Disclaimer: due to the coronavirus pandemic, this course description might be subject to changes.
Topics/Disciplines: Computational Criminology; Big Data; Data Ethics; Online Crime
Familiarity with computational techniques is of critical importance in a digital era. This course helps students familiarize with the fundamental concepts and techniques of programming in the social sciences, humanities, and law.
Students will develop skills to evaluate new research studies; assess both relevance and pitfalls of digitized data and automated procedures in practical applications (e.g. court sentencing);
And develop research using digitized data and computational methods.
NB No pre-knowledge of programming, computational methods, or statistics is required.
This course is an (extracurricular) Honours Class: an elective course within the Honours College programme. Third year students who don’t participate in the Honours College, have the opportunity to apply for a Bachelor Honours Class. Students will be selected based on i.a. their motivation and average grade.
No pre-knowledge of programming, computational methods, or statistics is required.
This course introduces students to the field of computational criminology, an emerging blend of criminology, computer science and applied mathematics.
A computational criminology offers students new perspectives and tools to approach crime and victimization problems with a data-driven approach. The digitization of data offers a wealth of information to explore various interesting research questions that previously could not be answered.
For example, how do we predict where crime will occur based on the combination of data? How does the media report about crime? How does information flow within criminal networks? Which crime and victimization problems are we all connected to?
In order to analyze these and other questions, students need to equip themselves with robust and innovative techniques to evaluate or apply digitized data methods.
This course teaches students the fundamental concepts and programming techniques needed to gain access to and analyze ‘big data’ on crime. Through a combination of critical readings, (guest-)lectures, and programming exercises, the course offers students the opportunity to develop and answer research questions with techniques learned during the course. In addition, the course challenges students to think critically about ethics and ethical dilemmas when answering important questions with "computational" techniques.
Even if students are not pursuing a career as a data scientist, this course will offer them the necessary skills to evaluate ‘big data analysis’ in practical applications.
Upon successful completion of this course, students will:
Understand the possible social implications of computational techniques, particularly in the field of criminology;
Understand ethical challenges and can contribute to a debate on ethics with knowledge about digitized data and computational methods;
Understand the fundamental concepts behind programming;
(Use Python to) write basic programming scripts to obtain, analyze, and visualize different data formats;.
Analyze digitized data to answer a question relevant to the field of criminology.
Programme and timetable:
Summerschool, two weeks.
In the week of 21 June and 28 June. Some preparation before; and a final assignment (paper) afterwards.
The program will look as follows:
Monday: Introduction to computational research methods in criminology (lecture, 2 hours),
Tuesday: Ethics in computational research (lecture and debate, 3 hours)
Possible guest lecturers: Prof. Dr. Matthew Salganik (Princeton)/Prof. Frederik Zuiderveen Borgesius Radboud University)/a colleague from e-law at Leiden University
Wednesday: Data collection (lecture 2 hours, tutorial 2 hours)
Thursday: Data analysis and visualisation (lecture 2 hours, tutorial 2 hours)
Friday: 2 Excursions, one in the morning, one in the afternoon (Options: Facebook Guest Lecture, CBS, Europol, Nationale Politie (Webcrawler)
Monday: An Introduction to Networks (tutorial 2 hours, 1 hour guest lecture)
Possible guest lecturer: Prof. Dr. David Lazer (Northeastern University)
Tuesday: An Introduction to unsupervised and supervised Machine learning (tutorial 2 hours, 1 hour lecture)
Wednesday: Students work independently on their assignments
Thursday: Integration and debate (lecture 3 hours), back-up for excursion
Friday: *Research symposium with student presentations *(3 hours)
Depends on physical or online education.
For the tutorials, in principle computer rooms (in KOG or Sterrewacht or Campus the Hague), or students can work on their own laptop.
The lectures: take place in a normal lecture room.
Examples of literature that will be used in this course:
Book Sections from: Matthew Salganik (2017). Bit by Bit: Social Research in the Digital Age. Princeton, NJ: Princeton University Press. It is available here.
Articles such as:
Safiya Noble (2018). Algorithms of Oppression: Introduction. New York University Press;
Kieran Healy and James Moody (2014). Data Visualization in Sociology. American Review of Sociology 40: 105-28;
Cathy O’Neil (2016). Weapons of Math Destruction: “Introduction.” Crown Publishing Company. (Blackboard); Williams, M. L., & Burnap, P. (2016). Cyberhate on social media in the aftermath of Woolwich: A case study in computational criminology and big data. British Journal of Criminology, 56(2), 211-238; Snaphaan, T., & Hardyns, W. (2019). Environmental criminology in the big data era. European Journal of Criminology, 1477370819877753; Brennan, T., & Oliver, W. L. (2013). Emergence of machine learning techniques in criminology: implications of complexity in our data and in research questions. Criminology & Pub. Pol'y, 12, 551.
Other possible literature will be announced in class or via Brightspace.
Course load and teaching method:
This course is worth 5 ECTS, which means the total course load equals 140 hours:
Seminars: lectures and tutorials, in total 20-22 hours
Final research symposium of 3 hours with student presentations
Excursion: 1 or 2 excursions of 3 hours (to be replaced by additional guest lectures if online)
Literature reading: 35 hours
Practical work: 40 hours
Final assignment and presentation: 30 hours
Aanwezigheids- en participatieplicht, participation is mandatory.
The assessment methods will look as follows:
(This is an example)
- 30% (15% each) Two reaction papers to a session’s reading(s) of 1000 words
- 20% Presentation during a research symposium
- 50% A final assignment (code paper or regular paper)
It is not required to successfully complete all partial exams in order to pass this course. Students are allowed to compensate a ‘fail’ (grades up to and including 5.0).
The assessment methods will be further explained in the first session of the class.
Brightspace and uSis:
Brightspace will be used in this course. Students can register for the Brightspace module one week prior to the start of the course.
Please note: students are not required to register through uSis for the Bachelor Honours Classes. Your registration will be done centrally.
Registration will be possible from Monday 9 November 2020 up to and including Thursday 19 November 2020 through the student website of the Honours Academy.
Prof.dr. Masja van Meeteren
Department of Criminology
Leiden Law School
firstname.lastname@example.org +31 71 527 3512
Dr. Ieke de Vries
College of Criminology and Criminal Justice
Florida State University, Tallahassee
email@example.com +1 617 943 1826