Squeezing the most from volunteered geographic information to monitor mountain biking in peri-urban protected and recreational areas at a metropolitan scale

How to cite this study

Mendes, R.M.N., Farías-Torbidoni, E.I. and da Silva, C.P. 2023. Squeezing the most from volunteered geographic information to monitor mountain biking in peri-urban protected and recreational areas at a metropolitan scale. Journal of Outdoor Recreation and Tourism 42(2023), 100624.

Overview

This study evaluates mountain bike use in five recreational areas using GPSies data at a regional scale in the Lisbon Metropolitan Area (LMA) in Portugal. Results indicate that 98% of users were from Portugal and 60.57% of the rides were going to at least one of the protected and recreational areas (P&RAs) studied. 80.77% of all rides in LMA were round trips, with an average length of 44.87 km.

Relevance

This study is relevant to those interested in using social media data to predict visitation patterns of outdoor recreation at a metropolitan scale. Using spatial analysis, this study indicates entrance hotspots into the parks which is useful for visitor management and developing relevant signage and information. 

Results from GPSies data are not representative of all mountain biking users, as not all users post their activities or use that platform. Comparing social media visitation data to on-site trail counters or other datasets can significantly improve the accuracy of these estimates, especially at a regional scale.

Location

This study is located in Lisbon, Portugal.

Trail Type

The study area comprised the entire Lisbon Metropolitan Area, including five recreational areas: the Parks of Sintra-Cascais, Arrabida National Park, the Protected Landscape of Costa da Caparica Fossil Cliff, the Monsanto Forest Park, and the National Sports Center of Jamor.  The first three are national protected areas and the last two are urban parks. Each park is used by residents and other users as both a recreational and bike-riding area.

Purpose

The purpose of this study was to monitor visitor behavior at a metropolitan scale using GPSies data. Financial support for this research was received from the Foundation for Science and Technology through National funds, and the National Institute of Physical Education of Catalonia at University of Lleida.

Findings

  • 98% of users were from Portugal. The days of the week with the highest number of submissions were Sundays, Mondays, Saturdays, Thursdays, and Fridays. 
  • 62% of users reported mountain biking to be their favorite activity in the app. 
  • 60.57% of the rides targeted at least one of the P&RAs studied. 80.77% of all rides in Lisbon Metropolitan Area were round trips, with an average length of 44.87 km.
  • Costa da Caparica Fossil Cliff had the highest average riding distance at 51.19 km, followed by Arrabida Natural Park, Jamor National Sports Center, and Sintra-Cascais Natural Park. Monsanto Forest Park had the shortest average distance at 38.09 km.

Methods

Data on mountain bike activities was collected from GPSies (now a part of AllTrails) and filtered,  resulting in 8,664 individual tracks from 2006 – 2017 in the Lisbon Metropolitan Area. Data collection included ID, username, length of activity, altitude, notes, start point, country of residence, registration date, and favorite activity. Qgis was used to convert the data to shapefiles which were then added to ArcGIS. Tracks were filtered for outliers, excluding tracks with a length above the 95th percentile. This created six datasets; one for each mountain biking area and one for all other tracks outside these areas. Statistical analysis was conducted at the regional and park scale. The commitment level of users was evaluated through their activity logs. Mountain bikers’ spatial preferences were evaluated using indicators such as the percentage of rides starting within 250 m of the designated mountain biking area, the percentage of ride length within or outside of the riding area, the average distance by bike to the mountain biking area, and finally maximum distance to the mountain biking area.


Added to library on November 27, 2023