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Sportspeople Tracking in the Mountains: a Methodological Proposal for the Spatial Analysis of GPS Data Produced by Trail Runners

Camille Savre et Marie Eveillard-Buchoux
Cet article est une traduction de :
La trace du sportif en montagne : proposition méthodologique pour l’analyse spatiale de données GPS produites par des traileurs [fr]


The aim of this article is to determine how GPS data produced by sportspeople can be useful in understanding the spatial uses of mountains. It presents an exploratory methodology carried out on routes recorded by trail runners and uploaded to the sports social network Strava™. Our methodology proposes to categorise the routes with elevation and distance criteria. The spatial translation of this categorisation will then be observed to understand the behaviour of sportspeople in their practice area. In a context where sporting activities offer new forms of human presence in mountain environments, this article provides a better understanding of sports practices in the mountains. This methodology paves the way for using geodata to analyse individual behaviour on a qualitative basis. It may complement other qualitative surveys and quantitative approaches, thus meeting the complex challenges of managing mountain environments.

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1The number of people visiting mountain areas has increased due to demographic growth (1.1% and 0.5% per year between 2014 and 2020 respectively in Haute-Savoie and Savoie; INSEE, 2022) and a rise in the number of tourists (a 4.7% overall annual increase in the number of overnight stays between 2014 and 2019 in Savoie and Haute-Savoie; Savoie Mont Blanc Observatory, 2023). This tourist expansion has been accompanied by the diversification of practices, leading to a growing human presence in the mountains, both spatially and temporally (Bourdeau, 2006; Perrin-Malterre, 2018). These new forms of human presence have led to the emergence of interactions with mountain environments which are still largely unknown. Among these practices, recreational activities have a prominent place and are now tracked thanks to the digital transition (Mao & Obin, 2018). Scientific research can take advantage of these digital devices; in this case the GPS tracks voluntarily recorded by trail runners.

Transdisciplinary Questions

2The following article is the result of a meeting between two researchers, a geographer and an anthropologist, around a common challenge: understanding mountain sports areas. They shared this challenge with a wider research group, including protected area managers, for whom the issue of sports areas was posed in terms of quantitative visitor numbers and their behaviours requiring qualitative characterisation. In their respective disciplines, they looked for ways to describe and analyse what happens when mountains are used for sporting activities. GPS recordings of individual movements stood out in their investigations since they are increasingly present in the landscape of sporting activities. By becoming a potential source of data, they raise scientific questions.

3It is around these recordings, and more specifically around the notion of “geodigital tracking”, that the authors came together. A digital footprint, or digital track, is an imprint left behind when you surf the web or use a service from a digital terminal. It is said to be geodigital if it is associated with geographical data (or metadata), enabling it to be spatialised (Mericskay et al., 2018). In the present case, the succession of GPS points recorded from the start to the end of a sporting activity constitutes the geodigital track. These tracks are the starting point for a methodology designed to test their relevance in answering research questions with both qualitative and quantitative, social and spatial formulations.

4What routes do sportspeople take? Do they have any particular habits? What characterises their routes? Do mountain areas differ from other geographical areas? We believe that geodigitally tracking athletes’ routes can help to answer these questions. This is why an exploratory methodology was chosen, with the aim of determining how the geodigital tracks produced by sportspeople can be useful in understanding the spatial uses of mountains. Ultimately, the aim is to be able to associate a “semantic trajectory” with these tracks (Cayèré, 2022; Eveillard-Buchoux et al., 2021), in other words to provide explanations for their form, location and temporality. Understanding the meaning of these routes from the point of view of the trail runner would make it possible, in particular, to adjust the management methods of the areas covered (Kerouanton, 2020).

5By focusing on trail running, we propose the hypothesis that a detailed understanding of this practice is necessary in order to carry out a relevant analysis of its spatial uses. To do so, geodigitally tracking trail runners’ routes helps to provide more objective monitoring of individual practices than information reported in discourse. In fact, the familiar or even routine nature of these practices can tend to unconsciously mask ordinary ways in which they are used, in favour of more exceptional ways that generate more interest from sportspeople. The objective parameters of geodigital tracking reveal differences within the trailer runner’s tracks and amongst other trail runners. In this sense, the practices studied can be characterised in greater detail, providing a new key for analysing the spatial uses of trail runners.

