Web-based spatial multi-criteria evaluation software for forestclim project

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This software will be developed from an existing module in ILWIS. GIS software. ... downloaded to desktop applications and many of the collaboration potentials of internet are lost, or data .... obvious best alternative among several alternatives.
Web-based spatial multi-criteria evaluation (SMCE) software

ITC Working paper 1 for the ForestClim Project

Version : 20090918 Authors: Luc Boerboom, Johannes Flacke, Ali Sharifi, Özgün Alan

TABLE OF CONTENTS 1 2 2.1 2.2 2.3 3 4 5 6 7

Rationale and status of this document .........................................2 Background concepts and terms.................................................4 Planning and decision making process..........................................5 Spatial planning and decision support systems................................7 Spatial multi-criteria evaluation ................................................8 Problem Statements............................................................. 10 Development Objectives........................................................ 12 Features and issues.............................................................. 14 References........................................................................ 15 Appendix A........................................................................ 17

1

Rationale and status of this document

Several of the ForeStClim project activities1 aim to develop and use spatial multicriteria evaluation software to evaluate and compare current and future forest states in order to choose between alternative future management pathways that adapt to climate change. These states result from predictions about impacts of alternative management options and scenarios expressing uncertainties about e.g. climate, market, technology development, et cetera. Other project activities aim to assess these future states. A need for spatial multi-criteria evaluation can occur at various moments and in various settings in planning and decision making processes. For instance in prioritization of problematic areas, or finding suitable areas for certain management interventions, or if alternative management plans exist, to evaluate the spatial impacts of these plans. Individuals can have an interest in these evaluations, but mostly these planning and decision making processes involve multiple actors or stakeholders, likely of various organizations. In spatial multi-criteria evaluations, spatial data used to be collected and used in desktop computers. However, increasingly we find data shared through intranet and internet. The sharing comes in different forms, e.g. in the form of maps 1

Action 3.1.2 A digital tool for multi-criteria analysis will be developed in order to assess land-use change dependent upon future regional climatic conditions and socio-economic forcing (KontextU). This multi-criteria analysis will be an enhancement of the eco-efficiency analysis tool that has been developed within the INTERREG III-project WaReLa. Action 3.2.8 Develop stand alone multicriteria evaluation software for integration of spatial and non-spatial criteria, including a scenario manager, multi-stakeholder analysis, and spatial sensitivity analysis to establish effect of data uncertainty on decision certainty. This software will be developed from an existing module in ILWIS GIS software. Action 3.2.9 Establish evaluation methodology with decision makers with transnational and national mandates to evaluate current state and future forest management strategies

(images) with the general public, as the French Inventaire Forestier National2 does, or in the form of data shared through intranet as practiced in the Landesforsten Rheinland Pfalz3. Currently great advantages exist of sharing data in terms forest management and planning practices, such as shared standards of data definitions and formats, multi-user access for ranges of functions, and the more mundane security, controlled access and back up management concerns. So far only a few web-based applications that facilitate processing of data and interaction between users exist. One could hypothesize that a reason why web-based data is not fully used, is the lack of analytical tools. Nobel Prize laureate Herbert Simon formulated sharply: "What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”(Simon, 1971) Until such time that these analytical applications exist, data either needs to be downloaded to desktop applications and many of the collaboration potentials of internet are lost, or data needs to be produced locally with all associated interoperability problems. That indeed is the state of current implementation of spatial multi-criteria evaluation (SMCE) software, including the SMCE module developed in ITC’s open source ILWIS geographic information system. However, the last five years we see these potentials emerge in Web2.0, such as web applications (e.g. full blown web-based geo-information systems4) open source standards5, modular and chainable geo-information functions6, and mashups7 that combine data and applications from different web sites to create new web applications. In the ForeStClim interregional development project we aim to develop spatial multi-criteria evaluation software that not only addresses for example questions such as where are areas most vulnerable to climate change, which areas are normatively8 more suitable for certain species or management practices, or how do forest management adaptation plans compare in relation to strategic 2

