Doctoral Track in Urban Science: Course Descriptions

PhD students who wish to pursue the urban science doctoral track must complete nine credits (or three elective courses) out of their total doctoral degree credit requirements through CUSP.

Electives are categorized into three speciality areas: Complexity, Informatics, and Sensing. Students must focus on at least two of the specialties, by taking at least three credits (one course) in each of the chosen areas. Upon consultation with the Departmental Research Advisor and the CUSP Director, students may be able to replace a CUSP elective with a relevant course in their home department.

Complexity

Complex Urban Systems

Course Description: This course offers an introduction to the broad field of complex urban systems, with a focus on project-based learning and computer coding. Using only basic concepts in probability and linear algebra, the course will introduce methods and principles of complex systems applied to urban systems, including geography laws, scaling principles, and mobility patterns.

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0

 

Disaster Risk Analysis and Urban Systems Resilience

Course Description: This course offers ample coverage of urban risks to different natural hazards such as earthquakes, hurricanes, floods, and wildfires. The class will discuss fundamental concepts in understanding hazards, infrastructure vulnerability, risk, and disaster recovery. Additionally, the course will cover introductory topics on disaster risk modeling with rigorous statistical methods and large datasets. The class will review critical elements that can exacerbate risks such as climate change, rapid urban growth, and deteriorating and precarious infrastructure. The course will include guest speakers who inform policymaking on large-scale risk mitigation and novel technologies for disaster risk reduction. The class is designed for graduate students interested in risk and resilience for practice and research. Knowledge of undergrad-level statistics and probabilities and experience in data visualization in Python, Matlab, or R is required. The class will meet regularly for lectures and discussion of reading assignments on state-of-the-art quantitative and qualitative concepts of disaster risk and risk management. An open project in the field of disaster risk and resilience is a crucial component of the class.

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0

Informatics

Urban Spatial Analytics

Course Description: In this course, students will learn how spatial analysis can support the exploration of geographical properties, patterns, and phenomena in urban context. The course will cover the foundations of spatial analysis in the spatial sciences, examining in particular how spatial science influences data collection, data modeling, data analysis, and data interpretation. The course will explore the derivation of core spatial statistics and geostatistics that are routinely used in geographical analysis. The course will also examine the use of spatial analysis in supporting spatial modeling. Students are expected to have undertaken prior coursework in Geographic Information Systems (GIS). Labs will focus on how to run spatial analysis methods from GIS platforms. Example sets in the course will focus on urban applications.

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0

 

Applied Data Science

Course Description: This course equips students with the skills and tools necessary to address applied data science problems with a specific emphasis on urban data. Building on top of the Principles of Urban Informatics (prerequisite for the class) it further introduces a wide variety of more advanced analytic techniques used in urban data science, including advanced regression analysis, time-series analysis, Bayesian inference, foundations of deep learning and network science. The course will also contain a team data analytics project practice. After this class the students should be able to formulate a question relevant to urban data science, find and curate an appropriate data set, identify and apply analytic approaches to answer the question, obtain the answer and interpret it with respect to its certainty level as well as the limitations of the approach and the data.

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0

 

Machine Learning for Cities

Course Description: Student teams engage in projects through the integration and analysis of data, definition and testing of possible solutions, identification of implementation strategies and constraints, and recommendation of preferred solutions and implementation plans. Student teams are challenged to utilize classroom learning within the real-world constraints of city operations and development, while cognizant of political, policy, and financial considerations and issues of data privacy, validity, and transparency. In so doing, student teams are tasked with creating innovative and replicable solutions to pressing urban problems.

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0

Sensing

Internet-of-Things Security and Privacy: A Data-Driven Perspective

Course Description: Smart home IoT (Internet-of-Things) devices are gaining popularity in average consumer homes. These “smart” devices, such as cameras, plugs, TVs, dishwashers, etc, are also known to pose various security and privacy threats (e.g., your Alexa listening to you), but the opaque nature of these devices makes it difficult to discover security and privacy vulnerabilities. This course introduces basic and advanced topics on Internet-of-Things (IoT) security and privacy from a data-driven perspective. It starts with preliminaries on networking and Internet security, followed by security research based on a data-driven approach. Students will read peer-reviewed academic papers from multiple disciplines, ranging from computer science, psychology, and policy/law. Furthermore, students will engage in hands-on projects to independently investigate real-world security and privacy issues of IoT devices and/or propose solutions to fix these issues. Students for this class are expected to have networking knowledge, such as how TCP/IP works, how packets get forwarded, and how to run tcpdump. Otherwise, students are encouraged to take relevant Coursera courses on these topics before the semester begins and to seek the instructor’s approval.

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0

 

Monitoring Cities

Course Description: The world’s urban population is growing by nearly 60 million per year; equivalent to four cities like New York annually. Monitoring the chronological growth of key attributes of cities, as well as quantifying their current conditions presents a great potential for positive change. Through the acquisition of new data, there are immediate opportunities to influence the sustainable growth of small and medium size cities. There is also the potential for alleviating the extremes in Megacities, where conditions have reached a critical and unmanageable state. Looking at cities as interdependent networks of physical, natural and human systems, this course provides a perspective on how to monitor the function and wellness of these systems. Students obtain an understanding of needs assessment, planning, and technical approaches for instrumenting a city. This includes monitoring patterns of activity, mobility, energy, land use, physical and lifeline infrastructure, urban ecology, vegetation, atmosphere and air quality. The expected outcomes of this course is a comprehensive understanding of what can be instrumented and the monitoring architecture for acquiring and generating new data about cities.

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0

 

Urban Sensing

Course Description: Remote sensing technologies are becoming increasingly available at better resolution levels and lower costs. This course will provide an overview of some of the most common technologies in the areas of imagery, video, sound, and hyperspectral data that can be facilitated through smart phones or other readily accessible means. Students will be given a formal introduction to the aforementioned four areas and then be afforded an opportunity for hands on training in data collection and data analysis. In the course, students will have the opportunity to work in small groups to investigate an urban problem of interest to them at a site of their choosing. The teams will use these new learned technologies in tandem with other publicly accessible data (either formally available or also collected by the researchers) to investigate a working hypothesis about their chosen urban problem for their particular site. 

Credits: 3
Weekly Schedule: Weekly Lecture Hours: 3 | Weekly Lab Hours: 0 | Weekly Recitation Hours: 0