impacts of mountain biking and hiking on subalpine grassland in Australia using an experimental protocol, Journal of Environmental Management, 92(12), 3049-3057.


Rice, W., Mueller, J.T., Graefe, A., Taff, B. D. (2019) Detailing an approach for cost-effective visitor-use monitoring using crowdsourced activity data, Journal of Park and Recreation Administration; Urbana, 37 (2).


Rossi, S., Pickering, C., and Byrne, J. (2013) Attitudes of local park visitors: assessing the social impacts of the South East Queensland horse riding trail network. Brisbane: Department of Science, Information Technology, Innovation and the Arts. Salas-Olmedo, M.H., Moya-Gómez, B., García-Palomares, J.C. and Gutiérrez, J. (2018) Tourists' digital footprint in cities: Comparing Big Data sources, Tourism Management, 66, 13-25.


Santos, T., Mendes, R. N., & Vasco, A. (2016) Recreational activities in urban parks: Spatial interactions among users, Journal of Outdoor Recreation and Tourism, 15, 1-9.


Strava (2020) Strava Metro. Available from: https://metro.strava.com/ [Accessed 5th August 2020].


Published in Tracking Tourists

Paperback format [Details]Price: £36.99Copies / Delivery by post
Terms and conditions of purchase | Privacy policy