After a natural disaster, the planning and prioritization of early recovery efforts are often difficult and can be misaligned with a community’s needs. Yet, planning has a unique role in building adaptation and resilience, especially in communities with high vulnerabilities, especially in the initial stages of recovery. Many researchers and responders have pointed to the emergence of digital data and emerging computational tools to assist in discovering and prioritizing needs in these communities. Still, there lacks a comprehensive investigation of how these may or may not be opportunities to shape policy decisions.

Collaborating with the National Disaster Preparedness Training Center (NDPTC), the students proposed, tested, and validated emerging approaches to analyzing digital data, including satellite imagery, social media, and crowdsourced sources, to aid recovery prioritization. The project used the contemporaneous recovery planning efforts of government, non-profits, and community groups after Hurricane Ida as a case study to test and evaluate these technologies. Through interviews with residents, professionals, government officials, and advocates in St. Charles, Jefferson, and Lafourche Parishes, Louisiana, the students adopted a “Whole of Society” perspective on recovery planning and technology development.

The impacts of this project go beyond a single planning/technology evaluation for the NDPTC. The students brought urban planning values to machine learning (all the while learning these technologies themselves), and their outcomes can shape future disaster research through the breadth of results for various audiences.

📸: Students from the course produced a public-facing website, report and Github repository. 

🏅: The work was recognized with a 2022 Michigan Association of Planning (Michigan APA) Graduate Student Project award. 

Situated within the spatial practices of urban studies and city planning, this course is a hands-on introduction to design principles, theory, software techniques, and strategies for communicating data to various audiences. Classes will be a combination of lectures, design workshops, and labs. Through readings, design critique, and code assignments, students will learn how visual representations can help understand complex data and design, and evaluate visualizations for analysis or communication.

Topics include visual perception, exploratory data analysis, task analysis, graphic design, narrative, etc. The course will not only introduce a suite of programs and skills, as visual languages play a much larger role in mediating our interactions, facilitating, and constraining our awareness of the systems we are embedded in. This course will actively maneuver through the inequal agency afforded by (and limitations to) visual and narrative representations for different communities. The course will ask in what ways representation is an act of advocacy or disenfranchisement. What are the effects of inequities in indexical data collection manifested in visual and narrative communication? More specifically, the course engages with how representation—in its practice of describing existing conditions or proposing imagined possibilities—can transcend supposed neutrality and promote the inclusion of communities otherwise excluded or counterweight the underlying data’s biases.

This course provides students an introduction to the technical, theoretical and practice-based dimensions of urban informatics, an interdisciplinary field of research and practice that uses data and information technology for the analysis, management, planning, inhabitation, and usability of cities. Situated at the intersection of digital technologies and the human environments, this course situates itself at the emergence of new disciplines— urban science, big data, smart cities, civic technologies among others. The course is centered around technical lectures interspersed with guest presentations and class debates grouped into five topical categories—data acquisition, numerical analysis, mapping and spatialization, visualization and interaction, and civic technologies­. Students will also have an opportunity to develop their project—based on their research question—that combines these technical aspects in a final analysis and demonstration. Within the seminar and lecture sessions, we will discuss the policy and design questions around the creation of, and use of urban data within the language of urbanism. Seminar and lecture sessions cover topics related to the context and practice models associated with urban technologies, including civic technology, indicators, smart cities, and performance management.

The proliferation of technologies has brought a concomitant wealth of spatiotemporal data. Within the context of this wealth of data, and the tools by which we can explore the
data, this course seeks to introduce students to advanced techniques in creating, utilizing, and critiquing methodologies for addressing questions relevant to urbanism. Aimed at covering a variety of topics in both practice and in research, the course operates with a two-fold mission: (1) to critically discuss the theories, concepts, and research methods involved in spatial analysis and (2) to learn the techniques necessary for engaging those theories and deploying those methods. The class will work to meet this mission with a dedicated focus on the urban environment and the spatial particularities and relationships that arise from the urban context.