6The first part of this article describes how geodigital tracking data is collected by sportspeople. We present the datasets used and the useful characteristics for the methodology followed. The second part presents the first methodological approach developed to qualify each track according to specific parameters (difference in distance and altitude). We pay attention to the diversity of individual forms of practice and formalise these distinctions through categorisation. Thirdly, we apply this categorisation to a new set of data. A spatial approach is adopted on two scales: meso and micro. The exploratory results are detailed for their heuristic value. The final section discusses the limitations and prospects of using geodigital tracking in social sciences.

Trail runners and digital tools

Conditions for producing digital data

7Trail running can be defined as:

A run in a natural environment including mountains, deserts, forests, coastlines, jungles/tropical forests, arid or grassy plains (e.g., dirt road, forest road, single-track road, sand, etc.) with a minimum of paved or asphalt roads, which do not exceed 20–25% of the total course. This category has no distance or elevation restrictions. (Scheer et al., 2020)

8The number of trail runners is increasing (Madoré & Loret, 2021). The boom in this activity is particularly visible through the development of organised races bringing together several thousand participants. This mobile practice also takes place beyond competitive spaces. In its everyday dimension, and like jogging (Cook et al., 2015), it deals with space and other users. Depending on the environment in which trail running takes place, the elevation gain is a key factor in the experience of trail runners. These effects are visible in significantly longer routes, which is why trail runners pay particular attention to it. The topography of the routes is also carefully examined by the trail runner (illustration 1). Finally, by looking for non-paved routes, the trail runners tend to go to less anthropised areas. Given these characteristics, mountain areas can be considered as preferred practice areas.

Illustration 1 : Ethnographic document showing the topographic profile of a trail running race. Available on the race website, the route is printed by the trail runners and studied until the start

Illustration 1 : Ethnographic document showing the topographic profile of a trail running race. Available on the race website, the route is printed by the trail runners and studied until the start

Camille Savre

9Trail running is also witnessing the emergence of digital tools (Buron, 2018). In particular, the use of digital devices (GPS watches, sports apps, heart rate monitors, etc.) raises the question of the effects on athletes’ experiences and, in particular, their relationship with their body (Verchère, 2016). Runners who consistently use self-tracking develop adapted habits, known as “expert metatechniques”, which guarantee the quality of their sporting experiences (Quidu & Favier-Ambrosini, 2022). Learning this “practical wisdom” could be linked to a social position that enables critical and reflexive distancing from the data produced by digital devices (ibid.), whereby the construction of a benevolent view of oneself would be a common objective (Pharabod, 2019). The question of differences in usage depending on the type of running practice, linked to socio-demographic profiles, is then open and remains to be investigated. On this point, the influence of gender seems to be an interesting avenue of research for understanding self-tracking practices, as well as their modes of acceptance and resistance (Esmonde, 2020; Vignal et al., 2022).

10As well as individual experiences, the data produced by these devices is also shared on networks dedicated to connecting sports enthusiasts, such as community platforms on which sportspeople can store and share all their sporting activities recorded via watches or mobile phones. These applications are built around self-tracking innovations and make a series of “promises” to users: self-knowledge, performance optimisation, integration into a community, and motivational support of various kinds (Soulé et al., 2022). Runners who share their data effectively strengthen their identity as runners by enabling the creation of shared meanings with other users (Carlén & Maivorsdotter, 2017). By making all runs visible, these platforms account for the investment made by runners. Indeed, preparing to run is a central part of running (Allen Collinson, 2008; Hockey, 2009), but this activity is difficult to perceive and quantify, since it is largely hidden from any organisation that can record such data.