http://www.ifn.fr/spip/?rubrique67 Personal communication Astrid Tesch and Ulrike Raible of Landesforsten Rheinland Pfalz.(Oct, 2008) 4 Examples are: UMN (University of Minnesota) MapServer, CartoWeb, or CommonGIS 5 Open source standards have been defined for development of various Web Services: Web Processing Services (http://www.opengeospatial.org/standards/wps), Web Feature Services (http://www.opengeospatial.org/standards/wfs), Web Mapping Services (http://www.opengeospatial.org/standards/wms), and Web Coverage Services (http://www.opengeospatial.org/standards/wcs) 6 E.g. in http://52north.org/ 7 http://en.wikipedia.org/wiki/Mashup_(web_application_hybrid) 8 Normatively here means that we recognize there is biophsysical suitability that can be defined by rule-based systems, but that trade-offs between such biophysical suitability and other considerations/criteria can be evaluated through methods of spatial multi-criteria evaluation. 3

objectives, but also software that facilitates intra-regional collaboration within forest management organizations or between forest management organizations and other organizations. In addition, we aim to develop software that facilitates inter-regional mashups of spatial multi-criteria evaluations done in different regions for communication between regions. We call this software web-based collaborative spatial multi-criteria evaluation software. In ForeStClim, a task force on Evaluation Methods and Tools Development has been established to, amongst other activities, guide development and use of spatial evaluation software. This document is primarily intended for that community. In the next chapters of this working paper, we first explain some of the concepts and terms about decision making, decision making processes, decision support systems in general and spatial multi-criteria evaluation in particular, in order to begin to establish a common ground. Then we give a series of problem statements reflecting the current state of the art in web-based collaborative spatial multicriteria evaluation. These statements are inverted to objectives in the next section. Finally we share a few ideas of features we aim to explore and issues that need to be discussed. It should be emphasized that this is a very first draft. Ideas are still immature and need exposure to real and varied planning and decision practices as well as policy and data environments. Discussions and collaboration with our project partners are the way to achieve this. Finally, as the reader will see in our section about problem statements, the prime statement is one of institutionalization of software. The consequence is that we aim to place process over product. That means that we have to become close to practitioners that operate in existing forest management processes, with policies and data in the different forest management organizations to have these drive software development, and develop a mutual (common?) understanding amongst these practitioners about the different roles of (spatial) evaluation in forest management. Creating this understanding and ultimately finding the ‘natural’ places and situations where and when such methods are of use and are trusted, is the biggest challenge. 2 Background concepts and terms We like to minimize confusion about the concepts and terms we use. In order to understand how we see the role of evaluation methods and tools in planning and decision making processes, we not only need to define evaluation methods and tools, but also define these processes. Since evaluation methods and tools are meant to support decision making, we define them as decision support systems. Therefore, we first discuss a model of the planning and decision making process, followed by a discussion about decision support systems (DSS), and finally we discuss evaluation methods and tools.

2.1 Planning and decision making process Decision making processes are diffuse non-linear processes where many people have different perceptions of what it is. A few definitions provide structure to these processes. First we need to recognize a decision when we see one. Second we have to have a model of decision making processes themselves to relate decision support systems to the processes. Third, decision processes may or may not lead to decision problem, and only if they do, evaluation methods are relevant. So we need to define decision problems. Finally, decision problems are surrounded with uncertainty for which the term scenario is used in various ways. We clarify our use of this term. Let’s start with the decision itself. A decision we define as a specific commitment to action (usually a commitment of resources) (Mintzberg et al., 1976). A decision process is a set of actions and dynamic factors that begins with the identification of a stimulus for action and ends with the specific commitment to action (Mintzberg et al., 1976). So the decision making process is a recursive process, i.e. a set of actions leading to a decision, i.e. a commitment to actions. Dynamic factors are what we will call “evidence” (Fig. 1). The decision process or decision making process, we use the terms interchangeable, can be divided into three non-linear (Mintzberg et al., 1976) semi-rational, or semi-irrational if you will, phases. They are phases of understanding a problem, finding alternative ways to resolve that problem, and choosing between these alternative ways. Simon refers to these phases as respectively the intelligence, design and choice phases (Simon, 1960). Figure 1 shows a more elaborated model of the decision making process that we are following (Sharifi and Rodriguez, 2002). A decision problem is defined as a situation where an individual or a group perceives a difference between a present state and a desired state (i.e. resulting from the intelligence phase) and where the individual or group has alternative course of actions available (i.e. resulting from the design phase), the choice of action can have a significant effect on this perceived difference, the individual or group is uncertain a priori as to which alternative should be selected (i.e. resulting to the choice phase) (Ackoff, 1981). So decision making processes that lead to a single solution do not carry a decision problem. Nor do processes that lead to an obvious best alternative among several alternatives. Paradoxically formulated by Heinz von Foerster: “Only those questions that are in principle undecidable, we can decide.”(Foerster, 2003).

