Proven proficiency with Python – read the “To satisfy the prerequisites” section at the bottom
The challenges of urbanization are complex, difficult to handle, ranging from socio-spatial inequalities to serious environmental and sustainability issues. Together with the increasing volume and nature of big data sets in cities, these challenges call for a better understanding and modeling of the structure and dynamics of cities using state-of-the-art mathematical and data mining tools and network approaches. In this course, we will discuss the current understanding of networks and human movements in cities and learn about computational approaches to characterize them. Large data sets will be computationally investigated, in particular, infrastructure networks and patterns of movements, and the limits of the used tools and algorithms will be discussed. Besides the mathematical theory, the course will have a practical approach with hands-on classes, data visualization, and with the development of a project. During the class, all examples and sample codes will be provided in Python and Jupyter notebooks.
This course will take place twice a week during the first half of the fall term. Starting date: September 18, 2018.
Master and PhD
Lectures: 12 classes of 100 min. Around two thirds of the classes will be theory only. The rest will include programming exercises or evaluation of data sets. Therefore, use of a computer will be required during some lectures. During class students can form groups and use their own laptops. Instructions on the required software will be provided during the first class.
- A science of cities: classical and modern approaches to understanding urban systems
- Urban scaling laws and agglomeration economies
- Spatial networks and transportation networks; multiplex networks and multimodality
- Random walks on networks; routing algorithms and network flows; Braess’ paradox
- Mobility tools, measurements, and spatial interaction models: gravity, radiation
- Using python to analyze spatial/geo/mobility data
- Using the library OSMnx to scrape and analyze street and mobility networks
- Introduction to the QGIS geographic information system application
- Community detection and null models for human interaction and spatial networks
- Human mobility and urban mixing, epidemiology, polycentricity
- Shareability networks and maximum matching
- Project presentations
Textbooks and reading
Marc Barthelemy. The structure and dynamics of cities. Cambridge University Press 2016.
A list of papers and online resources will be provided during classes
In short: don't do it. You may work with others to help guide problem solving or consult stack overflow (or similar) to work out a solution, but copying—from friends, previous students, or the Internet—is strictly prohibited. NEVER copy blindly blocks of code – we can tell immediately. If caught cheating, you will fail this course. Ask questions in recitation and at office hours. If you are stuck with a programming task and cannot get help, write code as far as you can and explain in the code comments where and why you are stuck.
Further information, such as the course website, assessment deadlines, office hours, contact details etc. will be given during the course. The instructor reserves the right to modify this syllabus as deemed necessary any time during the term. Any modifications to the syllabus will be discussed with students during a class period. Students are responsible for information given in class.