Search result: Catalogue data in Spring Semester 2025
Spatial Development and Infrastructure Systems Master  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 Master Studies (Programme Regulations 2021) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0488-00L | Master’s Project in Spatial Development and Infrastructure Systems  | W | 9 credits | 18A | Supervisors | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This seminar offers the students the opportunity to research and present a topic of their choice in depth resulting in a term paper. The topic can be freely chosen after consultation with the chair supervising the student. The chairs will also provide a list of proposed topics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Practise independent scientific working addressing a relevant topic from the range of the master's programme course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The students can work on a topic of their choice from the range of the he master's programme course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 701-1674-00L | Spatial Analysis, Modelling and Optimisation Prerequisites: 701-0951-00L "GIS - Introduction into Geoinformation Science" in autum semester or comparable preparatory training. | W | 5 credits | 4G | M. A. M. Niederhuber, V. Griess, A. Semiao Ramos Cordeiro Dias | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Problems encountered in forest- and landscape management often have a spatial dimension. Methods and technics of geoinformation sciences GIS and/or optimization give support to identify good solutions. Students learn to conceptualize, implement and combine I) spatial analysis & modeling of geodata and, II) optimization techniques, based on theoretical inputs and practical work on small projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understand, search for, and manage various types of geospatial data; Carry out conceptual data modelling for a spatial and/or optimisation problem and translate it into a tangible form within a GIS software; Conceptualize spatial and/or optimisation problems and design a workflow that transitions from "data processing" through "advanced spatial analysis" to "presentation of results"; Implement such a workflow in standard GIS and/or optimisation software, verify and validate the procedures, then present the final results. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Knowledge and skills equal those of the course "GIST - Einführung in die räumliche Informationswissenschaften und Technologien" | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 103-0427-00L | Regional Economics | W | 4 credits | 2G | B. Buser, C. Abegg | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The lecture on Regional Economics focusses on the theoretical aspects of spatial factor allocation and of growth determinants. The course takes a top down stance and looks at regional development from a macroeconomic perspective. Implications of theoretical models on regional and growth policy will be discussed in and connections to the course Site Management will be made. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students shall know the theoretical basics of spatial economy and growth theories an a regional scale; they shall gain the competence to apply concepts and theories of spatial science as well as regional economics to concrete problems of their area of study. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Origin of "Spatial Economics" Indices of regional economics and growth analysis Regional advantages in competition and growth theories Regional innovation theory (innovation processes, cluster theory and innovation policy) Regional labour markets Theory and political implications with examples (New Regional Policy NRP, Regional Innovation Systems RIS) Evaluation of policy instruments for regional development External Speaker and discussion of topicality by press | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Literature is optional, there will be given hints to: Bathelt, H., Glückler J. (2018): Wirtschaftsgeographie. Ökonomische Beziehungen in räumlicher Perspektive. 4. Auflage. ISBN: 978-3-8252-8728-3 Eisenhut, P. und Sturm J-E. (2024): Aktuelle Volkswirtschaftslehre 2024/2025. Rüegger Verlag, Zürich. ISBN: 978-3-7253-1092-0 Eckey, H.-F. (2008): Regionalökonomie. GWV Fachverlag GmbH, Wiesbaden. ISBN: 978-3-8349-0999-2 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | German | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-0576-00L | Advanced Sustainability Economics Does not take place this semester. PhD course, open for MSc students | W | 3 credits | 3G | to be announced | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course covers current resource and sustainability economics, including ethical foundations of sustainability, intertemporal optimisation in capital-resource economies, sustainable use of non-renewable and renewable resources, pollution dynamics, population growth, and sectoral heterogeneity. A final part is on empirical contributions, e.g. the resource curse, energy prices, and the EKC. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding of the current issues and economic methods in sustainability research; ability to solve typical problems like the calculation of the growth rate under environmental restriction with the help of appropriate model equations. Please note that the course takes places in Zurichbergstrasse 18, which requires an ETH card to enter. We kindly ask Non-ETH students to inform Aleksei Minabutdinov (aminabutdinov@ethz.ch) if they would like to attend. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 701-1522-00L | Multi-Criteria Decision Analysis | W | 3 credits | 2G | J. Lienert | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This introduction to "Multi-Criteria Decision Analysis" combines prescriptive Decision Theory (Multi-Attribute Value and Utility Theory) with practical application and computer-based decision support systems. Aspects of descriptive (behavioral) Decision Theory (psychology) are introduced. Participants apply the theory to an environmental decision problem (group work). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The main objective is to learn "Multi-Attribute Value Theory" (MAVT) and apply it step-by-step to an environmental decision problem. Multi-Attribute Utility Theory" (MAUT) is shortly introduced. At the end, participants should be able to carry out MCDA on their own, in research projects and in practice (e.g., working as consultant). The participants learn how to structure complex decision problems and break them down into manageable parts. An important aim is to integrate the objectives and preferences of different decision-makers or stakeholders. The participants will practice how to elicit subjective (personal) preferences from stakeholders with structured interviews. They will learn to include uncertainty in decision models and test assumptions with sensitivity analyses. Participants should have an understanding of people's limitations to decision-making, based on insights from descriptive Decision Theory. They will use formal computer-based tools to integrate "objective / scientific" data with "subjective / personal" preferences to find consensus solutions that are acceptable to different stakeholders. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | GENERAL DESCRIPTION Multi-Criteria Decision Analysis (MCDA) is an umbrella term for a set of methods to structure, formalize, and analyze complex decision problems involving multiple objectives (aims, criteria), many different alternatives (options, choices), and different stakeholders which may have conflicting preferences. Uncertainty (e.g., of environmental data) adds to the complexity of environmental decisions. MCDA helps to make decision problems more transparent and guides stakeholders into making rational choices. Today, MCDA-methods are being applied to many complex decision situations. This class is designed for participants interested in transdisciplinary approaches that help to better understand real-world decision problems and that contribute to finding sustainable solutions. The course focuses on "Multi-Attribute Value Theory" (MAVT). It gives a short introduction to "Multi-Attribute Utility Theory" (MAUT) and behavioral Decision Theory, the psychological field of Decision Analysis. STRUCTURE The course consists of a combination of lectures, exercises and discussion in the class, exercises in small groups, and reading. Some exercises are computer assisted, applying the ValueDecisions app, a browser-based MCDA software in a user-friendly R-shiny interface. For the analyses, participants need a laptop. The participants will choose an environmental case study to work on in small groups throughout the semester. They will summarize this work in a graded report. Additional reading of selected sections in the textbook Eisenführ et al. (2010) is required to understand the theory. Participants’ individual learning of MCDA will be tested in one mandatory quiz. GRADING The grade for the course is determined by one mandatory quiz at a fixed date that is individually completed during class (30%) and a semester-long group project with a final written group report to be delivered at the end of the semester (70%). There is no possibility to repeat the quiz! If participants miss the mandatory quiz, it is graded 1. Last cancellation / deregistration date for this graded semester performance: first Tuesday in March! Please note that after that date no deregistration will be accepted and the course will be considered as “fail” / unsatisfactory grade. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | No script (see below) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Theory is supported by reading selected sections in: Eisenführ, Franz; Weber, Martin; and Langer, Thomas (2010) Rational Decision Making. 1st edition, 447 p., Springer Verlag, ISBN 978-3-642-02850-2. Additional reading material will be recommended during the course. Lecture slides will be made available for download. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course requires some understanding of (basic) mathematics. The "formal" parts are not too complicated and we will guide students through the mathematical applications and use of the ValueDecisions app (software). Participants should bring their own laptop (let us know if this is not possible). The course is limited to 33 participants (first come, first served). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 101-0507-00L | Infrastructure Management: Optimisation Tools | W | 6 credits | 2G | B. T. Adey | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course will provide an introduction to the methods and tools that can be used to determine optimal inspection and intervention strategies and programs for infrastructure. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Upon successful completion of this course students will be able: - to use preventive maintenance models, such as block replacement, periodic preventive maintenance with minimal repair, and preventive maintenance based on parameter control, to determine when, where and what should be done to maintain infrastructure - to take into consideration future uncertainties in appropriate ways when devising and evaluating monitoring and management strategies for physical infrastructure - to use operation research methods to find optimal solutions to infastructure management problems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Part 1: Explanation of the principal models of preventative maintenance, including block replacement, periodic group repair, periodic maintenance with minimal repair and age replacement, and when they can be used to determine optimal intervention strategies Part 2: Explanation of preventive maintenance models that are based on parameter control, including Markovian models and opportunistic replacement models Part 3: Explanation of the methods that can be used to take into consideration the future uncertainties in the evaluation of monitoring strategies Part 4: Explanation of how operations research methods can be used to solve typical infrastructure management problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | A script will be given out at the beginning of the course. Class relevant materials will be distributed electronically before the start of class. A copy of the slides will be handed out at the beginning of each class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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