11The use of these digital devices has not been the subject of any specific study of trail runners. This population includes a high proportion of managers (53% compared with 9% for the French population) and graduates with 5 or more years of higher education (51% compared with 10%) and people with a significantly higher median annual income than the French population (€28,421 compared with €22,200) (Gruas, 2021). This social recruitment suggests financial accessibility to measurement tools as well as a willingness to develop self-preservation techniques, enabling them to persist in self-tracking without altering their sporting experiences (Quidu & Favier-Ambrosini, 2022). We therefore consider that these tracks are not produced by a specific fringe of trail runners and that they do not correspond to individual experiences; any type of trail runner can record any type of run.

Capturing New Data: Individualised Geodigital Tracks

12On this basis, our methodology focused on a specific type of data produced by sportspeople: a series of geolocalised points that retrace the route taken by the runner during their run. According to the geodigital track classification proposed by Mericskay et al. (2018), these are flow tracks, produced voluntarily by the user, and are explicitly geolocated and individual. This individual dimension, which is opposed to the notion of aggregated tracks, is of primary interest to the method presented. In fact, individual geodigital tracking makes it possible to distinguish the deployment of human behaviours, their temporality and their spatiality (Beaud, 2015). Depending on how they are exploited, these tracks can potentially serve as “informational megaphones of those who make places” (Mericskay et al., 2018, p. 51).

13The anthropologist suggested to the geographer to take a closer look at the geodigital tracking of trail runners. As part of her ethnographic survey combining participant observations and comprehensive interviews, she met with trail runners who systematically record their runs using a GPS watch before sharing them on a community platform called Strava™. With their informed consent, the researcher selected three respondents to look at the geodigital tracking of their sporting activities and more specifically their trail runs in 2021. The annual time scale was chosen as relevant because it allows potential seasonal variations in activities to be considered in the mountain context presented. In addition to the rigour of the recording, a second criterion was defined to diversify where the athletes live. A number of clues from the ethnographic survey and the literature on running led us to consider the place of residence as a relevant tracker to be investigated (Deelen et al., 2019; Qviström, 2016; Sanchez-Garcia et al., 2019) in that it represents a privileged point of departure for runners, influencing the possibilities of their movements. The first three respondents are identified as P1, P2 and P3. Their geodigital tracking data constitute an initial data set (n=425) (map 1 and table 1). The anthropologist was given the complete sporting life story of each individual as well as information on their involvement in trail running.

14To further explore the potential of the geodigital tracking thus produced, a second set of data (n= 329) was required (table 1). In addition to the rigour of the recordings, we chose a geographical variable, this time requiring residence in Savoie (73) or Haute-Savoie (74). This second parameter was defined in order to focus our attention on the mountain territories that ultimately concern the authors’ research. Based on these criteria, three other individuals were selected (P4, P5, P6), for whom all the running tracks throughout 2021 were collected.

Table 1 : overview of the two datasets with some sociodemographic variables



Run tracks




Lenght of time in practice

Data set 1



















Data set 2



















Map 1: presentation of the first dataset

Map 1: presentation of the first dataset

Marie Eveillard-Buchoux

15All the tracks in the two datasets were extracted manually from the Strava™ application by going directly through the respondents’ accounts according to their privacy settings. Each track is linked to an individual and includes a set of spatiotemporal data related to the route (date, distance, duration, elevation gain). Transposed to a geographical space, each track can then be read through the spaces it covers (type of space, relief, vegetation, etc.).

Individualised Geodigital Tracking to Categorise Runners’ Activities

16The use of digital devices by trail runners makes it possible to track all their sporting activities over time. This kind of longitudinal monitoring was carried out on the first set of data. Systematising the recordings of P1, P2 and P3 made it possible to statistically describe their involvement in trail running. Although the three respondents practise several sporting activities (skiing, cycling, climbing, swimming, etc.), they allocate a large proportion of their leisure time to running (table 2).

Table 2: categorisation characteristics and time percent of each respondent to each run type


Runs in 2021

Frequency (%)

Time (%)










17The aim of this first stage is to qualify the runs of these trail runners. The main purpose of this qualification is the possibility of objectivation thanks to the information contained in the geodata tracks and its exhaustiveness.