Figure 1. Framework for the planning and decision making process. So decision problems are uncertain as to what are normatively the more important impacts. Hence what Ackoff calls “uncertain a priori as to which alternative should be selected”. But although decision makers (or policy makers as in Fig. 2) may be uncertain as to what are the more important impacts, they have a sense of control both of the actions they take and the importance they attribute to the impacts (as measured by indicators) these actions may have. What they can not control are the external influences. We will call the uncontrollable variables scenarios, which is different from many other definitions that will call any assumption a scenario.

Figure 2. Differentiation of policy options and scenarios in policy making process. (Engelen, 2000)

2.2 Spatial planning and decision support systems Amongst the many definitions of decision support systems we prefer the definition by Rauscher, which says that decision support systems are a class of computer systems that help a manager or decision maker, where human judgment is an important contributor to the decision making process and human information processing capacity limits this process (Rauscher, 1995). This definition is attractive, because it does not so much describe what DSS look like but suggests what is does (help in judgment and integrate information), and distinguishes as such clearly DSS from information systems. This is very important because easily every bit of information or every information system is called a decision support system, and if everything is a decision support system, then what is a decision support system? A more descriptive definition of what DSS mostly look like is given by Turban: “At minimum we can say: A DSS is an interactive, flexible, and adaptable CBIS [computer-based information system], specially developed for supporting the solution of a particular management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights. The most sophisticated DSS definition will add to this: DSS also utilizes models (either standard and/or custom-made), it is built by an iterative process (frequently by end-users), it supports al the phases of the decision making, and it includes a knowledge base.”(Turban, 1995) (Fig. 3.)

Figure 3. Components of a decision support system. (Turban, 1995)

From Turban’s definition it follows that we are not only interested in judgment, i.e. knowledge management, but also in data management and aggregation of information through models. In the case of web-based evaluation models, data management is of particular concern. Spatial decision support systems address spatial decision problems, using spatial data, spatial models, spatial knowledge management and dialogue management in which map representation is important. 2.3 Spatial multi-criteria evaluation Multi-criteria evaluation (MCE) is a decision support methodology in which alternatives are compared and evaluated through tree-like hierarchies of objectives and criteria. In spatial multi-criteria evaluation (SMCE) the alternatives are locations in the form of points, lines, areas or grid cells and therefore criteria could occur in the form of maps (Herwijnen, 1999) (Fig. 4). Appendix A gives a more detailed description. The appendix contains excerpts from (Boerboom, 2009)

Figure 4. Aggregation of standardized criteria maps, all scaled to the same utility scale with values ranging from 0 to 1. Depending on criteria priorities, different areas become more or less suitable in the overall composite index map.

Figure 5. Criteria tree and four different optional lay-outs of a national park in Northern Italy. Green colors are better and red are worse. (Zucca et al., 2008) ITC has developed an SMCE module in its open source ILWIS geographic information system (GIS) software, mainly because of dissatisfaction with existing implementations of the methodology in GIS software, which do not adhere to a good underlying concept (Appendix A), do not appreciate criteria tree structuring, nor do they allow for use of both spatial and non-spatial criteria. Obviously, decision problems rarely are about spatial criteria only. Figure 5 shows a criteria tree structure and both spatial and non-spatial criteria applied to selection of alternative national park lay-outs in Northern Italy (Zucca et al., 2008). The current desktop application has also been applied in studies on for instance, urban poverty mapping in India (Baud et al., 2008), regional road routing evaluation (Keshkamat et al., 2009), town and country planning (Boerboom et al., 2006), nature conservation (Geneletti, 2007), or flood management (Sharifi and Boerboom, 2006, Kheirkhah Zarkesh et al., 2005). We consider the combination of existing software and applications as a good starting point for development as outlined under the first section of this document.

