Lecture 1-1
Welcome to
CUS 2025!
Esteban Moro
Network Science Institute | Northeastern University
NETS 7983 Computational Urban Science
2025-01-07
NETS 7983 Computational Urban Science
Welcome!
This course introduces the field of Computational Urban Science, focusing on methods to collect and analyze urban data.
Covers techniques such as Geographic Information Systems, network science, machine learning, spatial models, and causal inference to examine city dynamics and inform urban planning, policy, and research.
Explores the application of these methods to real-world urban datasets, including mobile phone data, social media, and transactional data, enabling critical insights into the complexities of urban systems.
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NETS 7983 Computational Urban Science
Learning objectives:
- Collect and Analyze Urban Data: (spatial models, spatial visualization, network science) + (
R and python) + (visualization).
- Use Computational Social Science tools for urban insights: (machine learning) + (spatial analysis) + (causal inference) = urban insights.
- Understand Urban Dynamics: analyze complex urban phenomena (mobility patterns, social urban networks, lifestyle patterns, urban decision-making) to inform urban policy and planning.
- Develop urban data solutions: design and implement data-driven solutions for (sustainability, transportation, epidemics, resilience) by creating (models and simulations).
- Design and evaluate urban interventions: use causal inference methods to evaluate interventions and policies
- Collaborate and communicate urban insights create decision-making tools.
Meet the instructors
- Full Professor in Physics, Department of Physics & Network Science Institute, Northeastern University.
- Affiliated Researcher at MIT Media Lab
- Find out more at estebanmoro.org
- Postdoctoral Researcher at Network Science Institute, Northeastern University.
- Find out more at hamishgibbs.net
Meet each other
![Red sticker with overlaid text "Hello, my name is..."]()
- What is your experience with Computational Social Science, Network Science, and Urban Science?
- What do you hope to get out of this course?
Communication
We will use:
Canvas for official announcements and private communications.
Slack for daily information, Q&A, team discussions, can casual conversations.
https://netsi-cus-course.slack.com
Join using the following link:
https://join.slack.com/t/netsi-cus-course/signup
Any one with a northeastern email address can join.
email Send an email to the instructors. Only if needed. Please use Slack for anything related to the course.
Material
- All course material will be available at the webpage socialurban.net/CUS
- This is the first edition of this course. Help us build/edit/expand it!
Computing
We are going to use our SUNLab sever stella.northeastern.edu for data access and computing:
Apply for an account: request an account on the server to the instructors
User login and password: You will be assigned a unique user login and password to access the server.
More information: socialurban.net/CUS > Computing
Rules: Check the rules to access and use the server
Working on the server. You can use RStudio, VSCode, or Jupyter Notebooks to work on the server.
Datasets. All datasets used in the labs and materials can be found in /data/CUS
Grading
Grading in this course will be as follows.
Attendance and Participation: 20%. Homework assignments: 20% each. Final project: 40%.
| Attendance and Participation |
20% |
| Homework assignments. |
20% each |
| Final project |
40% |
Homework assignments will be individual. Final project can be in groups of up to 2 persons.
