Computers, Environment and Urban Systems 27 (2003) 511–526 www.elsevier.com/locate/compenvurbsys
Why are spatial decision support systems not used? Some experiences from the Netherlands Oddrun Uran*, Ron Janssen Institute of Environmental Studies, Vrije Universiteit Amsterdam, De Boelelaan 1115, 1081 HV Amsterdam, The Netherlands Received 14 May 2002; accepted 21 July 2002
Abstract Spacial decision support systems (SDSS) are popular tools in decision-making processes. Despite their popularity there are many systema that are never or hardly used. The present study searches for explanations or reasons for success or failure of such tools by comparing five representative examples of SDSS for coastal zone and water management. The findings of the comparison show, amongst others, that SDSS only provide limited or no support for analysing and evaluating the output generated by SDSS. Also, no or poor support for spatial evaluation is offered which might be one of the reasons why some SDSS are not used. # 2003 Published by Elsevier Ltd. Keywords: Spatial decision support systems; Coastal zone management; Spatial analysis; Spatial evaluation
1. Introduction Decision Support Systems (DSS) and, more recently, Spatial Decision Support Systems (SDSS) are increasingly popular tools in decision-making processes. The reasons for the popularity of such tools can partly be found in the technological development, which makes it possible to install and use the systems on PCs, and partly in the need to manage the large amount of often complicated data that play a role in the decision-making processes. There are numerous definitions of DSS known but in this study that of Janssen (1992) is used: A DSS implies a computer program that:
* Corresponding author. Tel.: +31-20-444-9543; fax : +31-20-444-9553. E-mail addresses:
[email protected] (O. Uran),
[email protected] (R. Janssen). 0198-9715/03/$ - see front matter # 2003 Published by Elsevier Ltd. doi:10.1016/S0198-9715(02)00064-9
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assists individuals or groups of individuals in their decision process; supports rather than replaces judgements of individuals; and improves the effectiveness rather than the efficiency of a decision process. A SDSS is different from a DSS in the fact that it is used to support decision processes where the spatial aspect of a problem plays a decisive role. Despite ongoing discussion, there is no agreement on a single definition. Fotheringham (1990) compares this discussion to ‘‘defining a car: there seems to be general agreement on what it is, but not on what are the necessary attributes for giving it such a label’’. This explains why most authors simply end up with a list of characteristics of what a SDSS might comprise. For this study we prefer the statement by Densham (1991) that SDSS are ‘‘explicitly designed to provide the user with a decision-making environment that enables the analysis of geographical information to be carried out in a flexible manner’’. The lack of widely supported and unambiguous definitions of DSS and SDSS do not seem to hamper the development of such systems. On the contrary: there are innumerable systems under development for supporting a variety of decision questions. Most popular application areas are, at least in The Netherlands, coastal zone management, river management, water resource management and land-use planning (Scholten, Fabbri, Uran, & Romao 1999). Despite the high effort and large amounts of money spent on developing DSS, there are many that are never or hardly used (Adelman, 1992; Ascough, DeerAchough, Schaffer, & Hanson, 1998; LWI, 2000). There are many reasons for this lack of success of decision support systems. One reason can be that the users find the system too detailed, time consuming and costly to use (Ubbels & Verhallen, 2000). Other reasons are related the general complexity of the systems (Jones, El-Swaify, Graham, Stonehouse, & Whitehouse 1998), while still other reasons are related to the uncertainty of the model output and on the appropriateness for solving the decision question. Aschoug et al. (1998) state that the limited involvement of users in the development phase can lead to unsuccessful DSS. In addition, Loucks (1995) emphasis the need for training in the use of each particular DSS. Only then can a DSS be used effectively, the results free of errors and useful for decision-making. English and Dale (1999) describes four categories of factors which constrain the use of analytical tools in decisionmaking. These are too little, too much and wrong information, time and resource limitations, lack of access, and the hurdles of communication and trust. Some of the reasons for unsuccessful DSS mentioned earlier are strong indications that users are not always able to take systems into use as intended or expected by developers. In other words, the functionality of the systems is not in all cases optimal. Functionality is a very wide term and can include almost any aspect of a SDSS. In this paper, functionality is used in the context of how easily systems can be used for their purpose. There are probably just as many reasons for poorly functional SDSS and, therefore, unsuccessful SDSS as there are systems built. The following questions are important when searching for an explanation or reasons for success or failure:
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1. 2. 3. 4. 5.
