Courses

Fall semesters

Course # 34:816:502:01 & 34:970:502:01: Theory and Practice of Public Informatics

Data science and artificial intelligence (AI) are gradually transforming how cities and regions are planned, designed, and operated, as well as how public policies are formulated. This course focuses on Public Informatics, which is the application of translational data science to address complex economic, social, and health challenges facing the public.

Through 3 different modules, the course will concentrate on Urban Informatics and Public Policy Analytics, two interrelated and rapidly growing fields at the core of Public Informatics. The course will survey data-driven applications in key “subject areas” such as smart cities, sustainability science, climate strategies, urban mobility analytics, and urban design. Additionally, students will explore data science approaches and AI methods being used in housing and real estate analysis, labor market and workforce development analysis, and health informatics. The course will emphasize how data-intensive approaches for the common good continue long-standing traditions in urban planning, public policy analysis, community engagement, and civic activism.

While we examine the benefits of adopting such approaches, we will also delve into pitfalls of data-intensive strategies. Examples of disadvantages include the risks of violating people’s privacy, wrongfully targeting specific groups or areas due to algorithmic biases, and increasing the digital divide. Other examples include paradoxical increases in demand for scarce resources due to technology-enabled ease in accessing goods and services. We will also examine national AI policies, and open data policies, and draw implications for the subject areas. The course will offer students a glimpse of the fluid legal, policy and technical frameworks on responsible innovation; ethics, equity, explainability, fairness, and trust in data; and data governance and management approaches relating to security, privacy, reproducibility, metadata, interoperability, as well as data licensing and partnership-development with data owners. Other “softer” issues include capacity-building and culture change to be receptive to evidence-based decision making. These elements are collectively needed in order to transform data into the knowledge and insights for the public good.

Spring semesters

Course # 34:970:654:01 & 34:970:679:01: Smart, Sustainable and Healthy Cities

This Special Topics seminar course on Smart, Sustainable and Healthy (SSH) cities will consider planning for future cities through the lens of technology. Data science and Artificial Intelligence (AI) are slowly but steadily transforming how cities and regions are planned, designed and operated. A wide range of technologies – drones, blockchains, Internet of Things, generative AI, recommendation systems, delivery bots, Location-Based Services, real-time critical health alert systems, to name just a few – are rapidly changing the way services are offered, and the way we live and work in cities. These activities have tremendous potential to affect the wellbeing of cities and regions, but come with a range of challenges as well.

We will consider three case studies shaping the future of cities – smart cities, sustainable cities, and healthy cities – and study how new and emerging technologies are being leveraged to benefit transportation systems, urban design, citizen engagement, city infrastructure, climate action, disaster resilience, among other areas of importance in cities. We will also look at how technologies are employed in healthcare and wellbeing systems, specifically how new and emerging technology intends to eliminate health disparities and address Social Determinants of Health (SDOH).

The course will consider how the policy landscape is evolving to address new and unexpected consequences of technology implementation and how urban planners are incorporating the data and insights that result in planning. The stakeholder landscape is changing rapidly as well, with many organizations new to planning entering into the realm of cities practice with, for example, the creation of digital tools and open data analytics. While this has helped with the innovation ecosystem for citizen wellbeing, many issues such as bias, data justice and lack of transparency remain unresolved. The course will take a balanced approach and elaborate on the benefits that accrue from adopting such approaches, while delving in-depth into the disadvantages and pitfalls of data-intensive approaches with respect to equity, privacy, security, and other unintended consequences. The course will cover US cases, as well as international applications of SSH cities.

Course # 34:833:679:01: Advanced Quantitative Methods

This course will focus on empirical data modeling for planning and policy analysis. The overall objective of the course is to introduce a series of statistical methods beyond the linear model that are suited to making inference when working with non-normal (Qualitative and Limited dependent variable) data, and in the latent variable contexts. The course will also focus on considerations arising from modeling nonexperimental data and when modeling hierarchical, clustered, spatial and other atypical data structures.

Students will be exposed to new and emerging approaches to secondary data collection, for example, through the use of Application Programming Interfaces and responsible web scraping. The course will require the use of R, and there will be a lab session as a part of each week’s class.

The knowledge gained will enable urban planning and policy students to make inferences about problems of interest to them and to use data to implement specific research designs. The material covered is also likely to be of interest to students in other social science and health-related disciplines.

The course will be offered in four modules:

1. Module 1: Review of basic concepts related to the linear model, introduction to new and emerging ways to obtain secondary data, and models of conditional quantiles.

2. Module 2: Models of Qualitative and Limited dependent variables with a particular focus on count and censored data and other non-normal such as Hurdle models, and models for fractions/proportions.

3. Module 3: Models for hierarchical or multi-level designs and spatial structures.

4. Module 4: Structural modeling and causal inference.