Web-based Multicriteria Decision Analysis and Visualization for Reinvestments in Power Networks

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IN

DEGREE PROJECT CIVIL ENGINEERING AND URBAN MANAGEMENT,

SECOND CYCLE, 30 CREDITS STOCKHOLM SWEDEN 2017,

Web-based Multicriteria Decision Analysis and Visualization for

Reinvestments in Power Networks

NATALIE EKROTH

JOSEFIN LENNARTSSON

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Abstract

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.

Keywords: GIS, web-GIS, MCDA, Visualization, Reinvestment

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Acknowledgements

Daniel Sedell, (Digpro AB), co-supervisor. For providing help, ideas and support and for keeping us positive and focused throughout this thesis.

Associate Professor Gy˝oz˝o Gid´ofalvi, (KTH Geoinformatics), co-supervisor. For providing feeback, ideas and research material.

Professor Yifang Ban, (KTH Geoinformatics), examiner. For exmaniation of this thesis.

Fredrik Hilding, (Sweco Position AB). For providing help with the start-up phase of the prototype development.

Ella Syk, (Digpro AB). For providing ideas and help during the development of the prototype.

Bj¨orn Persson, (Digpro AB), senior supervisor and Jonas Jacobsson, (Digpro AB).

For feedback, inputs and sharing extensive knowledge throughout the work of this thesis.

Susanne Christoffersson (Växjö Energi), Peter Karlsson (Växjö Energi) and Örjan Kvist (Växjö Energi). For participating in interview and evaluation and for providing us with real world data.

Cathrin Bäckstrand (Jönköping Energi), Mats Javebrink (Jönköping Energi) and Ashfaq Taimor (Kraftringen). For participating in interviews and sharing important knowledge and experiences.

Finally we want to address a great thanks to everyone that attended our session at Dig- pro’s customer meeting for listening to the presentation and answering the questionnaire.

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Declaration of Individual Contribution

The work of this thesis has been divided equally between Natalie Ekroth and Josefin Lennartsson. The literature study about related work and common technology was mainly carried out by Josefin whereas the literature study about web GIS was mostly conducted by Natalie. The interviews and questionnaires were designed, held and summarized by both of the authors. The methodology, result and discussion were conducted together and it is impossible to divide the individual contribution on these parts of the thesis.

The functionality of the prototype was mostly developed by Natalie and the design of the prototype was mainly implemented by Josefin. However, both authors have con- tributed with ideas and suggestions of functionality and design of the prototype.

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List of Figures

1 Different representations of the same layer on different background maps 11

2 Flowchart showing the process of the research . . . 16

3 Image showing example of WLC for power networks . . . 19

4 The startpage of the prototype . . . 21

5 Meny button clicked . . . 21

6 Light gray basemap and age layer . . . 22

7 Dark basemap and age layer . . . 22

8 Parameter form . . . 22

9 Tooltip shown on hover . . . 22

10 Weight form . . . 23

11 Alert box with warning message . . . 23

12 Result from analysis . . . 23

13 Click and pan to polygon . . . 23

14 Result from questions 1 to 6 . . . 25

15 Result from questions 7 to 12 . . . 26

16 Result from question number 13 . . . 27

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List of Tables

1 Scale for Pairwise Comparison . . . 6 2 Pairwise Comparison of the Evaluation Criteria . . . 6

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Terms and Abbreviations

BI Business Intelligence CSS Cascading Style Sheets CTA Call to Action

DM Decision Maker

GIS Geographic Information System GUI Graphical User Interface

HCI Human Computer Interaction HTML HyperText Markup Language HTTP Hypertext Transfer Protocol JSON JavaScript Object Notation MCDA Multicriteria Decision Analysis MCE Multicriteria Evaluation

SA Sensitivity Analysis

SDSS Spatial Decision Support System

S-MCDA Spatial Multicriteria Decision Analysis UI User Interface

URL Uniform Resource Locator UX User Experience

UXD User Experience Design WLC Weighted Linear Combination

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1 Introduction

1.1 Background

Making decisions regarding reinvestments in power networks can be a hard and time consuming task. There are a lot of parameters that need to be considered and evaluated in order to reach a conclusion. For example, for a pipe these parameters can be age, number of outages, previous inspections and more. How to weigh these different parameters against each other is not trivial. Today, many electric companies go through these factors manually and try to get an overview of where an reinvestment should be made in the network (Sedell, 2016). For these companies, an interactive web-based tool would be of great use and a good way to support and back up their decisions. The tool will be a web application where, from case to case, the user can choose which parameters they want to prioritize and how to weigh these parameters of interest against each other. This will result in an output map that highlights the areas suggested for future reinvestments in the power network.

1.2 Problem Definition

Today a lot of the decisions for reinvestments in the power network are done on “gut feeling”, which requires expertise and previous knowledge of the network in question.

Decisions are highly dependent on who makes them and their knowledge of the network.

The people in charge of reinvestment decisions are often people with little or no knowledge and experience of working with GIS systems. There is no advanced analysis behind their decisions. Some software that deal with Business Intelligence (BI) and budget decisions are in use but few of them uses a map component and or an advanced analysis.

1.3 Objectives and Research Issues

The objective of this thesis is to develop a prototype of a web application that will aid in decision making for reinvestments in power networks. The purpose of the prototype is to:

1. perform multicriteria decision analysis, 2. list areas in need of reinvestments and

3. be visually helpful in decision making by showing these areas on a map.

The main question this research aims at answering is: What type of analysis is appropriate for layman users when making reinvestment decisions and how can this information be efficiently communicated to the decision maker? To reach the answer to this question, goals are set up to achieve along the way. These goals consist of finding out how companies make these decisions today, if they see a need for a tool that will aid in decision making and what type of analysis that is appropriate for this.

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1.4 Limitations and Delimitations

The prototype can be extended with more features that would be of great interest, but due to time limits only a small portion of features are implemented.

The developed prototype can be used for all types of reinvestment decisions. How- ever, since power network data is used in the present thesis, the prototype is applied to this. For other types of data, other parameters and aggregation methods need to be considered, as described in 3.1.2 and discussed in 5.1.2.

The prototype will only be evaluated by a small subset of users. Since these are the prospective users of the developed prototype they are assumed to be representative.

However, a greater subset of users with different kinds of backgrounds would probably have been an even more accurate representation of reality. Due to time limits, this was not considered in the present thesis.

1.5 Disposition

Section 2 presents related work and other research done on the subjects presented in this thesis as well as related technologies such as Digpro products and web GIS architecture.

This is followed by Section 3, where the methodology for this research is presented in detail. Further, in Section 4 the results of the research are presented together with screen shots of the developed prototype. The results are thereafter discussed in Section 5. Finally, conclusions and recommended future work are presented in Section 6.

2 Related Work

2.1 Common Concepts and Methodologies

The main focus of this research can be seen as a combination of decision making with the help of GIS and the presentation in the form of maps on the web. To provide the reader with the relevant background information, common concepts and methodologies are summarized in the following subsections.

2.1.1 Analysis with GIS

The abbreviation GIS stands for Geographical Information System (Heywood et al., 2011). There are several viewpoints as to how the term should be defined, one being that GIS is a computer system consisting of three principal components; the hardware, software and the appropriate procedures. GIS uses spatially referenced and geographical data to carry out various management and analysis tasks, hence it is said that the main goal of GIS is to assist when making spatial decisions (Malczewski, 1999).

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2.1.2 Spatial Decision Support Systems (SDSS)

Spatial decision support systems (SDSS) are systems that help the user solve complex problems that include a spatial component, such as site selection, routing or urban development (Sugumaran et al., 2011). For spatial problems it is unusual to have exactly the right kind of or enough data, thus both hard and soft information are almost always considered in spatial decisions. Hard information is verifiable data and knowledge while soft information includes feelings, opinions and preferences. Both hard and soft information involve some uncertainty, therefore spatial problems cannot be solved with absolute certainty. Spatial decision support system is used for the analysis in this research.

2.1.2.1 Multicriteria Decision Analysis A Multicriteria Decision Analysis (MCDA) problem can be described as a problem where the possibilities of actions are based on incomparable and contradictory criteria (Malczewski, 1999). Throughout this thesis the term MCDA is used although another commonly known name for this kind of analysis is Multicriteria Evaluation (MCE). When the problem also involves the spatial component i.e., when the result is based on the alteration and union of geographical data, it is called spatial MCDA (S-MCDA). The values of a group of evaluation criteria in combination with the preferences of the DM are considered to be the vital characteristics of spatial MCDA. This means that the final result will consist of the relation between the geographical components in combination with value judgments. Since spatial problems can be very complex, with their many components and the relationship among them, they might be of great difficulty to solve for a decision maker. GIS and MCDA can help the DM in reaching better effectiveness and efficiency. S-MCDA is a well known method for solving spatial problems with contradictory criteria, therefor this approach is used in the present thesis.

Malczewski (1999) suggests that there are seven steps in the process of decision making with MCDA. It starts with identifying the problem and ends with (a) recommendation. Among these there are three main components that need to be thoroughly considered; value scaling, criterion weighting and combination rules (Malczewski and Rinner, 2015). These three are described more in depth below.

Value Scaling

After defining the problem and deciding on the criteria to be included, the next step is to scale the values (Malczewski, 1999). To be able to compare attributes, they have to be on the same scale. There are several methods with which attributes can be made commensurate. The methods are usually divided into deterministic, probabilistic or fuzzy. Among the deterministic approaches, the linear scale transformation is considered to be the most commonly used one. With the linear scale transformation the data is stretched linearly.

There are two linear scale transformation methods mentioned in the literature; maximum score and score range, where the score range is the most frequently used method for standardizing evaluation criteria. When applying the linear scale transformations one has to first decide if the criterion is considered to be a benefit or cost criterion. Benefit criteria

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are criteria where high values are preferable, for example the further away from water the better the location. Cost criteria are thus the opposite and low values are preferable.

There are two mayor differences between these two methods. By standardizing with the score range procedure the worst possible scenario always gets the value of 0 and the best the value of 1, and all other values are ranged in between. For the max score method on the other hand, the highest value is not necessarily 1 and the lowest is not always equal to 0. The max score keeps the proportional changes whereas the score range does not.

x⁰_ij = x_ij − x^min_j

x^max_j − x^min_j (1)

x⁰_ij = x^max_j − x_ij

x^max_j − x^min_j (2)

Equations 1 and 2 display the calculations of the score range procedure. Equation 1 is applied to benefit criteria, whereas Equation 2 is applied to cost criteria.

x⁰_ij = x_ij

x^max_j (3)

x⁰_ij = 1 − x_ij

x^max_j (4)

Equations 3 and 4 display the calculations of the max score procedure. It standardizes the the data by dividing the raw data with the maximum value of each criterion. Equation 3 is applied to benefit criteria and Equation 4 is applied to cost criteria. For Equations 1 - 4,x⁰_ij is the standardized score for the i:th object and the j:th attribute.x_ij is the raw score,x^max_j is the maximum score for the j:th attribute andx^min_j the minimum score for the j:th attribute.

As mentioned above, there are multiple ways in which the attribute values can be scaled (Malczewski, 1999). For the deterministic methods there are something called value / utility function approaches. The shape of the value function is determined by the decision maker’s preferences and in practise it is often approximated by performing a mid-point value method (Malczewski and Rinner, 2015). This means that the decision maker first decides on the end points, i.e assigns the values of 0 and 1. After that the decision maker chooses what value would be the mid-point of these two and thus assigns it 0.5. After this a value function can be derived or more mid-points can be assigned to find the most fitting function.

Besides the deterministic ways to standardize maps other methods are based on probability theory and fuzzy logic. Scaling with fuzzy logic is done by first specifying a fuzzy set membership function and then assigning a value to a decision alternative based on its membership (Malczewski and Rinner, 2015). The common fuzzy set membership

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functions are sigmoidal, J-shaped, linear or user-defined. The probabilistic way of deriv- ing commensurate maps is based on probability theory (Malczewski, 1999). This has to do with the likelihood of a value occurring. Numbers between 0 and 1 can be used to represent the relative frequencies with which the different possible outcomes occur. 0 is assigned if it is impossible for the event to occur and 1 is assigned it it is certain that an event will occur.

In the case of this thesis the authors’ unawareness of the distribution of the parameters precludes any option but a linear standardization, as discussed in Section 5.

Criterion Weighting

When all criteria have been made commensurate it is time to weigh them against each other. The method that is considered to be the simplest one is called ranking (Malczewski, 1999). With ranking methods the criteria are ranked in ordered and then normalized. For example, with straight ranking the most important criterion is set to 1, the second most important to 2 and so on. After ranking the criteria there are different methods to derive normalized weights. Another way of assigning weights is by something called rating methods, where one of the simplest approaches is called the point allocation. It is a method in which the DM distributes 100 points between the criteria of interest. This means that if one criterion is given 100 points it is the only one that will influence the output and if a criterion is given 0 points it will be ignored. As an example, if the DM is considering three criteria, the points can be allocated to be [33,33,33] if they are considered to be of equal importance or [50,25,25] if one is more important but the other two of equal importance, and so on. In comparison to ranking methods, with rating several criteria can be considered as equally important and the 5th criterion does not have to be 5 times worse than first one. These two methods can be criticized for their lack of theoretical foundations. The attributes need to be clearly defined for these methods to be of any use. On the other hand, if the person distributing the weights is not familiar with advanced weighting techniques, the rating and ranking methods can be a simple way to assign weights. Since the prototype developed in this particular thesis addresses GIS laymen users, the weights will be assigned by rating. A more complex method, pairwise comparison, will be briefly described below since it is often mentioned and used. See Section 5 for the discussion of choice of weighting method.

The pairwise comparison, was originally developed as a part of the Analytic Hierar- chy Process (AHP) (Malczewski, 1999). The weights are derived from a matrix where the DM compare the relative importance of the criteria against each other. See Table 2 for an example of a pairwise comparison matrix and Table 1 for the comparison scale.

Once the pairwise comparison matrix is developed, a process of developing normalized weights can begin.

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Table 1: Scale for Pairwise Comparison (Malczewski, 1999, p.183) Intensity of Importance Definition

1 Equal importance

2 Equal to moderate importance

3 Moderate importance

4 Moderate to strong importance

5 Strong importance

6 Strong to very strong importance

7 Very strong importance

8 Very to extremely strong importance

9 Extreme importance

Table 2: Pairwise Comparison of the Evaluation Criteria (Malczewski, 1999, p.183)

Criterion Price Slope View

Price 1 4 7

Slope ¹₄ 1 5

View ¹₇ ¹₅ 1

Combination Rules

At the end of MCDA you want to know which alternatives are the best and / or the worst (Malczewski, 1999). To be able to order the alternatives you need some kind of decision / combination rule. Among the many decision rules to chose from, additive rules are best known and most widely used. When handling spatial multiattribute decision making the most commonly used techniques are simple additive weighing, or Weighted Linear Combination (WLC)which are based on weighted average. The acronym WLC refers to the process of the combination rule; the criterion is firstly scaled linearly, followed by being assigned a weight and then all of these are summed together. The criteria are directly given weights according to their relative importance as perceived by the DM. See Equation 5 for the mathematical expression of WLC.

A_i =X

j

w_jx_ij (5)

In Equation 5x_ij is the score of the i:th alternative with respect to the j:th attribute,w_j is a normalized weight. A_iis the sum of allx_ij multiplied by their respective weight.

A_i =X

j

w_jx_ij = 0.1 ∗ 0.5 + 0.2 ∗ 0.25 + 0.3 ∗ 0.25 = 0.05 + 0.05 + 0.075 = 0.25 (6)

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Equation 6 shows an example where WLC is used. Three criteria are firstly scaled to have values of 0.1, 0.2 and 0.3 and are then assigned the weights of 0.5, 0.25 and 0.25 respectively. According to Equation 5 the final sum for this attribute is 0.25.

If paying attention one can note that there is a trade-off to be aware of when using WLC (Drobne and Anka, 2009). If a parameter with a high original value gets weighed low (value of 0.3 * weight of 0.25=0.075 ) it might still add to the final value more than a parameter with a low original value that gets weighed high (value of 0.1 * weight of 0.5 = 0.05). WLC can be said to offer full trade-off, being that a low value can be compensated by a high given weight.

For this thesis the WLC combination rule is used since it lets the user set the weights in one straightforward step and leaves a lot of control to the decision maker. Section 5 will discuss the benefits and challenges with the method. To be able to discuss the difference of methods further, two other methods that are often mentioned regarding combination rules are also described, Analytic Hierarchy Process (AHP) and Ordered Weighted Aver- aging (OWA). The AHP was developed by Thomas Saaty in 1980 (Malczewski, 1999).

It is used with pairwise comparison and consists of three principles; decomposition, comparative judgment and synthesis of priorities. This means that the decision problem first has to be decomposed into a hierarchy. The comparative judgment refers to pairwise comparison of the elements within a level of the hierarchy. The final step is about con- structing an overall priority rating. In general one can say that AHP uses the pairwise comparison firstly when comparing the criteria and then again for comparing the alternatives to finally sum it all together. OWA is a weighted sum with ordered evaluation criteria. This means that both criterion weights and ordered weights are applied. With the order weights the level of trade-off between criteria can be chosen. In OWA the first step is to create ranked layers. The ranked layers are created by going through each pixel one by one and for the highest ranked layer only selecting the best pixel from all criterion layers. This means that the rank 1 layer will include only the pixels with the highest values from all criterion layers, rank 2 will have the second best pixels and so on. Each pixel in the ranked layers are then multiplied by its respective criterion weight. If taken from layer x, multiply by criterion weight a, if taken from layer y multiply by criterion weight b. Finally the ranked layers (with their criterion weights already multiplied in) are multiplied by their respective order weight. WLC can be seen as a version of OWA where the ordered weights are set to be equal (Drobne and Anka, 2009). When setting equal order weights the step of ranking and multiplying by order weights is thus unnecessary.

2.1.2.2 Sensitivity Analysis Sensitivity Analysis (SA) is a part of MCDA and refers to how the errors in the input data affect the error in the final output (Malczewski, 1999).

The aim of multicriteria spatial error analysis is basically to evaluate the effect of errors that the criterion maps and set weights have on the decision outcomes. To perform sensitivity analysis an analysis of uncertainties must first be done (Malczewski and Rinner, 2015). The main sources of uncertainty are the values and weight of the criteria. At this point there is no method for choosing the optimal MCDA model. If applying different multicriteria decision rules to the same decision problem the results will be inconsis-

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tent. When selecting a MCDA model one should consider the nature of the problem, data requirements, consistency of results and computational complexity. To summarize, a sensitivity analysis is done to conclude the robustness of the recommended solution.

The goal of this thesis is not to find the most accurate result from the analysis, but to see if a web-based analysis tool like the one presented in this thesis would be useful for decision makers in power network business. For this reason a sensitivity analysis in this sense is not preformed in this thesis although it is further discussed in Section 5.

2.1.3 Web GIS

Prior to the development of Web GIS most of the GIS applications were designed for specialists working on desktop computers (Fu and Sun, 2011). Web-based applications do not require a locally installed software and have therefor changed the use of GIS by making it more accessible to the public. There are a lot of benefits with Web GIS instead of desktop GIS (ESRI, 2017). One of the advantages with Web GIS is that the application is easily available via a Uniform Resource Locator (URL). The application can be easily accessed via any device that has access to the Internet. This means that the application can be reached by multiple users at the same time, in contrast to the desktop application which can only be used by one user at a time. This also brings a challenge to the Web GIS - it needs higher performance and scalability than the desktop GIS. Due to the fact that GIS applications are not only used by GIS experts but also by users with no GIS background, one of the challenges is to design for simplicity and comfort (ESRI, 2017).

This means that the User Interface (UI) and the User Experience (UX) need to be taken into consideration in order to create a successful GIS. In this section, these terms are deeper investigated.

2.1.3.1 Human Computer Interaction Before the later 1970s, the ones who interacted with computers were mainly information technology specialists (Carrol, 2017).

When computers became more available for layman users the need for research in Hu- man Computer Interaction (HCI) increased rapidly. HCI refers to design and use of computer technology with focus on the interfaces between people and computers. One purpose of HCI is to increase the usability of computer technology. Within HCI the term usability is continually reconstructed but it often includes properties like fun, flow and aesthetic tension to mention a few. Usability is a concept with no end and it can not be reduced to a static checklist.

As the focus of HCI is moving towards User Experience (UX), more research is done on what creates a good experience for the user (Vermeeren et al., 2016). Therefor, further studies are done on emotional aspects rather than only functional aspects. Design for experience needs to affect the users feelings in a positive way, thus the role of design has become more and more important for HCI. This is further looked into in Section 2.1.3.1 and Section 2.1.3.1.

HCI needs to overcome many obstacles, one of them is that different users have different cognitive style, i.e different ways of learning and keeping knowledge (Rouse, 2017).

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Furthermore, the user interface technology is a field that is changing rapidly, causing new interaction possibilities that previous research cannot be applied to. This means that there will always be a need for new research in the field of HCI.

User Interface

The User Interface (UI) is the way a user interacts with a computer or other electronic devices. The UI consist of both software interface, i.e. menus, buttons and other controls in a web page, and hardware interface, i.e. keyboard and mouse, to control the software (Stopper, 2012).

The first user interface for GIS was command-line interface, where the user interacted with the computer by typing commands and the system responded with text in the command prompt (Egenhofer and Kuhn, 2010). These GIS were mainly accessed and used by experts. In the second half of 1980 GIS was developed with a Graphical User Inter- face (GUI) which included windows, menus, buttons, icons and other controls. The GUI allowed the user to interact with the software mainly through hardware such as keyboard and mouse (Stopper, 2012). With GUI, GIS became more accessible since the user no longer needed to remember and understand all commands, they were integrated with the GUI (Egenhofer and Kuhn, 2010).

GIS has long been developed by need of functionality and the user interface has not been prioritized (Egenhofer and Kuhn, 2010). Recently, GIS developers have started to understand the importance of a good GUI as well, not just good functionality. Although, since GIS often includes a lot of data and functionality it is a hard task to design a good UI that all users, despite their GIS knowledge, can understand and appreciate (Andersen, 2015).

User Experience

User Experience (UX) is all the experiences that a person has when interacting with a digital tool. These experiences are for example physical, emotional and mental (Stokes, 2017). It refers to the overall satisfaction a user gets from interaction. When designing for the user, the following questions are good to think about:

• Who is the user?

• What are the user’s wants and needs from your platform?

• What are the user’s capabilities, web skills and available technology?

One of the major things of UX is usability. Usability is about making the interaction easy and intuitive (Stokes, 2017). The users should not have to think, they should just do. The most important part of usability is sticking to standards, for example to have navigation menus at the top or left of the web page. Another thing to think about is to design the components in the same way (Andersen, 2015). For example, if the close button is in the top-right corner in one window, it should be located in the same place in other windows as well. Furthermore, the mouse click should behave in the same way throughout the application. One example of this is that a right click on one object in the

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application should generate the same response as a right click on another object in the application.

Another major thing in UX is simplicity, the simpler the better (Stokes, 2017). One part of simplicity is to have a lot of empty spaces since it makes it easier for the user to navigate in the web page. Further, when the user needs to make a lot of choices there is a physiological part that kicks in. The user might worry about making the right choice and similar, therefor fewer options makes the experience better for the user. Additionally, clear, simple and plain language is important when designing a good UX. If there are a lot of functions in the application, it is a good idea to group similar functions together to achieve a cleaner interface (Andersen, 2015). This will make the interface feel lighter for the user. It is also good to keep the main functionality easily accessible for the user.

Besides usability and simplicity, credibility is of great importance in UX (Stokes, 2017). If the web page looks professional and trustworthy the experience will be better for the user.

User Experience Design

The success of a digital product, a web page for example, depends on how the user in- terprets it (Stokes, 2017). A good User Experience Design (UXD) can please a customer and generate more customers whereas a bad UXD can lead to less customers. A great UXD is reliable, functional, convenient and more importantly it is enjoyable and gives an experience worth sharing.

When developing a web page with focus on UXD, it is good to start with creating the basic structure of the page (Stokes, 2017). Most web pages have a hierarchical structure with broad important pages on the top and narrow less important pages in the bottom of the structure. The second step is to analyze the content of the web page i.e what content is needed and how and when it should be created. This is done by analyzing what the site should achieve, what the user wants and needs and the tone and language of the site, to mention a few. The next step is to create a sitemap of the web page. A sitemap is a structured plan for how the pages of the web page will be organized. Further, the layout of the web page needs to be visualized. The layout of a web page is designed based on the type of page but it typically consists of four elements; header, footer, side bars and central content. The main navigation menu, search tools and login features should be placed in the header. The footer is used for important but not leading features, such as legal information and additional navigation elements. Secondary content and tools should be presented in the side bar. Finally, the central content area is used to present the main content of the web page. The final step is to fit together all the other elements of the web page such as Call To Actions (CTA) and forms.

It is important to understand that one user experience design cannot and will not work for everyone in every situation, since all human beings are different (Stokes, 2017). Thus, the user experience design from one web page cannot be directly copied to another web page. The design needs to be adapted to the goals, values and product for every individual web page.

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2.1.3.2 Web Mapping

It is difficult to detect patterns in raw data, for example several rows of number or text (Murray, 2013). However, when data is visualized in a bar chart, a pie chart or other similar visualization techniques, even small children can detect patterns in the data. Thus, visualization is a good way of communicating data.

A map is a graphic representation that shows spatial relationships (Tyner, 2010).

Maps are often generalized in the sense that only the interesting information are extracted, in contrast to a photography where all available information is present. When creating a map, the mapmaker needs to know the purpose of the map and extract the right kind of information. For example, a sea map does not need to have road information.

Since the mapmaker decides what to show on the map, a map is always biased in one sense. The map has moved from paper to digital, thus the maps are no longer designed for map readers but to map users (Muehlenhaus, 2013). Web maps are expected to be interactive, responsive and possible to manipulate to fit the user’s need.

Figure 1: Different representations of the same layer on different background maps According to the Swedish Standards Institute (SIS), web map services should offer map layers that include few and associated object types (SIS, 2015). This means that the user should be able to choose the object types that are relevant for the application and be able to turn on and off object types in the map. Additionally, the web map service should offer different styling of the background map to be able to emphasize the object types in the map, this can be seen in Figure 1. The level of details in the map should partly be dependent on the zoom level; zoomed in views should show more details than zoomed out views. SIS states that this principle is often used for background maps, but is not as commonly used for other map layers. The guidelines from SIS is taken in consideration for the prototype development in the present thesis.

2.2 Previous Work

The field of GIS can be seen as fairly wide and several studies can be found that covers its evolution. The following section gives a brief summary of some of the things that have been researched regarding SDSS, MCDA and GIS on the Web. This provides an overview of what has been done and what experts in the field think regarding different approaches, which is of great importance when making a decision regarding the methodology for this thesis.

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Jankowski et al. (2001) presented a map-centred exploratory approach to multiple criteria decision making. The authors state that maps are sometimes used to visualize and / or evaluate the results however they are usually not used as decision support tools.

With this, they saw the relevance of presenting new spatial decision support tools where maps play a more important role in the structure of multiple criteria spatial decision problems. With this tool the user is for instance allowed to order decision options and assign priorities to decision criteria. They ask the question “What are the effective means of using maps in order to support decision problem exploration and structuring?”. They then present four multicriteria cases, with a different decision support tool for each. Finally they conclude that a high level of interaction between maps and attribute data graphs aids the decision maker in an understanding of the problem structure. Just as in this article, the current thesis presents interaction between maps and attribute data. The result of the analysis is presented in a map as well as listed in a table.

Karnatak et al. (2007) developed a multicriteria decision analysis tool for spatial decision making in the Web GIS environment. The authors emphasize how the traditional GIS can only serve devoted users with desktop software and how the web enabled GIS gives users convenient and efficient access to the system. Although multicriteria techniques such as AHP help selecting the optimal alternative, they discovered that expert knowledge is of great importance when assigning weights. The present thesis also uses a web-GIS environment and the experts are allowed to select the weights, thus keeping the importance of the experts’ knowledge. The current thesis uses the combination rule WLC and not AHP as in this article.

Rinner (2007) suggested in this article the principles of combining MCE methods with Geographic visualization (GeoViz) to support spatial decision making. The approach is tested on the Urban Quality of Life in Toronto, Canada. The method chosen for measuring the quality of life is the Analytic Hierarchy Process (AHP) which according to the author gives the user a way of interacting with decision making strategies and showing spatial patterns in the evaluation results. The method was evaluated on utility and usability by interviewing three users. Rinner (2007) suggests that since the use of GeoViz tools is accelerating, there is also a requirement of correctly evaluating their usefulness. When looking at design principles from studies within HCI, researchers have found a difficulty in measuring the success of GeoViz tools. In the case study presented in the article one of the difficulties lies in the fact that there is no agreed upon definition and measure of QoL. The challenges faced when evaluating the QoL include the definition of the neighbourhoods, the choice of parameters that affect QoL and the processing of these parameters. By MCE the QoL indicators can be weighted individually. In the case study two models are compared, one modern and one contemporary. The difference between the models is the indicators involved in deciding the QoL. The two models are then compared by weights that are set by sliders. By putting one model at 100 percent weight and the other at 0, the areas displayed are significantly different. For the evaluation, domain experts were interviewed and they answered questions while an investigator operated the

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tool. It was concluded that when an analyst is allowed to manipulate the MCE settings they can observe changes in the result and compare it to their previous knowledge. Re- sults from the case study showed that even though the interviewee was already aware of some spatial patterns regarding the QoL in the area, the visual analytic approach was well received. Future research should look at the possibility of decision makers to manipulate the original attribute values and their weights. Just as in this article, the present thesis evaluates the prototype by letting the potential user see the prototype in action, ask question and fill out a questionnaire. Weights are set by using sliders. HCI and UX is taken into consideration when developing the user interface. The difference between this article and the present thesis is the choice of combination rule. The article uses AHP whereas this research uses WLC.

Jankowski et al. (2008) presented a concept of a Web-based spatial multiple criteria evaluation tool for individual and group called Choice Modeler (CM). The objective of the article was to present a prototype that used current web-based technologies and with this contribute to the process of developing MCE as either a part of SDSS or a stand alone methodology. The purpose of the CM was to be used as a tool for evaluation of decision variants, which would help to reduce the complexity of the decision having to be made by the decision maker regarding the multiple decision options, evaluation criteria and criterion weights. The authors mention previous research done in the area of web-based spatial decision support systems where they believe that a lot of it has covered application specific models for what-if scenarios and visualization of such, although not much has been done on the study of tools that amplify the human judgment about the components of the decision situation. The article questions whether MCE tools should be moved from desktop and towards web services. For the Choice modeler the three-tier architecture, a version of a distributed architecture, is used as system implementation. The three-tier architecture uses a client tier, middle-ware tier and the data storage tier. In this research the CM server retrieves data from the database and executes MCE functions selected by the user. The CM represented the beginning of providing MCE functions in the shape of distributed web services. The authors suggest that the next step in research should be to develop MCE functions as open source standardized Web services. In accordance with this article, the present thesis also presents GIS trough web services and with a three tier architecture.

Ligmann-Zielinska and Jankowski (2008) presented a framework for performing sensitivity analysis in spatial multiple criteria evaluation. The framework is organized as a guide in selecting the SA technique most appropriate for the problem . The authors stress the lack of research regarding spatial sensitivity analysis. Since the spatial MCE problems involve the spatial component there should also be a way of evaluating if the methods and weights should be the same for an entire layer throughout space. Maybe one criteria should be looked at differently in different places, for example urban and rural. The goal of the framework is to help when making a decision about what kind of SA best suits the problem at hand. This framework was looked at to help figure out

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what kind of evaluation should be done for the current research. No spatial SA is done in the current thesis due to constraints such as time and underdeveloped methods but is definitely something that could be considered in the future when more research has been done.

Drobne and Anka (2009) did a study on merging GIS and MCDA as well as comparing the two MCE methods WLC and OWA. The authors claim that about 80 percent of data used by managers or decision makers include a geographical component. Maps have been used as support in decision making for a very long time. It is important to note that no GIS by themselves make decisions, they are simply there to aid the decision maker. According to the authors, the limitations of WLC include the trade-off among criteria as well as the scaling. As the name suggest the scaling of WLC is linear, but in some cases a non-linear scaling might make more sense. One advantage with WLC is the ability to give relative weights to each of the factors. WLC can be seen as one variant of OWA, where the order weights are given equal importance. With order weights one more step is included where the decision makers themselves can decide how much trade-off is desired. In using OWA there are three groups that the criteria should be divided into;

hard constraints, factors that can trade-off and factors that should not trade-off. When OWA method is used in the case study, the factors are first divided into two groups; environmental concerns and development costs, since they do not have the same level of trade-off. For the cost factors a full trade-off and average risk was selected and thus the WLC method was used. For the environmental factors order weights that gave both less risk and less trade-off was selected. When finally combining the two layers an OWA approach with low risk and no trade-off was chosen (order weights of 1 and 0). According to the authors the main purpose of the application was not to find a suitable place in the area of the case study, but rather to describe and test the WLC and OWA methods of MCE. The practise and research of GIS-based multicriteria decision making is rapidly growing, and the tools provided today give decision makers of spatial decision problems advantages. Still, there are some topics that need to be further investigated and developed. The authors list them as: selection of attributes, weights, scale and methods for aggregation, error assessment and the inclusion of database and decision rule uncertainty and sensitivity analysis. The pros and cons of WLC and OWA in this article are looked at and for the current thesis the WLC is chosen.

According to Silva et al. (2014) there is a need to investigate how to integrate GIS, MCDA, the Internet, modelling and databases with the goal of creating Web Multicri- teria Spatial Decision support systems (Web MC-SDSSs). Therefor this article presents a fully integrated system for combining GIS and the MCDA method called ELECTRE TRI with the help of ArcGIS software. This specific article applies this method on a case study where the sustainability of dairy farms is analyzed. The authors claim that the greatest benefit of using Web services is the fact that there are no limitations in terms of time, data and communication. Through the web, the services can be accessed conve- niently and effectively. Silva et al. (2014) list the three major advantages of integration

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of GIS and MCDA as; enhancing the evolution of GIS, improving the desired level of usability and enriching approaches to problem solving. Within the MCDA-GIS integration several decision rules have been suggested, among them the most common ones are Weight Summation/Boolean Overlay, Ideal Reference Point, Analytic Hierarchy Process and outranking methods (being the one mentioned in this article). In the concluding part the authors mention that a limitation with this approach is the requirement of Inter- net connection. The current thesis also uses web-services to create a prototype, but the prototype stands alone and not on top of ArcGIS.

2.3 Related Technology

In this section related technologies such as Digpro products and web GIS architecture is described.

2.3.1 Digpro Products

Digpro is a company that offers different kinds of GIS solutions. Their application, dp- Power, can be used for representation of power networks. Different modules can be added for further functionality, for example the module Operator includes functions for trouble call handling and outage management. The module Maintainer is as the name implies used for planning, execution and follow-up of inspections. Data from dpPower, Operator and Maintainer will be used for this thesis.

dpWebmap is a web-based GIS solution that is available as complement to all Dig- pro’s utilities. In dpWebmap, the network and background data can be visualized with any web browser. This allows the customers of Digpro to make their data available both externally and internally. The base of dpWebmap is used as a guideline for the prototype development in this thesis.

2.3.2 Web GIS Architecture

A three-tier architecture is often used when developing a web application. This means that the user-interface (presentation tier), the data access (logical tier) and the data storage (data tier) are developed as three different modules (AL-Mukhtar and Hadi, 2012).

In a web GIS the data is stored in a GIS database, the data access is done by a GIS server and the user interface is a client (Fu and Sun, 2011). The client can be a web browser, desktop application or a mobile application. Upon interaction from the user, the client sends a request to the GIS server which sends the request to the GIS database. The GIS database sends back the requested data to the GIS server which processes the data and then sends the result back to the client. Finally the client presents the results to the user.

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The functions and the performance of the web GIS server are the most important parts of a web GIS application (Fu and Sun, 2011). A GIS database stores and manages the data for a GIS (Fu and Sun, 2011). This kind of database can handle both spatial data (points, lines and polygons) and non-spatial data. Some GIS databases store collections of features while others store the data model that defines spatial relationships, for example topologies or networks. The database can range from small, single-user database up to large multiple-user database where the latter allows simultaneously accessing and editing.

The GIS application is never better than the quality of the data stored in the database.

Therefor, it is important to think about the purpose of the GIS application when the data is collected - a professional application needs good, current geographic information. The client can either be a web browser, a desktop application or a mobile client (Fu and Sun, 2011). Web browsers are the most commonly used clients for web GIS. Earlier, the browser clients where static and tedious. Nowadays, with technologies like AJAX (Asynchronous JavaScript and XML) and additional APIs, one can create a dynamic, interactive and user-friendly interface which can perform many types of GIS operations.

3 Research Methodology

Figure 2: Flowchart showing the process of the research

The methodology can be divided into three main parts consisting of five steps as seen in Figure 2. The preparation phase, followed by the development phase and finally the evaluation phase. After the first evaluation the prototype might be adjusted according to recommendation before the final evaluation.

3.1 Preparation

Before the development can start some preparation is necessary. The following sections describe this process.

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3.1.1 Interviews

Visualization can be very different for someone who is used to looking at maps compared to someone who normally work with lists and tables. At the same time creating a tool when you are not sure what the important features are can be really hard without asking someone who is familiar with the subject at hand. Therefore, to understand what the customer actually wants and demands from a tool like the one handled in this research, interviews are carried out with three power company customers of Digpro. All of the interviews are conducted with people involved in the decision making process of reinvestments.

The purpose of the interviews is to understand how the companies make their decisions today and if they see a need for a tool that will help them provide means for decisions. Apart from general questions like the subjects role in the decision making process and their GIS experience, open-ended questions are asked in order to allow for free-form answers. The main questions are listed below:

• How is the decision process carried out today?

• Which are the important parameters when looking for reinvestments?

• How should the result be aggregated and visualized?

3.1.2 Data Handling

The data for this thesis is extracted from a real customer of Digpro and consist of both spatial and non-spatial data that is stored in an Oracle database. The spatial data contains line objects (for example cables) and node objects (for example substations). The customer data includes a lot of sensitive information, therefor the data needs to be anonymized before it can be used in the web application. Furthermore, the database that the data is extracted from consists of a lot of data that is not of interest in this thesis, therefor only a few schemas are extracted. The extracted schemas include base data (ID, installation year and geometries to mention a few), outage data (for example length of outage and number of affected customers) and inspection data (for example number of inspection remarks and the degree of the remark).

The outage data gathered for this thesis is grouped at bay level and this data cannot be extracted for individual objects. To be able to perform a consistent analysis, the rest of the data needs to be aggregated at bay level as well. A bay can be defined as the physical box in a power station along with the outgoing line that includes all the objects that are connected under the bay, for example cables and delivery points. Every object belongs to one and only one bay and in the data the bay is stored as an attribute for each object.

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3.1.3 Basemap

The basemap provides the geographic context of the application. Since the application in this thesis is developed for power networks, a basemap that includes main roads and buildings is chosen. Since too many details in the basemap can disturb the visualization of the network, a light gray-scale with a low level of detail is used for the basemap. To be able to enhance the colors of some of the operational layers, a dark basemap is also available.

3.2 Prototype Development

In order to see if the chosen analysis and visualization are conceivable, a prototype is developed. The development is described in detail in the following sections.

3.2.1 Front-End Development

The front-end of the web is what the user can see and interact with i.e. the UI. The UI of this prototype is created with HTML5, CSS and JavaScript. The biggest challenge of the front-end development is to design the web page so that the user is faced with relevant and useful information. In the ideal web application, the user does not have to think to much about the functionality, they should understand by the interface how it works. In order to try to achieve this and to create a good UX, the keywords usability, simplicity and similarity are taken into consideration. Since most of the intended users are familiar with Digpro’s software, the GUI of dpWebmap is used as inspiration. This can mainly be seen in the side navigation bar of the prototype. Furthermore, the concealed functionality to click on an entry in the resulting table and zoom into the corresponding feature in the map is also a behaviour that is included in dpWebmap.

The open source JavaScript library OpenLayers 3 is used to create the map and the vector layers. OpenLayers 3 also includes a lot of functions that allow for interaction and manipulation of spatial data that is included in the prototype, for example convex hull.

3.2.2 Back-End Development

The back-end of the web consists of the parts that the user cannot see or interact with, i.e.

server and database. In the web application developed in this thesis the communication between the database and the client is done by a Python server. This server was created by Ella Syk and Fredrik Hilding in a master thesis conducted 2016, Syk and Hilding (2016).

After the relevant data is extracted from the original database, the Oracle geometry objects need to be converted into GeoJSON format. This is done directly in the database with a in Oracle 12.2 built in function. The original data consists of base data, outage data and inspection data and is stored in separate tables. In order to get all of the relevant data in one single table, these different tables are joined together. This makes it easier to write the queries on the server side and also improves the response time for the prototype.

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Due to the fact that data sent between the server and the browser have to be plain text, the Python server has to query the database for JSON-data. JSON is a text that easily can be converted into JavaScript objects and vice versa. In Oracle 12.2 there are built in functions to aggregate data into JSON-data, these functions are used in the queries on the server in order to get data that is easy to handle in JavaScript.

3.2.3 Analysis

The combination rule for this research is WLC. It is done with a linear score range scaling. The scaling will give all parameters a value between 0 and 1, where 1 refers to an element with poor values and thus in need of reinvestment. The weighing is done through the rating method of point allocation.

Figure 3: Image showing example of WLC for power networks

Figure 3 shows an example of how WLC is performed on power network data that has been aggregated to bay level (as mentioned in Section 3.1.2). In Figure 3 the bays are represented as blue polygons. The first step of the WLC is the scaling of the parameters, which is done as a function in the JavaScript. The weights are set by the user through an HTML form presented in the interface and the final summation is done with a function in the JavaScript after the parameters and weights are chosen.

For the presentation of the result, a table is displayed. In this resulting table, the bays are sorted in accordance to their final sum. The bay with the highest sum is ranked as number 1 and displayed at the top of this resulting table. This is the bay in greatest need of reinvestment. Besides the rank, this resulting table also shows information about the ID of the bays and a colored dot that indicates the final score of the bay. The coloring of the dots in this resulting table is red for values between 1 and 0.8, yellow for values between 0.8 and 0.4 and green for values from 0.4 and below. An example of how this resulting table looks for the prototype developed in this thesis can be seen in Figure 12 in Section 4.2.

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3.2.4 Operational Layers

The operational layers are layers that the user work directly with or layers that are a result of an algorithm in the application. The operational layers are placed on top of the basemap. In this thesis one of the operational layers includes the network which consists of all arcs and points. This layer is styled similar to how the network is styled today in dpPower in order to achieve similarity.

According to a new law, the power companies are not allowed to charge for network parts that are over 40 years old. Due to this, a layer that includes arcs and points that are over 35 years old is created. This layer makes it easy for the customer to quickly see the old network parts in the map.

Another operational layer is created from the result of the analysis in the application.

The features of this layer (the bays) are created by a convex hull around the arc and point features. Each arc and point belongs to a specific bay. After several joins in the database the associated bay is stored as an attribute for each arc and point feature. This attribute is then used to create the convex hull for each bay.

3.3 Evaluation

Evaluation of the research is done by sending out a questionnaire to the customers. The questionnaire is first sent out to the interviewed customers. After this, the prototype is adapted to the opinions that are expressed by the customers. The questionnaire is also answered by the customers that are attending the presentation of this thesis at Digpros customer meeting. However, the prototype is not adapted after this questionnaire due to lack of time. The questionnaire concerns questions about functionality, design and visualization techniques. The questionnaire can be found in Appendix 6.

4 Results and Analysis

This part starts with presenting the result from the interviews in Section 4.1. Followed by several screen shots of the developed prototype in Section 4.2. Lastly, a summary of the evaluations is presented in Section 4.3.

