Social Networks of Sport and Their Potential in Smart Urban Planning Processes

Social Networks of Sport and Their Potential in Smart Urban Planning Processes

Raquel Pérez-Delhoyo Higinio Mora Rubén Abad-Ortiz Rafael Mollá-Sirvent

Department of Building Sciences and Urbanism, University of Alicante, Spain

Department of Computer Technology and Computation, University of Alicante, Spain

Available online: 
| Citation

© 2020 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (



Information and data have become a new working tool for many disciplines including urbanism. Its incorporation into the field of urban planning is currently a process with great development potential. Within this context, citizens are one of the most important sources of data, providing relevant information for better smart city planning, adapted to their preferences and needs. In this sense, social networks are very powerful tools that city planners have to know directly from users the use they make of public space. It is clear that this information cannot be left out of the process of smart planning and design of today’s cities. Specifically, this work focuses on the study of sport social networks and aims to determine which sport social networks offer the greatest potential for improving urban planning processes. To this end, the main existing social networks in this field are studied and, as a conclusion, the advantages and disadvantages that make these sports networks an opportunity to move towards smarter, more participatory and inclusive urban planning are discussed.


citizen participation, citizen-centric urban planning, inclusive city, smart city, smart urban planning, social networks of sport, technology-aided urban planning


[1] Landry, C., The changing face of urban planning: towards collaborative and creative cities. Human Smart Cities: Rethinking the Interplay Between Design and Planning, Springer, pp. 239–250, 2016.

[2] Hanzl, M., Potential of the Information Technology for the Public Participation in the Urban Planning. Geoinformatics for the Natural Resources Management, Nova Science Publishers: New York, pp. 475–498, 2009.

[3] Mueller, J., Lu, H., Chirkin, A., Klein, B. & Schmitt, G., Citizen Design Science: A strategy for crowd-creative urban design. Cities, 72, pp. 181–188, 2018.

[4] Social network, “Atleto” website,

[5] Social network, “Gotzam” website,

[6] Social network, “Runkeeper” website,

[7] Szark-Eckardt, M., Mobile applications as a tool conditioning health of young generation. AIP Conference Proceedings, 2040(1), pp. 070004, 2018.

[8] Martinez-Nicolas, A., Muntaner-Mas, A. & Ortega, F.B., Runkeeper: a complete app for monitoring outdoor sports. British Journal of Sports Medicine, 51(21), 1560–1561, 2017.

[9] Stragier, J., Vanden Abeele, M. & De Marez, L. Recreational athletes’ running motivations as predictors of their use of online fitness community features. Behaviour & Information Technology, 37(8), 815–827, 2018.

[10] Social network, “Sports Tracker” website,

[11] Ferrari, L. & Mamei, M. Identifying and understanding urban sport areas using Nokia Sports Tracker. Pervasive and Mobile Computing, 9(5), pp. 616–628, 2013.

[12] Social network, “Runtastic” website,

[13] Antón, A.M. & Rodríguez, B.R., Runtastic PRO app: an excellent all-rounder for logging fitness. British Journal of Sports Medicine, 50(11), 705–706, 2016.

[14] Social network, “Endomondo” website,

[15] Vickey, T.A., Ginis, K.M., Dabrowski, M. & Breslin, J.G., Twitter classification model: The ABC of two million fitness tweets. Translational Behavioral Medicine, 3(3), 304– 311, 2013.

[16] Fioravanti, A., Cursi, S., Elahmar, S., Gargaro, S., Loffreda, G., Novembri, G. & Trento, A. Visualizing and Analising Urban Leisure Runs by Using Sports Tracking Data. Proceedings of the 35th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe), vol. I, pp. 533–540, 2017.

[17] Social network, “MyMapRun” website,

[18] Social network, “Strava” website,

[19] Hochmair, H.H., Bardin, E. & Ahmouda, A., Estimating bicycle trip volume for MiamiDade county from Strava tracking data. Journal of Transport Geography, 75, 58–69, 2019.

[20] Sun, Y. & Mobasheri, A., Utilizing Crowdsourced data for studies of cycling and air pollution exposure: a case study using Strava Data. International Journal of Environmental Research and Public Health, 14(3), 274, 2017.

[21] “Strava Metro” website,

[22] Lee, K. & Sener, I.N., Understanding potential exposure of bicyclists on roadways to traffic-related air pollution: findings from El Paso, Texas, Using Strava Metro Data. International Journal of Environmental Research and Public Health, 16(3), 371, 2019.