In recent years, several new Artificial Intelligence methods have been developed to make models more explainable and interpretable. The techniques essentially deal with the implementation of transparency and traceability of black box machine learning methods. Black box refers to the inability to explain why the model turns the input into the output, which may be problematic in some fields. To overcome this problem, our approach provides a comprehensive combination of predictive and explainability techniques. Firstly, we compared statistical regression, classic machine learning and deep learning models, reaching the conclusion that models based on deep learning exhibit greater accuracy. Of the great variety of deep learning models, the best predictive model in spatio-temporal traffic datasets was found to be the Adaptive Graph Convolutional Recurrent Network. Regarding the explainability technique, GraphMask shows a notably higher fidelity metric than other methods. The integration of both techniques was tested by means of experimental results, concluding that our approach improves deep learning model accuracy, making such models more transparent and interpretable. It allows us to discard up to 95% of the nodes used, facilitating an analysis of its behavior and thus improving the understanding of the model.

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.

Urban researchers and planners are often interested in understanding how economic activities are distributed in urban regions, what forces influence their special pattern and how urban structure and functions are mutually dependent. In this paper, we want to show how an algorithm for ranking the nodes in a network can be used to understand and visualize certain commercial activities of a city. The first part of the method consists of collecting real information about different types of commercial activities at each location in the urban network of the city of Murcia, Spain. Four clearly differentiated commercial activities are studied, such as restaurants and bars, shops, banks and supermarkets or department stores, but obviously we can study other. The information collected is then quantified by means of a data matrix, which is used as the basis for the implementation of a PageRank algorithm which produces a ranking of all the nodes in the network, according to their significance within it. Finally, we visualize the resulting classification using a colour scale that helps us to represent the business network.