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We attempted to identify the various modes of urban events from the urban routine, and the relationship between events and the transmission of messages on the Twitter social network for Paris and the Ile de France region. The locations and rhythm of tweets emitted in this territory, reveals places with regular or others with sporadic activity. For the same urban space, there are rarely regularity in the transmitting force of a hotspot, but, on the contrary, a great variability of frequencies and rhythms, and this lack of automaticity and arrythmia make it possible to approach temporary clusters and discontinuity. According to whether the event is singular or cyclical, planned or unforeseen, whether it takes place in a single place, following a route or tends to appropriate a specific neighborhood, these tweets highlight chronotopic urban spaces.

Is built environment the most influential factor on travel behavior when compared to individual socioeconomic characteristics? This paper extends the empirical knowledge by providing and comparing quantitative estimates of these various effects on both commuting distance and mode choice in a European city spatial context, while using up-to-date and novel methodology. Eight indicators of built and social environment are identified in order to characterize clusters of residential locations, giving a rich view of spatial and social diversity of locations. To disentangle the causal effects of residential self-selection and built environment, both sample selection and specific matching preprocessing (“coarsened exact matching”, a novel approach in the field) are implemented. Regarding commuting distance, the true effect of built and social environment appears modest with an increase of distance in the range of 10-20%. It comes behind individual socioeconomic characteristics such as car availability and skill. Regarding commuting mode choice, again the true effect of built and social environment is modest, with a near 20%pt increase of car share and around 10%pt decrease or public transport share for the most prominent effects, and it comes behind car availability. These results suggest the primary importance of directly influencing car use, if not car ownership, in the European context, while trying to modify the built environment would provide only limited results.

With the digital turn, data from connected devices are a new resource for understanding urban rhythms and mobility. Using the geolocated messages (1, 540, 000 tweets) of Twitter social network users recorded in 2015, this article uses dynamic graphs to visualize Parisian mobilities. Using a clustering algorithm, we show how to move from a geolocated message to an individual displacement and on to collective trajectories. These trajectories illustrate the preferential mobility of users of the social network in Paris, as well as the areas of concentration and poles of interest.