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Current social networks provide information with high correlation with events that are occurring worldwide. Twitter is a microblogging network of real time messages in which people post about various classes of events. A relevant topic is traffic congestion; user-generated content is useful to assist drivers in avoiding crowded areas. This work proposes a model to predict traffic-related events, based on a set of machine learning methods, in which a spatio-temporal dataset is obtained from Twitter messages. The training stage uses geocoded traffic events, in order to generate possible sites with traffic congestion at a given time. As a case study, partial areas of the Mexico City were taking into consideration. [ABSTRACT FROM AUTHOR]

Environmental noise is a big problem related to the environmental pollution in cities, which affects the quality of people life. In this paper, a methodology that uses an approach based on Volunteered Geographic Information (VGI) for the monitoring, analysis and prediction of environmental noise is proposed. It can be very useful to propose alternatives and initiatives that improve the life in a city. So, this work is composed of the following stages: data acquisition, analysis and, data processing, as well as the information visualization, considering the temporality of the same and taking into account macro and micro levels of analysis for the study surface. In addition, some details of the design and development of a geographic information system are presented, consisting of a web-mapping system, an application for mobile devices called "NoiseMonitor", geospatial analysis and machine learning methods (support vector machines and artificial neural networks) for the prediction of environmental noise; by using contextual information; that is, some data related to the city. This kind of work seeks to take advantage of the willingness of citizens to participate collaboratively to sense their environment and be considered as human sensors, which unlike traditional approaches, the cost associated with the development and implementation of this project is much lower. Likewise, a case study based on the Mexico City is presented and discussed, particularly the fourth quadrant of the Historic Center of the City, which is very representative for the variety of environmental noise that is generated in that area. The application domain of this approach is oriented towards big data from a collaborative perspective, Internet of Things and smart cities. [ABSTRACT FROM AUTHOR]