A. Grêt-Regamey and M.J. van Strien
Multi-Criteria Decision Analysis
Final Project: A Site Selection Problem in the Canton of Zurich
Autumn Semester, 2016
1) DESCRIPTION
For the final project, you are asked to use the multicriteria decision making (MCDM) and the statistical
tools you will learn throughout the semester to solve a site selection problem for the construction of a
new urban project in the Canton of Zurich. You can select the type of project to be developed from the
list below:
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A new hospital
A Landfill
A Nuclear energy power station
A Techno park
A new airport
Alternative, you can propose another urban project which must be of similar magnitude or causing a
similar socioeconomic or environmental impact on the Canton of Zurich to one of the projects listed
above.
You must consider yourself as an external consultant and support urban planners selecting the optimal
location for the new urban project. For this purpose, you are asked to conduct the following studies:
1) Firstly, as a pre-feasibility study, you are asked to find a set of suitable sites where your project
might be constructed. To this end, you must first define from the dataset provided the relevant
attributes for you decision-making process (between 5 and 10). After that, use all the MCDM and
statistical tools covered in the course to find the set of suitable sites for your project.
2) Secondly, from the set of suitable sites found in 1), use the appropriated MCDM decision tools to
find the optimal location where, according to your analysis, the project should be constructed.
The project must be handed in by groups of two students
2) WHAT MUST THE FINAL REPORT INCLUDE?
The final report must include the following sections:
a) Problem definition: Give a brief definition of the spatial decision-problem of your project. What
is the gap between the desired and the actual state of the study area defining your spatial
decision-problem? How do you expect that the development of your project will contribute to the
solution of this problem?
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A. Grêt-Regamey and M.J. van Strien
b) Evaluation criteria: Expand on the objectives that reflect all concerns relevant to the decisionproblem. Similarly, expand on the selection of the relevant attributes associated with the
objectives.
c) Site selection methodology: In this section you must describe in details the methods and criteria
employed in finding a set of possible locations for your project (point 1 above) and selecting the
optimal or the most recommended location for the project (point 2 above). Use this section to
expand on the selection of alternatives, types of decision variables, criterion weights and decision
rules. You must also justify the selection of each method or criterion. That is, explain for example
why do you think that a particular method or criterion is superior to the other existing methods
or criteria for your case study?
d) Results: Present the final results of your analysis. Please, use tables and maps to illustrate your
results. Additionally, give a brief and concise technical description of your project. For example,
you can include technical characteristics such as the extent of the area that your project should
cover as well as a general estimation of the amount of people who would benefit from the
operation of the project.
e) Discussion of results and recommendations: Present a critical analysis of your results as well as
recommendations for future actions.
3) HOW LONG SHOULD THE REPORT BE?
Maximally 10 pages including text, tables, figures and maps.
4) LANGUAGE AND DEADLINE
The report has to be written in English and handed in no later than the 22nd of December at the HIL H
42.3 office. You can also hand in the report just before your oral presentations.
5) R FILES
All calculations for your project must be done using the statistical software R. In this way, each group is
also asked to provide both the file with the R code and the file with the R workspace used in the project.
Please, email these files to [email protected], also no later than the 22nd of December. You can also
save your files on a CD if these are too big.
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A. Grêt-Regamey and M.J. van Strien
6)
ORAL PRESENTATION OF THE PROJECTS
In addition to the final report, one 10 to 15 minutes oral presentation will be required of each group,
followed by 5 minutes of discussion. The presentation should basically cover the entire project and pretty
much follow the report outline. Each student must present some part of the presentation.
Date and time of presentations: Thursday 22nd of December, from 10:00 to 12:00 hrs.
Venue: To be confirmed at the proper time
All presentations must be held in English.
6. GRADING
We will be grading the final implementation of your project based on its performance in the presentation,
your final report as well as your R code and R workspace. When grading, we will pay special attention to:
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The definition and understanding of your decision problem
The clarity and the understanding of the methodology used in the analysis.
The critical thinking used in identifying a solution, reaching conclusions and proposing
future actions.
We will also try to reconcile the wide variation in methodology and scope between projects. In this way,
if you took on a rather simple site selection problem with easy-to-meet requirements you will need to
demonstrate in more details the thoroughness of your problem definition and robustness of the decision
rules and sensitivity analysis, than if you took on a more complex site selection problem.
The oral presentation and the final report count for 25% and 75% of project grade, respectively. Though
the R code and the R workspace will not be explicitly graded, they will have to reflect and support your
results.
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A. Grêt-Regamey and M.J. van Strien
7. DESCRIPTION OF THE DATASET
The dataset provided contains various data of which you are to define at least 5 relevant attributes for
your decision-making process. The list below gives you an overview of the existing data. Feel free to collect
further information and data for your specific site selection project independently.
7.1 The data includes the following layers from vector25:
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Hauptstrassen (shp)
Flüsse (shp)
Gebaeude (shp)
It also contains:
 Bahnhöfe (Bhf) (shp)
 Arealstatistik (Raster 100m)
 Spitäler (shp)
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Eisenbahn (shp)
Gemeindegrenzen (shp)
Kantonsgrenze (shp)
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DHM25 (Digitales Höhenmodell, Raster 25m)
Slope (in degrees and percentages)
Aspect
7.2 An extended dbf file of the Zurich house price data (zurich_data.dbf) with the following variables:
Variable
P
SQM
ISHOUSE
CARTT_CBD
PTACC
RAILSTATIO
AUTOBAHN
AIRNOISE
SLOPE
VIEW_LAKE
Description
Monthly net asking rent in CHF
Floor area in square meter
Is 1 if the property is a single family house, otherwise 0
Average travel time to the Zurich Central Business District by car in minutes
Regional public transport accessibility to employment
Euclidean distance to next rail station in km
Is 1 if highway located within 100m, otherwise 0
Is 1 if daily average air noise is above 52dB, otherwise 0
Slope by 25m raster
Visibility of lake surface (> km2) in hectares (calculated based on 25m DEM and
is therefore not considering objects such as buildings or trees)
SOLAR_EVE
HOTREST_JO
POP_DENS
FOREIGNERS
Evening solar exposure index
Number of jobs in hotel and restaurant industry within 1 km
Number of inhabitants in hectare
Percentage of foreigners in hectare (foreigners are here defined as inhabitants
with nationalities outside of North-Western Europe, North America and Australia)
TAXLEVEL
Local income tax level as percentage of the basic cantonal tax
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A. Grêt-Regamey and M.J. van Strien
7.3 Finally, the following set of data is provided.
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Bodenkarte (shp)
Feuchtgebiete
Forstkreise
Gefahrenkarte
Gewässerschutzkarte
Grundwasserkarte
Haltestellen
Inventar_
Naturschutzobjekte
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Kant_Richtplan
Kies_Rohstoffkarte
NO2_Modellierung
Nutzungszonen
Pixelkarte
Populationsdichte (1 ha)
Reg_Richtplan
Schutzwald
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Stadtkreise
SVO
Wärmenutzungsatlas
Waldstandorte
Zonen_OeV
A detailed description of the data listed above can be found directly in the data-folder itself.
Students will also be allowed to save the data on their personal computers subject to the signature of a
declaration stating that the data must be used only for the purposes of this lecture and that the data must
be deleted at the end of the semester.
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