18In view of the characteristics of trail running already mentioned, we used the parameters of distance and elevation to form a single criterion for distinguishing all types of run. We calculated the ratio (X in m/km) of the total elevation to the total distance of the run to categorise each one (table 3). Travel time and speed were not used in this categorisation because they imply a notion of performance that we wanted to neutralise in order to define a typology that does not reflect the type of runner but rather the type of run. The choice of this ratio ignores the total distance and total altitude difference, which can also be related to a level of practice. Although the type of run can be used to characterise the runners, it is more broadly a description of what they do without any preconceived ideas about the meaning they give to these actions.

Table 3: Categorisation characteristics and percentage of time allocated by each participant to each type of outing


Ratio (X)






X < 25

Time (%)





25 ≤ X ≤ 40





X > 40




19We therefore defined three types of run corresponding to three elevations to distance ratios. When X<25, the run is described as “flat”, i.e., the difference in altitude does not restrict the runner’s running movement. When 25 ≤ X ≤ 40, the run is of the “elevation” type, i.e., the elevation during the run is noticed and generally planned. Finally, when X>40, the run is of the “mountain” type. The choice of this term is based on the physical definition of a mountain:

A protruding part or relief of the earth’s crust that is both high (several hundred metres above its bedrock), with declining slopes, and occupies a large area of space (at least several square kilometres). (George & Verger, 2013)

20It is these topographical attributes that make this type of ratio possible. The geographical area in which these types of “mountain” runs take place can be investigated elsewhere. This categorisation makes it possible to describe the sample in terms of the distribution of runs for each respondent (table 3). The distributions for P1 and P3 follow the same trends, while that for P2 stands out. We hypothesise that the length of time P2 has been practising and/or their living environment may be a factor in explaining this unusual distribution. The rest of the methodology focused on using this categorisation as a tool for spatial analysis. By retaining information on the type of run, it provides a useful tool for interpreting the spatial uses of mountain areas.

21We propose that the results of this initial qualitative approach, focusing on a few individuals, contribute to the definition of trail running. Today, trail running has a broad definition that does not exclude any distance or elevation (Scheer et al., 2020). The categorisation carried out follows this definition, but provides a broader view of all the runs made by trail runners and their different possible distributions. In other words, each trail runner can do more or less “flat”, “elevation” or “mountain” type runs. Ultimately, this categorisation highlights the diversity of the runs they undertake. Qualifying this information makes it possible to include the experiential dimension of the trail runner in analyses, in this case spatial analyses, because a type X<25 or type X>40 run does not involve the same actions, which is a first step towards interpreting a semantic trajectory.

Comparing Geodigital Tracks for Spatial Analysis

22Each geodigital track in the second dataset was manually associated with a type of output following the categorisation presented above. Depending on the scale of the analysis, the tracks can be considered as a local activity, geographically associated with the municipality in which its point of departure is recorded (which does not systematically correspond to the point of arrival). They can also be considered in their entirety, as a linear representation of the entire route, from the first GPS transmission point to the last.

Meso-Level Approach

23The three respondents (P4, P5, P6) mainly run in the region where they live. This spatial approach differs from that of the region in the administrative sense of the term, which corresponds more to an area of around a hundred kilometres around the home accessible by a few hours’ drive at most. They visit the Swiss and French Alps, including the departments of Savoie, Haute-Savoie and Isère. Although they do most of their runs there, P6 occasionally travels further afield, to other mountain ranges (the Pyrenees and the Canary Islands), while P4 also goes running in Spain. These trips are mainly linked to participation in organised races.

24Within their region of residence, the geographical distribution of their running is heterogeneous (map 2). P6 is more spatially diversified: they run from thirty-seven towns between France and Switzerland, in five French departments (01, 05, 38, 73 and 74). Half of their runs are concentrated in three towns: Mieussy (23% of runs), Archamps (16%) and Chamonix (15%). At the same time, P4’s running is geographically restricted to their home town (91%), while P5’s running is intermediate, with 68% of their runs taking place in their home surroundings.

Map 2: Runs distribution of three respondents living in Haute-Savoie (P4, P5 et P6)

Map 2: Runs distribution of three respondents living in Haute-Savoie (P4, P5 et P6)

Marie Eveillard-Buchoux

25This first result highlights, on the one hand, how place of residence is a spatial determining factor, where between 54% and 91% of the respondents’ runs are concentrated and, on the other hand, the existence of occasional runs generally confined to the Alpine region.

26If we look more closely at these local runs, we can see that for P4 and P5, they all correspond to “mountain” type runs (X > 40 m/km) and to significantly longer practice times, while they are more varied for P6. These results led us to investigate where the runners live and their occasional runs on a micro-scale.

Micro-level approach

27If we look at the individual scale of routes, the space used for running can be precisely distinguished into two spatial entries relating to running habits: firstly, the daily space, where the respondents run regularly (depending on where they live or work); and secondly, occasional runs where the respondents rarely run (< 10 times, i.e. < 1 time/month).

28The daily space is unique for P4 and P5 and multiple for P6. All three partake in runs belonging to the three categories identified (“flat”, “elevation” and “mountain”). The diversity of running types implies different spatial uses, particularly in relation to the topography. In addition to the desire to do different types of run, analysing the tracks enabled us to distinguish two modes of spatial behaviour (map 3).

29By observing the track itineraries, we were able to identify superimposed tracks. The recurrence of these superimpositions enabled us to determine the existence of typical routes. These repetitions correspond to an initial use of space, which is P5’s main spatial behaviour. Of the runs around their home, 44% were made along two routes (route type 1 and route type 2; see map 3). The second behaviour identified is based on the diversity of the runner’s routes. Only 15% of the routes run by P4 were strictly on the same course. We analysed this type of behaviour using an approach based on the sectors used. P4 made 49% of their runs in two sectors, one to the southwest of their home, the other to the northeast (sectors 1 and 2; see map 3). Finally, between replication and diversification, there is a whole gradient of behaviour in which the respondents fall. A larger panel of respondents would enable us to determine whether one of these spatial behaviours is dominant in trail running.

Map 3: Two spatial uses of the daily space: route type and sector type

Map 3: Two spatial uses of the daily space: route type and sector type

Marie Eveillard-Buchoux

30Regular tracks also show the organisation of space in relation to topography. Whether repeated or original, “flat” runs are restricted to the valleys or lower slopes around where people live. They are usually short in terms of distance, and therefore time. “Mountain” runs, on the other hand, take you further and, above all, higher. At Taninges, for example, P5 systematically climbs towards the Pointe de Marcelly and the Lac de Roy plateau (map 4). On the other hand, the diversity of P4’s runs led them to cover several different types of area for their “mountain” runs, identified through different sectors (hillsides, terraces, wooded hills, alpine ridges and plateaux). Thus, one (P5) covers a limited area in which they take advantage of differences in altitude, while the other (P4) covers a larger proportion of the area available for their activities around their home, while also taking advantage of differences in altitude (map 5). In the end, it is above all the behaviour of the trail runner that determines the variability of the environments and landscapes used for trail running.

Map 4: routes (n=32) of the daily space of P5 depending on the runs categorisation

Map 4: routes (n=32) of the daily space of P5 depending on the runs categorisation

Marie Eveillard-Buchoux

Map 5: Daily space practice area: comparison between two types of behaviours, replication (P5) and diversification (P4)

Map 5: Daily space practice area: comparison between two types of behaviours, replication (P5) and diversification (P4)

Marie Eveillard-Buchoux

31The areas used for occasional runs open up another area for analysis. These runs are all “mountain” type for P4 and P5 and 63% for P6. What form do these runs take? What types of landscape do they cross? Do they connect with particular geographical features? These analyses need to be carried out through the prism of the space invested, with the aim being to compare the different runs made by trail runners in the same area.

