ARTICLES
IMPROVING SPATIAL DECISION SUPPORT SYSTEMS METHODOLOGICAL DEVELOPMENTS FOR NATURAL RESOURCES AND LAND MANAGEMENT Hedia Chakroun, Researcher, Institut National de Recherche en Génie Rural, Eaux et Forêts (Tunisia) Correspondance to Hedia Chakroun:
[email protected] Professor Goze Bertin Benie, Centre d’Application et de Recherche en Télédétection (Canada) Correspondance to Goze Bertin Benie:
[email protected]
The use of geographic information systems (GIS) in the past two decades helped in formulating and solving spatial decision-making problems. In spite of their huge capacities in the acquisition and the storage of spatial data, GIS have some limits when it is a matter of solving semi-structured problems that represent most real-world spatial decision cases. The improvement of GIS analytic capacities can provide support required in multiple decision-making phases. We use advanced spatial analysis techniques applied to raster data representing a set of constraints that may be encountered in a land management project. Digital maps and a digital elevation model (DEM) have been used to produce the constraint spatial database for the case study. Each spatial feature had been subject to an evaluation process and a utility value was given to represent its tolerance to the management project according to the constraints identified previously. Results obtained from this methodology have been compared to conventional cases of suitability mapping from the original set of constraint maps. Results show that suitability maps for the management project derived from this study represent multiple scenarios leading to the improvement of the design and choice phases of decision-making process.
1. INTRODUCTION Nowadays, land managers and decision makers are daily faced with problems related to a growing development under a constant diminution of resources; they are frequently encountering planning issues characterized by the complexity of interactions between environmental and socioeconomic systems. This complexity is essentially related to the diversity of decision alternatives and their variability in the space, the diversity of criteria nature (some may be qualitative while others may be quantitative), and the fact that decisions are often surrounded by uncertainty. For these reasons, the availability of powerful tools should help to afford a flexible problem-solving environment in which problems can be explored, understood and solved under the multiple conflicting objectives. In the past two decades, the use of spatial information technologies, especially remote sensing (RS) and geographic information systems (GIS) had widely assist managers in their work. However, more technical expertise is required to improve the decision-making process. Indeed, in spite their huge capacities in the acquisition and storage of spatial data, GIS have some limits when it is a matter of solving most real-world spatial decision problems. Hence, improvement of spatial information integration in decision-making support is becoming a necessity under the multiple challenges facing decision makers who are supposed to present efficient solutions under many forms of pressure like demographic and urban growth and scarceness of earth resources. This is the principal aim of the present work where we explore the effects of interfacing spatial analysis tools within the decision- making environment. We begin by a review
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of decision-making process in a spatial context. Next, we present a methodological framework designed to improve the decision making process in a land planning project.
2. DSS AND SDSS: A REVIEW 2.1. DEFINITIONS Decision Support systems (DSS) have been developed in the early sixties for business applications (corporate strategic planning, scheduling of operations, etc.). Simon (1960) suggests that any decision-making process can be structured into three major phases: intelligence, design and choice. According to Malczewski (1997), the “Intelligence phase” involves searching or scanning the environment for conditions calling for decisions. The “design phase” involves inventing, developing, and analyzing a set of possible decision alternatives for the problem identified in the intelligence phase, and the “Choice phase” involves selecting a particular decision alternative from those avail able; each alternative is eval uated and ana lyzed in re lation to oth ers in terms of particular decision rules. There are interactions between the phases of a decision making process as it will be explained later (section 4.1). A Spatial Decision Support System (SDSS) has the main characteristics of a DSS. In addition, it should be adapted to the specificity of spatial data. Densham et al. (1994) defined a SDSS as a geoprocessing system designed to support the decision research process for complex spatial problems. SDSS are also defined as conceptual framework that assists decision makers in solving complex spatial problems. Hence, a SDSS has to provide input for spatial data and to allow storage of complex structures common in spatial data. This kind of system should also include analytical techniques that are unique to spatial analysis and produce outputs in the form of maps, reports, charts and other spatial forms. 2.2. PRINCIPAL COMPONENTS OF SDSS Many authors have given definitions to what a SDSS should contain. These definitions may be gathered into three components represented by Figure 1. The Data Base Management System (DBMS) integrating functions to manage spatial and attribute data. In a spatial context, the DBMS is inherent to the Geographic Information System (GIS). The Model Base Management System (MBMS) containing functions to manage models. The efficiency of this component is related to the analytic capabilities of the SDSS. The linkage between the DBMS (in the GIS) and the MBMS may be a weak coupling based on the exchange of data between DBMS and MBMS; or a strong coupling where models are embedded within GIS or vice versa (Batty 1995). The Dialog Generation and Management System (DGMS) that manages the interface between the user and the rest of the system. One of the most important characteristics of a SDSS is to support users while solving semistructured or ill-structured spatial decision problems (Figure 1). According to Simon (1960), decision problems fall on a continuum ranging from completely structured to unstructured decisions: the first ones occur when the decision-making problem can be structured either by the decision maker or on the basis of relevant theory, whereas the second ones must be solved by the decision maker without any assistance from a computer (they are non programmable).