3 Problem Statements The development of the new SMCE software is driven by the following observation of problems that can be classified into five groups. Although classified, several problems could have been grouped into different classes. We try to summarize each problem in a shorter term in grey font. A. Problems related to embedding in planning and decision practices. 1. DSS are not commonly institutionalized in decision making processes. Need of understanding institutionalization. 2. Decision makers sometimes show a kind of mistrust in DSS, considering it to be a black box with no transparency of how outputs result from inputs and the assumptions in the code of the software. Mistrust to DSS. 3. Decision makers consider their decision making as a private activity which may be (partially shared) with specific other actors. Transparency vs. privacy between actors B. Problems related to the nature of the decision making process 4. In the decision making processes evaluation questions arise in which people want to know in which location is the problem or opportunity (intelligence), in which location are solutions (design), and if we have solutions in different locations, which ones are the better solutions (choice) where substantial amounts of (spatial) information is considered. Need of spatial evaluation tools. a. Such evaluation is not based on monetary indicators only, but also nonmonetary. b. Decision making is not only based on spatial indicators, but also nonspatial or spatial metrics. 5. Decision processes are explained through political and public administration lenses (Zahariadis, 1998) and analyzed through such theories as Advocacy Coalitions Framework (Sabatier, 1991), Policy Arrangements Approach (Veenman et al., 2009), or Framing Theory (Nutt, 1998a, Curseu and Schruijer, 2008) 6. Collaborative decision making processes have and need a dynamic of periods of divergence and convergence. Divergence-convergence dichotomy problem. 7. Decision processes take place in different contexts (e.g. resources inputs, and time) and involve multiple actors (with different skills and background – culture, education). Variety of decision processes and actors. 8. Multiple actors (individuals or organizations) may have multiplicity of interests in evaluation that need to converge on decision outcome rather than interests. Finding convergence and compromise solutions. 9. It is not possible to apply different criteria trees to different areas within one project. Multiple criteria tree for multiple area problem. 10. It is not possible to compare evaluation by organizations that have different mandate areas to which different interests, policies, and evaluations apply. Spatial evaluations comparison problem.

11. Different actors have different criteria trees for one area. Multiple criteria tree for one area problem. 12. Decision makers find it difficult to consider uncertainty in the decision making process, yet it is relevant to avoid making the wrong decision. Sources of such uncertainty are in their judgment and in the facts, which leads to uncertainty of evaluation outcome. Uncertainty in decision making. C. User experience and user communication problems 13. Users may be remote or (partially) together. Multi-locality problem. 14. Analysis needs to be communicated with different users and their constituencies. Need of communication of analysis among actors. 15. After generating the evaluation result, decision makers need to understand how the interaction of scores, weights and uncertainties happen. Which factors have contributed to what degree to final outcome. Decision explanation problem. 16. Planning and decision processes are dynamic and require reports of the state of the evaluation. Because: a. Actors do not necessarily (want to) take notes. b. Actors leave with different understandings, for better or for worse. c. Communication is required with the people who were not available in the process. Capturing planning and decision process / multi-temporal problem. D. Data access/use problems 17. In many situations internet or web-data are not available. Data availability problem. 18. Spatial data can not be downloaded over internet because it is too heavy, bandwidth is limited or data is not shared. Data access problem. 19. Data considered in an evaluation may be heterogeneous in terms of format, type, size, ownership, source, and it is hard to process these data in a convenient manner. Heterogenous data problem. E. Changing requirements problems 20. Different users prefer, or are used to different methods in multi-criteria evaluation: a. In spatial aggregation (e.g. different spatial metrics), see appendix A. b. In thematic aggregation (e.g. weighted summation or Electre), see appendix A. 21. We can not expect that we develop the best algorithms for SMCE and other developers may have smart components. For instance calling a spatial metric for the spatial aggregation functions in resp. Path 1 and Path 2. Problem of using external services & resources, (by non-programmers). 22. Current software systems for SMCE are inflexible regarding the adoption of newly developed SMCE functionalities. Need of interchangeability of functionality, (by non-programmers).

