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Because it enables a synthesis of “where” and “how many” data derived from connected devices has embarked upon a new era of data liable to be mobilised to analyse geographical realities, not forgetting, either, the question of “who?”, as well as the ethical issues attached to the use of this data and to whether or not the data is “volunteered”1 (Goodchild 2007). A reversal of the qualification of individuals is running from a categorization organized around the profession, income, age, lifestyle or place of residence, in a way categorization of marks of behavior mobility and uses of the city.Since the seminal work by C. Ratti, numerous contributions (Blondel2 et al, 2015) have deepened this field of study. In the understanding of the functioning of geographical space, traces left by individuals on the social networks and/or by their mobile phone use have become essential sources to gain better apprehension of daily mobility patterns, or specific behaviours on the occasion of exceptional events.From a theoretical viewpoint, this data is not solely the arrival of new types of information among the classic methods. It also amounts to a breakaway in the approaches to the different spaces involved, via its potential to construct new, relevant aggregates. For some time, the issue of a renewal in urban geography has been abroad, with an abandonment of the analysis of the perennial structures of urban space, based on social categories, gender, age, socio-professional category or place of residence. If this data is not available with APIs of social networks, we can move on to something different. In this issue of NETCOM, we propose to gather contributions showing the geographical interest of performing analyses, not on the basis of the usual categories, but by mobilising and breaking down the information derived from geo-localised data. This data is not a mere spatial localisation of coordinates. If it is crossed with temporal data, which can be converted into durations or frequencies, it enables the identification of spatial-temporal functioning within geographical space (Lucchini, Elissalde et al, 2011, 2013, 2014, 2015).By perusing urban heartbeats and paces, the refinement of the temporal divisions provided by legal recordings of crowdsourcing data gives access to the variability of urban functioning according to a new temporal sequencing process which can in particular accommodate numerous different time patterns. In recent years, new research has used exchanges of “tweets” in the New York urban area (França, 2014, Visualising the “heartbeat” of a city with tweets) or photos posted online via Instagram (Yan-Tao Zheng et al, 2013).In this issue we intend to gather contributions where the purpose is to promote the new ways of categorising geographical space based on data from the social networks. These contributions can take on the following forms :analyses of the urban space and variations in user concentrations using data collected via the APIs of various social networks.probabilistic models for crowd movements, and also other types of model integrating the hypothesis that phenomena of self-organisation or self-fulfilling may emerge from randomness.experimentation performed using agent-based simulation platforms.REFERENCES:AGUITON C., CARDON D., SMOREDA Z. (2009). Living Maps. First international Forum of application and Management of Personal Electronic Information, MIT, Cambridge, MA, USA.BLONDEL V., DECUYPER A., KRINGS G. (2015). A survey of results on mobile phone datasets analysis. EPJ Data Science (2015) 4:10, Doi 10.1140/epjds/s13688-015-0046-0.CALABRESE F., PEREIRA F., DI LORENZO G., LIANG L. (2010). The geography of taste: analyzing cell-phone mobility and social events. Computer Science, vol. 6030, 2010, 22-37.CANDIA J., GONZALEZ M., WANG P., SCHOENHARL T., MADEY G., BARABASI A.L. (2008). Uncovering individual and collective human dynamics from mobile phone records, 2008. Journal of Physics A, Math and Theorical, vol. 41, 22.CARDON D. (2015). A quoi rêvent les algorithmes, Nos vies à l’heure des big data, col. La république des idées, Seuil.CRANG M., (2001). Rhythms of the city, in May J. &#38; Thrift N. (ed) Timespace, geographies of temporality, London, Routledge.ELISSALDE B., LUCCHINI F., FREIRE-DIAZ S. (2013). Caractériser l’attractivité des quartiers urbains par les données de téléphonie mobile. L’Information géographique, n° 1, vol. 77, 2013, 44-62.FRANÇA U., SAYAMA H., MCSWIGGEN C., DANESHWAR R., BAR-YAM Y. (2015). Visualizing the “Heartbeat” of a City with Tweets. Complexity, Avril 21, 2015. Doi :10.1002/cplx.21687, http://www.necsi.edu/research/social/nypattern.htmlGOODCHILD M.F. (2007). Citizens as Sensors. The World of Volunteered Geography. VGI Specialist Meeting Position Papers, 2007: Santa Barbara, http://web.simmons.edu/~benoit/infovis/Goodchild.pdfLUCCHINI F. et al. (2014). Urban events and emerging phenomena, ICCSA 2014, The 4th International Conference on Complex Systems and Applications, Le Havre.RATTI, SEVTSUK, HUANG, PAILER (2005). Mobile Landscapes: Graz in Real Time. Proceedings of the 3rd Symposium on LBS &#38; TeleCartography.RESCH B., ZIPF A., BEINAT E., BREUSS-SCHNEEWEIS P., BOHET M. (2012). Towards the Live City, Paving the Way to Real-time Urbanism. International Journal on Advances in Intelligent Systems, vol 5 n° 3&#38;4, http://iariajournals.org/intelligent_systems/SEVERO M., ROMELE A. (2015). Traces numériques et territoires, col. Territoires numériques, Presses des Mines.SMOREDA Z., AGUITON C., FOURESTIE B., MORLOT F. (2010). Taking the Urban Pulse with Mobile Networks, ParisTech Review, june 2010.TAILLANDIER P., GRIGNARD A., GAUDOU B., DROGOUL A. (2014). Des données géographiques à la simulation à base d’agents : application de la plate-forme GAMA. Cybergeo : European Journal of Geography, Systèmes, Modélisation, Géostatistiques, document 671.Important Dates :February 2016: Call for contribution;01 May 2016: texts submitted;June 01: Back assessments by reviewers;June 15, 2016: Summary of evaluation returns realized by NETCOM and addressed to the authors with texts return request for September 2016.