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Most real-world spatial decision problems, are somewhere between these two extreme cases: they can be solved partially by the use of computer programs and the final solution is generally given by the intervention of the user or from a negotiation of a group of actors; this is the area where the SDSS concept has major applications.
Figure 1. Principal components of SDSS Adapted from Malczewski (1997)
3. CHALLENGES IN SOLVING SPATIAL DECISION PROBLEMS Impact studies for infrastructure projects such as dams or mountain lakes, the determination of suitable areas for waste deposits or the selection of the best network for water alimentation or power lines are examples of projects where environmental, technical and economical constraints could be advantageously integrated into a GIS by means of appropriate integration models. The multiple actors implicated in such projects use their expertise and knowledge to affect priorities, defined also as scores, to spatial entities that are subject to an alteration by the project or that represent a potential area to improve the project. For instance, when it is a matter of choosing the best site for a culture, the terrain slope is an important factor to consider in the study; thus the agriculture engineer uses previous knowledge to determine a kind of a mathematical functionrelating slope to a suitability factor. This process corresponds to the elaboration of evaluation models whose formulation is necessary in the SDSS. The use of spatial information technologies affords large possibilities in formulating evaluation models inside the SDSS. Many factors are improving the capacities of using GIS as principal components in SDSS. Improvement of data capacities storage either by compression techniques
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or by hardware capacities increase makes it relatively easy the update of territory information. Else, the availability of high-resolution satellite images makes them a reference tool in updating land use and landscape evolution. These factors combined with accessibility to huge amounts of data via the World Wide Web make spatial information available for the integration into land planning and natural resources management. However, this is not sufficient to explore all the possible solutions for a given problem under the diversity of constraints and the multiplicity of interest groups. For example, in the case of land management projects implicating numerous constraints that may be represented by spatial layers, digital maps overlay, widely used for its simple conceptual bases, may lead to very restricted suitable areas because of the binary rigidity (a spatial site is either accepted or rejected). Limits of commercial GIS are then easily highlighted when it is a matter of complex decision problems related to land management characterized by numerous actors and points of view. Then, more research and development efforts should be made to explore new tools to evaluate theoretical alternative scenarios for territory management projects and to improve the interaction among the members of the project (decision makers, community members, associations interested in the effects of the project, etc.). In the next paragraph, we present an approach having as objective to provide tools for the user to help him in the exploration of the space solution of his planning project.
4. METHODOLOGICAL APPROACH Most of land planning projects and natural resources management use land suitability assessment whose objectives are to maximize economic efficiency and to minimize environmental impacts. The general process of land suitability assessment may be divided into the following four steps:
1. 2. 3. 4.
identifying and mapping land use, environmental and technical impacts on separate maps; constructing several combinations of maps based on priorities determined by the evaluation process; deriving suitability; making choice by decision maker(s).
4.1. FRAMEWORK DEVELOPMENT The SDSS elaborated in the present study is given by Figure 2. The first step consists in gathering multi-thematic data representing principal constraints identified by the project actors. These data may derive from existing spatial database (analogical and numeric) or may be produced for the study. For example, remote sensing data, field surveying and GPS technologies constitute multi-source data acquisition. Studies, statistics and expertise reports describing the management problem represent knowledge and information that are generally combined with spatial data in order to elaborate spatialized evaluation model that will help the analyst in the formulation and the resolution of the problem. All the expertise and the previous similar impact projects are gathered in knowledge base. In the case study section, we will present a description of the evaluation model used for the development of the project knowledge base.