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How are alternatives specified? How do users get from start to finish? How is the output presented? How is evaluation of the results supported? Does the SDSS do what it was meant to do?
These five questions are the basis for comparison of five representative examples of spatial decision support systems for coastal zone management. The answer to these questions may provide clues to the overall question of this paper: why are spatial decision support systems not used? Section 2 provides a short introduction to the five spatial decision support systems used. The five research questions of this paper are dealt with in the following five sections. Section 3 describes the input a user has to provide to define an alternative or a scenario. Clear navigation through a system is important for a user. Section 4 deals with the way the different systems guide the user from start to finish. Section 5 describes the different types of maps used to present the output to the user and Section 6 how evaluation of results is supported. Section 7 compares what the SDSS was meant to do with what it is able to do. Finally, using all this information an attempt is made to answer the question why the systems are not used in Section 8
2. Five examples of spatial decision support systems for coastal zone management There are several suggestions to be found in the literature describing the characteristics of a DSS on which to base a comparison or an evaluation of the systems. Ubbels and Verhallen (2000) evaluate a set of DSS according to their suitability for collaborative planning processes using the characteristics user-friendliness, prerequisite for collaborative processes, transparency, flexibility, and the way the effects of possible actions are estimated. Using these characteristics the authors determine the suitability of tools for specific user groups and decision-making phases. Crossland, Wynne and Perkins (1995) investigated the impact on the decision makers performance when using SDSS in a decision making process. The performance was measured by the accuracy of the solutions and the time it took to come to the solutions. It was concluded that SDSS users have a shorter solution time and fewer errors compared to those not using SDSS. Adelman (1992) presents three methods for evaluating a DSS in technical, empirical, and subjective evaluation terms. These methods of evaluating a DSS are intended for implementation during the development phase of the DSS, to keep the development on track. However, the methods can also be used when evaluating a system after completion. The technical evaluation method focuses on system characteristics and includes, among other criteria, the adequacy of the selected analytical methods, software development costs, software tests and verification, and adequacy of the knowledge base. The empirical evaluation method on the other hand focuses on obtaining objective measures of the system performance. An important question
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in this type of evaluation is whether decision-makers perform better with a system than without. The subjective evaluation method focuses on assessing the system from the perspective of the potential user in order to identify whether the end-users find the system useful or not. This study focuses on the functionality of SDSS. Because the emphasis is on characteristics of the system this approach can be labelled as a technical evaluation. It is very difficult to perform an empirical or subjective evaluation because of the inevitable dependency on end-users. As stated earlier many SDSS are not taken into use and if they are, documentation of end-users experiences is rare. Five examples of spatial decision support systems for coastal zone management are used to test the research questions. These examples can be seen as representative for the state of the art in the Netherlands in this field. 1. Nature Development and Valuation (NDV Module)—which supports the design, development and valuation of nature on the Dutch coast. The potential for nature development is thereby quantified, analysed and presented. The application is the land reclamation project Maasvlakte 2, located South of Rotterdam (Ruijgrok, de Vries, Baptist, Meulen, & Zaadnoordijk, 1999). 2. Ecotope evaluation DSS (ECOPEIL)—which supports questions concerning the expected ecotopes and the consequences of water level changes on the naturalness and biodiversity in the IJsselmeer area (Jans, Platteeuw, Tosserams, & Schiereck, 2000). 3. Shoreline DSS (Shoman)—which supports suitability assessment of locations for large-scale nature development projects on the Dutch coast. The application is the development of a wet dune valley in the Province of NorthHolland (Heuvel, Katwijk, Triest, & Goedhart, 1993). 4. Wadden Sea DSS (WadBOS)—which supports the analysis of current policy and management issues in the Wadden Sea. It permits assessment of the effects of policy decisions and the feasibility of management measures (Uljee & Englen, 2000). 5. Eco-morphological DSS (EMM)—which supports the analysis and evaluation of morphological and ecological effects of different dredging and deposition interventions. The application area is the Western Scheldt estuary (Baptist, de Vries, & Wang, 1998). Table 1 provides a few characteristics of the five systems. It shows that all systems support both the development and selection decision phases, that development time is usually substantial and that the systems main users are analysts.