This course will draw upon various disciplines to interrogate the state-of-the-discussion of GIScience as well as extend it to the realm of “social physics” and network science (as a subset of topics from complexity science), as the volume and velocity extend our opportunity to extract useful information from digital traces of human activity. This course will, therefore, also extend to teach algorithms to model and to characterize complex networks, as it is applied to questions of urbanism, planning and design within the domain of physical space. However, with the nascency of these practices, this course situates itself in the uncomfortable middle of providing both the methods for answering complex socio-spatial questions and critiquing them within a professional practice discourse.

tl;dr: We will critically explore and discuss advanced methods in analyzing spatiotemporal data within an urban context, with practice in
common and exposure to emerging methods.

tl;dr: Build sensors. Consider how data can support agendas in spatial and environmental justice, as well as enumerating the use of public space. Play as a critical practice will be our M.O.

In recent years, interest in “public life” — people’s daily interactions within the built environment (Gehl 2011) — has been renewed as urban spaces are being transformed into areas for recreation, socializing, and human activity. However, many commonly-accepted theories in environmental psychology and planning were generated from limited observations — limited by time and space. This course asks in what ways sensing technologies can validate or challenge these theories of public space and social interaction, and how do we intersect them with aspects of environmental quality and justice, sustainability, equity, and overall general well-being?

Participants in this hands-on workshop will design and implement prototypes for the creation of data on human activity, environmental conditions, and quality. Students will also learn methodologies to analyze and present the data. We will use the university context as a living laboratory to test and reevaluate the commonly-accepted theories of public life while engaging in critical conversations that balance the positive aspects of better-informed design and policy with the challenges concerning data ethics, surveillance, and privacy.

📸: The course website for 2019 and 2020 archives critical and process work from the course, while selected student work can be found on the respective course pages for 2019 and 2020.  course website, and select work can be found on the GSAPP website.

With the proliferation of digital data, new opportunities are being availed to measure, understand and propose changes to the communities in which we live, work, and play. This has led to a host of new terms and disciplines—urban science, big data, smart cities, civic technologies—that seek to understand the intersection of digital technologies and the human environment. These forces have created new opportunities for planners to make data actionable through analysis and visualization, as well as avenues for new types of practice, including startups and data advocacy. Furthermore, many of the most urgent problems facing cities—including those as a result of climate change—are problems known, presently, through their representations in the output of predictive models. These forces and challenges have placed an imperative for planners to develop the facilities to collect, analyze, and communicate (visually and textually) using these large and often-messy data sets.

This Introduction to Urban Data and Informatics is intended to provide students an entrée into the technical, theoretical, and practice-based dimensions of data analysis in cities. The course is centered around technical lectures interspersed with guest presentations and class debates grouped into five topical categories—data acquisition, numerical analysis, spatialization, visualization and interaction, and civic technologies. Students will also have an opportunity to develop their project—based on their research question—that combines these technical aspects in a final analysis and visualization. Within the seminar and lecture sessions, we will discuss the policy and design questions around the creation of and use of urban data within the language of planning.

📸: Select work from 2019 can be found on the GSAPP website. 

This subject introduces graduate students to theories about how cities are formed and the practice of urban design and development, using US and international examples. The subject is organized into two parts: Part 1 analyses the Forces which act to shape and change cities. Starting with Boston as a reference, we will examine key forces affecting contemporary urban development: market economics, social forces, industrial production, the natural environment, public development, private development, and incentives to encourage good design. Finally, we will consider how cities define a vision for their future and how these are articulated in plans and proposals. Lectures will be supplemented by guest presentations, case studies, and field trips. Part 2 surveys key Models of physical form and social intervention that have been deployed to resolve competing forces acting on the city. The models reflect discrete languages of city-making. We will discuss the evolution of each model, practical consequences, and potentials for resolving emerging urban problems and opportunities. The models include Tradition, Art, Efficiency, Ecology, Security, Emotion, and Intelligence. The application of the models will be illustrated in historical and contemporary project cases from Europe, Asia, Latin America, and the US.