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Figure 2. General framework for SDSS in a planning management problem
The knowledge base elaboration consists in translating expertise and information concerning the planning project into priorities or scores. The score is a numerical value resulting from an evaluation process (Voogd 1983); it may be a qualitative or a quantitative entity. In some cases, priorities are expressed in the form of mathematical functions called “utility functions”. The elaboration of consistent database and flexible interface is an important step in the intelligence phase of the decision process because it allows the analyst to refine the definition of his problem’s principal components. Also, the capacity of the system to incorporate new data and updated information is very important for the implementation of open SDSS. In the design phase, the user needs to explore the solution space (the options available) by applying multiple models inside the system to generate a series of feasible alternatives to his problem. At this stage, it is important to extract the potential solutions or alternative for solving the problem. Thus, we make use of spatial analytic integration tools, which were developed in previous research in order to improve the design phase of the decision process. 4.2. CONTEXT-ORIENTED INTEGRATION OF MULTI-THEMATIC DATA Our methodology has been basically inspired from the research trend calling to replace conventional perception of territory represented by multi-thematic layers overlaying available in all commercial GIS. Thus, we made the following hypothesis: considering spatial context in the formulation of suitability model leads to the elaboration of more potential scenarios that afford more feasible alternatives. We suggest a new concept of multi-thematic maps integration inside the SDSS based on the development of a context-oriented analysis. In the following sections, we first give a general formalism of suitability spatialization process, next we illustrate the method-
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ology by a simple computation example and finally we explain the adaptation of the framework to a Voronoi diagram structure. 4.2.1. FORMALISM OF SUITABILITY SPATIALIZATION
Let CI denote a constraint spatial feature map set, n is the number of distinct spatial elements in CI. Let UI denote the corresponding utility map: each input feature from CI set is associated with a utility value derived from a knowledge base produced by experts. For example, if CI set is a land-use map produced from a classified satellite image, then the number of classes is equal to n; each class is given a utility value that reflects its sensitivity to the planning project. Interaction among these sets is symbolized by: CI -------(UFI)-----------> UI -----------------(SFI)-------> SI ci----------(UFI)-------> ui = UF( ci) -----------(SFI)---------> si = SFI (ui) CI : Constraint map number I ci : a class feature belonging to the constraint map CI UFI : Utility function applied on constraint map CI UI : Utility map number I ui : utility value corresponding to ci SFI : Suitability function applied on UI SI : Suitability Map number I si : suitability value for class ci. Utility function (UFI) is formulated according to the expertise stored in a knowledge base. Suitability function (SFI) translates utilities into suitability values producing a suitability map (SI) where the study area is classified from the least to the most suitable for the planning action.
Figure 3. Suitability modelisation using spatial analytic capacities
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If the constraint map is in a raster format where X is a cell, X belongs to a unique constraint class ci and has a unique utility value ui, thus it is possible to compute its suitability. In Figure 3 we show two ways of integrating multi-thematic constraints maps:
1. 2.
the first model is a conventional one and it is based on an algebraic suitability computation model; the second model is a regionalized model where context has been integrated in the suitability computation process.
Formulation of suitability function is expressed as the simple standardization of utility value relatively to all other utility values. For example, see Equation 1.
Equation 1.
If cell X is within a context where there is only m features classes (m < n), then we formulate model 2 suitability computation by Equation 2.
Equation 2.
In spatial management and planning problems, constraints are represented by several maps. If we have N constraint maps, the total suitability of an X cell is calculated as the average of all X suitability values. See Equation 3.
Equation 3.
A computation example is given by Figure 4. In this example, we consider a landuse map composed of four classes (forest, agriculture, urban, recreation) as shown in Figure 4(a). For each class a utility value had been affected (Figure 4(b)). The study area was divided into two parts called context A and context B (Figure 4(c)). We choose two cells XA and XB belonging to forest class, XA is within context A and XB is within context B. Suitability computation by models 1 and 2 highlights a different suitability value found by the context oriented model when compared to conventional model. This spatialization consists in the adaptation of utility functions to the context inside which they are being applied.
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Figure 4. Computation example of suitability values by model 1 and model 2
4.2.2. SUITABILITY SPATIALIZATION IN VORONOI DIAGRAMS
The development of a context-oriented integration tool had been the object of research work in the field of advanced spatial analysis methodologies (Chakroun et al. 1997; Chakroun et al. 2000 and Chakroun et al. 2003). The summary of this work is as follows: we have considered the local neighbourhood defined inside a Voronoi structure (Voronoi, 1908) which is a neighbourhood framework well adapted to raster and vector maps. See Figure 5.
Figure 5. Voronoi diagram
For more details about this choice justification, refer to Chakroun et al. (2003). The “context” illustrated in Figure 3 is then defined by the neighbouring structure of this diagram by overlaying it on the study area in order to subdivide it into contiguous regions called context. The Voronoi diagram structure offers many ways to consider the context:
• • •
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the context is the space within each polygon; the context is the first order Voronoi diagram of a given polygon; the context is the second order Voronoi diagram of a given polygon.
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For each context, utility functions may be adapted and, consequently, derived suitability maps are different from each other. The main implication of such spatialization is the definition of locally utility functions adapted for each situation; as a consequence, for the same spatial feature, its suitability is not only dependant on the absolute utility value but also on the context where the feature belongs. The study case presented in the next section explains how it is possible to explore the effect of the context-oriented modelisation of suitability and its implications on spatial decision process, especially the improvement of solution space exploration that is essential to accomplish in the choice phase of a planning project. 5. CASE STUDY The developed approach has been tested in the research of appropriate zones for power line implementation in a study area in southern Quebec (Canada). This is a typical spatial planning problem where constraints represent spatial features sensitivity from environmental, technical and economical points of view. The public institution responsible for the implementation of power lines made an impact study where the multiple effects of the project had been examined (Hydro-Quebec 1990). We use this study to apply the SDSS described above (Figure 3). 5.1. SPATIAL DATABASE DESIGN Spatial features identified as the most sensitive to the project in the impact study have been gathered by categories; next a raster map was produced for each constraint. Table 1 summarizes the maps, their categories and the utility values associated with each spatial feature (explanation of utility determination is given in section 5.2). Landuse, agriculture, particular areas and urban maps were produced from the digitalization of existing paper maps. Relief and landscape constraint maps were derived from an existing digital elevation model (DEM). The slope of a terrain generates a technical constraint for the implementation of pylons. The DEM of the study site has been used to calculate the slope by applying derivative operators in the x and y directions and computing the magnitude of the resultant vector. The DEM was also used to elaborate a visibility map because it is important to determine the landscape alteration by the project. Computation of visibility was as follows: the highest points in the study area were identified, then we compute for each cell the number of points from which the cell may be seen. Spatial resolution of constraint project raster maps was unified to 100x100 meters. We build the spatial database for the project in the raster format inside a GIS. Figure 6 shows the six raster maps elaborated for the study.
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Table 1. Principal components of constraint spatial database
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Figure 6 (a–b). Raster maps used in the study
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Figure 6 (c–d). Raster maps used in the study
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Figure 6 (e–f). Raster maps used in the study
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5.2. KNOWLEDGE BASE The knowledge base allows the user to feed the SDSS with all the expertise concerning the project. As mentioned above, the existing “Hydro-Quebec” impact study was the model applied in this research. The main purpose of this model was to characterize each spatial feature encountered in the study region by a value reflecting its resistance to the power line project. Two steps helped in the formulation of this resistance:
• •
the evaluation of project impact on the spatial feature on the basis of three impact levels (high, medium, low); the intrinsic value of each spatial feature based on the evaluation of its scarceness, its importance and the legislation. This results in five value levels.
An evaluation matrix relating impact levels and intrinsic values was elaborated by the experts; thus for each spatial feature, a resistance value has been affected on an ordinal scale. We use this model in the present research by expressing resistance values into utility function values shown in Figure 7. Since data are multi-thematic and represent a diversity of spatial features categories, we note that scales representing these data vary from nominal scale (landuse, agriculture, particular areas and urban maps) to ordinal scale (visibility) and cardinal scale (slope).
Figure 7. Utility functions applied elaborated from the evaluation process
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Utility functions are expressed in terms of cost: a spatial feature that does not represent a strong resistance to the project obtains the least utlity values. Thus, our objective is to highlight spatial areas with the weakest utilities in all constraint maps because they represent the most suitable areas for the project. 5.3. SUITABILITY COMPUTATION Models described in section 4.2 and represented in Figure 3 have been used to produce suitability maps from the integration of constraint maps set. Model 1 (cell-based algebraic model) was applied on utility maps by the way of normalization (Equation 1) and average suitability computation (Equation 3). Application of model 1 is done by means of layers algebraic combinations available in all commercial GIS raster format. The application of model 2 designed as the cell-contextoriented integration in Voronoi diagram structure, takes advantage of a stand-alone “C” program integrating multi-thematic raster maps by considering the contextual information in the calculation of suitability. We briefly describe the basic stages of model 2 program (detailed algorithms of model 2 are described in Chakroun et al. 2000 and Chakroun et al. 2003). The context is defined inside each polygon: first, categorical spatial features are identified, next suitability functions are computed. The algorithm was designed to check the homogeneity level inside the polygons. On the light of this test, polygons with high heterogeneity are divided and homogeneous polygons are merged together. We applied the developed algorithm to our case study: the plan of the six constraint maps was originally partitioned into 1500 Voronoi polygons whose generators were chosen randomly. We made an algorithm configuration so that the user can choose to compute a suitability map for each partition.
Figure 8. Voronoi diagram segmentation of map resulting from the summation of the six utility maps
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Figure 8 illustrates subdivision of the study area into polygons. Both model 1 and model 2 compute suitability values on a cell base. Next, inside each polygon, an average suitability value is computed. 5.4. ANALYSIS Generic suitability maps produced from model 1 and model 2 are elaborated for a 200 polygons partition; their statistical distribution are illustrated by histograms of Figure 9.
Figure 9. Representation of model 1 and model 2 suitability variation
The main difference observed between the two charts is the dynamic of suitability values variation in model 2 that is very important compared to model 1. The example given at the end of section 4.2.1. shows that for the same spatial feature, the context-oriented model led to two different suitability values. This explains the wide range of possible suitability values from model 2 compared to model 1. For comparison purpose between the two models, we have made a reclassification process of suitability values calculated by model 1 into four classes: very high, high, moderate and low. Thus, we may represent model 2 suitability values within these four classes. This representation is illustrated by Figure 10. This crossed histogram reflects the fact that for a single coarse suitab-
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ility class resulting from model 1, the context-cell-based model allows its refinement into much more suitability classes.
Figure 10. Crossed histogram of model 2 suitability within model 1 suitability
Context-oriented suitability computation is more than a combination of all utility values from constraint maps, it contains also a supplement information which is a spatial information reflecting the interaction of the cell with its neighborhood. Indeed, the knowledge base represented by the utility functions expression is being “spatialized” by the adaptation of suitability computation to local context. In terms of scenario diversity, this result may be very interesting: the rigidity of simplistic algebraic layer combination is compensated by the integration of a contextual information. Concretely, this means that computation of spatial area suitability is being related to interactions between spatial features located in this area. Thus, we may obtain as many scenarios of suitability as the partition space schemes since each partition represents a background for a new potential alternative that would not be identified if the contextual dimension were ignored. 5.5. IMPROVEMENT OF CHOICE PHASE 5.5.1. PLANNING MODEL: POWER LINE PATHS
The SDSS applied in the present study (Figure 3) takes advantage of the development of the spatial analytic integration tool described above. We showed in the previous section how the context-oriented suitability model leads to a wide variety of scenarios that improves the design phase of spatial decision process. As a consequence, the choice phase will also be improved because each suitability scenario generates at least an alternative for the planning project. Indeed, the potential number of alternatives may be higher than one since for each partition scheme, it is possible to consider different levels of Voronoi neighbourhood (Figure 5). Thus, the analysts have to make a choice from a largest solution space.
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In our case study, the objective is to determine the least cost power lines path from technical, environmental and economical points of views. For this purpose, we applied the suitability developed approach in order to calculate impedance maps that are integrated in the research of the least cost paths. The raster version of the shortest path algorithm (Dijkstra 1959) has been used so that for each suitability map scenario, it is possible to determine a path and to make an evaluation according to the constraints fixed in the beginning. The least cost-path model represents here the planning model in Figure 3. For each suitability scenario, it is possible to compute a power line path. Thus, a decision table known in multicriteria analysis systems may be integrated into the SDSS to make the choice of the final solution more systematic and to help the analyst in the feed back process (Figure 2). Indeed, feed back process is essential in a SDSS if we take into account the fact that ill-structured problems are generally subject partially or totally to negotiation between the multiple actors. 5.5.2. INTEGRATION OF MULTICRITERIA EVALUATION PROCESS INTO SDSS
The potential solutions for power line paths are derived from the application of Dijkstra shortest path algorithm applied on each suitability map. Thus, for the same partition of the study area into Voronoi polygons, we may calculate two impedance maps, one for each model (model 1 and model 2). Next, we make the evaluation of obtained paths seeking the determination of least cost ones. To achieve this objective, we use a multicriteria decision making technique (MCDM). The elaboration of the use of this technique in SDSS is beyond the scope of this work. Many studies have already dealt with this issue, especially the adaptation of MCDM to GIS context; see, for example Carver (1991), Pereira et al. (1993), Jankowski (1995), and Jankowski et al. (2001). As it is well known, an MCDM process has three components:
• • •
the definition and formulation of criteria; the generation of possible choices or alternatives; the evaluation of each alternative in the light of multiple criteria.
The last component needs the choice of an evaluation technique from a wide variety of existing techniques (refer to Vincke, 1986). The evaluation of paths obtained by different spatial partition should be done according to the spatial criteria represented by original maps used as input and reflecting the essential spatial constraints to the implementation of power lines. The other criterion that should be considered is the monetary cost of the path, which is directly influenced by its length. Paths calculated from each partition of the plan maps are characterized by a score cost (S) resulting from their superposition to each one of the six raster maps ((S) equals the product of the spatial feature score value by the area of the spatial feature crossed by the path) and the path length (L) that may be is expressed in length distance. The principal stages of the evaluation process are illustrated in the flow chart of Figure 11.
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Figure 11. Flowchart choice phase in a SDSS applied to power line implementation
5.5.2.1. DESCRIPTION OF AN MCDM TECHNIQUE: IDEAL POINT TECHNIQUE
The IP technique known as compromise programming is based on the comparison of alternatives to an ideal alternative by some measure of distance (Goicoechea et al. 1982). The ideal point is a hypothetic alternative with the least scores observed in all criteria. All available alternatives are ranked according to a multidimensional distance to the ideal point. Zeleny (1982) gave a general expression of such a distance: see Equation 4.
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Equation 4.
E(I,J): effectiveness values corresponding to the standardized score cost or length of alternative I for criterion J; E*(I,J): effectiveness value for the «ideal» alternative WJ: weight of criterion J NC: number of criteria E*(I,J) is the «ideal» alternative whose scores are the minimum ones observed from all the alternatives on every criterion. The p factor is an integer number, which determines the degree of compensation between criteria. Pereira et al. (1993) give the following explanation for the effect of p factor: for example, when p = 1, there is a total compensation between criteria, (a decrease of one unit on a criterion is totally compensated by an increase of one unit in another criterion). The more p increases, the less compensation is observed between factors. For a large value of p there is no compensation at all between criteria. 5.5.2.2 RESULTS AND ANALYSIS
We derive shortest paths from weight maps corresponding to 18 schemes of study area partition into Voronoi diagrams (190 to 20 polygons with a 10 polygons step). For each partition, two weights maps are calculated from models 1 and 2. The effectiveness value (E(I,J) in Equation 4) for each alternative (path) is being calculated. Details of these computations are given in annex. Since there is a compromise between the path score cost and its total length, we suggest to study three cases of criteria weight affectation (generally obtained by a consensus between the multiple actors of the project):
•
• •
Case 1: the cost in terms of score is weighted in the same way than the length of the path (in this case, each spatial criteria has a weight equal to 0.5/6 and the length has also a weight equal to 0.5; total sum of weights equals to 1); Case 2: the spatial criteria have much more importance than the length (each spatial criterion has a weight equals to 0.9/6 and the length has a weight equal to 0.1); Case 3: the length has much more importance than the raster maps (each raster map has a weight equals to 0.1/6 and the length has a weight equal to 0.9).
Standardized effectiveness values (table 3 in annex) have been used in the calculation of the parameters of the IP technique. The ideal point or alternative is determined from table 3 in annex as the least value observed in each criterion [E*(I,J) = (0; 0.463; 0.439; 0.515; 0.462; 0.436; 0)]. We compute the distance dp for three critical values of factor p (p = 1, p = 2 and p = 10). Results are reported in table 4 in annex. Next, for each partition scheme, we calculate the difference between the distances dp for alternatives obtained with model 2 compared to alternatives obtained with model 1.