3. How are alternatives specified? One of the main objectives for taking a SDSS into use is that it must be possible to test alternative solutions. In creating alternatives, users can incorporate ideas and knowledge of the decision problem and possible solutions can be tested in terms of
Decision process phase
Long/medium or short term calculations
Development time
Type of user
Final version
NDV
Development Selection
Long term (75 years)
3 years
Analysts and decision makers
Yes
ECOPEIL
Identification Development Selection
Short (minimum 1 year), medium and long (maximum 50 years)
4 years
Analyst
Yes
Shoman
Development Selection
Not applicable
1 year
Analyst
Yes
WadBOS 2
Identification Development Selection
Medium (standard 10 years), user decides if less
3 years
Analysts and decision makers
No
EMM
Development Selection
User defined but preferably long term (20 years)
2 years
Analysts
No
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Table 1 General characteristics of the selected SDSS
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Fig. 1. Standard input of data in the Eco-morphological DSS for the decision variable dredging for one time step (Baptist et al., 1998).
their feasibility. It is important that definition of alternatives can be managed by users. When making an alternative gets too complex for a user, the user can easily feel overwhelmed by the task and it is less likely that the SDSS will be a success. The complexity of making an alternative depends, among others, on how many parameters need to be changed and how easy it is to make changes to the parameters. An example of a situation in which some users can feel overwhelmed by the task of making alternatives can be found in the Eco-morphological DSS (Baptist et al., 1998). Here only two decision variables play a role, which implies that it should be an easy task to make an alternative. However, this is not the case. The decision variables are the amount of dredged material and the amount of deposited material, both in m3. These are entered using two separate maps, one for deposition and one for dredging. Fig. 1 shows an example of a dredging input map. Input data must be provided for 112 possible locations for each time step. Not only does the user, therefore, have to think of the amount of material either to dredge or deposit when making an alternative, but also the spatial pattern of the input data has to be taken into consideration. The time step of the Eco-morphological DSS is 30 days and because the DSS calculates long-term morphological and ecological effects on the estuary, putting in data for a time period of 20 years is not inconceivable. In addition to the challenging task of putting in data for one time step, the user has to repeat the input task a large number of times, both for the dredging and deposition decision variable. Not only is then the task very time consuming but also users easily lose the overview of the spatial patterns of the input data that very likely change in time. Generating alternatives therefore becomes a very complicated task. In the four other SDSS, generating an alternative is different from the Eco-morphological DSS and is more directed towards entering and changing parameters. In the Nature Development and Valuation DSS users need to design a land reclamation area and add management practices and functions of the designed area. A similar option is also available in the Ecotope Evaluation DSS where users can change the elevation of an area and add the management practise of this area. In the Shoreline DSS users must delineate an area which represents the form of a wet dune slack. The elevation of this new area must then also be filled in. In the Wadden Sea DSS, contrary to the Shoreline DSS, users are expected to fill in many, highly diverse, parameters. The emphasis on putting in data using maps is much less
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Fig. 2. A diagram used in the Nature Development DSS to perform tasks and at the same time give an overview what tasks that need to and can be done next (Ruijgrok et al., 1999).
apparent, with the only input of data using maps being when users wish to open or close areas for functions like fishery or recreation.
4. How are users guided through the system? Acceptance of a DSS to a large extent dependent of its ease of use. An important element in this respect is how well a user is guided though the system, for example whether the next step is self-explanatory or not. The organisation and number of windows at any one time is also important as is navigation through the system and its facilities. An example of a screen that is self-explanatory is shown in Fig. 2 and is taken from the Nature Development DSS (Ruijgrok et al., 1999). The boxes represent different tasks. The tasks that already have been performed are shown in green while those still not done in yellow. Red is used to indicate tasks that for some reason cannot be done at this moment and lilac shows the currently running task. In this way, users keep a clear overview while performing the different tasks in the SDSS. The same type of guidance is found in the Eco-morphological DSS. However, the developers of the Wadden Sea DSS have chosen a very different approach, where the path through the system consists of many levels. Because of this it is easy for users to lose overview of which level they are on and what task to perform next. Another disadvantage is that the number of windows open at any given moment can be very large. Systems like the Shoreline DSS and the Ecotope Evaluation DSS also possess
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Table 2 Presentation of output
NDV Module ECOPEIL Shoman WadBOS 2 EMM
Presentation
No. of maps per time step
Time series?
Spatial resolution (m)
Know what is important?
++ ++ ++ ++ ++
199 8 1 29 138
Yes Yes No Yes Yes
200200 1010 1515 500500 6060
+ +++ +++ ++ +
Key: +++=good, ++=moderate, +=poor, 0=not available.
hierarchical structures but the number of levels is much smaller, so the paths through these systems are much easier to follow than is the case with the Wadden Sea DSS.
5. How is the output presented? The simulation results of the alternatives are presented in tables, graphs or maps. The users of SDSS have to be able to understand and use a variety of output data with varying importance to the problem to be solved. When the amount of output increases a user will have difficulties picking out those results that are relevant and
Fig. 3. An Ecotope Evaluation DSS output map showing the distribution of ecotopes with the highest chance of developing or remaining after changes in the water levels have occurred. The main part of the map shows the IJsselmeer but also the much smaller Ketelmeer and Zwarte Meer located in the South Eastern part of the map is included (Jans et al., 2000).
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an overload of information can be the result. In addition, the presentation of the output can be imperfect. Output in the form of maps, i.e. spatial output, adds an additional challenge to the user (see among others Golledge, 1992; MacEachren, 1995; Openshaw, 1991). When the number, and different types of maps is large, the overall complexity for the user increases, which again leads to a decrease in functionality (Herwijnen, 1999). How output is presented in the five systems is summarised in Table 2. An example of output is the map generated in the Ecotope Evaluation DSS (Jans et al., 2000) shown in Fig. 3. This map presents the distribution of ecotopes that have the highest chance of developing or remaining after changes have occurred in the water levels. In Table 2 some elements of output are presented for each of the five SDSS. The way output is presented can be decisive for whether a user can comprehend the information produced. There are many visualisation methods available and developers must choose the ones they feel applicable. In Ecotope Evaluation DSS, for example, the developers have put extra care into making tables easily readable by applying a uniform set of limited colors to the tables. When dealing with spatial data represented in the form of maps, there are cartographic rules that one can follow to ensure map readability. Unfortunately, these rules are not always followed and there are examples to be found of maps without legends, incomplete legends, etc. which decrease the readability of the maps and, therefore, the functionality of the system. Examples of this can be found in the Eco-morphological DSS and the Nature Development DSS. The map in Fig. 3 is an example of output from the Ecotope Evaluation DSS and has as many as 15 legend classes. This is a high number of classes and the question is whether it is too many classes for users to distinguish. MacEachren (1994) states that in the complexity of a map, no more than six different colour values (i.e. variations in darkness of a colour) can be distinguished easily. Map readers can on the other hand distinguish more colour hues (e.g. red, green, blue) which is used in the legend in Fig. 3. It is uncertain, however, how many classes users can distinguish. The number of maps varies greatly among the systems compared from 1 (Shoreline DSS) to 199 (Nature Development and Valuation DSS). Even though the amount can indicate something about the functionality for users, it is difficult to draw conclusions based only on the number because a high number of maps do not automatically indicate that a user is likely to be overloaded with information. If the maps are well structured and presented, a high number of maps is not in itself an obstacle. The same line of reasoning applies to cases with just one map as output: if this map contains a high density of (complex) information users can find them too much of a challenge to use. Spatial resolution is one measure to characterise the complexity of information presented. The higher the resolution and, therefore, the amount of detail, the more complex the spatial patterns can be. This does not necessarily need to be the case. Although the spatial resolution of the map in Fig. 3 is 1010 m, which is high, the spatial pattern is still distinct. The problem with a high resolution arises when patterns get complex and several patterns needs to be compared at the same time.
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In some SDSS, changes over time are important and hence time series of maps are provided. Either the relevant maps change while the calculations are ongoing, so that the user can observe the changing map(s) simultaneously as in the Wadden Sea DSS, or the maps are all presented at the end of the calculations. The latter is the case in the Ecotope Evaluation DSS, the nature development DSS and the Ecomorphological DSS. Either way it means that by offering output of different stages of the calculations the information load on users is increased. Sometimes it is difficult to know what output is important. The Eco-morphological DSS, for example, produces a lot of maps but users need to know exactly which species are sensitive to change to find the maps that show morphological or ecological effects. The Nature Development DSS is similar. In the Wadden Sea DSS, however, users have to make a choice of which maps they want to study out of a list of output maps, which forces users to make a conscious choice. Also, because users have to decide in advance for what time steps the system should output results, it is easier for the user to maintain an overview of what is important. These options, however, do not ensure users set the ‘right’ output time or that they look at all the maps that are of importance.
6. How is evaluation of the results supported? Model output is not always suitable for direct use in decision making. To make output useful forevaluation, comparison and ranking of alternatives in many cases methods for spatial analysis and evaluation methods are needed. How well the analysis and evaluation tasks are supported is summarised in Table 3. All the decision support systems studied have some kind of analysis method that users can apply. In the Eco-morphological DSS and the Nature Development DSS statistical and mathematical operations can be applied on the parameters of interest using so-called Case Analysis Tools, which are then displayed graphically or in table format. In the Shoreline DSS users can analyse the results visually by choosing what to include in the display and by drawing cross sections. In the Ecotope Evaluation DSS no additional support is given for users to analyse the results. However, the results are presented to the users in the form of summed indicators in Excel format, which means that only users skilled in this program can perform additional analysis. Table 3 Analysis and evaluation
NDV ECOPEIL Shoman WadBOS 2 EMM
Support of analysis
Comparison of alternatives
Spatial analysis
Support of evaluation
Ranking of alternatives
Spatial evaluation
+++ + ++ + +++
++ ++ +++ +++ ++
0 ++ ++ +++ 0
+++ ++ ++ ++ +
0 0 0 0 0
0 + + 0 0
Key: +++=good, ++=moderate, +=poor, 0=not available
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Fig. 4. A difference map of recreation intensity in the Wadden Sea under two different economic growth scenarios (Uljee & Engelen, 2000). The units of the intensity maps are the average number of vessels per 500500m.
In the Wadden Sea DSS an analysis of the results takes place along with the calculation itself and users can observe the result while running the models. However, the system does not enable users to apply other analysis methods that they may find necessary. A common objective for taking a SDSS into use in the first place is that one wants to compare different alternatives to better be able to undertake necessary trade-offs. As can be seen in Table 3, all SDSS offer the possibility of comparing alternatives. An example of a comparison between two alternatives from the Wadden Sea DSS is shown in Fig. 4, where the difference in recreation intensity between a high and a low economic growth scenario is calculated. The map shows that an increase in economic growth leads to an increase in recreation intensity in certain areas. In three of the five SDSS it is possible to analyse maps spatially. In the Ecotope Evaluation DSS and the Shoreline DSS this is possible because the systems are built as GIS applications. This leaves users with a large range of possible analysis methods that can be performed on the spatial output. A considerable drawback, however, is the fact that users have to be skilled in GIS to be able to know which methods to perform to get the wanted result and to perform these in a correct way. It is questionable whether this can be expected by all users of these systems. The Wadden Sea DSS has an analysis tool that supports spatial analysis with different kinds of numerical overlay techniques: the map in Fig. 4 is an example of this. In the Nature Development DSS and the Eco-morphological DSS, however, it is not possible to analyse a map spatially even though these are the two systems that generate the greatest numbers of maps. Users also need to be able to evaluate the output. The reason for this is that the output sometimes needs simplification, aggregation, structuring or another form of processing in order for it to be used in a decision-making process. In some systems this is done automatically, or ‘hidden’, so the user is unaware of the fact that an evaluation step has been made. An example of this are maps showing the total of a parameter, e.g. biomass as in the Eco-morphological DSS. Here, all maps showing the biomass of species are overlaid and summed and a new aggregated map is pro-
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Fig. 5. Target species index calculated by Ecotopes Valuation DSS for six taxonomic groups for four areas (Jans et al., 2000).
duced. In the Nature Development DSS, the Wadden Sea DSS, the Shoreline DSS and in the Ecotope Evaluation DSS evaluation is built-in in which users can fill in criteria or scores. In the Nature Development DSS extra attention to evaluation is given and monetary, perception and ecological values of an area are taken into consideration in the results. An example of an evaluation in The Ecotope Evaluation DSS is given in Fig. 5. This table shows so-called target species indexes. A target species is either an animal of a plant species that fulfils at least two of the following three criteria: (1) the species is rare in the Netherlands, (2) the species shows a population decline in the Netherlands, and (3) the species occur in the Netherlands is a number that is important internationally (Bal, Beye, Hooogeveen, Jansen, & Reest, 1995). The index is calculated by multiplying the number of expected target species within an area with the size of that area. This is done for six taxonomic groups and four areas. The developers claim this to be a measure of the value of the area for the target species. Because the size of the four areas are included in the index and these differ for the four areas, a comparison between columns is meaningless. Also comparison between rows is meaningless without additional information. The index is calculated as the product of size of area and the number of target species that are expected to occur. However, the number of target species is only relevant in relation to the maximum number of target species that can be expected under ideal conditions. The total number is no doubt much higher for birds than for mammals. The table is only suitable for a cell by cell comparison between tables. If three alternatives are presented, each in one table, three tables such as Table 3 needs to be compared. This implies that the user has to compare 24 indices on a one by one basis. In conclusion, Table 4 shows that none of the SDSS support ranking of alternatives even though this can be an important activity in a decision making process. Because the Ecotope Evaluation DSS (ECOPEIL) and the Shoreline DSS (SHOMAN) are built as GIS applications, spatial evaluation of these systems output is possible. Again, however, the question arises whether one can expect users of SDSS to be skilled in GIS to such an extent that performing spatial evaluation does not constitute a problem. This is doubtful seen the complexity of spatial evaluations methods.
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Table 4 Does the SDSS do what it was meant to do? NDV ECOPEIL Shoman WadBOS 2 EMM
+++ +++ ++ +++ ++
Key: +++=good, ++=moderate, +=poor, 0=not available
7. Does the SDSS do what it was meant to do? It is well known that system design can change during development. It is, therefore, interesting to know if a DSS as implemented meets the functional specifications as defined at the start of the project. Table 4 gives an overview on how well each of the SDSS meets the functional specifications as defined at the start of the project. As can be seen, all of the five SDSS do what they were meant to do, however, some better than others. A reason for the good score is that the system objectives are often very broadly defined and only very large shortcomings of system can lead to the objectives not being fulfilled. In this section, therefore, also the shortcomings of the systems and impediments for users will be discussed. The objective of the Nature Development DSS is to support design, development and valuation of nature on the coast. The potential of nature development is thereby quantified and presented. The DSS fulfils this objective. However, users of this DSS can easily feel overloaded by the high number of spatial information produced and no spatial analysis methods are provided to help users handle the large number of maps generated. The objective of the Ecotope Evaluation DSS is to calculate the to be expected ecotopes and the consequences of changing water levels on naturalness and biodiversity of the IJsselmeer area. This DSS also fulfils the objectives. The output is well presented and evaluation steps are made to make the generated information easier to handle. However, to be able to apply spatial analysis methods to the generated maps users have to be skilled in GIS, because the system is build as a GIS application. This approach leaves users with a high degree of freedom when analysing the maps but also requires that they possess the knowledge and experience of which analysis methods to apply. The objective of the Shoreline DSS is to support assessments of the impacts of management actions on the development of the coastline. The Shoreline DSS assesses the suitability of locations for development of wet dune valleys and this objective is fulfilled using the system. However, it is difficult to say whether an area found suitable for a wet dune slack also is ‘the most’ suitable for a wet dune valley, as is stated in the documentation. The wet dune valley can be built on several locations and it is up to the user to choose one of these suitable areas for the development. There is no way of determining which is the best location for a wet dune valley as long as the distance to roads, pipelines, drink water infiltration areas, etc. are respected.
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The two main objectives of the Wadden Sea DSS are to facilitate communication between stakeholders and to link information and knowledge of the Wadden Sea. It is not possible to draw conclusions whether the DSS have fulfilled these two criteria. The DSS should in addition give users insight into the area in its present state, the policy objectives, policy indicators and criteria, the policy measures and the area in its future state. These objectives are fulfilled. Unfortunately, the Wadden Sea DSS is a complicated system to use with many parameters that can be changed and with many levels and windows that need to be opened. The objectives of the Eco-morphological DSS are twofold: the first is to develop a generic module, which can be used when analysing and evaluating morphological and ecological effects of different interventions (strategies) under different conditions (scenarios). The second objective is to apply the Eco-morphological DSS on the Western Scheldt Estuary. The Eco-morphological DSS meets these objectives. The shortcoming of the Eco-morphological DSS is that it does not allow a widespread evaluation of the results on the morphology and the ecology as stated in the objectives. The evaluation steps offered can be regarded as insufficient. The Eco-morphological DSS does generate a large number of maps necessary for an evaluation but does not support the evaluation itself, neither does it support spatial analysis methods that users can apply to the maps.
8. Conclusions and recommendations Despite the high effort, time and money that are put into developing SDSS, there are many that are not taken into use. The reasons are probably as many as there are unsuccessful SDSS and can be found in the systems themselves and also in the decision processes they are suppose to support. To find clues to the central question ‘‘Why are spatial decision support systems not used?’’ five representative SDSS from the field of coastal zone and water management are compared using the following questions: 1. 2. 3. 4. 5.
How are alternatives specified? How do users get from start to finish? How is the output presented? How is evaluation of the results supported? Does the SDSS do what it was meant to do?
Specification of alternatives is a real challenge in most of the systems. Although the task is complex and requires a lot of knowledge this task is not well supported in any of the systems. This means that it is difficult to generate a complete set of relevant alternatives and also that the effort involved is high. Navigation through the system is supported well by most systems. However, the large number of options available sometimes represented by a high number of windows open at the same time, may easily be confusing and add to a lower functionality of the systems. Presentation of output, is on the other hand, adequate in most systems. Concern for the use of SDSS can be raised, however, by the fact that the SDSS only provide limited
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or no support for analysing and evaluating the output generated by SDSS. As can be seen in the previous section, there are exceptions. However, these are few and in some cases dependent on users having skills in the use of GIS. None of the systems provide support for ranking alternatives, which can be an important task in a decision-making process. In addition, no or poor support for spatial evaluation is offered. These limitations of the support offered can be one reason why SDSS are not used. All systems do what they were meant to do: they meet the functional specifications as defined at the start of the project. These specifications are usually rather vague and not clearly linked to the decision problem the system is designed to support. This means that if the question is interpreted as ‘‘does the SDSS provide adequate support to the decision process?’’ the answer would not be as positive. In all of the systems contact with the decision process seem to be lost during the development of the SDSS. The need for a closer link between developers and users during development is probably the most important lesson from this paper.
Acknowledgements This research has been funded by the project group Estuaries and Coasts of the Land, Water Environment Information technology program (CUR/LWI, Gouda).
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Oddrun Uran holds a Master in Environmental Sciences from the Vrije Universiteit, Amsterdam. Worked a year as a Young Graduate Trainee at the European Space Agency on remote sensing application studies. Uran is currently writing a Ph.D. on spatial decision support systems for coastal zone and water management. Ron Janssen specializes in the use of multicriteria methods to support environmental decision-making. He published a book: ‘‘Multiobjective decision support for environmental management’’ and developed decision support software. Current projects focus on the use of multicriteria methods in a spatial context.