Course Mission

The past decade has seen the rapid proliferation of large-scale digital data sets, from cell phone records to social media posts, from calls for government services to digitized historical records. With their unprecedented volume and detail, these new resources have opened up a window through which we can closely examine the behavioral and social dynamics of everyday life, and nowhere is this truer than in urban areas, which have the greatest density of both people and technology. This has given rise to urban informatics, and a renewed opportunity to understand the city, its people, and its neighborhoods, and to develop new policies and programs that improve urban life. Big Data for Cities takes an experiential approach that enables students to contribute to this young field. Given unprecedented times, we will apply our newfound skills to the urgent question of the shifting urban landscape in the age of COVID-19.

Course Outcomes

Course Content

Big Data for Cities introduces students to the burgeoning field of urban informatics with an eye towards five learning outcomes:

  1. Describe the Field of Urban Informatics: Students will read articles and websites that expose them to the researchers, policymakers, practitioners, and companies that are incorporating big data into the study and management of the city, from which they will seek to articulate the themes and connections that constitute the field of urban informatics.
  2. “Seeing” Neighborhoods through Naturally-Occurring Data: Students will acquire the methods necessary to manage, analyze, and interpret modern digital data (e.g., administrative records) using R statistical software, with a focus on translating data gathered from internet platforms into knowledge that meaningfully describes the dynamics of the city. This experiential aspect of the course is facilitated by the Boston Area Research Initiative (BARI), a center at Northeastern that focuses on urban informatics in the region. The data come from BARI’s efforts to build the Boston Data Portal, which gathers and coordinates data from many sources describing the region; our work will also be informed by the input BARI partners who work in the public, private, and non-profit sectors locally. Data work this semester will focus on data sets that capture the shifting urban landscape in the time of COVID-19.
  3. Situating Internet Data in their Real-World Context: Naturally-occurring data are a rich source of information about a city, but it is necessary to verify what real-world events they actually describe and how they are meaningful. Students will undertake City Explorations in order to compare the patterns in their data to independent information about neighborhood contexts. They will also discuss their work with various local stakeholders, including public officials and community leaders. These experiences will ground our interpretation of the data while also empowering us to utilize them to support the real communities that they describe.
  4. Communicating Discoveries: Analysis is only as useful as the insights that can be derived from it. Students will practice reporting and presenting the results of data analyses both orally and in writing in forms that would be used to communicate to a range of audiences.
  5. Creating Public Data Products: Students will modify and document improvements they make to the data sets used in the course. These will be stored in BARI’s Boston Data Portal, thereby creating new data products that can be used by others, including our local partners and future classes.

Learning Dimensions: SAIL

Big Data for Cities subscribes to Northeastern University’s campus-wide emphasis on experiential learning and does so with a focus on service-learning—that is, applying our newfound technical skills and substantive knowledge to the real-world needs and challenges of local partners. We will do this directly through our analyses of data describing the shifting urban landscape in Boston during COVID-19.

As such, the value of the course will be more than just the five content-specific learning outcomes described above; it will also help students to develop general competencies that are useful across contexts. These learning dimensions are captured by the Center for Advancement of Teaching and Learning through Research’s Self-Authored Integrated Learning (SAIL) framework. This course will particularly highlight three of their five dimensions.

SAIL Dimension Definition This Course
Intellectual Agility Learners develop the ability to use knowledge, behaviors, skills, and experiences flexibly in new and unique situations to innovatively contribute to their field. Leverage quantitative thinking for problem solving. Connect data to real-world phenomena. Adjust project goals with new insights.
Social Consciousness & Commitment Learners develop the confidence, skills, and values to effectively recognize the needs of individuals, communities, and societies and make a commitment to constructively engage in social action. Identify civic challenges. Apply data to better understanding and serving the city. Recognize how to use data to better understand and advocate around problems.
Professional & Personal Effectiveness Learners develop the confidence, skills, behaviors, and values to effectively discern life goals, form relationships, and shape their personal and professional identities to achieve fulfillment. Coordinate individual and group-level efforts on a collaborative team. Gather and leverage the perspectives of external partners. Select project directions strategically by aligning analytic opportunities with public interest.

Course Format:

Data Collaborations:


Each week will entail: readings with reading responses; and R-based data explorations. Each unit will also include one city exploration assignment, one midterm (or, for the third unit, the final project), and one service-learning reflection.

Reading Responses

R-based Data Exploration

City Exploration Assignments

Service-Learning Reflections

Midterms and Finals

Each section of the course will conclude with a major project. These are:


Academic Honesty:


Other Expectations: