The main aims of the course are: to illustrate the key concepts of decision making and Multicriteria Decision Analysis to provide background in GIS modeling techniques in the vector and raster data model to provide background on fuzzy set theory and it’s application in a spatial context to develop a case study for optimal siting of an industrial facility implementing a Spatial Decision Support Systems (SDSS)
Successful completion of the following modules: ENM.514 – GIS Theory ENM.515 – GIS Analysis & Applications ENM.525 – Integrated GIS/RS Case Studies
The main learning outcomes of the component will be the following: Become familiar with the guiding principles of multicriteria analysis, decision theory and decision support systems foundations applied in a GIS context; Demonstrate a working knowledge of spatial analysis methods relevant for environmental management decisions; Be able to structure a decision problem according to the classic workflows of a decision making analysis by integrating and transforming the relevant data sources Acquire the knowledge to translate stakeholders’ preferences into relative importance Apply the GIS knowledge acquired in the previous units to an applied research field of crucial important in the context of environmental management Understand the challenges of researchers involved in quantitative analysis to solve socio-environmental and oftentimes conflicting problems
The course presents the techniques to integrate GIS, spatial analysis, databases to develop a Spatial Decision Support System (SDSS) for the sustainable use of environmental resources (natural and artificial).
The course is articulated in a series of lectures and practicals to be carried out both individually and as groups to tackle the various steps entailed in a SDSS application. Throughout the course, students will develop a multicriteria analysis scenario creating an automated model within the modeling environment of ArcGIS (Model Builder). Two case studies will be carried out during the course: The first case study concerns the siting of an industrial facility in a mountain area. The students will be given the full set of basemaps required to derive a number of criteria to obtain a suitability map of the most favourable site that meets the objective of the analysis. The second case study concerns the routing of an electric power line. The students will be given the full set of basemaps required to derive a number of criteria to obtain a cost surface. Spatial analysis techniques will be applied to derive the most suitable corridor to connect two power stations.
Individual SDSS scenario development and paper-style presentation (100%) The purpose of the coursework is to develop a case study by choosing one of the proposed scenarios described below. The student is asked to structure the project in a scientific paper-style presentation of no more than 10 pages presenting the relevant steps and discussing the results obtained. Multi-criteria and multi-scenario – Alternative Scenario #1 - Siting Create a multi-criteria evaluation scenario to find suitable areas for one objective considering at least 4 different factors and at least 2 constraints. Use different standardization functions to assess suitability for each factor. Use 2 different weight assignment methodologies (including pairwise comparison as one of the methods and checking the consistency of the comparisons matrix) to simulate the evaluations of different stakeholders and their relative weights. Use the Weighted Linear Combination decision rule for the aggregation of the factors and derive the 2 suitability maps. Use 2 post-aggregation constraints, for example: i) Choose an overall suitability threshold and ii) Create regions of pixels above a certain threshold for the 2 results and consider size (find the largest region) and/or suitability (sum or average) to evaluate the results. Quantify and discuss the differences in the 2 resulting suitability maps. Multi-criteria and multi objective – Alternative Scenario #2 - SitingCreate a multi-criteria evaluation scenario to find suitable areas for two objectives considering at least 3 different factors and at least 1 constraint (express constraints using Boolean maps) for each scenario. Some criteria can be shared between the two objectives. Use different standardization functions to assess suitability for each factor. Use pairwise comparisons to derive the weights for the different factors, checking the consistency of the comparison matrix. Use the Weighted Linear Combination decision rule for the aggregation of the factors and derive the 2 suitability maps. Use 2 post-aggregation constraints, for example: i) Choose an overall suitability threshold and ii) Create regions of pixels above a certain threshold for the 2 results and consider size (find the largest region) and/or suitability (sum or average) to evaluate the results. Quantify and discuss the differences in the 2 resulting suitability maps, using Zonal statistics or other techniques to quantify the overlay between the candidate sites. Instructions to students When you compare two resulting maps, you can convert them to Boolean and find out the amount of overlap compared to the areas that are exclusive of one or the other final maps. The lower the “concordance”, the higher the impact of the different techniques you have applied to either combine the factor maps, or weight the factors.You can also use histograms in your paper to represent the distribution of suitability values in the result map. The indications of the two exercises represent the MINIMUM REQUIREMENTS. It is up to you to eventually extend the exercise you choose and apply additional thresholding, post-aggregation, weighting and alternative aggregation techniques. If you think that the scenario you are developing requires more than the required number of constraints and/or factors you can include them in your Decision support system. Your capacity of being creative in designing your scenario will contribute to the evaluation. Assessment criteria The evaluation of the project will take into consideration the following criteria: - Clear demonstration of individual research skills and use of class materials, experience and information material - The capability to write in a paper-like style the scientific evidence supporting the results - The structure of the work and the clarity of the illustration - The capacity to present the relevant information and write as if a non-GIS-technician was to read the essay - The avoidance of details such as the functionalities of the software used.