A Data-Centric Approach for Social and Spatiotemporal Sensing in Smart Cities

Part of the current smart cities research aims to efficiently measure its properties, improving our perceptions of objects, people, and the environment where we live. However, the implementation of sensing technologies in city scale is a critical factor in terms of cost and coverage. Thus, researchers have resorted to data-centric approaches to provide sensing systems based on big data. In this paper, we investigate data streams of online social networks to provide insights about people, venues, and city regions using a big data approach for urban sensing. We take advantage of the popularity of these applications to investigate social and spatiotemporal characteristics, demonstrating the feasibility of sensors based on data analytics.