Spatial Datasets and Urban Applications


The proliferation of the mobile web and the availability of large scale digital datasets has enabled a new wave of research studies that are largely driven by these new types of data generated in urban environments. This tutorial aims to offer an overview of the opportunities and challenges posed by geolocated datasets with a particular emphasis on their use for the study of urban data science, guiding participants through the entire process of mining such datasets to using them to analyze different aspects of urban science with a theory-backed approach. We will provide an extensive overview of some of the theory underlying the study of urban systems followed by a practical introduction on how to use several different datasets and APIs in the second part of the day. Inspired by a fusion of computational approaches and complex systems, this tutorial will integrate elements from geography, computer science, urban studies, sociology, physics and complex systems. This will involve the description of methodologies for the collection of geo-referenced and spatial datasets, techniques for the analysis and modeling of geographic data and mobility, network science as a tool to understand cities, machine learning as a medium to solve optimization problems and define prediction tasks in urban environments, and finally, ways to visualize raw datasets and corresponding outputs on maps.