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Mobile information systems agendas are increasingly becoming an essential part of human life and they play an important role in several daily activities. These have been developed for different contexts such as public facilities in smart cities, health care, traffic congestions, e-commerce, financial security, user-generated content, and crowdsourcing. In GIScience, problems related to routing systems have been deeply explored by using several techniques, but they are not focused on security or crime rates. In this paper, an approach to provide estimations defined by crime rates for generating safe routes in mobile devices is proposed. It consists of integrating crowd-sensed and official crime data with a mobile application. Thus, data are semantically processed by an ontology and classified by the Bayes algorithm. A geospatial repository was used to store tweets related to crime events of Mexico City and official reports that were geocoded for obtaining safe routes. A forecast related to crime events that can occur in a certain place with the collected information was performed. The novelty is a hybrid approach based on semantic processing to retrieve relevant data from unstructured data sources and a classifier algorithm to collect relevant crime data from official government reports with a mobile application.

During the last years, we faced a tremendous development of mobile sensing applications powered by innovative technologies related to ubiquitous and pervasive computing, volunteered geographic information, crowdsourcing and social networks. Nowadays, we are living in the next digitally enriched generation of social media in which communication and interaction for user-generated content is mainly focused on improving the sustainability of smart cities. Thus, urban computing is defined as the technology for acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, for tackling the major issues that cities face. Moreover, this technology is seeking ways to reduce inefficiencies and to be more agile in responding to citizens' needs in order to create smart cities. In this position paper, we address the content to describe the urban applications and the challenges for open research problems that are presented in the big cities.

, where he leads the SESAR Research Lab and is Head of the Ph.D. program in computer science. His research interests include secure service-oriented architectures, privacy-preserving big data analytics, and cyber-physical systems security. PEIQUAN JIN received the Ph.D. degree in computer science from the University of Science and Technology of China (USTC), in 2003. He spent two years in the Department of Electronic Engineering and Information Science, USTC, for his postdoctoral research. He was a Visiting Scientist with the University of Kaiserslautern, Germany, in 2009, hosted by Prof. T. Härder.

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