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Urban Computing


Admission requirements

Not applicable.


Urban computing, as an interdisciplinary field, is the science of using computing technology in solving urban challenges such as traffic, and pollution. This course provides a comprehensive introduction to this topic. Throughout the course, students will get familiar with data acquisitions, integration, and modeling skills required for urban computing research.
Spatio-temporal data is the data collected as a result of movement of mobile objects, such as cars and people, over space. Spatio-temporal data mining is the core to most urban computing research projects. Most previously designed machine learning algorithms do not consider the specific characteristics of spatio-temporal data. In this course, we will talk about how different classes of machine learning algorithms have been designed so far to perform on such data. We will also learn how additional data sources can be fused and integrated with spatio-temporal data. The concepts throughout the course are explained using examples extracted from the state of the art urban computing research literature. Students will put the knowledge of spatio-temporal data mining they acquire in this course into practice, using different assignments and a final project.

Course objectives

This course aims at providing knowledge on computational skills for urban computing research. Topics to be addressed are:

  • Acquiring data for urban computing research

  • Processing time-series data

  • Processing spatial data

  • Processing spatio-temporal data

  • Visualization techniques for spatio-temporal data

  • Machine learning algorithms for urban computing research

  • Data integration

  • Deep learning for urban computing research


The most recent timetable can be found on the students' website.

Mode of instruction

  • Online video lectures

  • Online interactive work group sessions

  • Assignments

  • Paper

  • Online video presentation

Course load

Total hours of study: 168 hrs. (= 6 EC)
Lecture hours (lectures + presentations): 24 hrs.
Practical assignments: 40 hrs.
Final project: 104 hrs.

Assessment method

The final score of the course is composed of:

  • Peer review (10%)

  • Assignments (25%)

  • Online presentation (15 %)

  • Project (50%)

Reading list

  • Extra reading material composed of recent papers will become available through Blackboard.


  • You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.

  • Please also register for the course in Blackboard as soon as the lecturer has made it available.

Contact information

Lecturer: dr. M. Baratchi
Skype: mitra.baratchi


  • Daniela Gawehns (Skype: danielagawehns)

  • Jaco Tetteroo (Skype: jacotetteroo)

Course website: Urban Computing