The transformation towards sustainable mobility has become a strategic action of local governments, and Barcelona is no exception. The city has established itself internationally as a model of sustainable urbanism, faithful to a long urban tradition –which elevates public space as a fundamental substrate of the social fabric– and to the 2030 United Nations agenda. Fixing these principles in the Urban Mobility Plan and the Neighborhood Plan, the City Council has proposed and implemented urban reforms, often in a participatory way, aimed at redesigning public space towards a model of decarbonized and fair mobility.

Barcelona currently has on average one of the most equitable distribution of urban space, but with room for improvement (e.g., Trinitat Vella). Beyond the fundamental pillar of Superblocks (PMU 2013-18), the City Council puts on the horizon 2024 the pacification of 32 kilometers of streets. Not only for sustainable mobility and a more equitable redistribution of space: but also for the development of new social spaces; revitalization of local commerce, entrepreneurship; and new activities in the urban fabric. However, what data support each performance? Which streets need to be pacified? Which areas have abnormal pedestrian flows?

In this context, LIKE-BCN addresses the analysis of public space to detect shortcomings by exploiting GIS information on walkable space and real data on pedestrian dynamics of TC Group, participant of the project, to: (a) construct detailed origin-destination matrices for pedestrian mobility and (b) estimate sidewalk level pedestrian flows to (c) be able to measure the efficiency of the sidewalk network considering pedestrian demand and (d) quantify the socio-economic interaction potential at the local level.

Thus, LIKE-BCN is presented as a tool for accurate measurement and based on data to guide processes of urban transformation, towards the achievement of the challenges of Barcelona posed in the PMU 2019-24 and beyond.


Online social networks (OSN) constitute nowadays mainstream communication channels to interact, exchange opinions, and reach consensus. In recent years, it has increasingly become evident that competition significantly shapes the topology and the dynamics on these information-driven platforms: users thrive for visibility, while memes resemble entities that compete for users’ attention. This analogy has fostered many researchers’ struggle to model user behavior on one hand, and so-called semiotic dynamics on the other. However, these represent a partial picture, accounting only for user-user, or meme-meme, interactions. Turning to user-meme (bipartite) networks, non-competitive relationships become apparent as well, under the form of mutualism: the choice of more frequent memes increases the visibility of individuals, which in turn makes the popularity of those memes even larger.

The presence of competitive and mutualistic interactions in OSN is reminiscent of natural ecological systems, where species may interact in many different ways. For example, animal pollinators and plants engage in mutually beneficial connections, but concurrently compete with species in their own class. The introduction of a networked perspective in ecological modelling since the early 2000s represented a major conceptual leap, establishing the link between ecosystem dynamics and species interaction patterns. In particular, the emergence of ubiquitous structural arrangement, nestedness, has been observed in many different ecosystems, which has ignited several research in order to investigate the relation between the species network properties and the system’s stability and biodiversity –notwithstanding other structural arrangements such as modularity or in-block nestedness.

Such qualitative resemblance between natural and information ecosystems indicate that there is ample room to exploit a long tradition in Ecology in this new informational context. While modelling information ecosystems is hardly in its birth –or, specifically, at the descriptive level– a Statistical Mechanics of ecological systems, the conceptual and mathematical framework to relate the macro and micro structural levels has been developed in Ecology in the last decade and can be exploited in this context.
Thus, the project seeks the following objectives:

1. Structural characterization of information ecosystems.
2. Structural patterns in time-evolving information ecosystems.
3. Population dynamics in information ecosystems.

Espacio Persona

The growing sensitivity of citizens and authorities to the dehumanization of cities is promoting innovative concepts and ideas that aim to increase the role of people in the urban environment, consequently reducing the area allocated to vehicles. The current situation favors a frequent interaction between vehicles and pedestrians. In this scenario, combined with the current high levels of stress, it is not rare that such interactions lead to accidents and run-overs, also affecting particularly sensitive groups: the elderly, children and citizens with disabilities.

In this context, the general objective of the Espacio Persona project has been to design an indicator to assess the safety of pedestrians in urban environments according to their structural, functional and dynamic characteristics. To this end, it was proposed to identify and quantify, through images of public space, the most important characteristics for pedestrian road safety, and add them in a single indicator –with the additional advantage of its potential application on any urban space for which the proper data is available. The methods to meet this challenge combine geometric, visual (urban images), and vehicle flow data, with analysis tools based on Artificial Intelligence (Deep Learning).

The resulting micro-indicator characterizes urban areas (in cells between 100 and 300sqm.) according to their level of safety, taking into account the main causes that lead to pedestrian collisions. The implementation of these micro-indicators was based on public and accessible Big Data through open data portals, focusing on the two cities in Spain with the largest population, Madrid and Barcelona. In particular, from the Ayuntamiento de Madrid, the Institut Cartogràfic i Geològic de Catalunya (ICGC), Google Street View, Mapillary, Openstreetmap, the Policía Municipal de Madrid and the Guàrdia Urbana de Barcelona.

As a result of all this, four notable results were achieved: (1) an architecture for the automatic prediction of vehicle-pedestrian accidents based on urban images; (2) an algorithmic process to extract geographic information on the walkable space of cities from public topographic databases; (3) an innovative method for measuring the presence of perceptual impairments in urban environments, mixing urban images and topographical data; and finally (4) a platform for viewing and querying the data produced.

The main and secondary results of the Espacio Persona project are of possible interest to public entities (DGT, municipalities, cartographic institutes, security forces, etc.), private entities (traffic and mobility consultancies, GIS companies, civil works, etc.) and other organizations (parents’ associations, groups with functional diversity, etc.).