Urban Data Analytics

The integration of big data and interconnected technology, along with the increasing population, will lead to the necessary creation of smart cities. The availability of sensors in city infrastructure provides high granular data at unprecedented spatiotemporal scales. This course will introduce scientific techniques that will allow the analysis, inference and prediction of large-scale data (e.g. GPS vehicular data, social media data, mobile phone data, individual social network data, etc.) that are present in city networks. Basics of the data science methods to analyze these datasets will be presented. The course will focus both on the methods and their application to smart-city problems. Python will be used to demonstrate the application of each method on datasets. We will examine real world examples from for example traffic management, logistics, telecommunications and crowd sensing. Examples of problems that will be discussed include ridesharing platforms, smart and energy-efficient buildings, evacuation modeling, decision making during extreme events & urban resilience.