| Summary: | It can be a hard and time consuming task for a decision maker to decide which parts of a network to reinvest in. There are a lot of parameters to take into consideration regarding reinvestments, for example age, number of outages, number of inspection remarks and the degree of inspection remarks. Without any visualization, it is difficult to detect patterns in the data. Therefor, the decision maker is required to really know the network he/she is working with and to have a gut feeling of where to reinvest. The purpose of this thesis is to show that the decision making process can be much simpler and better supported when using GIS tools for analysis and visualization. This is done by designing a prototype of a web application that can produce multicriteria decision analysis on the parameters of interest for reinvestments in a power network.Traditionally, heavy desktop clients are for expert users while web-based clients are better for layman users. One of the greatest advantages of a web-based client over a desktop client is that it can be reached externally from any device that has access to internet. Because of this, the prototype is developed as a web-based client. Customer data can be sensitive information, this means that the data needs to be secure and directly accessible for the users of the application. Therefor, a 3-tier architecture with client, server and database is used. The result is visualized in a map, which makes it easy for anyone to interpret the result. Since the prototype is developed to be used by none GIS experts, the weighted linear combination method is used for the analysis. The prototype is not fully automated and does not deliver an absolute decision, the goal is rather for it to function as an aid for the decision maker when deciding on the final reinvestment area. The prototype is evaluated by the prospective users of the application through a questionnaire and the results show that a tool like this would be very useful for reinvestments decisions. Since the prototype does not rely on topology or network structure, it can be adapted to other spatial decision problems than just reinvestments in power networks.
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