A massive amount of information as geo-referenced data is now emerging from the digitization of contemporary cities. Urban streets networks are characterized by a fairly uniform degree distribution and a low degree range. Therefore, the analysis of the graph constructed from the topology of the urban layout does not provide significant information when studying topology-based centrality. On the other hand, we have collected geo-located data about the use of various buildings and facilities within the city. This does provide a rich source of information about the importance of various areas. Despite this, we still need to consider the influence of topology, as this determines the interaction between different areas. In this paper, we propose a new model of centrality for urban networks based on the concept of Eigenvector Centrality for urban street networks which incorporates information from both topology and data residing on the nodes. So, the centrality proposed is able to measure the influence of two factors, the topology of the network and the geo-referenced data extracted from the network and associated to the nodes. We detail how to compute the centrality measure and provide the rational behind it. Some numerical examples with small networks are performed to analyse the characteristics of the model. Finally, a detailed example of a real urban street network is discussed, taking a real set of data obtained from a fieldwork, regarding the commercial activity developed in the city.

Among social networks, Foursquare is a useful reference for identifying recommendations about local stores, restaurants, malls or other activities in the city. In this article, we consider the question of whether there is a relationship between the data provided by Foursquare regarding users’ tastes and preferences and fieldwork carried out in cities, especially those connected with business and leisure. Murcia was chosen for case study for two reasons: its particular characteristics and the prior knowledge resulting from the fieldwork. Since users of this network establish, what may be called, a ranking of places through their recommendations, we can plot these data with the objective of displaying the characteristics and peculiarities of the network in this city. Fieldwork from the city itself gives us a set of facilities and services observed in the city, which is a physical reality. An analysis of these data using a model based on a network centrality algorithm establishes a classification or ranking of the nodes that form the urban network. We compare the data extracted from the social network with the data collected from the fieldwork, in order to establish the appropriateness in terms of understanding the activity that takes place in this city. Moreover, this comparison allows us to draw conclusions about the degree of similarity between the preferences of Foursquare users and what was obtained through the fieldwork in the city.