PublicSense: Refined Urban Sensing and Public Facility Management with Crowdsourced Data

Urban sensing and computing has become hot with the recent surge of Internet of Things, smart phones, and mobile social networks. Complementary to static sensing infrastructure, mobile crowd sensing (MCS) has become an important way to achieve large-scale urban sensing. However, few studies pay attention to public sensing to improve city management. In this paper, we propose an MCS-based framework to improve urban sensing and public facility management. We use a unique crowdsourced data which collects complaint calls from citizens in Xi'an city, as a case study for public facility sensing and management. We analyze the complaint call data from four different perspectives, and obtain several interesting findings. These findings are useful for applications such as public facility management, urban infrastructure maintenance and emergency sensing.