Limitations and Potential Uses of Geodigital Tracking in Social Sciences

32The geodigital tracking data collected for this study appeared to be particularly well suited to the inductive logic pursued by several disciplines in social sciences, particularly when they examine subjects that have not yet been studied in-depth. Indeed, the data facilitated the emergence of research questions directly related to the information they contain and at the intersection of the disciplinary issues of geography and anthropology. The methodology described above has made it possible to formulate hypotheses about the spatial behaviour of the sportspeople surveyed, which offers a starting point for future research.

33Without providing a possible substitute for qualitative surveys carried out directly with participants, geodigital tracks can complement them. They can be used as a support for recalling and deepening memories linked to a specific route (Layton, 2021). Geodigital tracking data provide a complementary quantitative view. In our case, this ability to make previously unmeasurable activities measurable and comparable has led to the categorisation of running practices. While the limitations of this categorisation may be debatable, particularly in view of the limited size of the sample, we would like to emphasise that this tool enabled us to qualify the runs practised in greater detail, while ensuring the reproducibility of this classification. In this way, the runs of each respondent are distributed in a way that can be used for spatial analysis.

34While one of the strengths of geodigital tracking is that it accumulates exponentially, making it possible to aim for representativeness, or even exhaustiveness, this study did not explore this potential. As our processing was based on a limited number of geodigital tracking data, it probably circumvented one of the major limitations to their use: geodigital tracking has “transparency defects” (inequalities and intra-territorial differentiation, deliberate blurring, failure to take account of circumstantial events, temporal desynchronisation due to collection time, etc.) (Vidal, 2015). The transition to larger datasets should therefore raise questions about the quality of the data and metadata, and also require an ethical stance on the use of data, the future and exploitation of which are left in a grey area for the contributors (Soulé, 2022).

35Finally, by covering a wide range of information, geodigital tracking makes it possible to articulate different scales of analysis. Sometimes considered as occasional runs on a meso scale, tracking data can also be analysed to provide a precise account of localised individual behaviour. By enabling this dual focus, we argue that geodigital tracking has a strong heuristic value for identifying and understanding places as they are experienced by humans. Further research on this data would then provide an integrated understanding of different places and a desirable horizon for meeting the challenges posed by this complex reality.

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Table des illustrations

Titre Illustration 1 : Ethnographic document showing the topographic profile of a trail running race. Available on the race website, the route is printed by the trail runners and studied until the start
Crédits Camille Savre
Fichier image/jpeg, 266k
Titre Map 1: presentation of the first dataset
Crédits Marie Eveillard-Buchoux
Fichier image/png, 251k
Titre Map 2: Runs distribution of three respondents living in Haute-Savoie (P4, P5 et P6)
Crédits Marie Eveillard-Buchoux
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Titre Map 3: Two spatial uses of the daily space: route type and sector type
Crédits Marie Eveillard-Buchoux
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Titre Map 4: routes (n=32) of the daily space of P5 depending on the runs categorisation
Crédits Marie Eveillard-Buchoux
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Titre Map 5: Daily space practice area: comparison between two types of behaviours, replication (P5) and diversification (P4)
Crédits Marie Eveillard-Buchoux
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Référence électronique

Camille Savre et Marie Eveillard-Buchoux, « Sportspeople Tracking in the Mountains: a Methodological Proposal for the Spatial Analysis of GPS Data Produced by Trail Runners »Journal of Alpine Research | Revue de géographie alpine [En ligne], 111-3 | 2023, mis en ligne le 15 février 2024, consulté le 21 avril 2024. URL : ; DOI :

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Camille Savre

Doctorante, UMR 5204 EDYTEM

Marie Eveillard-Buchoux

Maîtresse de conférences, Université Toulouse Jean-Jaurès, UMR 5602 GEODE

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Le texte seul est utilisable sous licence CC BY-NC-ND 4.0. Les autres éléments (illustrations, fichiers annexes importés) sont « Tous droits réservés », sauf mention contraire.

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