OASV4100 Urban Governance Course description

Modern urban governments face challenges due to rapid changes in social, economic and environmental conditions. This course focuses on ideas, models and trends in the governance of urban areas, and students are provided with a set of analytical tools to study contemporary changes and transformations in governance practices and arrangements. These changes include, for example, new types of citizens participation, new types of delivery, emphasis on urban entrepreneurship, aims for sustainable development and smart city initiatives.

The course provides a cross- disciplinary perspective, including political science and sociology. In addition, we follow a comparative approach to urban governance, where urban areas both within and across national contexts are compared. The course will focus explicitly on national and international experiences from three policy areas where the governance challenges are particularly prominent: public spaces, housing, and infrastructure. The main focus are the processes and mechanisms that shape an urban area, as well as strategies and instruments in the governance of these areas.

The course will be taught in English if attended by international students. The course will be taught in Norwegian if attended by only Norwegian speaking students.

None.

A student who has completed his or her qualification has the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student has

• Thorough knowledge about processes and mechanisms that shape a city, including its atmosphere and its functioning
• Thorough knowledge about strategies and instruments to promote sustainable cities, emphasizing economic, social and environmental sustainability
• Thorough knowledge about national and international approaches to coordinated urban governance, its challenges, benefits and solutions
• Thorough knowledge about national and international approaches to democracy and participation in urban governance
• Thorough knowledge about managing public space, housing, and infrastructure in cities and urban areas

Skills

The student

• Can contribute to the development of urban strategies and implement measures to steer processes in the desired direction
• Is able to assess the need for and use participatory measures in city policy formulation and implementation
• Is able to interact effectively with market actors
• Is able to understand, manage and critically discuss public space, housing and physical infrastructure in an urban context

General competence

The student

• Can contribute to public sector innovation and reforms

The course is organized in series of lectures, working groups and student presentations.

We are witnessing the era of Big Data where data is generated, collected, and processed at an unprecedented scale and data-driven decisions influence many aspects of modern life.

Data mining is the process of discovering patterns in large data sets involving methods in statistics and database systems. A large number of applications such IoT sensors generate large amounts of data streams. The necessity of data stream mining and learning from the data is increasingly becoming more prevalent and urgent.

Extracting knowledge from data sets requires not only computational power but also programming abstractions as well as analytical skills. In this course, the students will be exposed to the different approaches for data mining and stream processing such as association rules learning, anomaly detection, data clustering, visualizations, and extracting statistical features on the fly from large data streams. The students will be exposed to concrete data mining and neural network architectures including deep learning models for handling large data streams such as convolutional neural networks, recurrent neural networks, autoencoders, transformers and attentions. In this course, the student will also be exposed to different data mining systems, working end-to-end pipelines including performance evaluation, detecting overfitting, underfitting, and data defects. With a focus on data mining applications, we will study some powerful numerical linear algebra methods.

The exam in the course is an individual course paper. The paper must have a scope of 10-15 pages (in addition to front page, table of content and list of references). Font and font size: Arial/Calibri 12 points. Line spacing: 1.5.

The exam can be answered in English or in a Scandinavian language (Norwegian, Danish, Swedish).

Students awarded a fail grade are given one opportunity to submit an improved version of the assignment for assessment.

This course is divided into two parts. The first part with focus on covering the principles of data mining and stream processing. Different seminars will be given on the different methodological aspects of data mining and stream processing as well as the programming paradigms and software tools that enable them.

The second part will focus on the students completing a programming project. The project can be chosen from a portfolio of available problems. The student will work in a group on the project and submit a final code-base with a report.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

None.

Group project (2-4 students) (15 000 - 17 500 words)

The exam can be appealed.

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.