Applied Urban Science and Informatics, OAC

Cities are increasingly data-rich environments, and data-driven approaches to operations, policy, and planning are beginning to emerge as a way to address global social challenges of sustainability, resilience, social equity, and quality of life. Understanding the various types of urban data and data sources – structured and unstructured, from land use records to social media and video – and how to manage, integrate, and analyze these data are critical skills to improve the functioning of urban systems, more effectively design and evaluate policy intervention, and support evidenced-based urban planning and design. While the marketing rhetoric around Smart Cities is replete with unfulfilled promises, and the persistent use (and mis-use) of the term Big Data has generated confusion and distrust around potential applications. Despite this, the reality remains that disruptive shifts in ubiquitous data collection (including mobile devices, GPS, social media, and synoptic video) and the ability to store, manage, and analyze massive datasets require students to have new capabilities that respond to these innovations.

Visualization and visual analytics systems help people explore and explain data by allowing the creation of both static and interactive visual representations. A basic premise of visualization is that visual information can be processed at a much higher rate than raw numbers and text. Well-designed visualizations substitute perception for cognition, freeing up limited cognitive/memory resources for higher-level problems. This course aims to provide a broad understanding of the principals and designs behind data visualization. General topics include state-of-the-art techniques in both information visualization and scientific visualization, and the design of interactive/web-based visualization systems. Hands-on experience will be provided through popular frameworks such as matplotlib, VTK and D3.js.

A variety of technical skills are needed to build analyses that can help us to solve urban challenges. This course is designed to develop programming skills and to gain familiarity with the techniques, concepts, and models of urban informatics computing. Students will learn to program in python through a series of online tutorials, and will be exposed to the leading thinking on urban challenges through readings and discussion. Weekly lectures will demonstrate how these skills can be used to construct analyses through detailed code reviews. Finally, students will have the opportunity to practice these skills as they build an analysis of an urban challenge using real data.

This course will introduce you to urban governance and its current innovation trends. Urban governance comprises the various forces, institutions, and movements that guide economic, politics, social and physical development, the distribution of resources, social interactions, and other aspects of daily life in cities. Public-sector innovation is indispensable to solve the complex urban challenges we are facing and can bring significant improvements in the services that the government has a responsibility to provide, including those delivered by third parties. Following a Discovery-Design-Delivery approach, students will learn the complex nature of cities, different strategies to solve public problems, how urban administration works and how public policies are crafted, how we can promote urban governance innovation, why collaboration is a must and which are the best tactics to promote effective public-private partnerships and networks, how we can support public engagement at all stages of the policymaking cycle, how to promote effective communications using current technology available, ethical issues that may arise when applying analytics to policy problems, how we can connect artificial and collective intelligence, and different approaches to measuring organizational performance. This course will help students to become public entrepreneurs that know how to effectively deliver data and innovation projects into an urban environment.

This course will provide an accessible introduction to the fundamental concepts and operations that underpin Geographic Information Systems (GIS). At their core, GIS rely on geography as an interface to structured and unstructured data that are stored and managed in what are often complex information systems. The course will introduce students to the central components of GIS as commonly deployed in enterprise software, free and open source code libraries, and experimental systems. The course gives equal treatment to the methodology underlying GIS (particularly in spatial science and computer science), and the software operations that collectively enable GIS functionality and applications (particularly data structures, spatial data access, and geometric operations). Hand-on lab sessions will walk students through the use of GIS methods in popular software, including the Google Maps Javascript Application Programming Interface (API), ESRI ArcGIS (ArcMap and ArcGIS Pro), GeoDa, and GIS libraries in R Studio. The course is intended to prepare students for more advanced coursework at NYU CUSP, particularly Urban Spatial Analytics and Advanced Spatial Analytics.

This course equips students with the basic skills and tools necessary to address urban data science problems. It starts with basic computational skills, statistical analysis, error analysis, good practices for handling data, and further covers a variety of approaches and techniques used in applied data science, including regression analysis, clustering, and classification. The course considers those techniques through the prism of the Machine Learning paradigm, introducing the concepts of supervised and unsupervised learning, discussing common challenges such as over-fitting, multicollinearity and the ways to address those. The course also starts the project-oriented practice in urban data science. After this class you should be able to formulate a question relevant to urban science, find and curate an appropriate data set, identify and apply common analytic approaches to answer the question, obtain the answer and assess it with respect to its certainly level as well as the limitation of the approach and the data.