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Development Objectives

Problems formulated in the previous section give directions for development of a collaborative web-based spatial multi-criteria evaluation software system. In this section we derive an objective from each of the problem statements. By no means these objectives can all be achieved within the ForeStClim project, however, basic architecture assumptions have to be known in order to avoid lock-in later leading to the impossibility of achieving some of these objectives later. Arguably we find the first and last group of objectives the most challenging. This is not a final listing. We hope discussions with task force members, the software development process, and application of software to practivces will lead to more, improved, and prioritized objectives. A. Objectives concerning embedding in practice. With these objectives we aim to establish trust and institutionalization of the system. Therefore the software system: 1. Should be developed as a socio-technical artifact, considering the variability of needs and regulations of organizations resulting from networks of individual actors and other artifacts (e.g. hardware systems, policies, etc.) 2. Should Provide insight in algorithms, the flow of collaborative and analytical processes in the software, and make development process and assumptions traceable and understandable. 3. Should be able to hide or expose the steps in decision making for privacy or transparency respectively (think of e.g. Google wave) B. Objectives to embed concepts and frameworks about decision making processes in general and SMCE in particular in the software system Therefore, the software system: 4. Should clearly refer to the different roles SMCE methodologies play in different non-linear phases of the (spatial) planning and decision making process, the phases of MCE, the pathways of SMCE, as explained in this working paper. 5. Should be seen as a tool that expresses belief systems, shapes discourse, is framed in and frames discourses and affects power relations. 6. Should follow a clear model that guides processes of divergence and convergence in collaborative decision making. 7. Should provide convenient user interfaces for different types of decision processes (e.g. the “quick and dirty” versus the more elaborate) and different kinds of users in (e.g. in terms of skill levels, languages, map/color reading experience). 8. Should facilitate tracing of different evaluation outcomes back to decision assumptions about interests, and indicate directions for “compromise solutions”.

9. Should facilitate mashups of different SMCE analysis in different areas, with clear reference to mandates, policies, rationales, and interests. (Think of e.g. tagging locations with considerations/arguments/discourses) 10. Should allow applying more than one (and different) criteria tree to evaluate alternatives for different areas. 11. Should allow applying more than one (and different) criteria trees to evaluate alternatives for one specific area. 12. Should provide methods and tools to assess spatial uncertainty and sensitivity involved in decision making and help user to consider other options when uncertainty and random errors are simulated. C. Objectives for User Experience & Communication Related Problems With these objectives we want to solve problems which cause inefficient humanhuman and human-computer interactions. Solution will lead users to a better experience & communication. Therefore the software system: 13. Should be able to be used simultaneously by multiple users in different locations. 14. Should provide a convenient and efficient communication mechanism for the users involved in planning and decision making processes 15. Should provide meaningful reports to demonstrate how decision makers have reached the outcome. 16. Should keep the history and the current state of the decision making process from different decision makers perspectives to help decision makers to communicate, keep track of changes and for developing a common understanding. D. Objectives for Data Access/Use Problems Wıth these objectives we want to solve problems regarding the input data of the decision support systems which can be addressed by using a proper architecture. Solutions should make the software functionality less internet dependent and data specific. Therefore the software system: 17. Should be usable (maybe not with full functionality) when users have local data or when they are not able to access remote resources (or when there is no access to internet/intranet). 18. Should provide services which can access data easily (such as services that reside on the data site) when downloading data over internet is not feasible. These remote services process data and return results to the users. 19. Should be able to process heterogeneous data by the aid of standards compliant services that expose those data sets using standard protocols. E. Objectives for the Flexibility and Changing Needs Problems With these objectives, we are seeking some solutions for the challenging problems of changing demands for the system and adopting new features. Possible solutions will provide sustainability and popularity for the software system. Therefore the software system:

20. Should provide flexible workflow of decision making process for different user needs and methodologies. Order of steps and the algorithms used in these step can be substitutable. 21. Should be able to access and use different algorithms/methods exposed on the web as soon as those services are compatible with the (OGC) standards that the software system complies to. 22. Should have an open architecture to adopt new functionalities. 5 Features and issues We are not yet in a phase where we can think of specific features to be developed. However, some features we may have to consider are database interface and security, multi-user evaluation and (a-)synchronous communication, We can already see that several issues will need to be explored. Eventually we will have to address all problems and objectives. However, at this early point in the process we see three issues emerge. 1) What are current practices of (spatial) evaluation of forest strategy and management planning in the forest management organization that are partner to ForeStClim? How would these practices shape software development under ForeStClim? We have seen for instance in Landesforsten use of a rule-based evaluation tools to establish forest stands in terms of habitat, production and recreation suitability. Possibly a similar role can be attributed to the ecological site classification tool used in the UK. What 2) What are the current tools in use for evaluation and what are their strengths and weaknesses? How would existing tools or tools under development shape software development under ForeStClim? Again, a web-based version of the ecological site classification tool is under development in Forest Research, and in Landesforsten a suitability analysis tool is being used. 3) What kind of database contents, policies, and ontologies exist? How would these database characteristics shape software development under ForeStClim? As mentioned earlier, the sharing comes in different forms, e.g. in the form of images with the general public, as the French Inventaire Forestier National does, or in the form of data shared through intranet as practiced in the Landesforsten Rheinland Pfalz Certainly this list will increase and become more specific after discussion in the task force.

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Appendix A

Excerpts from BOERBOOM, L. G. J. (2009) Avoid lock - in for development of online spatial multi criteria evaluation software. In: Proceedings of the 11th international conference on Computers in Urban Planning and Urban Management, 16-18 June 1009, Hong Kong, CUPUM 20th anniversary / ed. by A.G.O. Yeh and F. Zhang. Hong Kong : University of Hong Kong, 2009. ISBN 978-962-7589-0. 16 p. […] 2. DESCRIPTION OF SPATIAL MULTI-CRITERIA EVALUATION […] I have encountered a lot of mechanical use of the methodology. To name a few examples, default evaluation structures of economic, social and environmental concerns are used, whereas a decision problem may hinge around quite a different trade off. Or, only a list of criteria is used without any effort to explore structures. Or, evaluations are data driven classification rather than value driven evaluations. Or, the evaluation is focused on obtaining agreement on the value systems expressed in criteria trees rather than on the outcome of the evaluation. After all, different considerations about alternatives may lead to the same conclusions. The abundance of mechanical applications makes me prefer to formulate a description rather than a definition of the methodology, because a definition does not do justice to the art behind the methodology. As a matter of fact sharing the art, as little an artist that I am, is the biggest difficulty I am facing. It is like explaining a child what life is. You have to live it. I will therefore highlight some problems that can be encountered in the application of the methodology and in software. But most importantly I will make an overview of the models of SMCE using the figures in this section, that apply to different phases in the planning and decision making process and to different GIS data environments, i.e. the raster and vector environment. As you will see, it is rather complicated, but in my opinion the models are essential to make these approaches to SMCE intuitive to laymen. I would want to start by positioning SMCE in processes of planning and decision making. The difference between MCE and SMCE becomes apparent when we identify the alternatives in SMCE. I briefly have to go over the phases of SMCE, particularly given the fact that in current applications of SMCE in different GIS environments, no strong model of SMCE is being applied. I close the section with a description of implementation of SMCE in raster and vector environments. The practice of planning, problem solving, policy making or decision making, consists of a nonlinear and parallel sequences of recursive and iterative actions of searching for solutions, understanding a problem, coming to action and building up argumentation and persuasion. I purposefully place these activities in seemingly odd order and avoid the use of “cycles” or “phases” (Dunn, 2004) that suggest an ever

presence control and order, whereas the practice is much more complex (Parson, 1995). Individual decision makers are bounded by limitations of information acquisition and processing in rational choice (Simon, 1991). Decision processes show backtracking and interrupts (Mintzberg et al., 1976). Policy advocacy groups with different policy beliefs broker decisions (Sabatier, 1991). The different lenses of rational choice, advocacy coalitions, policy streams can be shown to provide complementary insights (Zahariadis, 1998). Different evaluation tactics (analytical, bargaining, subjective, and judgement) and their success in decision making are well illustrated and discussed by Nutt (Nutt, 1998b) using 317 real decisions with different degrees of complexities. In these non-linear and parallel sequences of action, moments may arise when, for whatever reason, alternatives need rational judgment, comparison and ranking. Multi-criteria decision methods are an approach for such judgment, comparison and ranking. The family of multi-criteria decision methods (MCDM) consists of two branches, the multi-attribute decision methods (MADM) branch and the multi-objective decision methods (MODM) branch. In the MADM branch a finite number of alternatives are evaluated, and multi-criteria evaluation (MCE) methodology belongs to this branch, whereas in the MODM branch an infinite number of alternatives are evaluated towards optimality. Unlike MCE, which is most often associated with ranking of alternatives only for the purpose of choice, spatial multi-criteria evaluation (SMCE), also is concerned with the ranking of alternatives for the purpose of problem understanding, e.g. environmental impact assessment, or planning, e.g. suitability evaluation or strategic environmental assessment. SMCE compares and ranks spatial units within maps, i.e. raster cells, polygons, lines or points, as well as the layout differences of spatial units between maps as wholes, or in other words, their relations to the space around them. Let me illustrate the different approaches to SMCE both in raster and vector GIS environments. First I discuss raster environments, followed by vector environments. Multi-criteria evaluation is methodology to make judgment about choice alternatives through explicit hierarchically organized sets of objectives (Fig. 1). The methodology is distinguished by five different phases. They are evaluation problem structuring, standardization of criteria with different units through value functions, prioritization of criteria, aggregation of standardized values, and analysis of uncertainty and sensitivity.

W1

Objective 1 Criterion 1

W2

Standarized dimensionless data

Original data in different units

Planning evaluation goal

V-Fn

Objective 2 W2,1 Criterion 2

V-Fn

Criterion 3

V-Fn

W2,2

* W1

* W1 * W2,1

* W1 * W2,2

+ Alternative 1 Alternative 2

Alternative 3

Figure 1. Generation of alternatives using SMCE in raster environment. “W i” are weights. V-Fn refers to value function. In the case of raster maps the alternatives are raster cells. A tree-like hierarchy of objectives is established. For each objective one or more criteria factually assess the degree of achievement of the objective. The tree is also referred to as value tree (Borcherding and von Winterfeldt, 1988). In the case of SMCE, maps represent these factual assessments i.e. original data, for (some of) the criteria (Fig. 1). To my knowledge none of the GIS applications allows structuring of objectives and criteria. After evaluation problem structuring and factual assessment, be it quantitative or qualitative, these assessments are evaluated through value functions (Beinat, 1997). These value functions rescale the different criteria units into a dimensionless scale, also referred to as standardization (Fig. 1). But more importantly value functions are expressions of interpretation of data. For instance legal constraints differentiate allowable from unallowable alternatives. Criteria in policy directives differentiate the more desirable from the less desirable alternatives. Value functions also express for example diminishing marginal values, and risk avoidance or risk taking behavior differentiating the regrettable and opportunistic alternatives. Raster and vector maps consist of values or classes. Different approaches to standardization exist for each. (Beinat, 1997, Voogd, 1983). Standardization in GIS environments is not always carried out in correct ways. Sometimes utility scales are automatically used relative to a map’s values instead of an absolute scale e.g. from 0 to 1. Or different value functions are applied to the different maps of a criterion that assess performance of different layouts of alternative plans (Fig. 2) Given interpretation of assessed data, priority is assigned to different criteria. Of course, if standardized values of a criterion do not differ substantially between alternatives, the criterion does not discriminate the alternatives well. Although a criterion may be considered important, if it does not discriminate and therefore is not likely to receive a lot of priority. Different methods exist to prioritize criteria (Voogd, 1983).

Standardized and prioritized maps are then aggregated. A simple form of aggregation is the weighted summation of maps (Fig. 1). Here too alternative methods exist (Roy and Vincke, 1981). Two problems can be encountered. First, it is not always clear how different software packages multiply the different standardization scales they use for different criteria with the criteria weights. Second, as a matter of principle, poor performance of an alternative on one criterion can be compensated by good performance in another, unless non-compensatory constraints are imposed. However, somehow most GIS students I work with and I suspect many practitioners find it difficult to think in terms of compensation. They rather think of masks and overlays, boolean operators, black and white. Finally, a ranking of alternatives is only certain if the criterion scores, priorities, value functions and/or constraint are certain and if all different evaluation methods give the same ranking. Considering uncertainties in criterion scores, standardization, as well as prioritization, rankings of alternatives can change. Uncertainty analysis gives the probability that each of the alternatives ranks in each of the rank positions. Sensitivity analysis gives the nearest weight vector or assessment score values where rank reversal takes place. This much for the summary of the five different phases in the methodology of MCE. In all five phases different assumptions can be made because different actors may make different judgments. I referred to this as multiplicity in the introduction of this paper. Therefore if SMCE is used for planning purposes (Fig.1) different spatial alternatives are likely to result. These alternative layouts, plans, solutions represented in maps, in turn can be evaluated using SMCE. Van Herwijnen summarizes this evaluation in two pathways (Fig. 2). Either spatial impacts are spatially aggregated (SA) to single values (dots). Then multi-criteria analysis (MCA) is applied to obtain a ranking of alternatives (Path 1). Alternatively, in Path 2 MCA is applied directly to the objects in a map (raster cells, polygons, lines or points). Finally, Path 3 refers to the straight interpretation of map series to come to a ranking of alternatives as it is done without multi-criteria analysis. Although representing the essence of SMCE, Fig. 2 overlooks several problems. First of all, it suggest, against van Herwijnen’s intention, that results of the two pathways are the same. Secondly it suggests that criteria maintain the same meaning as they are spatially aggregated, but they don’t and therefore qualify

Figure 2. Two paths to rank alternatives with spatial impacts (adapted from source: (Herwijnen, 1999). objectives differently and possibly even qualify different objectives. Thirdly, the diagram ignores that rarely evaluation is done on the basis of maps only. Usually non-spatial criteria such as the costs of different alternatives are to be considered as well in the evaluation. Unlike a raster map, a vector map has an attribute table associated to it. It means that MCE can be performed right away on the polygons, lines or points through that attribute table (Fig. 3). For instance the GeoChoice software application operates in this mode, as well as the currently available web-based applications of SMCE. This works well for situations where objects to be chosen from do not change locations. i.e. the layout remains constant.. The evaluation between different (planned) layouts of objects and their spatial impacts is more complicated (Fig. 4). The different impacts have to be crossed leading to a map of Uniform Analysis Zones (UAZs) before again an MCE is applied to these UAZ’s. The What if? software application is an example of this approach. But the real problem is about how to aggregate utility from the maps that result from the MCE analysis, because what is the meaning of a small utility of a large polygon and/or line, and a large utility for a small polygon and/or line in the different plans that under evaluation. It is a necessary step to compare maps, but this question needs to be explored further. Another problem is that standardization of a criterion between different maps for different alternatives should occur considering all criterion values in all maps.

Figure 3. Evaluation of objects within a vector map inside a single map and table.

Figure 4. Evaluation of objects between different alternative Uniform Analysis Zone vector maps and tables. Many choices can me made of how to implement SMCE. The best illustration is the number of desktop and on-line implementations to be discussed in the next section. For none of the existing applications discussed in this paper it was made explicit what the inscribed assumptions of the approaches followed are. However if we look at the practice of the forest service evaluation models many inscriptions exist. The challenge is to understand and negotiate these inscriptions.

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