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Figure 12. Distance difference to ideal point between alternatives obtained with model 2 and model 1
Figure 12 summarizes the results represented as cumulative area of the dp difference. When the path length criterion is as important as the spatial criteria or more important, alternatives obtained by model 1 are costless than those obtained with model 2. This last model gives costless alternatives when spatial constraints are highly weighted than the length. In this latter case, the
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compensation effect introduced by the p factor is more perceptible: when there is total compensation between criteria (p = 1), most of alternatives resulting from model 2 are closer to the ideal point than those resulting from model 1. As the compensation factor increases, the distance difference between the two sets of alternatives decreases. In summary, this multicriteria analysis shows two major results: The improvement of choice phase of decision process was verified since we have obtained alternatives that are substantially different from those obtained without integration of contextual information. Context-oriented suitability mapping led to more advantageous solutions (costless paths) when we give more importance to spatial criteria traducing environmental and technical constraint than economic criteria (path length).
6. DISCUSSION AND CONCLUSION The principal scope of the present work was the development of a methodological approach to improve decision-making process in land planning and natural resources management. We integrate tools elaborated in the field of advanced spatial analysis in a spatial decision support system. We showed how it is possible to interface these tools within the different stages of SDSS process. The developed methodology was applied to the problem of suitability assessment because many planning and resource management problems use this information support in negotiation between the multiple actors and as a support for representing feasible solutions. The practical case study was based on an impact study where multiple environmental, technical and economical constraints are present. Combined with multi-thematic spatial data, these constraints were tarnsformed into spatial evaluation database used as input for suitability computations models. These models are the principal component in the improvement of SDSS design and choice phases. Two major results have been obtained by the present research: The elaboration of context-oriented suitability model characterized by the adaptation of conventional algebraic models to the local context. This led to the improvement of the SDSS design phase by adapting utility functions to the local context where they are applied. Thus, numerous potential solutions have been added to the solution space; these solutions represent the bases of elaborating new potential scenarios for the planning or management studied project. The context-oriented suitability applied to least cost power line research highlights more advantageous solutions than conventional suitability model. This had been proved by a multicriteria evaluation process. Although the huge possibilities offered by the developed approach, it is clear that the adaptation of utility functions to local context is highly affected by the definition of the context itself. For example, if we consider the first-order Voronoi diagram, the solution space may be totally different than the case where second-order neighborhood is considered. Besides, the partition schema of study area may be infinite, and so does the solution space. It is then clear that the developed approach is not an optimization process that allows the convergence to «the» partition of space which is «the» best one for spatialization of suitability model. Nevertheless, the strength of the developed tool lies in the huge possibilities of generating potential scenarios translating relations between spatial features.
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APPENDIX Each alternative effectiveness computation E(I,J) has been determined on this basis: For each path, S and L are computed. Results are showed in Appendix Table 1.
Appendix Table 1 Effectiveness matrix of 36 alternatives resulting from the application of suitability models 1 and 2 on 18 partition scheme of the study area
In order to standardize the effectiveness values, we calculate two limit cases for each score map The case where the weight layer is the score map; The case where the weight layer is a constant-value grid with equal-size score map. Case 1 affords a path with a score cost Smin and length Lmax and case 2 gives a path with Smax and Lmin. Results are summarized in Appendix Table 2.
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Appendix Table 2 Min-Max values of score cost and length
Appendix Model 1 Min-Max values of score cost and length
Appendix Model 2
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Appendix Table 3 Standardized effectiveness matrix of 36 alternatives resulting from the application of suitability models 1 and 2 on 18 partition scheme of the study area
Appendix Table 3 summarizes standardized effectiveness matrix.
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Appendix Table 4 Results of dp calculation from the ideal point for the alternatives obtained with model 2 and model 1
Appendix Table 4 summarizes the results of dp calculation from the ideal point for the alternatives obtained with model 2 and model 1.
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Cite this article as: Chakroun, Hedia; Benie, Goze. ‘Methodological development for improving spatial decision support systems in natural resources and land management’. Applied GIS, 1, DOI: 10.2104/ag050005
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