4.1 Interviews

After conducting three telephone interviews with customers of Digpro the relevant background information was gathered to be able to proceed with the prototype. The main questions asked were how the decision process is carried out today, if they think it works perfectly or if they see a need for a new tool and also what they consider to be the important parameters when deciding on their reinvestments.

The current decision making process is dependent on expertise and it is important that the decision maker knows the network. The companies have the information, for example age of the network parts, in an excel document that they take into consideration when they

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are looking for reinvestments in the network. None of the interviewed companies use any analysis or algorithm to support the reinvestment decisions today. After describing essential functionalities of the proposed prototype they all thought that a web application like the one presented in this thesis could be helpful.

In the interviews, the companies presented which parameters that they often looked at when doing reinvestments. These parameters are listed below:

• Age of the network

• The outages in the network

• The result of inspections in the network

• What type of network part (free or isolated)

4.2 Prototype

Figure 4: The startpage of the prototype Figure 5: Meny button clicked In Figure 4 the startpage of the prototype is shown. The menu button is placed in the top left corner. In Figure 5 the menu button has been clicked and the left side navigation bar has been extended. In this menu the user can select which base map to show and which operational layers to place over the base map.

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Figure 6: Light gray basemap and age layer Figure 7: Dark basemap and age layer In Figure 6, the age layer and the light background map has been chosen. The dark basemap overlayed with the age layer can be seen in Figure 7. In the age layer, the network parts between 35 and 40 years old are styled orange and network parts over 40 years old are styled red. This information is shown in the legend displayed in the bottom right corner of the map.

Figure 8: Parameter form Figure 9: Tooltip shown on hover When the ”Run analyze” button is clicked, a form with selectable parameters is opened. This form is showed in Figure 8. As seen in Figure 9, the user can hover over the parameters to get a short explanation of the respective parameter.

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Figure 10: Weight form Figure 11: Alert box with warning message When the wanted parameters are selected, a form where the user can select weights for each parameter opens. This form can be seen in Figure 10. If the total weight does not add up to 100% an alert box is displayed to the user, this is shown in Figure 11.

Figure 12: Result from analysis Figure 13: Click and pan to polygon The result from the analysis is presented with a table and polygons in the map. The red polygons are automatically visualized in the map after the analysis, this can be seen in Figure 12. The user can click on the rows in the resulting table to show and pan to the corresponding polygon in the map, this behaviour is shown in Figure 13. Figure 13 also shows that it is possible to click on the polygons in the map to get a popup with information about that polygon (bay).

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4.3 Evaluation

For the questionnaire a total of 30 answers were gathered. The questions in the questionnaire are listed below. Thereafter, the results are summarized and presented in stacked charts.

1. Are the selectable parameters appropriate for reinvestment analysis?

2. Is it appropriate to aggregate the data on bay level?

3. Is any important operational layer missing?

4. Does the pop-up show the right information about the object?

5. Does the pop-up show the right information about the bay?

6. Is the right information shown in the resulting table?

7. Is the placement of the main menu good?

8. Is the placement of the resulting table good?

9. Is the background map showing the right level of detail?

10. Is the coloring of the age layer easy to interpret?

11. Is the coloring of the result of the analysis easy to interpret?

12. Do you think a tool like this would be useful for you?

13. Overall impression of the design

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q1 q2 q3 q4 q5 q6 10

20 30

#participants

Yes No Don’t know Figure 14: Result from questions 1 to 6

In Figure 14, the result from the questions regarding functionality is presented. The stack for q1 shows that most of the subjects thought that the selectable parameters were appropriate for reinvestment analysis in power networks. The subjects who said no wanted to be able to select more parameters such as land use and cost. There were a few comments about being able to select which kinds of objects the analysis is done on, for example stations or cables. The stack for q2 shows that most of the subjects thought that it was appropriate to aggregate the data on bay level. Some of the subjects wanted to aggregate on specific types of objects, for example stations and cables. Another sug- gestion was to do the analysis on every single object with no level of aggregation. When looking at the result regarding q3 it is clear that many subjects were missing some operational layers. The suggestions were to also include operational layers for land use, cost, airborne cables and outages. Many of the subjects had comments regarding the questions about the information in the popups of arc/node objects and bay objects and the information in the resulting table as seen in the stacks for q4, q5 and q6. Most of them thought that the chosen id should be replaced with another id that is more informative for the user.

Some of the subjects desired that the type of object, for example feeder cable, should be stated in the popup. The subjects also thought it would be desirable to show the total suitability score from the analysis in the resulting table.

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q7 q8 q9 q10 q11 q12 10

20 30

#participants

Yes No Don’t know Figure 15: Result from questions 7 to 12

The questions regarding design and visualization is presented in the stacks for q7 to q11 in Figure 15. All of the subjects thought that the placement of the menu and the resulting table was good, this can be seen in stack for q7 and q8. Some of the subjects did not answer the questions regarding the visualization but all of the subjects who did thought the visualization was good. This is shown in the stacks for q10 and q11.

There was a comment about the need to take color blindness into account for the coloring throughout the prototype. Some of the subjects did not answer the question regarding usefulness of the prototype but all of the subjects who did thought that an application like the one developed in this thesis would be useful for reinvestment decisions, this can be seen in the stack for q12.

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7 % 50 % 3

4

43 %

5

Figure 16: Result from question number 13

In Figure 16 the result from the question regarding the overall impression of the design is summarized. The overall impression was measured on a scale from 1-5, 1 meaning that the overall impression was not at all good and 5 meaning a very good impression. Most of the subjects rated it as 4 or 5, only a few rated it as 3, as seen in the figure.

5 Discussion

5.1 Preparation

Before getting started on the actual development of a prototype, relevant information needed to be gathered. It was chosen to do so through literature study for the GIS analysis and visualization parts. For knowledge about the current decision making process in terms of reinvestments of power networks and the demand for a new process, performing interviews was considered to be the best way to go.

5.1.1 Interviews

The interviews were conducted at an early stage of the development process as a way of gaining more insight in the workings of power networks. At this early stage it is a difficult task to know what questions to ask, if not familiar with the subject at hand.

Thus, presenting several companies with a questionnaire to get quantitative information did not seem appropriate for this thesis. Instead, to gain a general understanding and insight it was chosen to conduct fewer, qualitative interviews where the customers were asked open questions in the hopes of describing the process and needs in more than a yes or a no. Furthermore, it is easier to orally describe the concept of the proposed prototype than in writing.