A data integration framework for urban systems analysis based on geo-relationship learning

The world is rapidly urbanizing, and for the first time in history over 50% of the world’s population reside in urban areas. This rapid urbanization brings about tremendous challenges at the intersection of governance, infrastructure and the environment. Advanced sensing and data analytics techniques have been developed in the context of so called “smart cities” with the goal of providing insights on how urban systems could be designed and managed more effectively. However, the proliferation of data from heterogeneous sources makes interoperability and mining of such urban data streams difficult. Facilitating the extraction of insights that support data-informed policymaking and program recommendations will require frameworks to integrate such heterogeneous data streams. In this paper, we introduce a novel data integration framework that utilizes a RDF (Resource Description Framework) model to integrate disparate urban data streams based on geo-relationships that are iteratively learned from semantic information and the structure of relational databases. The development of our framework was driven by interviews and observations of city officials responsible for managing and integrating urban data and a review of the various types of disparate datasets generated from sources like departmental databases, sensors, and crowdsourcing. Finally, we apply our proposed framework to an urban data scenario in order to demonstrate the applicability and usefulness of the framework.