Section: New Results

Urban Computing Leveraging Location-Based Social Network Data: a Survey

Participants : Thiago H. Silva [UTFPR (Brazil) - Dept. of Computer Science] , Aline Carneiro Viana, Antonio Loureiro.

Urban computing is an interdisciplinary area in which urban issues are studied using state-of-the-art computing technologies. This area is at the intersection of a variety of disciplines: sociology, urban planning, civil engineering, computer science, and economics, to name a few. More than half of the world's population today live in cities and, consequently, there is enormous pressure on providing the proper infrastructure to cities, such as transport, housing, water, and energy. To understand and partly tackle these issues, urban computing combines various data sources such as those coming from Internet of Things (IoT) devices; statistical data about cities and its population (e.g., the Census); and data from Location-Based Social Networks (LBSN), sometimes also termed as location-based social media. One fundamental difference between data from LBSNs and data from other sources is that the former offers unprecedented geographic and temporal resolutions: it reflects individual user actions (fine-grained temporal resolution) at the scale of entire world-class cities (global geographic resolution).

Urban computing with LBSN data has its particularities. For instance, users who share data in Foursquare, a popular LBSN, usually have the goal of showing to their friends where they are while also providing personalized recommendations of places they visit. Nevertheless, when correctly analyzed for knowledge extraction, this data can be used to better understand city dynamics and related social, economic, and cultural aspects. To achieve this purpose, new approaches and techniques are commonly needed to explore that data properly.

In order to better study such needs, we have published at ACM Computing Survey Journal (the ACM journal with highest impact factor) a survey that provides an extensive discussion of the related literature, focusing on major findings and applications. Although its richness concerning knowledge provision, LBSN data presents several challenges, requiring extra attention to its manipulation and usability, which drives future research opportunities in the field of urban computing using LBSN data. Our work is complementary to two existing surveys in the area of urban computing (i.e., by Jiang et al. and by Zheng et al.) since they only mention briefly few studies that explore LBSN data, neglecting key challenges that revolve around LBSNs. We hope that taken together, our effort and these existing ones, provide a broad perspective of urban computing studies and its development through the lens of different data-driven approaches.